Item talk:Q141908

From geokb

{

 "OpenAlex": {
   "id": "https://openalex.org/A5053923666",
   "orcid": "https://orcid.org/0000-0001-6464-3054",
   "display_name": "Giles M. Foody",
   "display_name_alternatives": [
     "G. M. Foody",
     "G.A. Foody",
     "Giles M. Foody",
     "G.M Foody",
     "G. Foody",
     "Glles M. Foody",
     "Giles Foody"
   ],
   "works_count": 392,
   "cited_by_count": 32419,
   "summary_stats": {
     "2yr_mean_citedness": 5.862068965517241,
     "h_index": 82,
     "i10_index": 238
   },
   "ids": {
     "openalex": "https://openalex.org/A5053923666",
     "orcid": "https://orcid.org/0000-0001-6464-3054",
     "scopus": "http://www.scopus.com/inward/authorDetails.url?authorID=7007014233&partnerID=MN8TOARS"
   },
   "affiliations": [
     {
       "institution": {
         "id": "https://openalex.org/I142263535",
         "ror": "https://ror.org/01ee9ar58",
         "display_name": "University of Nottingham",
         "country_code": "GB",
         "type": "education",
         "lineage": [
           "https://openalex.org/I142263535"
         ]
       },
       "years": [
         2024,
         2023,
         2022,
         2021,
         2020,
         2019,
         2018,
         2017,
         2016,
         2015
       ]
     },
     {
       "institution": {
         "id": "https://openalex.org/I4210142203",
         "ror": "https://ror.org/03msk5846",
         "display_name": "Museum of the American Revolution",
         "country_code": "US",
         "type": "archive",
         "lineage": [
           "https://openalex.org/I4210142203"
         ]
       },
       "years": [
         2023
       ]
     },
     {
       "institution": {
         "id": "https://openalex.org/I4210105990",
         "ror": "https://ror.org/01gkn6j11",
         "display_name": "Institute of Geodesy and Geophysics",
         "country_code": "CN",
         "type": "facility",
         "lineage": [
           "https://openalex.org/I19820366",
           "https://openalex.org/I4210105990"
         ]
       },
       "years": [
         2016
       ]
     },
     {
       "institution": {
         "id": "https://openalex.org/I19820366",
         "ror": "https://ror.org/034t30j35",
         "display_name": "Chinese Academy of Sciences",
         "country_code": "CN",
         "type": "government",
         "lineage": [
           "https://openalex.org/I19820366"
         ]
       },
       "years": [
         2016
       ]
     },
     {
       "institution": {
         "id": "https://openalex.org/I91125648",
         "ror": "https://ror.org/04jcykh16",
         "display_name": "Wuhan Institute of Technology",
         "country_code": "CN",
         "type": "education",
         "lineage": [
           "https://openalex.org/I91125648"
         ]
       },
       "years": [
         2016
       ]
     },
     {
       "institution": {
         "id": "https://openalex.org/I3124059619",
         "ror": "https://ror.org/04gcegc37",
         "display_name": "China University of Geosciences",
         "country_code": "CN",
         "type": "education",
         "lineage": [
           "https://openalex.org/I3124059619"
         ]
       },
       "years": [
         2016
       ]
     },
     {
       "institution": {
         "id": "https://openalex.org/I4210135175",
         "ror": "https://ror.org/044wmmj34",
         "display_name": "Hefei Institute of Technology Innovation",
         "country_code": "CN",
         "type": "facility",
         "lineage": [
           "https://openalex.org/I4210135175"
         ]
       },
       "years": [
         2016
       ]
     },
     {
       "institution": {
         "id": "https://openalex.org/I43439940",
         "ror": "https://ror.org/01ryk1543",
         "display_name": "University of Southampton",
         "country_code": "GB",
         "type": "education",
         "lineage": [
           "https://openalex.org/I43439940"
         ]
       },
       "years": [
         2009,
         2007,
         2006,
         2005,
         2004,
         2003,
         2002,
         2001,
         2000,
         1999
       ]
     },
     {
       "institution": {
         "id": "https://openalex.org/I2801402261",
         "ror": "https://ror.org/037tapg39",
         "display_name": "Kingston College",
         "country_code": "GB",
         "type": "education",
         "lineage": [
           "https://openalex.org/I2801402261"
         ]
       },
       "years": [
         2005,
         1989,
         1988,
         1987,
         1986
       ]
     },
     {
       "institution": {
         "id": "https://openalex.org/I154570441",
         "ror": "https://ror.org/02t274463",
         "display_name": "University of California, Santa Barbara",
         "country_code": "US",
         "type": "education",
         "lineage": [
           "https://openalex.org/I154570441"
         ]
       },
       "years": [
         2001
       ]
     }
   ],
   "last_known_institutions": [
     {
       "id": "https://openalex.org/I142263535",
       "ror": "https://ror.org/01ee9ar58",
       "display_name": "University of Nottingham",
       "country_code": "GB",
       "type": "education",
       "lineage": [
         "https://openalex.org/I142263535"
       ]
     }
   ],
   "topics": [
     {
       "id": "https://openalex.org/T10111",
       "display_name": "Remote Sensing in Vegetation Monitoring and Phenology",
       "count": 165,
       "subfield": {
         "id": "https://openalex.org/subfields/2303",
         "display_name": "Ecology"
       },
       "field": {
         "id": "https://openalex.org/fields/23",
         "display_name": "Environmental Science"
       },
       "domain": {
         "id": "https://openalex.org/domains/3",
         "display_name": "Physical Sciences"
       }
     },
     {
       "id": "https://openalex.org/T10689",
       "display_name": "Hyperspectral Image Analysis and Classification",
       "count": 101,
       "subfield": {
         "id": "https://openalex.org/subfields/2214",
         "display_name": "Media Technology"
       },
       "field": {
         "id": "https://openalex.org/fields/22",
         "display_name": "Engineering"
       },
       "domain": {
         "id": "https://openalex.org/domains/3",
         "display_name": "Physical Sciences"
       }
     },
     {
       "id": "https://openalex.org/T11164",
       "display_name": "Mapping Forests with Lidar Remote Sensing",
       "count": 66,
       "subfield": {
         "id": "https://openalex.org/subfields/2305",
         "display_name": "Environmental Engineering"
       },
       "field": {
         "id": "https://openalex.org/fields/23",
         "display_name": "Environmental Science"
       },
       "domain": {
         "id": "https://openalex.org/domains/3",
         "display_name": "Physical Sciences"
       }
     },
     {
       "id": "https://openalex.org/T10757",
       "display_name": "Volunteered Geographic Information and Geospatial Crowdsourcing",
       "count": 49,
       "subfield": {
         "id": "https://openalex.org/subfields/3305",
         "display_name": "Geography, Planning and Development"
       },
       "field": {
         "id": "https://openalex.org/fields/33",
         "display_name": "Social Sciences"
       },
       "domain": {
         "id": "https://openalex.org/domains/2",
         "display_name": "Social Sciences"
       }
     },
     {
       "id": "https://openalex.org/T10226",
       "display_name": "Global Analysis of Ecosystem Services and Land Use",
       "count": 49,
       "subfield": {
         "id": "https://openalex.org/subfields/2306",
         "display_name": "Global and Planetary Change"
       },
       "field": {
         "id": "https://openalex.org/fields/23",
         "display_name": "Environmental Science"
       },
       "domain": {
         "id": "https://openalex.org/domains/3",
         "display_name": "Physical Sciences"
       }
     },
     {
       "id": "https://openalex.org/T13890",
       "display_name": "Applications of Remote Sensing in Geoscience and Agriculture",
       "count": 45,
       "subfield": {
         "id": "https://openalex.org/subfields/1902",
         "display_name": "Atmospheric Science"
       },
       "field": {
         "id": "https://openalex.org/fields/19",
         "display_name": "Earth and Planetary Sciences"
       },
       "domain": {
         "id": "https://openalex.org/domains/3",
         "display_name": "Physical Sciences"
       }
     },
     {
       "id": "https://openalex.org/T10895",
       "display_name": "Species Distribution Modeling and Climate Change Impacts",
       "count": 41,
       "subfield": {
         "id": "https://openalex.org/subfields/2302",
         "display_name": "Ecological Modeling"
       },
       "field": {
         "id": "https://openalex.org/fields/23",
         "display_name": "Environmental Science"
       },
       "domain": {
         "id": "https://openalex.org/domains/3",
         "display_name": "Physical Sciences"
       }
     },
     {
       "id": "https://openalex.org/T10770",
       "display_name": "Digital Soil Mapping Techniques",
       "count": 37,
       "subfield": {
         "id": "https://openalex.org/subfields/2305",
         "display_name": "Environmental Engineering"
       },
       "field": {
         "id": "https://openalex.org/fields/23",
         "display_name": "Environmental Science"
       },
       "domain": {
         "id": "https://openalex.org/domains/3",
         "display_name": "Physical Sciences"
       }
     },
     {
       "id": "https://openalex.org/T12157",
       "display_name": "Machine Learning for Mineral Prospectivity Mapping",
       "count": 30,
       "subfield": {
         "id": "https://openalex.org/subfields/1702",
         "display_name": "Artificial Intelligence"
       },
       "field": {
         "id": "https://openalex.org/fields/17",
         "display_name": "Computer Science"
       },
       "domain": {
         "id": "https://openalex.org/domains/3",
         "display_name": "Physical Sciences"
       }
     },
     {
       "id": "https://openalex.org/T11659",
       "display_name": "Multispectral and Hyperspectral Image Fusion",
       "count": 29,
       "subfield": {
         "id": "https://openalex.org/subfields/2214",
         "display_name": "Media Technology"
       },
       "field": {
         "id": "https://openalex.org/fields/22",
         "display_name": "Engineering"
       },
       "domain": {
         "id": "https://openalex.org/domains/3",
         "display_name": "Physical Sciences"
       }
     },
     {
       "id": "https://openalex.org/T11106",
       "display_name": "Trajectory Data Mining and Analysis",
       "count": 19,
       "subfield": {
         "id": "https://openalex.org/subfields/1711",
         "display_name": "Signal Processing"
       },
       "field": {
         "id": "https://openalex.org/fields/17",
         "display_name": "Computer Science"
       },
       "domain": {
         "id": "https://openalex.org/domains/3",
         "display_name": "Physical Sciences"
       }
     },
     {
       "id": "https://openalex.org/T10005",
       "display_name": "Biodiversity Conservation and Ecosystem Management",
       "count": 19,
       "subfield": {
         "id": "https://openalex.org/subfields/2309",
         "display_name": "Nature and Landscape Conservation"
       },
       "field": {
         "id": "https://openalex.org/fields/23",
         "display_name": "Environmental Science"
       },
       "domain": {
         "id": "https://openalex.org/domains/3",
         "display_name": "Physical Sciences"
       }
     },
     {
       "id": "https://openalex.org/T11880",
       "display_name": "Estimation of Forest Biomass and Carbon Stocks",
       "count": 14,
       "subfield": {
         "id": "https://openalex.org/subfields/2309",
         "display_name": "Nature and Landscape Conservation"
       },
       "field": {
         "id": "https://openalex.org/fields/23",
         "display_name": "Environmental Science"
       },
       "domain": {
         "id": "https://openalex.org/domains/3",
         "display_name": "Physical Sciences"
       }
     },
     {
       "id": "https://openalex.org/T11819",
       "display_name": "Digital Epidemiology and Disease Surveillance",
       "count": 12,
       "subfield": {
         "id": "https://openalex.org/subfields/2713",
         "display_name": "Epidemiology"
       },
       "field": {
         "id": "https://openalex.org/fields/27",
         "display_name": "Medicine"
       },
       "domain": {
         "id": "https://openalex.org/domains/4",
         "display_name": "Health Sciences"
       }
     },
     {
       "id": "https://openalex.org/T10930",
       "display_name": "Global Flood Risk Assessment and Management",
       "count": 12,
       "subfield": {
         "id": "https://openalex.org/subfields/2306",
         "display_name": "Global and Planetary Change"
       },
       "field": {
         "id": "https://openalex.org/fields/23",
         "display_name": "Environmental Science"
       },
       "domain": {
         "id": "https://openalex.org/domains/3",
         "display_name": "Physical Sciences"
       }
     },
     {
       "id": "https://openalex.org/T10640",
       "display_name": "Chemometrics in Analytical Chemistry and Food Technology",
       "count": 12,
       "subfield": {
         "id": "https://openalex.org/subfields/1602",
         "display_name": "Analytical Chemistry"
       },
       "field": {
         "id": "https://openalex.org/fields/16",
         "display_name": "Chemistry"
       },
       "domain": {
         "id": "https://openalex.org/domains/3",
         "display_name": "Physical Sciences"
       }
     },
     {
       "id": "https://openalex.org/T10320",
       "display_name": "Neural Network Fundamentals and Applications",
       "count": 12,
       "subfield": {
         "id": "https://openalex.org/subfields/1702",
         "display_name": "Artificial Intelligence"
       },
       "field": {
         "id": "https://openalex.org/fields/17",
         "display_name": "Computer Science"
       },
       "domain": {
         "id": "https://openalex.org/domains/3",
         "display_name": "Physical Sciences"
       }
     },
     {
       "id": "https://openalex.org/T10801",
       "display_name": "Synthetic Aperture Radar Interferometry",
       "count": 11,
       "subfield": {
         "id": "https://openalex.org/subfields/2202",
         "display_name": "Aerospace Engineering"
       },
       "field": {
         "id": "https://openalex.org/fields/22",
         "display_name": "Engineering"
       },
       "domain": {
         "id": "https://openalex.org/domains/3",
         "display_name": "Physical Sciences"
       }
     },
     {
       "id": "https://openalex.org/T10319",
       "display_name": "Drivers and Impacts of Tropical Deforestation",
       "count": 11,
       "subfield": {
         "id": "https://openalex.org/subfields/2306",
         "display_name": "Global and Planetary Change"
       },
       "field": {
         "id": "https://openalex.org/fields/23",
         "display_name": "Environmental Science"
       },
       "domain": {
         "id": "https://openalex.org/domains/3",
         "display_name": "Physical Sciences"
       }
     },
     {
       "id": "https://openalex.org/T11312",
       "display_name": "Remote Sensing of Soil Moisture",
       "count": 10,
       "subfield": {
         "id": "https://openalex.org/subfields/2305",
         "display_name": "Environmental Engineering"
       },
       "field": {
         "id": "https://openalex.org/fields/23",
         "display_name": "Environmental Science"
       },
       "domain": {
         "id": "https://openalex.org/domains/3",
         "display_name": "Physical Sciences"
       }
     },
     {
       "id": "https://openalex.org/T10199",
       "display_name": "Wildlife Ecology and Conservation Biology",
       "count": 9,
       "subfield": {
         "id": "https://openalex.org/subfields/2303",
         "display_name": "Ecology"
       },
       "field": {
         "id": "https://openalex.org/fields/23",
         "display_name": "Environmental Science"
       },
       "domain": {
         "id": "https://openalex.org/domains/3",
         "display_name": "Physical Sciences"
       }
     },
     {
       "id": "https://openalex.org/T10766",
       "display_name": "Urban Heat Islands and Mitigation Strategies",
       "count": 8,
       "subfield": {
         "id": "https://openalex.org/subfields/2305",
         "display_name": "Environmental Engineering"
       },
       "field": {
         "id": "https://openalex.org/fields/23",
         "display_name": "Environmental Science"
       },
       "domain": {
         "id": "https://openalex.org/domains/3",
         "display_name": "Physical Sciences"
       }
     },
     {
       "id": "https://openalex.org/T11588",
       "display_name": "Global Methane Emissions and Impacts",
       "count": 8,
       "subfield": {
         "id": "https://openalex.org/subfields/2306",
         "display_name": "Global and Planetary Change"
       },
       "field": {
         "id": "https://openalex.org/fields/23",
         "display_name": "Environmental Science"
       },
       "domain": {
         "id": "https://openalex.org/domains/3",
         "display_name": "Physical Sciences"
       }
     },
     {
       "id": "https://openalex.org/T11333",
       "display_name": "Arctic Permafrost Dynamics and Climate Change",
       "count": 7,
       "subfield": {
         "id": "https://openalex.org/subfields/1902",
         "display_name": "Atmospheric Science"
       },
       "field": {
         "id": "https://openalex.org/fields/19",
         "display_name": "Earth and Planetary Sciences"
       },
       "domain": {
         "id": "https://openalex.org/domains/3",
         "display_name": "Physical Sciences"
       }
     },
     {
       "id": "https://openalex.org/T10266",
       "display_name": "Global Forest Drought Response and Climate Change",
       "count": 7,
       "subfield": {
         "id": "https://openalex.org/subfields/2306",
         "display_name": "Global and Planetary Change"
       },
       "field": {
         "id": "https://openalex.org/fields/23",
         "display_name": "Environmental Science"
       },
       "domain": {
         "id": "https://openalex.org/domains/3",
         "display_name": "Physical Sciences"
       }
     }
   ],
   "topic_share": [
     {
       "id": "https://openalex.org/T10111",
       "display_name": "Remote Sensing in Vegetation Monitoring and Phenology",
       "value": 0.0011568,
       "subfield": {
         "id": "https://openalex.org/subfields/2303",
         "display_name": "Ecology"
       },
       "field": {
         "id": "https://openalex.org/fields/23",
         "display_name": "Environmental Science"
       },
       "domain": {
         "id": "https://openalex.org/domains/3",
         "display_name": "Physical Sciences"
       }
     },
     {
       "id": "https://openalex.org/T10689",
       "display_name": "Hyperspectral Image Analysis and Classification",
       "value": 0.0008601,
       "subfield": {
         "id": "https://openalex.org/subfields/2214",
         "display_name": "Media Technology"
       },
       "field": {
         "id": "https://openalex.org/fields/22",
         "display_name": "Engineering"
       },
       "domain": {
         "id": "https://openalex.org/domains/3",
         "display_name": "Physical Sciences"
       }
     },
     {
       "id": "https://openalex.org/T10770",
       "display_name": "Digital Soil Mapping Techniques",
       "value": 0.0003692,
       "subfield": {
         "id": "https://openalex.org/subfields/2305",
         "display_name": "Environmental Engineering"
       },
       "field": {
         "id": "https://openalex.org/fields/23",
         "display_name": "Environmental Science"
       },
       "domain": {
         "id": "https://openalex.org/domains/3",
         "display_name": "Physical Sciences"
       }
     },
     {
       "id": "https://openalex.org/T11164",
       "display_name": "Mapping Forests with Lidar Remote Sensing",
       "value": 0.0003637,
       "subfield": {
         "id": "https://openalex.org/subfields/2305",
         "display_name": "Environmental Engineering"
       },
       "field": {
         "id": "https://openalex.org/fields/23",
         "display_name": "Environmental Science"
       },
       "domain": {
         "id": "https://openalex.org/domains/3",
         "display_name": "Physical Sciences"
       }
     },
     {
       "id": "https://openalex.org/T11659",
       "display_name": "Multispectral and Hyperspectral Image Fusion",
       "value": 0.0003057,
       "subfield": {
         "id": "https://openalex.org/subfields/2214",
         "display_name": "Media Technology"
       },
       "field": {
         "id": "https://openalex.org/fields/22",
         "display_name": "Engineering"
       },
       "domain": {
         "id": "https://openalex.org/domains/3",
         "display_name": "Physical Sciences"
       }
     },
     {
       "id": "https://openalex.org/T10757",
       "display_name": "Volunteered Geographic Information and Geospatial Crowdsourcing",
       "value": 0.0002981,
       "subfield": {
         "id": "https://openalex.org/subfields/3305",
         "display_name": "Geography, Planning and Development"
       },
       "field": {
         "id": "https://openalex.org/fields/33",
         "display_name": "Social Sciences"
       },
       "domain": {
         "id": "https://openalex.org/domains/2",
         "display_name": "Social Sciences"
       }
     },
     {
       "id": "https://openalex.org/T10226",
       "display_name": "Global Analysis of Ecosystem Services and Land Use",
       "value": 0.0002476,
       "subfield": {
         "id": "https://openalex.org/subfields/2306",
         "display_name": "Global and Planetary Change"
       },
       "field": {
         "id": "https://openalex.org/fields/23",
         "display_name": "Environmental Science"
       },
       "domain": {
         "id": "https://openalex.org/domains/3",
         "display_name": "Physical Sciences"
       }
     },
     {
       "id": "https://openalex.org/T11819",
       "display_name": "Digital Epidemiology and Disease Surveillance",
       "value": 0.0001817,
       "subfield": {
         "id": "https://openalex.org/subfields/2713",
         "display_name": "Epidemiology"
       },
       "field": {
         "id": "https://openalex.org/fields/27",
         "display_name": "Medicine"
       },
       "domain": {
         "id": "https://openalex.org/domains/4",
         "display_name": "Health Sciences"
       }
     },
     {
       "id": "https://openalex.org/T10801",
       "display_name": "Synthetic Aperture Radar Interferometry",
       "value": 0.0001584,
       "subfield": {
         "id": "https://openalex.org/subfields/2202",
         "display_name": "Aerospace Engineering"
       },
       "field": {
         "id": "https://openalex.org/fields/22",
         "display_name": "Engineering"
       },
       "domain": {
         "id": "https://openalex.org/domains/3",
         "display_name": "Physical Sciences"
       }
     },
     {
       "id": "https://openalex.org/T13890",
       "display_name": "Applications of Remote Sensing in Geoscience and Agriculture",
       "value": 0.0001573,
       "subfield": {
         "id": "https://openalex.org/subfields/1902",
         "display_name": "Atmospheric Science"
       },
       "field": {
         "id": "https://openalex.org/fields/19",
         "display_name": "Earth and Planetary Sciences"
       },
       "domain": {
         "id": "https://openalex.org/domains/3",
         "display_name": "Physical Sciences"
       }
     },
     {
       "id": "https://openalex.org/T10895",
       "display_name": "Species Distribution Modeling and Climate Change Impacts",
       "value": 0.0001439,
       "subfield": {
         "id": "https://openalex.org/subfields/2302",
         "display_name": "Ecological Modeling"
       },
       "field": {
         "id": "https://openalex.org/fields/23",
         "display_name": "Environmental Science"
       },
       "domain": {
         "id": "https://openalex.org/domains/3",
         "display_name": "Physical Sciences"
       }
     },
     {
       "id": "https://openalex.org/T13282",
       "display_name": "Automatic Road Extraction from Remote Sensing Images",
       "value": 0.0001114,
       "subfield": {
         "id": "https://openalex.org/subfields/2212",
         "display_name": "Ocean Engineering"
       },
       "field": {
         "id": "https://openalex.org/fields/22",
         "display_name": "Engineering"
       },
       "domain": {
         "id": "https://openalex.org/domains/3",
         "display_name": "Physical Sciences"
       }
     },
     {
       "id": "https://openalex.org/T11312",
       "display_name": "Remote Sensing of Soil Moisture",
       "value": 9.75e-05,
       "subfield": {
         "id": "https://openalex.org/subfields/2305",
         "display_name": "Environmental Engineering"
       },
       "field": {
         "id": "https://openalex.org/fields/23",
         "display_name": "Environmental Science"
       },
       "domain": {
         "id": "https://openalex.org/domains/3",
         "display_name": "Physical Sciences"
       }
     },
     {
       "id": "https://openalex.org/T11704",
       "display_name": "Crowdsourcing for Research and Data Collection",
       "value": 9.09e-05,
       "subfield": {
         "id": "https://openalex.org/subfields/1706",
         "display_name": "Computer Science Applications"
       },
       "field": {
         "id": "https://openalex.org/fields/17",
         "display_name": "Computer Science"
       },
       "domain": {
         "id": "https://openalex.org/domains/3",
         "display_name": "Physical Sciences"
       }
     },
     {
       "id": "https://openalex.org/T10319",
       "display_name": "Drivers and Impacts of Tropical Deforestation",
       "value": 8.91e-05,
       "subfield": {
         "id": "https://openalex.org/subfields/2306",
         "display_name": "Global and Planetary Change"
       },
       "field": {
         "id": "https://openalex.org/fields/23",
         "display_name": "Environmental Science"
       },
       "domain": {
         "id": "https://openalex.org/domains/3",
         "display_name": "Physical Sciences"
       }
     },
     {
       "id": "https://openalex.org/T11106",
       "display_name": "Trajectory Data Mining and Analysis",
       "value": 8.2e-05,
       "subfield": {
         "id": "https://openalex.org/subfields/1711",
         "display_name": "Signal Processing"
       },
       "field": {
         "id": "https://openalex.org/fields/17",
         "display_name": "Computer Science"
       },
       "domain": {
         "id": "https://openalex.org/domains/3",
         "display_name": "Physical Sciences"
       }
     },
     {
       "id": "https://openalex.org/T10766",
       "display_name": "Urban Heat Islands and Mitigation Strategies",
       "value": 7.99e-05,
       "subfield": {
         "id": "https://openalex.org/subfields/2305",
         "display_name": "Environmental Engineering"
       },
       "field": {
         "id": "https://openalex.org/fields/23",
         "display_name": "Environmental Science"
       },
       "domain": {
         "id": "https://openalex.org/domains/3",
         "display_name": "Physical Sciences"
       }
     },
     {
       "id": "https://openalex.org/T10616",
       "display_name": "Precision Agriculture Technologies",
       "value": 7.04e-05,
       "subfield": {
         "id": "https://openalex.org/subfields/1110",
         "display_name": "Plant Science"
       },
       "field": {
         "id": "https://openalex.org/fields/11",
         "display_name": "Agricultural and Biological Sciences"
       },
       "domain": {
         "id": "https://openalex.org/domains/1",
         "display_name": "Life Sciences"
       }
     },
     {
       "id": "https://openalex.org/T12157",
       "display_name": "Machine Learning for Mineral Prospectivity Mapping",
       "value": 6.79e-05,
       "subfield": {
         "id": "https://openalex.org/subfields/1702",
         "display_name": "Artificial Intelligence"
       },
       "field": {
         "id": "https://openalex.org/fields/17",
         "display_name": "Computer Science"
       },
       "domain": {
         "id": "https://openalex.org/domains/3",
         "display_name": "Physical Sciences"
       }
     },
     {
       "id": "https://openalex.org/T13058",
       "display_name": "Land-Use Suitability Assessment Using GIS",
       "value": 6.67e-05,
       "subfield": {
         "id": "https://openalex.org/subfields/2308",
         "display_name": "Management, Monitoring, Policy and Law"
       },
       "field": {
         "id": "https://openalex.org/fields/23",
         "display_name": "Environmental Science"
       },
       "domain": {
         "id": "https://openalex.org/domains/3",
         "display_name": "Physical Sciences"
       }
     },
     {
       "id": "https://openalex.org/T14427",
       "display_name": "Hydrologic Data Management and Analysis",
       "value": 6.66e-05,
       "subfield": {
         "id": "https://openalex.org/subfields/1907",
         "display_name": "Geology"
       },
       "field": {
         "id": "https://openalex.org/fields/19",
         "display_name": "Earth and Planetary Sciences"
       },
       "domain": {
         "id": "https://openalex.org/domains/3",
         "display_name": "Physical Sciences"
       }
     },
     {
       "id": "https://openalex.org/T11880",
       "display_name": "Estimation of Forest Biomass and Carbon Stocks",
       "value": 6.59e-05,
       "subfield": {
         "id": "https://openalex.org/subfields/2309",
         "display_name": "Nature and Landscape Conservation"
       },
       "field": {
         "id": "https://openalex.org/fields/23",
         "display_name": "Environmental Science"
       },
       "domain": {
         "id": "https://openalex.org/domains/3",
         "display_name": "Physical Sciences"
       }
     },
     {
       "id": "https://openalex.org/T10930",
       "display_name": "Global Flood Risk Assessment and Management",
       "value": 6.11e-05,
       "subfield": {
         "id": "https://openalex.org/subfields/2306",
         "display_name": "Global and Planetary Change"
       },
       "field": {
         "id": "https://openalex.org/fields/23",
         "display_name": "Environmental Science"
       },
       "domain": {
         "id": "https://openalex.org/domains/3",
         "display_name": "Physical Sciences"
       }
     },
     {
       "id": "https://openalex.org/T13030",
       "display_name": "Statistical Methods for Sensitive Survey Questions",
       "value": 5.89e-05,
       "subfield": {
         "id": "https://openalex.org/subfields/2613",
         "display_name": "Statistics and Probability"
       },
       "field": {
         "id": "https://openalex.org/fields/26",
         "display_name": "Mathematics"
       },
       "domain": {
         "id": "https://openalex.org/domains/3",
         "display_name": "Physical Sciences"
       }
     },
     {
       "id": "https://openalex.org/T10005",
       "display_name": "Biodiversity Conservation and Ecosystem Management",
       "value": 5.88e-05,
       "subfield": {
         "id": "https://openalex.org/subfields/2309",
         "display_name": "Nature and Landscape Conservation"
       },
       "field": {
         "id": "https://openalex.org/fields/23",
         "display_name": "Environmental Science"
       },
       "domain": {
         "id": "https://openalex.org/domains/3",
         "display_name": "Physical Sciences"
       }
     }
   ],
   "x_concepts": [
     {
       "id": "https://openalex.org/C205649164",
       "wikidata": "https://www.wikidata.org/wiki/Q1071",
       "display_name": "Geography",
       "level": 0,
       "score": 84.4
     },
     {
       "id": "https://openalex.org/C41008148",
       "wikidata": "https://www.wikidata.org/wiki/Q21198",
       "display_name": "Computer science",
       "level": 0,
       "score": 81.1
     },
     {
       "id": "https://openalex.org/C127313418",
       "wikidata": "https://www.wikidata.org/wiki/Q1069",
       "display_name": "Geology",
       "level": 0,
       "score": 74.5
     },
     {
       "id": "https://openalex.org/C62649853",
       "wikidata": "https://www.wikidata.org/wiki/Q199687",
       "display_name": "Remote sensing",
       "level": 1,
       "score": 67.3
     },
     {
       "id": "https://openalex.org/C86803240",
       "wikidata": "https://www.wikidata.org/wiki/Q420",
       "display_name": "Biology",
       "level": 0,
       "score": 65.6
     },
     {
       "id": "https://openalex.org/C121332964",
       "wikidata": "https://www.wikidata.org/wiki/Q413",
       "display_name": "Physics",
       "level": 0,
       "score": 59.2
     },
     {
       "id": "https://openalex.org/C18903297",
       "wikidata": "https://www.wikidata.org/wiki/Q7150",
       "display_name": "Ecology",
       "level": 1,
       "score": 58.9
     },
     {
       "id": "https://openalex.org/C154945302",
       "wikidata": "https://www.wikidata.org/wiki/Q11660",
       "display_name": "Artificial intelligence",
       "level": 1,
       "score": 55.4
     },
     {
       "id": "https://openalex.org/C127413603",
       "wikidata": "https://www.wikidata.org/wiki/Q11023",
       "display_name": "Engineering",
       "level": 0,
       "score": 53.6
     },
     {
       "id": "https://openalex.org/C33923547",
       "wikidata": "https://www.wikidata.org/wiki/Q395",
       "display_name": "Mathematics",
       "level": 0,
       "score": 44.1
     },
     {
       "id": "https://openalex.org/C39432304",
       "wikidata": "https://www.wikidata.org/wiki/Q188847",
       "display_name": "Environmental science",
       "level": 0,
       "score": 39.8
     },
     {
       "id": "https://openalex.org/C31972630",
       "wikidata": "https://www.wikidata.org/wiki/Q844240",
       "display_name": "Computer vision",
       "level": 1,
       "score": 38.3
     },
     {
       "id": "https://openalex.org/C119857082",
       "wikidata": "https://www.wikidata.org/wiki/Q2539",
       "display_name": "Machine learning",
       "level": 1,
       "score": 31.9
     },
     {
       "id": "https://openalex.org/C105795698",
       "wikidata": "https://www.wikidata.org/wiki/Q12483",
       "display_name": "Statistics",
       "level": 1,
       "score": 29.8
     },
     {
       "id": "https://openalex.org/C120665830",
       "wikidata": "https://www.wikidata.org/wiki/Q14620",
       "display_name": "Optics",
       "level": 1,
       "score": 29.6
     },
     {
       "id": "https://openalex.org/C138885662",
       "wikidata": "https://www.wikidata.org/wiki/Q5891",
       "display_name": "Philosophy",
       "level": 0,
       "score": 29.6
     },
     {
       "id": "https://openalex.org/C58640448",
       "wikidata": "https://www.wikidata.org/wiki/Q42515",
       "display_name": "Cartography",
       "level": 1,
       "score": 28.8
     },
     {
       "id": "https://openalex.org/C15744967",
       "wikidata": "https://www.wikidata.org/wiki/Q9418",
       "display_name": "Psychology",
       "level": 0,
       "score": 26.5
     },
     {
       "id": "https://openalex.org/C124101348",
       "wikidata": "https://www.wikidata.org/wiki/Q172491",
       "display_name": "Data mining",
       "level": 1,
       "score": 26.3
     },
     {
       "id": "https://openalex.org/C147176958",
       "wikidata": "https://www.wikidata.org/wiki/Q77590",
       "display_name": "Civil engineering",
       "level": 1,
       "score": 26.3
     },
     {
       "id": "https://openalex.org/C4792198",
       "wikidata": "https://www.wikidata.org/wiki/Q1165944",
       "display_name": "Land use",
       "level": 2,
       "score": 25.5
     },
     {
       "id": "https://openalex.org/C111368507",
       "wikidata": "https://www.wikidata.org/wiki/Q43518",
       "display_name": "Oceanography",
       "level": 1,
       "score": 25.0
     },
     {
       "id": "https://openalex.org/C2780648208",
       "wikidata": "https://www.wikidata.org/wiki/Q3001793",
       "display_name": "Land cover",
       "level": 3,
       "score": 24.2
     },
     {
       "id": "https://openalex.org/C59822182",
       "wikidata": "https://www.wikidata.org/wiki/Q441",
       "display_name": "Botany",
       "level": 1,
       "score": 24.0
     },
     {
       "id": "https://openalex.org/C199360897",
       "wikidata": "https://www.wikidata.org/wiki/Q9143",
       "display_name": "Programming language",
       "level": 1,
       "score": 24.0
     }
   ],
   "counts_by_year": [
     {
       "year": 2024,
       "works_count": 5,
       "cited_by_count": 1590
     },
     {
       "year": 2023,
       "works_count": 15,
       "cited_by_count": 2420
     },
     {
       "year": 2022,
       "works_count": 11,
       "cited_by_count": 2753
     },
     {
       "year": 2021,
       "works_count": 18,
       "cited_by_count": 2685
     },
     {
       "year": 2020,
       "works_count": 15,
       "cited_by_count": 2509
     },
     {
       "year": 2019,
       "works_count": 13,
       "cited_by_count": 2207
     },
     {
       "year": 2018,
       "works_count": 9,
       "cited_by_count": 2075
     },
     {
       "year": 2017,
       "works_count": 11,
       "cited_by_count": 1869
     },
     {
       "year": 2016,
       "works_count": 15,
       "cited_by_count": 1706
     },
     {
       "year": 2015,
       "works_count": 16,
       "cited_by_count": 1660
     },
     {
       "year": 2014,
       "works_count": 6,
       "cited_by_count": 1600
     },
     {
       "year": 2013,
       "works_count": 16,
       "cited_by_count": 1357
     },
     {
       "year": 2012,
       "works_count": 12,
       "cited_by_count": 1310
     }
   ],
   "works_api_url": "https://api.openalex.org/works?filter=author.id:A5053923666",
   "updated_date": "2024-08-23T20:50:43.462628",
   "created_date": "2023-07-21",
   "_id": "https://openalex.org/A5053923666"
 },
 "ORCID": {
   "@context": "http://schema.org",
   "@type": "Person",
   "@id": "https://orcid.org/0000-0001-6464-3054",
   "mainEntityOfPage": "https://orcid.org/0000-0001-6464-3054",
   "givenName": "Giles",
   "familyName": "Foody",
   "affiliation": {
     "@type": "Organization",
     "name": "University of Nottingham",
     "alternateName": "Geography",
     "identifier": {
       "@type": "PropertyValue",
       "propertyID": "RINGGOLD",
       "value": "6123"
     }
   },
   "@reverse": {
     "creator": [
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1016/j.jhydrol.2024.131512",
         "name": "DeepWaterFraction: A globally applicable, self-training deep learning approach for percent surface water area estimation from Landsat mission imagery",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "doi",
           "value": "10.1016/j.jhydrol.2024.131512"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.3390/geomatics4010005",
         "name": "Ground Truth in Classification Accuracy Assessment: Myth and Reality",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "doi",
           "value": "10.3390/geomatics4010005"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/10095020.2022.2100285",
         "name": "Assuring the quality of VGI on land use and land cover: experiences and learnings from the LandSense project",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "doi",
           "value": "10.1080/10095020.2022.2100285"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/15481603.2023.2217573",
         "name": "Regression-based surface water fraction mapping using a synthetic spectral library for monitoring small water bodies",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "doi",
           "value": "10.1080/15481603.2023.2217573"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1371/journal.pone.0291908",
         "name": "Challenges in the real world use of classification accuracy metrics: From recall and precision to the Matthews correlation coefficient",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "doi",
           "value": "10.1371/journal.pone.0291908"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.3390/rs15174336",
         "name": "Spectral Characteristics of Beached Sargassum in Response to Drying and Decay over Time",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "doi",
           "value": "10.3390/rs15174336"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1109/lgrs.2023.3234306",
         "name": "Deep Feature and Domain Knowledge Fusion Network for Mapping Surface Water Bodies by Fusing Google Earth RGB and Sentinel-2 Images",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "doi",
           "value": "10.1109/lgrs.2023.3234306"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1109/tgrs.2023.3308902",
         "name": "Unmixing-Based Spatiotemporal Image Fusion Based on the Self-Trained Random Forest Regression and Residual Compensation",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "doi",
           "value": "10.1109/tgrs.2023.3308902"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.3390/rs14215380",
         "name": "Global and Local Assessment of Image Classification Quality on an Overall and Per-Class Basis without Ground Reference Data",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "doi",
           "value": "10.3390/rs14215380"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.3390/s22197672",
         "name": "Towards an Automated Approach for Monitoring Tree Phenology Using Vehicle Dashcams in Urban Environments",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "doi",
           "value": "10.3390/s22197672"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1029/2022jg007026",
         "name": "The Spectral Species Concept in Living Color",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "doi",
           "value": "10.1029/2022jg007026"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1111/1365-2745.13844",
         "name": "Making (remote) sense of lianas",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "doi",
           "value": "10.1111/1365-2745.13844"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1109/lgrs.2020.3020395",
         "name": "Superresolution Land Cover Mapping Using a Generative Adversarial Network",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "doi",
           "value": "10.1109/lgrs.2020.3020395"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1109/tgrs.2020.3041724",
         "name": "Object-Based Area-to-Point Regression Kriging for Pansharpening",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "doi",
           "value": "10.1109/tgrs.2020.3041724"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1002/rse2.197",
         "name": "Remote sensing liana infestation in an aseasonal tropical forest: addressing mismatch in spatial units of analyses",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "doi",
           "value": "10.1002/rse2.197"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.3390/rs13142774",
         "name": "Detection of Spatial and Temporal Patterns of Liana Infestation Using Satellite-Derived Imagery",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "doi",
           "value": "10.3390/rs13142774"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.3390/ijerph18147242",
         "name": "Seasonal SUHI Analysis Using Local Climate Zone Classification: A Case Study of Wuhan, China",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "doi",
           "value": "10.3390/ijerph18147242"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1002/rse2.188",
         "name": "Let your maps be fuzzy!\u2014Class probabilities and floristic gradients as alternatives to crisp mapping for remote sensing of vegetation",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "doi",
           "value": "10.1002/rse2.188"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1111/2041-210x.13583",
         "name": "rasterdiv\u2014An Information Theory tailored R package for measuring ecosystem heterogeneity from space: To the origin and back",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "doi",
           "value": "10.1111/2041-210x.13583"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1111/geb.13270",
         "name": "From zero to infinity: Minimum to maximum diversity of the planet by spatio\u2010parametric Rao\u2019s quadratic entropy",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "doi",
           "value": "10.1111/geb.13270"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1101/2021.02.09.430391",
         "name": "rasterdiv - an Information Theory tailored R package for measuring ecosystem heterogeneity from space: to the origin and back",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "doi",
           "value": "10.1101/2021.02.09.430391"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1109/tgrs.2020.2999943",
         "name": "Spatiotemporal Fusion of Land Surface Temperature Based on a Convolutional Neural Network",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "doi",
           "value": "10.1109/tgrs.2020.2999943"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.3390/rs13030358",
         "name": "Scrutinizing Relationships between Submarine Groundwater Discharge and Upstream Areas Using Thermal Remote Sensing: A Case Study in the Northern Persian Gulf",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "doi",
           "value": "10.3390/rs13030358"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.3390/land10010035",
         "name": "Comparison of Simple Averaging and Latent Class Modeling to Estimate the Area of Land Cover in the Presence of Reference Data Variability",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "doi",
           "value": "10.3390/land10010035"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1109/tgrs.2020.2996064",
         "name": "Iterative Training Sample Expansion to Increase and Balance the Accuracy of Land Classification From VHR Imagery",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "doi",
           "value": "10.1109/tgrs.2020.2996064"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.3390/su12239834",
         "name": "Investigating the Potential of Radar Interferometry for Monitoring Rural Artisanal Cobalt Mines in the Democratic Republic of the Congo",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "doi",
           "value": "10.3390/su12239834"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1126/science.aay4490",
         "name": "Active restoration accelerates the carbon recovery of human-modified tropical forests",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "doi",
           "value": "10.1126/science.aay4490"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.3390/rs12071186",
         "name": "Use of Automated Change Detection and VGI Sources for Identifying and Validating Urban Land Use Change",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "doi",
           "value": "10.3390/rs12071186"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.3390/rs12030503",
         "name": "Spatio-Temporal Sub-Pixel Land Cover Mapping of Remote Sensing Imagery Using Spatial Distribution Information From Same-Class Pixels",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "doi",
           "value": "10.3390/rs12030503"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/13658816.2019.1593422",
         "name": "Crowdsourced geospatial data quality: challenges and future directions",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "doi",
           "value": "10.1080/13658816.2019.1593422"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1109/lgrs.2019.2894805",
         "name": "Optimal Endmember-Based Super-Resolution Land Cover Mapping",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "doi",
           "value": "10.1109/lgrs.2019.2894805"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1029/2018wr024136",
         "name": "Measuring River Wetted Width From Remotely Sensed Imagery at the Subpixel Scale With a Deep Convolutional Neural Network",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "doi",
           "value": "10.1029/2018wr024136"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1109/tgrs.2019.2894773",
         "name": "Spatial\u2013Temporal Super-Resolution Land Cover Mapping With a Local Spatial\u2013Temporal Dependence Model",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "doi",
           "value": "10.1109/tgrs.2019.2894773"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/2150704x.2019.1587196",
         "name": "Super-resolution land cover mapping by deep learning",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "doi",
           "value": "10.1080/2150704x.2019.1587196"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1016/j.gloenvcha.2019.01.004",
         "name": "Exploring temporality in socio-ecological resilience through experiences of the 2015\u201316 El Ni\u00f1o across the Tropics",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "doi",
           "value": "10.1016/j.gloenvcha.2019.01.004"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.3390/rs11030266",
         "name": "Earth Observation and Machine Learning to Meet Sustainable Development Goal 8.7: Mapping Sites Associated with Slavery from Space",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "doi",
           "value": "10.3390/rs11030266"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1111/2041-210x.12941",
         "name": "Measuring \u03b2\u2010diversity by remote sensing: A challenge for biodiversity monitoring",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "doi",
           "value": "10.1111/2041-210x.12941"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.3390/ijgi7030080",
         "name": "Increasing the Accuracy of Crowdsourced Information on Land Cover via a Voting Procedure Weighted by Information Inferred from the Contributed Data",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "doi",
           "value": "10.3390/ijgi7030080"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1016/j.ecolind.2017.09.055",
         "name": "Remotely sensed spatial heterogeneity as an exploratory tool for taxonomic and functional diversity study",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1016/j.ecolind.2017.09.055"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-85036478630"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1016/j.isprsjprs.2018.02.012",
         "name": "Slavery from Space: Demonstrating the role for satellite remote sensing to inform evidence-based action related to UN SDG number 8",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1016/j.isprsjprs.2018.02.012"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-85042604549"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1016/j.rse.2018.05.010",
         "name": "Spatial-temporal fraction map fusion with multi-scale remotely sensed images",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-85047057538"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1016/j.rse.2018.05.010"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1016/j.rse.2017.11.024",
         "name": "Supervised methods of image segmentation accuracy assessment in land cover mapping",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1016/j.rse.2017.11.024"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-85037527926"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1016/j.rse.2018.04.014",
         "name": "Using volunteered geographic information (VGI) in design-based statistical inference for area estimation and accuracy assessment of land cover",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-85046169385"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1016/j.rse.2018.04.014"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.3390/rs9111148",
         "name": "Impervious Surface Change Mapping with an Uncertainty-Based Spatial-Temporal Consistency Model: A Case Study in Wuhan City Using Landsat Time-Series Datasets from 1987 to 2016",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "doi",
           "value": "10.3390/rs9111148"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.3390/rs9111175",
         "name": "Monitoring Thermal Pollution in Rivers Downstream of Dams with Landsat ETM+ Thermal Infrared Images",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "doi",
           "value": "10.3390/rs9111175"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.3390/app7090888",
         "name": "Impacts of Sample Design for Validation Data on the Accuracy of Feedforward Neural Network Classification",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "doi",
           "value": "10.3390/app7090888"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1016/j.scitotenv.2016.12.038",
         "name": "Anticipating species distributions: Handling sampling effort bias under a Bayesian framework",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1016/j.scitotenv.2016.12.038"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-85014825767"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1016/j.rse.2017.05.011",
         "name": "Generating a series of fine spatial and temporal resolution land cover maps by fusing coarse spatial resolution remotely sensed images and fine spatial resolution land cover maps",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1016/j.rse.2017.05.011"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-85019386206"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/01431161.2017.1292073",
         "name": "Improving specific class mapping from remotely sensed data by Cost-Sensitive learning",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-85028874290"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/01431161.2017.1292073"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1111/tgis.12189",
         "name": "The Scale of VGI in Map Production: A Perspective on European National Mapping Agencies",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-85011660628"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1111/tgis.12189"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1016/j.rse.2016.12.017",
         "name": "Using mixed objects in the training of object-based image classifications",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-85008210901"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1016/j.rse.2016.12.017"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.3390/ijgi5110199",
         "name": "The Sensitivity of Mapping Methods to Reference Data Quality: Training Supervised Image Classifications with Imperfect Reference Data",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "doi",
           "value": "10.3390/ijgi5110199"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.3390/ijgi5050064",
         "name": "Investigating the Feasibility of Geo-Tagged Photographs as Sources of Land Cover Input Data",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "doi",
           "value": "10.3390/ijgi5050064"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.3390/ijgi5050055",
         "name": "Crowdsourcing, Citizen Science or Volunteered Geographic Information? The Current State of Crowdsourced Geographic Information",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "doi",
           "value": "10.3390/ijgi5050055"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1109/tgrs.2016.2528583",
         "name": "A superresolution land-cover change detection method using remotely sensed images with different spatial resolutions",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1109/tgrs.2016.2528583"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-84959933954"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1016/j.dib.2016.02.058",
         "name": "A virtual species set for robust and reproducible species distribution modelling tests",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-84960192540"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1016/j.dib.2016.02.058"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1109/tgrs.2016.2598534",
         "name": "An Iterative Interpolation Deconvolution Algorithm for Superresolution Land Cover Mapping",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-84983631049"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1109/tgrs.2016.2598534"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.3390/rs8080642",
         "name": "Assessing a temporal change strategy for sub-pixel land cover change mapping from multi-scale remote sensing imagery",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-84983749140"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.3390/rs8080642"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1002/rse2.18",
         "name": "Earth observation archives for plant conservation: 50\u00a0years monitoring of Itigi-Sumbu thicket",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-85021348063"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1002/rse2.18"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1007/s10707-016-0248-z",
         "name": "Geographically weighted evidence combination approaches for combining discordant and inconsistent volunteered geographical information",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1007/s10707-016-0248-z"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-84964723119"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/01431161.2016.1148288",
         "name": "Improving super-resolution mapping through combining multiple super-resolution land-cover maps",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-84978388193"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/01431161.2016.1148288"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1109/tgrs.2016.2527841",
         "name": "Learning-Based Superresolution Land Cover Mapping",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1109/tgrs.2016.2527841"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-84977951529"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1002/rse2.9",
         "name": "Satellite remote sensing to monitor species diversity: potential and pitfalls",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1002/rse2.9"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-84994559423"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/00087041.2015.1108658",
         "name": "Accurate attribute mapping from volunteered geographic information: Issues of volunteer quantity and quality",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/00087041.2015.1108658"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-84956663590"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1109/igarss.2015.7327053",
         "name": "Citizen science in support of remote sensing research",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-84962599793"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1109/igarss.2015.7327053"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1002/joc.4210",
         "name": "Crowdsourcing for climate and atmospheric sciences: Current status and future potential",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1002/joc.4210"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-84940889436"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1016/j.scs.2015.04.007",
         "name": "Enhancing the spatial resolution of satellite-derived land surface temperature mapping for urban areas",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1016/j.scs.2015.04.007"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-84944166820"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.3390/ijgi4042496",
         "name": "Impacts of species misidentification on species distribution modeling with presence-only data",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.3390/ijgi4042496"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-84952802470"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.14358/pers.81.6.451",
         "name": "Integrating user needs on misclassification error sensitivity into image segmentation quality assessment",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-84930066941"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.14358/pers.81.6.451"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1109/igarss.2015.7326952",
         "name": "The effect of mis-labeled training data on the accuracy of supervised image classification by SVM",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1109/igarss.2015.7326952"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-84962619558"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/13658816.2015.1018266",
         "name": "Usability of VGI for validation of land cover maps",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-84938422185"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/13658816.2015.1018266"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1016/j.ecolecon.2015.01.003",
         "name": "Valuing map validation: The need for rigorous land cover map accuracy assessment in economic valuations of ecosystem services",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-84921522459"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1016/j.ecolecon.2015.01.003"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "name": "Assessing the accuracy of volunteered geographic information derived habitat classification",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "eid",
           "value": "2-s2.0-84991387598"
         }
       },
       {
         "@type": "CreativeWork",
         "name": "Exploring the accuracy of crowdsourced annotations of post-disaster building damage derived from fine spatial resolution satellite sensor data",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "eid",
           "value": "2-s2.0-84991409016"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1016/j.rse.2014.02.015",
         "name": "Good practices for estimating area and assessing accuracy of land change",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1016/j.rse.2014.02.015"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-84897951081"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/17538947.2013.839008",
         "name": "Rating crowdsourced annotations: evaluating contributions of variable quality and completeness",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-84902486384"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/17538947.2013.839008"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/01431161.2014.857862",
         "name": "Recent developments in publishing on remote sensing",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/01431161.2014.857862"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-84890929667"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "name": "Volunteered geographic information",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "eid",
           "value": "2-s2.0-84950236173"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1007/s12517-011-0411-7",
         "name": "Assessing flash flood hazard in an arid mountainous region",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-84874949608"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1007/s12517-011-0411-7"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1111/tgis.12033",
         "name": "Assessing the accuracy of volunteered geographic information arising from multiple contributors to an internet based collaborative project",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1111/tgis.12033"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-84888860604"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1016/j.ecoinf.2012.04.002",
         "name": "Calculating landscape diversity with information-theory based indices: A GRASS GIS solution",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1016/j.ecoinf.2012.04.002"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-84884928525"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/2150704x.2013.798708",
         "name": "Ground reference data error and the mis-estimation of the Area of land cover change as a function of its abundance",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/2150704x.2013.798708"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-84879322724"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1016/j.rse.2012.10.031",
         "name": "Making better use of accuracy data in land change studies: Estimating accuracy and area and quantifying uncertainty using stratified estimation",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-84870202759"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1016/j.rse.2012.10.031"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1109/igarss.2013.6721249",
         "name": "Rating the quality of post-disaster damage maps: Mapping building damage after the 2010 Haiti earthquake",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1109/igarss.2013.6721249"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-84894261226"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1038/498037d",
         "name": "Satellites: Ambition for forest initiative",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-84878723784"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1038/498037d"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1016/j.cageo.2012.05.022",
         "name": "Uncertainty in ecosystem mapping by remote sensing",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1016/j.cageo.2012.05.022"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-84870814570"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1016/j.jag.2012.11.002",
         "name": "Using control data to determine the reliability of volunteered geographic information about land cover",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1016/j.jag.2012.11.002"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-84880321994"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1109/jstars.2013.2250257",
         "name": "Using volunteered data in land cover map validation: Mapping west African forests",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1109/jstars.2013.2250257"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-84880301861"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1109/igarss.2012.6351018",
         "name": "A contour-based pixel swapping method for super-resolution mapping",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-84873166378"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1109/igarss.2012.6351018"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1109/jstars.2012.2191537",
         "name": "Combining hopfield neural network and contouring methods to enhance super-resolution mapping",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1109/jstars.2012.2191537"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-84869494118"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1109/jstars.2012.2216514",
         "name": "Combining pixel swapping and contouring methods to enhance super-resolution mapping",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-84869504907"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1109/jstars.2012.2216514"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1016/j.isprsjprs.2012.03.011",
         "name": "Estimating tropical forest biomass with a combination of SAR image texture and Landsat TM data: An assessment of predictions between regions",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-84860520922"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1016/j.isprsjprs.2012.03.011"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1109/jstars.2012.2215310",
         "name": "Evaluation of SVM, RVM and SMLR for accurate image classification with limited ground data",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1109/jstars.2012.2215310"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-84869488312"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1109/lgrs.2011.2170810",
         "name": "Evaluation of envisat MERIS terrestrial chlorophyll index-based models for the estimation of terrestrial gross primary productivity",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1109/lgrs.2011.2170810"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-84858071405"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "name": "Exploring the potential role of volunteer citizen sensors in land cover map accuracy assessment",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "eid",
           "value": "2-s2.0-84975748779"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1109/jstars.2012.2191145",
         "name": "Impact of land cover patch size on the accuracy of patch area representation in HNN-based super resolution mapping",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-84869429066"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1109/jstars.2012.2191145"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1109/tgrs.2011.2174156",
         "name": "Latent class modeling for site- and non-site-specific classification accuracy assessment without ground data",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1109/tgrs.2011.2174156"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-84863008703"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1016/j.landurbplan.2012.05.016",
         "name": "Spatial non-stationarity in the relationships between land cover and surface temperature in an urban heat island and its impacts on thermally sensitive populations",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1016/j.landurbplan.2012.05.016"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-84863879982"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1016/j.jag.2011.06.002",
         "name": "Super-resolution mapping of lakes from imagery with a coarse spatial and fine temporal resolution",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-84864507901"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1016/j.jag.2011.06.002"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1109/igarss.2012.6352675",
         "name": "Using volunteered data in land cover map validation: Mapping tropical forests across West Africa",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-84873185446"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1109/igarss.2012.6352675"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1016/j.ecoinf.2010.07.007",
         "name": "An overview of recent remote sensing and GIS based research in ecological informatics",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1016/j.ecoinf.2010.07.007"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-79651474138"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1111/j.1466-8238.2010.00605.x",
         "name": "Impacts of imperfect reference data on the apparent accuracy of species presence-absence models and their predictions",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-79953782022"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1111/j.1466-8238.2010.00605.x"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "name": "Remote sensing of barley stressed with CO<inf>2</inf>and herbicide",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "eid",
           "value": "2-s2.0-84868629201"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/21507040903538130",
         "name": "A fresh start",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-79955409384"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/21507040903538130"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1016/j.rse.2010.05.003",
         "name": "Assessing the accuracy of land cover change with imperfect ground reference data",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1016/j.rse.2010.05.003"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-77955275042"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/01431160903516119",
         "name": "Editorial: A new launch",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-85009577047"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/01431160903516119"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1109/igarss.2010.5654088",
         "name": "Estimating terrestrial gross primary productivity with the envisat medium resolution imaging spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI)",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1109/igarss.2010.5654088"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-78650905227"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/01431160902887339",
         "name": "Estimating the relative abundance of C<inf>3</inf>and C<inf>4</inf>grasses in the Great Plains from multi-temporal MTCI data: Issues of compositing period and spatial generalizability",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-77649165358"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/01431160902887339"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1109/tgrs.2009.2039484",
         "name": "Feature selection for classification of hyperspectral data by SVM",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-77951295936"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1109/tgrs.2009.2039484"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/01431160902922888",
         "name": "Geostatistically estimated image noise is a function of variance in the underlying signal",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-77951135424"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/01431160902922888"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1016/j.ecoinf.2010.06.001",
         "name": "Remotely sensed spectral heterogeneity as a proxy of species diversity: Recent advances and open challenges",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1016/j.ecoinf.2010.06.001"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-77956878537"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "name": "Spatial entropy for the measurement of the spatial accuracy of classified remote sensing imagery",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "eid",
           "value": "2-s2.0-84975746784"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1109/igarss.2010.5649083",
         "name": "Super-resolution analysis for accurate mapping of land cover and land cover pattern",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-78650856186"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1109/igarss.2010.5649083"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "name": "Super-resolution mapping of landscape objects from coarse spatial resolution imagery",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "eid",
           "value": "2-s2.0-84923950843"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1117/12.865092",
         "name": "Super-resolution mapping using multiple observations and Hopfield neural network",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-78649741998"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1117/12.865092"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "name": "An estimation of tropical forest biomass with a combination of JERS-1 and Landsat TM data",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "eid",
           "value": "2-s2.0-84879896452"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1016/j.rse.2009.03.014",
         "name": "Classification accuracy comparison: Hypothesis tests and the use of confidence intervals in evaluations of difference, equivalence and non-inferiority",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1016/j.rse.2009.03.014"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-67349093551"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1109/igarss.2009.5417349",
         "name": "Correcting estimates of land cover change and change detection accuracy for error in ground reference data",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-77951286015"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1109/igarss.2009.5417349"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "name": "On Training and Evaluation of SVM for Remote Sensing Applications",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "eid",
           "value": "2-s2.0-84869498235"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/01431160903131059",
         "name": "Preface: Spatial accuracy in remote sensing",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/01431160903131059"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-70449428661"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/01431160903130937",
         "name": "Sample size determination for image classification accuracy assessment and comparison",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/01431160903130937"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-70449338714"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/01431160902755346",
         "name": "The impact of imperfect ground reference data on the accuracy of land cover change estimation",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-70449441439"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/01431160902755346"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1111/j.1475-4762.2009.00908.x",
         "name": "The nature of publishing and assessment in Geography and Environmental Studies: Evidence from the Research Assessment Exercise 2008",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1111/j.1475-4762.2009.00908.x"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-68749102184"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "name": "A Look to the Future",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "eid",
           "value": "2-s2.0-84949769009"
         }
       },
       {
         "@type": "CreativeWork",
         "name": "Accuracy Assessment",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "eid",
           "value": "2-s2.0-84949812908"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/01431160701758152",
         "name": "All change?",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/01431160701758152"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-37249066993"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/01431160701395203",
         "name": "Crop classification by support vector machine with intelligently selected training data for an operational application",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-40349110669"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/01431160701395203"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/01431160802029685",
         "name": "DEM and bathymetry estimation for mapping a tide-coordinated shoreline from fine spatial resolution satellite sensor imagery",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-48249150046"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/01431160802029685"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/01431160802290568",
         "name": "Estimating per-pixel thematic uncertainty in remote sensing classifications",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-57049119072"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/01431160802290568"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1177/0309133308094656",
         "name": "GIS: Biodiversity applications",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1177/0309133308094656"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-55249092068"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/01431160701442120",
         "name": "Harshness in image classification accuracy assessment",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/01431160701442120"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-40349114181"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1177/0309133308093606",
         "name": "Measuring and modelling biodiversity from space",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-55249100510"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1177/0309133308093606"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1109/lgrs.2008.915597",
         "name": "Multiclass and binary SVM classification: Implications for training and classification users",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-57649140412"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1109/lgrs.2008.915597"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/01431160701822115",
         "name": "RVM-based multi-class classification of remotely sensed data",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/01431160701822115"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-40349100592"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1016/j.ecoinf.2008.02.002",
         "name": "Refining predictions of climate change impacts on plant species distribution through the use of local statistics",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1016/j.ecoinf.2008.02.002"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-48749098066"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "name": "Remote Sensing Policy",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "eid",
           "value": "2-s2.0-84949773559"
         }
       },
       {
         "@type": "CreativeWork",
         "name": "Remote Sensing Scale and Data Selection Issues",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "eid",
           "value": "2-s2.0-84949812448"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.4135/9780857021052",
         "name": "The SAGE Handbook of Remote Sensing",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.4135/9780857021052"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-84949780315"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1016/j.ecoinf.2007.03.009",
         "name": "Discriminating and mapping the C3 and C4 composition of grasslands in the northern Great Plains, USA",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1016/j.ecoinf.2007.03.009"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-34548474765"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1016/j.ecoinf.2007.06.001",
         "name": "Editorial: Ecological applications of remote sensing and GIS",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1016/j.ecoinf.2007.06.001"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-34548481046"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.14358/pers.73.7.841",
         "name": "Exploring the geostatistical method for estimating the signal-to-noise ratio of images",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.14358/pers.73.7.841"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-34447101055"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1117/12.738437",
         "name": "Image-based method for noise estimation in remotely sensed data",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1117/12.738437"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-42449144733"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/01431160701244872",
         "name": "Increasing soft classification accuracy through the use of an ensemble of classifiers",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/01431160701244872"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-34748885521"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1111/j.0906-7590.2007.04726.x",
         "name": "Investigating spatial structure in specific tree species in ancient semi-natural woodland using remote sensing and marked point pattern analysis",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1111/j.0906-7590.2007.04726.x"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-33847229497"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/01431160600784259",
         "name": "Land cover classification using multi-temporal MERIS vegetation indices",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-33947423832"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/01431160600784259"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1177/0309133307081294",
         "name": "Map comparison in GIS",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-34848882176"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1177/0309133307081294"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/01431160600962566",
         "name": "Mapping a specific class with an ensemble of classifiers",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-34547111054"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/01431160600962566"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1016/j.ecoinf.2007.04.003",
         "name": "Mapping specific habitats from remotely sensed imagery: Support vector machine and support vector data description based classification of coastal saltmarsh habitats",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1016/j.ecoinf.2007.04.003"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-34548510616"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/01431160600981533",
         "name": "Modelling geometric and misregistration error in airborne sensor data to enhance change detection",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/01431160600981533"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-34249914209"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1111/j.1472-4642.2007.00344.x",
         "name": "Non-stationarity and local approaches to modelling the distributions of wildlife",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1111/j.1472-4642.2007.00344.x"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-34247354914"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1109/tgrs.2006.890414",
         "name": "One-class classification for mapping a specific land-cover class: SVDD classification of fenland",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-33947711252"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1109/tgrs.2006.890414"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1109/igarss.2007.4423373",
         "name": "Reducing the impacts of intra-class spectral variability on soft classification and its implications for super-resolution mapping",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1109/igarss.2007.4423373"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-79954572905"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.2112/04-0421.1",
         "name": "Shoreline mapping from coarse-spatial resolution remote sensing imagery of Seberang Takir, Malaysia",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-38349058007"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.2112/04-0421.1"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.14358/pers.73.8.923",
         "name": "Variability in soft classification prediction and its implications for sub-pixel scale change detection and super resolution mapping",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.14358/pers.73.8.923"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-34547609867"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "name": "Current Status of Uncertainty Issues in Remote Sensing and GIS",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "eid",
           "value": "2-s2.0-9144260429"
         }
       },
       {
         "@type": "CreativeWork",
         "name": "Deriving thematic uncertainty measures in remote sensing using classification outputs",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "eid",
           "value": "2-s2.0-57049142104"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/01431160500396527",
         "name": "Dynamics of ENSO drought events on Sabah rainforests observed by NOAA AVHRR",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/01431160500396527"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-33747143722"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1177/0309133306071152",
         "name": "GIS: Health applications",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1177/0309133306071152"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-33947402589"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1109/igarss.2006.536",
         "name": "Impacts of class spectral variability on soft classification prediction and implications for change detection",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-34948843456"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1109/igarss.2006.536"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1117/12.695460",
         "name": "Issues in training SVM classifications",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1117/12.695460"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-33751438835"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/01431160500396741",
         "name": "Localized soft classification for super-resolution mapping of the shoreline",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/01431160500396741"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-33747112013"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/01431160600554348",
         "name": "Mapping a specific class for priority habitats monitoring from satellite sensor data",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-33747107396"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/01431160600554348"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1016/j.ecolmodel.2005.11.007",
         "name": "Mapping the species richness and composition of tropical forests from remotely sensed data with neural networks",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1016/j.ecolmodel.2005.11.007"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-33646155530"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "name": "Pattern recognition and classification of remotely sensed images by artificial neural networks",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "eid",
           "value": "2-s2.0-84865785612"
         }
       },
       {
         "@type": "CreativeWork",
         "name": "Preface",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "eid",
           "value": "2-s2.0-84948845495"
         }
       },
       {
         "@type": "CreativeWork",
         "name": "Remote Monitoring of the Impact of ENSO-related Drought on Sabah Rainforest Using NOAA AVHRR Middle Infrared Reflectance: Exploring Emissivity Uncertainty",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "eid",
           "value": "2-s2.0-84948766907"
         }
       },
       {
         "@type": "CreativeWork",
         "name": "The evaluation and comparison of thematic maps derived from remote sensing",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "eid",
           "value": "2-s2.0-84959343631"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1016/j.rse.2006.04.001",
         "name": "The use of small training sets containing mixed pixels for accurate hard image classification: Training on mixed spectral responses for classification by a SVM",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-33745756516"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1016/j.rse.2006.04.001"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1016/j.rse.2006.03.004",
         "name": "Training set size requirements for the classification of a specific class",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1016/j.rse.2006.03.004"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-33747610735"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1002/0470035269",
         "name": "Uncertainty in Remote Sensing and GIS",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-84885532231"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1002/0470035269"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "name": "Uncertainty in Remote Sensing and GIS: Fundamentals",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "eid",
           "value": "2-s2.0-84948760931"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1007/s10109-006-0023-z",
         "name": "What is the difference between two maps? A remote senser's view",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1007/s10109-006-0023-z"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-33646735739"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1016/j.rse.2005.03.001",
         "name": "Appointment of new editorial board members",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1016/j.rse.2005.03.001"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-20144388072"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1111/j.1466-822x.2005.00142.x",
         "name": "Clarifications on local and global data analysis",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-12444346852"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1111/j.1466-822x.2005.00142.x"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1890/04-1061",
         "name": "Identification of specific tree species in ancient semi-natural woodland from digital aerial sensor imagery",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1890/04-1061"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-23044503721"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1109/igarss.2005.1526217",
         "name": "Increasing soft classification accuracy through the use of an ensemble of classifiers",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-33745713885"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1109/igarss.2005.1526217"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/01431160500254999",
         "name": "Interpreting image-based methods for estimating the signal-to-noise ratio",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/01431160500254999"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-33745103492"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/01431160512331326521",
         "name": "Local characterization of thematic classification accuracy through spatially constrained confusion matrices",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-17144416175"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/01431160512331326521"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/01431160500165716",
         "name": "Mapping the richness and composition of British breeding birds from coarse spatial resolution satellite sensor imagery",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-27744511267"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/01431160500165716"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/01431160500213292",
         "name": "Super-resolution mapping of the waterline from remotely sensed data",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-33745106415"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/01431160500213292"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1109/tgrs.2004.827257",
         "name": "A relative evaluation of multiclass image classification by support vector machines",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1109/tgrs.2004.827257"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-3042654673"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1191/0309133304pp407pr",
         "name": "GIS: Stressing the geographical",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-84990370512"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1191/0309133304pp407pr"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "name": "Land cover classification by support vector machine: Towards efficient training",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "eid",
           "value": "2-s2.0-15944418297"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1016/j.jhydrol.2003.12.045",
         "name": "Predicting locations sensitive to flash flooding in an arid environment",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1016/j.jhydrol.2003.12.045"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-2342479993"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1111/j.1466-822x.2004.00097.x",
         "name": "Spatial nonstationarity and scale-dependency in the relationship between species richness and environmental determinants for the sub-Saharan endemic avifauna",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-3142751254"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1111/j.1466-822x.2004.00097.x"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/01431160310001648019",
         "name": "Supervised image classification by MLP and RBF neural networks with and without an exhaustively defined set of classes",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-3242759024"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/01431160310001648019"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/01431160310001654969",
         "name": "Thematic labelling from hyperspectral remotely sensed imagery: Trade-offs in image properties",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/01431160310001654969"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-3242769079"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.14358/pers.70.5.627",
         "name": "Thematic map comparison: Evaluating the statistical significance of differences in classification accuracy",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-3042661357"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.14358/pers.70.5.627"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1016/j.rse.2004.06.017",
         "name": "Toward intelligent training of supervised image classifications: Directing training data acquisition for SVM classification",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1016/j.rse.2004.06.017"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-4544272407"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1016/j.rse.2003.08.004",
         "name": "Geographical weighting as a further refinement to regression modelling: An example focused on the NDVI-rainfall relationship",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1016/j.rse.2003.08.004"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0842306347"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "name": "Potential improvements in the characterization of forest canopy gaps caused by windthrow using fine spatial resolution multispectral data: Comparing hard and soft classification techniques",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "eid",
           "value": "2-s2.0-0038209052"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1016/s0034-4257(03)00039-7",
         "name": "Predictive relations of tropical forest biomass from Landsat TM data and their transferability between regions",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0037986221"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1016/s0034-4257(03)00039-7"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/0143116031000103853",
         "name": "Remote sensing of tropical forest environments: Towards the monitoring of environmental resources for sustainable development",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/0143116031000103853"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0142197746"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "name": "Spatio-temporal Response of Extreme Events on Bornean Rainforests",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "eid",
           "value": "2-s2.0-0242710519"
         }
       },
       {
         "@type": "CreativeWork",
         "name": "Super-resolution mapping of the shoreline through soft classification analyses",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "eid",
           "value": "2-s2.0-0242625767"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1046/j.1365-2699.2003.00887.x",
         "name": "Tree biodiversity in protected and logged Bornean tropical rain forests and its measurement by satellite remote sensing",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1046/j.1365-2699.2003.00887.x"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0037897270"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1191/0309133303pp345pr",
         "name": "Uncertainty, knowledge discovery and data mining in GIS",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0037342756"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1191/0309133303pp345pr"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "name": "Characterizing the flash flood hazards potential along the Red Sea coast of Egypt",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "eid",
           "value": "2-s2.0-0036126947"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1016/s0143-6228(02)00048-6",
         "name": "Evaluation of approaches for forest cover estimation in the Pacific Northwest, USA, using remote sensing",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1016/s0143-6228(02)00048-6"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0036812380"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/01431160210163128",
         "name": "Exploring the utility of NOAA AVHRR middle infrared reflectance to monitor the impacts of ENSO-induced drought stress on Sabah rainforests",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/01431160210163128"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0037059198"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/01431160110069791",
         "name": "Forest regeneration on abandoned clearances in Central Amazonia",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/01431160110069791"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0037051192"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/01431160110109570",
         "name": "Hard and soft classifications by a neural network with a non-exhaustively defined set of classes",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0037144618"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/01431160110109570"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "name": "Remote sensing of biodiversity: Using neural networks to estimate the diversity and composition of a Bornean tropical rainforest from Landsat TM data",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "eid",
           "value": "2-s2.0-0036031101"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1007/s005210200017",
         "name": "Sharpened mapping of tropical forest biophysical properties from coarse spatial resolution satellite sensor data",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0036665988"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1007/s005210200017"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1016/s0034-4257(01)00295-4",
         "name": "Status of land cover classification accuracy assessment",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0036213079"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1016/s0034-4257(01)00295-4"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "name": "The role of soft classification techniques in the refinement of estimates of ground control point location",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "eid",
           "value": "2-s2.0-0036731661"
         }
       },
       {
         "@type": "CreativeWork",
         "name": "Accuracy of image classifications. Problems in evaluating thematic maps derived from imagery",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "eid",
           "value": "2-s2.0-0035037741"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/01431160050505883",
         "name": "Fully-fuzzy supervised classification of sub-urban land cover from remotely sensed imagery: Statistical and artificial neural network approaches",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/01431160050505883"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0035048764"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1191/030913301680193841",
         "name": "GIS: The accuracy of spatial data revisited",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1191/030913301680193841"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0034753302"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "name": "Land cover classification from hyperspectral remotely sensed data: An investigation of spectral, spatial and noise issues",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "eid",
           "value": "2-s2.0-0035574595"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1046/j.1466-822x.2001.00248.x",
         "name": "Mapping the biomass of Bornean tropical rain forest from remotely sensed data",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1046/j.1466-822x.2001.00248.x"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0034957101"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "name": "Monitoring the magnitude of land-cover change around the southern limits of the Sahara",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "eid",
           "value": "2-s2.0-0034959023"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/01431160116869",
         "name": "Relationship between green leaf biomass volumetric density and ERS-2 SAR backscatter of four vegetation formations in the semi-arid zone of Israel",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/01431160116869"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0035918545"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "name": "Remote monitoring of impacts of ENSO related drought stress on Sabah rainforests",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "eid",
           "value": "2-s2.0-0035575031"
         }
       },
       {
         "@type": "CreativeWork",
         "name": "The effect of a non-exhaustively defined set of classes on neural network classifications",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "eid",
           "value": "2-s2.0-0035573303"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1007/pl00011477",
         "name": "Thematic mapping from remotely sensed data with neural networks: MLP, RBF and PNN based approaches",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1007/pl00011477"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-1342343252"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/01431160050110188",
         "name": "Assessing the ground data requirements for regional scale remote sensing of tropical forest biophysical properties",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/01431160050110188"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0033904637"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1016/s0378-1127(00)00284-x",
         "name": "Characterising windthrown gaps from fine spatial resolution remotely sensed data",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1016/s0378-1127(00)00284-x"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0034666591"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/01431160050121276",
         "name": "Characterizing tropical forest regeneration in Cameroon using NOAA AVHRR data",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/01431160050121276"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0033821354"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1016/s0098-3004(99)00125-9",
         "name": "Estimation of sub-pixel land cover composition in the presence of untrained classes",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0034010010"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1016/s0098-3004(99)00125-9"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1117/12.406583",
         "name": "General approach to assessing the value of hyperspectral imagery and its application to sensor concept evaluation",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1117/12.406583"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0034505225"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "name": "Linking remote sensing, land cover and disease",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "eid",
           "value": "2-s2.0-0033835012"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1023/a:1008112125526",
         "name": "Mapping land cover from remotely sensed data with a softened feedforward neural network classification",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1023/a:1008112125526"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0034548685"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/01431160050121285",
         "name": "Mapping the regional extent of tropical forest regeneration stages in the Brazilian Legal Amazon using NOAA AVHRR data",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/01431160050121285"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0033810997"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/01431160050029620",
         "name": "The relationship between ERS-2 SAR backscatter and soil moisture: Generalization from a humid to semi-arid transect",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/01431160050029620"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0033909994"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1016/s0304-3800(99)00094-0",
         "name": "Applications of the self-organising feature map neural network in community data analysis",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1016/s0304-3800(99)00094-0"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0033578381"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/014311699211769",
         "name": "Detection of partial land cover change associated with the migration of inter-class transitional zones",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0033434022"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/014311699211769"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1007/s101090050003",
         "name": "Fuzzy mapping of tropical land cover along an environmental gradient from remotely sensed data with an artificial neural network",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0013367757"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1007/s101090050003"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "name": "The continuum of classification fuzziness in thematic mapping",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "eid",
           "value": "2-s2.0-0033005338"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/014311699213055",
         "name": "The relationship between the biomass of cameroonian tropical forests and radiation reflected in middle infrared wavelengths (3.0-5.0 m\u00b5)",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0033586413"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/014311699213055"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/014311699211192",
         "name": "The significance of border training patterns in classification by a feedforward neural network using back propagation learning",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0033372875"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/014311699211192"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/014311698214479",
         "name": "A fuzzy classification of sub-urban land cover from remotely sensed imagery",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0032552476"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/014311698214479"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "name": "Estimating biophysical properties of tropical forests using radiation reflected in middle infrared wavelengths (3.0 - 5.0 \u03bcm)",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "eid",
           "value": "2-s2.0-0031625878"
         }
       },
       {
         "@type": "CreativeWork",
         "name": "Issues in training set selection and refinement for classification by a feedforward neural network",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "eid",
           "value": "2-s2.0-0031642397"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/014311698214659",
         "name": "Sharpening fuzzy classification output to refine the representation of sub-pixel land cover distribution",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0032505168"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/014311698214659"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1117/12.326720",
         "name": "Soft classifications for the mapping of land cover from remotely sensed data",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1117/12.326720"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0342722872"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1068/a301929",
         "name": "Unmixing aggregate data: estimating the social composition of enumeration districts",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1068/a301929"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0032434177"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/014311697218764",
         "name": "An evaluation of some factors affecting the accuracy of classification by an artificial neural network",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0031105722"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/014311697218764"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1007/bf01424229",
         "name": "Fully fuzzy supervised classification of land cover from remotely sensed imagery with an artificial neural network",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-21744459008"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1007/bf01424229"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/014311697218755",
         "name": "Log-linear modelling for the evaluation of the variables affecting the accuracy of probabilistic, fuzzy and neural network classifications",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0031106423"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/014311697218755"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1023/a:1009775619936",
         "name": "Mapping tropical forest fractional cover from coarse spatial resolution remote sensing imagery",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1023/a:1009775619936"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0030984130"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/014311697218845",
         "name": "Non-linear mixture modelling without end-members using an artificial neural network",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0031105570"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/014311697218845"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/014311697219024",
         "name": "Observations on the relationship between SIR-C radar backscatter and the biomass of regenerating tropical forests",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0031077818"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/014311697219024"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/01431169608949003",
         "name": "An assessment of radiance in landsat tm middle and thermal infrared wavebands for the detection of tropical forest regeneration",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/01431169608949003"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0029728982"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/01431169608948706",
         "name": "Approaches for the production and evaluation of fuzzy land cover classifications from remotely-sensed data",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/01431169608948706"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0030135691"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/01431169608948777",
         "name": "Classification of tropical forest classes from landsat tm data.",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/01431169608948777"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0030208643"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "name": "Coupling remotely sensed data to an ecosystem simulation model - An example involving a coniferous plantation in upland Wales",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "eid",
           "value": "2-s2.0-0008743113"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/07038992.1996.10874666",
         "name": "Estimation of the Areal Extent of Land Cover Classes that Only Occur at a Sub-Pixel Level",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0030392042"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/07038992.1996.10874666"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "name": "Fuzzy modelling of vegetation from remotely sensed imagery",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "eid",
           "value": "2-s2.0-0002780271"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1016/s0034-4257(95)00196-4",
         "name": "Identifying terrestrial carbon sinks: Classification of successional stages in regenerating tropical forest from Landsat TM data",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0029769688"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1016/s0034-4257(95)00196-4"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1016/s0167-8655(96)00095-5",
         "name": "Incorporating mixed pixels in the training, allocation and testing stages of supervised classifications",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0030292025"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1016/s0167-8655(96)00095-5"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "name": "Relating the land-cover composition of mixed pixels to artificial neural network classification output",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "eid",
           "value": "2-s2.0-0029750642"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/01431169608948707",
         "name": "Relations between tropical forest biophysical properties and data acquired in AVHRR channels 1-5",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0030136457"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/01431169608948707"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/10106049609354519",
         "name": "Representation of ecological trends in remotely sensed data: Relating the probability of class membership to canopy composition and a vegetation ordination",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0030456229"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/10106049609354519"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "name": "The mystery of the missing carbon",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "eid",
           "value": "2-s2.0-6244293672"
         }
       },
       {
         "@type": "CreativeWork",
         "name": "The mystery of the missing carbon",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "eid",
           "value": "2-s2.0-0029750042"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1002/bimj.4710380206",
         "name": "Weighting class importance in agricultural crop classification from remotely sensed data with an artificial neural network",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0030526510"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1002/bimj.4710380206"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1016/0924-2716(95)90116-v",
         "name": "Cross-entropy for the evaluation of the accuracy of a fuzzy land cover classification with fuzzy ground data",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1016/0924-2716(95)90116-v"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0028982899"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1007/bf00807952",
         "name": "Estimation of land coverage from a land cover classification derived from remotely sensed data",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1007/bf00807952"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0028981335"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/02693799508902054",
         "name": "Land cover classification by an artificial neural network with ancillary information",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/02693799508902054"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0029478734"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1016/0198-9715(95)00025-9",
         "name": "Mapping despoiled land cover from Landsat thematic mapper imagery",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1016/0198-9715(95)00025-9"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0029415395"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "name": "The effect of sampling on the species-area curve",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "eid",
           "value": "2-s2.0-0028572837"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/01431169508954507",
         "name": "The effect of training set size and composition on artificial neural network classification",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0029473455"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/01431169508954507"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1007/bf01414080",
         "name": "Training pattern replication and weighted class allocation in artificial neural network classification",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0346256369"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1007/bf01414080"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/01431169508954396",
         "name": "Using prior knowledge in artificial neural network classification with a minimal training set",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0028874010"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/01431169508954396"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "name": "Classification of remotely sensed data by an artificial neural network: issues related to training data characteristics",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "eid",
           "value": "2-s2.0-0028560774"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/01431169408954289",
         "name": "Crop classification from c-band polarimetric radar data",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/01431169408954289"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0028552715"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "name": "Environmental remote sensing from regional to global scales",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "eid",
           "value": "2-s2.0-85041151672"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.2307/2845527",
         "name": "Estimation of tropical forest extent and regenerative stage using remotely sensed data",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.2307/2845527"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0028163167"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/07038992.1994.10874582",
         "name": "Multi-source image classification II: An empirical comparison of evidential reasoning and neural network approaches",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/07038992.1994.10874582"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0028740098"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "name": "Ordinal-level classification of sub-pixel tropical forest cover",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "eid",
           "value": "2-s2.0-0028165056"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/01431169408954276",
         "name": "Separability of tropical rain-forest types in the tambopata-candamo reserved zone, peru",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0028594257"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/01431169408954276"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/01431169408954100",
         "name": "Sub-pixel land cover composition estimation using a linear mixture model and fuzzy membership functions",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0028184024"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/01431169408954100"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/01431169308904419",
         "name": "Characterizing tropical secondary forests using multi-temporal landsat sensor imagery",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/01431169308904419"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0027698706"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1002/ldr.3400040306",
         "name": "Determining the extent and spectral separability of industrially despoiled land in South Wales from satellite sensor data",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1002/ldr.3400040306"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0027846682"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1007/bf00807535",
         "name": "Non-classificatory analysis and representation of heathland vegetation from remotely sensed imagery",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1007/bf00807535"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0027832861"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "name": "Using cover-type likelihoods and typicalities in a geographic information system data structure to map gradually changing environments",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "eid",
           "value": "2-s2.0-0027371435"
         }
       },
       {
         "@type": "CreativeWork",
         "name": "A fuzzy sets approach to the representation of vegetation continua from remotely sensed data: an example from lowland heath",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "eid",
           "value": "2-s2.0-0026614862"
         }
       },
       {
         "@type": "CreativeWork",
         "name": "Derivation and applications of probabilistic measures of class membership from the maximum-likelihood classification",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "eid",
           "value": "2-s2.0-0027007203"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/01431169108955183",
         "name": "Soil moisture content ground data for remote sensing investigations of agricultural regions",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/01431169108955183"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0026358111"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/01431169008955148",
         "name": "Directed ground survey for improved maximum likelihood classification of remotely sensed data",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0025573157"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/01431169008955148"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "name": "Remote sensing of soils and vegetation in the USSR",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "eid",
           "value": "2-s2.0-85040890543"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/01431168908903855",
         "name": "Analysis and representation of vegetation continua from landsat thematic mapper data for lowland heaths",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/01431168908903855"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0024471330"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/10106048909354218",
         "name": "Multi\u2010temporal airborne synthetic aperture radar data for crop classification",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0024816843"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/10106048909354218"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "name": "Some aspects of the accuracy and comparability of soil ground data collected for microwave remote sensing investigations",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "eid",
           "value": "2-s2.0-0024807598"
         }
       },
       {
         "@type": "CreativeWork",
         "name": "Classification decision rule modification on the basis of information extracted from training data",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "eid",
           "value": "2-s2.0-0024224303"
         }
       },
       {
         "@type": "CreativeWork",
         "name": "Classification decision rule modification on the basis of information extracted from training data",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "eid",
           "value": "2-s2.0-0024078941"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/01431168808954884",
         "name": "Crop classification from airborne synthetic aperture radar data",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0024248564"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/01431168808954884"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "name": "Crop classification with multi-temporal X-band SAR data",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "eid",
           "value": "2-s2.0-0024152048"
         }
       },
       {
         "@type": "CreativeWork",
         "name": "Crop classification with multi-temporal X-band SAR data",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "eid",
           "value": "2-s2.0-0024078747"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/10106048809354161",
         "name": "Incorporating remotely sensed data into a GIS: The problem of classification evaluation",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/10106048809354161"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-3342892569"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/01431168808954989",
         "name": "The effects of viewing geometry on image classification",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/01431168808954989"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0024199256"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/10106048709354091",
         "name": "A method for thematic classification with synthetic aperture radar data",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/10106048709354091"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-84946341770"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "name": "Image classification: the spatial component.",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "eid",
           "value": "2-s2.0-0023482564"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/01431168708948700",
         "name": "Remote sensing letters: Radiometric balancing a comment",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/01431168708948700"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0023504188"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "name": "The use of Landsat TM data in a GIS for environmental monitoring.",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "eid",
           "value": "2-s2.0-0023471475"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/07038992.1986.10855104",
         "name": "An assessment of the topographic effects on sar image tone",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0022879252"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/07038992.1986.10855104"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "name": "Crop mapping with multi-feature synthetic aperture radar data ( East Anglia, UK).",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "eid",
           "value": "2-s2.0-0022851525"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/07038992.1986.10855095",
         "name": "Land-cover mapping from synthetic aperture radar: The importance of radiometric correction",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0022927017"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/07038992.1986.10855095"
           }
         ]
       },
       {
         "@type": "CreativeWork",
         "name": "Viewing geometry effects on SAR image tone and its importance for land cover mapping.",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "eid",
           "value": "2-s2.0-0022850282"
         }
       },
       {
         "@type": "CreativeWork",
         "name": "The influence of SAR viewing geometry on land cover mapping accuracy.",
         "identifier": {
           "@type": "PropertyValue",
           "propertyID": "eid",
           "value": "2-s2.0-0022235380"
         }
       },
       {
         "@type": "CreativeWork",
         "@id": "https://doi.org/10.1080/01431168408948892",
         "name": "Sectoring radar images to improve land cover map accuracy",
         "identifier": [
           {
             "@type": "PropertyValue",
             "propertyID": "doi",
             "value": "10.1080/01431168408948892"
           },
           {
             "@type": "PropertyValue",
             "propertyID": "eid",
             "value": "2-s2.0-0021613390"
           }
         ]
       }
     ]
   },
   "identifier": {
     "@type": "PropertyValue",
     "propertyID": "Scopus Author ID",
     "value": "7007014233"
   }
 }

}