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Title: Robust forest cover indices for multispectral images
Authors: Becker, Sarah J.
Daughtry, Craig S.T.
Russ, Andrew L.
Keywords: Trees
Forest canopies
Land cover--Remote sensing
Multispectral imaging
Publisher: Geospatial Research Laboratory (U.S.)
Engineer Research and Development Center (U.S.)
Series/Report no.: Miscellaneous Paper (Engineer Research and Development Center (U.S.)) ; no. ERDC/GRL MP-19-1
Is Version Of: Becker, Sarah J., Craig ST Daughtry, and Andrew L. Russ. "Robust forest cover indices for multispectral images." Photogrammetric Engineering & Remote Sensing 84, no. 8 (2018): 505-512.
Abstract: Trees occur in many land cover classes and provide significant ecosystem services. Remotely sensed multispectral images are often used to create thematic maps of land cover, but accurately identifying trees in mixed land-use scenes is challenging. We developed two forest cover indices and protocols that reliably identified trees in WorldView-2 multispectral images. The study site in Maryland included coniferous and deciduous trees associated with agricultural fields and pastures, residential and commercial buildings, roads, parking lots, wetlands, and forests. The forest cover indices exploited the product of either the reflectance in red (630 to 690 nm) and red edge (705 to 745 nm) bands or the product of reflectance in red and near infrared (770 to 895 nm) bands. For two classes (trees versus other), overall classification accuracy was >77 percent for the four images that were acquired in each season of the year. Additional research is required to evaluate these indices for other scenes and sensors.
Description: Miscellaneous Paper
Gov't Doc #: ERDC/GRL MP-19-1
Rights: Approved for Public Release; Distribution is Unlimited
Size: 34 pages / 2.28 MB
Types of Materials: PDF/A
Appears in Collections:Miscellaneous Paper

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