ACCURACY ASSESSMENT OF LANDSAT-DERIVED CONTINUOUS FIELDS OF TREE COVER PRODUCTS USING AIRBORNE LIDAR DATA IN THE EASTERN UNITED STATES
Knowing the detailed error structure of a land cover map is crucial for area estimation. Facilitated by the opening of the Landsat archive, global land cover mapping at 30-m resolution has become possible in recent years. Two global Landsat-based continuous fields of tree cover maps have been genera...
Main Authors: | , |
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Format: | Article |
Language: | English |
Published: |
Copernicus Publications
2015-08-01
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Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
Online Access: | http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-7-W4/241/2015/isprsarchives-XL-7-W4-241-2015.pdf |
Summary: | Knowing the detailed error structure of a land cover map is crucial for area estimation. Facilitated by the opening of the Landsat
archive, global land cover mapping at 30-m resolution has become possible in recent years. Two global Landsat-based continuous
fields of tree cover maps have been generated by Sexton et al. (2013) and Hansen et al. (2013) but the accuracy of which have not been
comprehensively evaluated. Here we used canopy cover derived from airborne small-footprint Lidar data as a reference to evaluate the
accuracy of these two datasets as well as the National Land Cover Database 2001 canopy cover layer (Homer et al. 2004) in two entire
counties in Maryland, United States. Our results showed that all three Landsat datasets captured well the spatial variations of tree cover
in the study area with an <i>r</i><sup>2</sup> ranging between 0.54 and 0.58, a mean bias error ranging between -15% and 5% tree cover, and a root
mean square error ranging between 27% and 29% tree cover. When the continuous tree cover maps were converted to binary
forest/nonforest maps, all three products were proved to have an overall accuracy >= 80% but with significant differences in producer’s
accuracy and user’s accuracy. Data users are thus suggested to beware of these accuracy patterns when selecting the most appropriate
dataset for their specific applications. |
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ISSN: | 1682-1750 2194-9034 |