Item talk:Q250142
From geokb
{
"USGS Publications Warehouse": { "@context": "https://schema.org", "@type": "Article", "additionalType": "Journal Article", "name": "Characterizing the spatial structure of endangered species habitat using geostatistical analysis of IKONOS imagery", "identifier": [ { "@type": "PropertyValue", "propertyID": "USGS Publications Warehouse IndexID", "value": "70029560", "url": "https://pubs.usgs.gov/publication/70029560" }, { "@type": "PropertyValue", "propertyID": "USGS Publications Warehouse Internal ID", "value": 70029560 }, { "@type": "PropertyValue", "propertyID": "DOI", "value": "10.1080/01430060500099394", "url": "https://doi.org/10.1080/01430060500099394" } ], "journal": { "@type": "Periodical", "name": "International Journal of Remote Sensing", "volumeNumber": "26", "issueNumber": "12" }, "inLanguage": "en", "isPartOf": [ { "@type": "CreativeWorkSeries", "name": "International Journal of Remote Sensing" } ], "datePublished": "2005", "dateModified": "2020-08-27", "abstract": "Our study used geostatistics to extract measures that characterize the spatial structure of vegetated landscapes from satellite imagery for mapping endangered Sonoran pronghorn habitat. Fine spatial resolution IKONOS data provided information at the scale of individual trees or shrubs that permitted analysis of vegetation structure and pattern. We derived images of landscape structure by calculating local estimates of the nugget, sill, and range variogram parameters within 25 ?? 25-m image windows. These variogram parameters, which describe the spatial autocorrelation of the 1-m image pixels, are shown in previous studies to discriminate between different species-specific vegetation associations. We constructed two independent models of pronghorn landscape preference by coupling the derived measures with Sonoran pronghorn sighting data: a distribution-based model and a cluster-based model. The distribution-based model used the descriptive statistics for variogram measures at pronghorn sightings, whereas the cluster-based model used the distribution of pronghorn sightings within clusters of an unsupervised classification of derived images. Both models define similar landscapes, and validation results confirm they effectively predict the locations of an independent set of pronghorn sightings. Such information, although not a substitute for field-based knowledge of the landscape and associated ecological processes, can provide valuable reconnaissance information to guide natural resource management efforts.\u00a0", "description": "23 p.", "publisher": { "@type": "Organization", "name": "Taylor & Francis" }, "author": [ { "@type": "Person", "name": "Marsh, S.E.", "givenName": "S.E.", "familyName": "Marsh" }, { "@type": "Person", "name": "Wallace, C.S.A.", "givenName": "C.S.A.", "familyName": "Wallace" } ], "funder": [ { "@type": "Organization", "name": "Southwest Geographic Science Center", "url": "https://www.usgs.gov/programs/national-geospatial-program" }, { "@type": "Organization", "name": "Western Geographic Science Center", "url": "https://www.usgs.gov/centers/western-geographic-science-center" } ], "spatialCoverage": [ { "@type": "Place", "additionalType": "country", "name": "United States", "url": "https://geonames.org/4074035" }, { "@type": "Place", "additionalType": "state", "name": "Arizona" }, { "@type": "Place", "additionalType": "unknown", "name": "Barry M. 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