Item talk:Q305885
From geokb
{
"USGS Publications Warehouse": { "@context": "https://schema.org", "@type": "Article", "additionalType": "Journal Article", "name": "Integrating forest inventory data and MODIS data to map species-level biomass in Chinese boreal forests", "identifier": [ { "@type": "PropertyValue", "propertyID": "USGS Publications Warehouse IndexID", "value": "70201610", "url": "https://pubs.usgs.gov/publication/70201610" }, { "@type": "PropertyValue", "propertyID": "USGS Publications Warehouse Internal ID", "value": 70201610 }, { "@type": "PropertyValue", "propertyID": "DOI", "value": "10.1139/cjfr-2017-0346", "url": "https://doi.org/10.1139/cjfr-2017-0346" } ], "journal": { "@type": "Periodical", "name": "Canadian Journal of Forest Research", "volumeNumber": "48", "issueNumber": "5" }, "inLanguage": "en", "isPartOf": [ { "@type": "CreativeWorkSeries", "name": "Canadian Journal of Forest Research" } ], "datePublished": "2018", "dateModified": "2018-12-18", "abstract": "Timely and accurate knowledge of species-level biomass is essential for forest managers to sustain forest resources and respond to various forest disturbance regimes. In this study, maps of species-level biomass in Chinese boreal forests were generated by integrating Moderate Resolution Imaging Spectroradiometer (MODIS) images with forest inventory data using\u00a0k\u00a0nearest neighbor (kNN) methods and evaluated at different scales. The performance of 630\u00a0kNN models based on different distance metrics,\u00a0k\u00a0values, and temporal MODIS predictor variables were compared. Random Forest (RF) showed the best performance among the six distance metrics: RF, Euclidean distance, Mahalanobis distance, most similar neighbor in canonical correlation space, most similar neighbor computed using projection pursuit, and gradient nearest neighbor. No appreciable improvement was observed using multi-month MODIS data compared with using single-month MODIS data. At the pixel scale, species-level biomass for larch and white birch had relatively good accuracy (root mean square deviation < 62.1%), while the other species had poorer accuracy. The accuracy of most species except for willow and spruce was improved up to the ecoregion scale. The maps of species-level biomass captured the effects of disturbances including fire and harvest and can provide useful information for broad-scale forest monitoring over time.", "description": "19 p.", "publisher": { "@type": "Organization", "name": "Canadian Science Publishing" }, "author": [ { "@type": "Person", "name": "Hawbaker, Todd tjhawbaker@usgs.gov", "givenName": "Todd", "familyName": "Hawbaker", "email": "tjhawbaker@usgs.gov", "identifier": { "@type": "PropertyValue", "propertyID": "ORCID", "value": "0000-0003-0930-9154", "url": "https://orcid.org/0000-0003-0930-9154" }, "affiliation": [ { "@type": "Organization", "name": "Geosciences and Environmental Change Science Center", "url": "https://www.usgs.gov/centers/geosciences-and-environmental-change-science-center" }, { "@type": "Organization", "name": "Rocky Mountain Geographic Science Center", "url": "https://www.usgs.gov/programs/national-geospatial-program" } ] }, { "@type": "Person", "name": "Zhang, Qinglong", "givenName": "Qinglong", "familyName": "Zhang", "affiliation": [ { "@type": "Organization", "name": "CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Shenyang 110016, China" } ] }, { "@type": "Person", "name": "He, Hong S.", "givenName": "Hong S.", "familyName": "He", "affiliation": [ { "@type": "Organization", "name": "Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China; School of Natural Resources, University of Missouri, 203 ABNR Building, Columbia, MO, USA", "url": "https://www1.usgs.gov/coopunits/unit/WisconsinWildlife" } ] }, { "@type": "Person", "name": "Liang, Yu", "givenName": "Yu", "familyName": "Liang", "affiliation": [ { "@type": "Organization", "name": "Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China" } ] }, { "@type": "Person", "name": "Henne, Paul D. phenne@usgs.gov", "givenName": "Paul D.", "familyName": "Henne", "email": "phenne@usgs.gov", "identifier": { "@type": 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