Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS vegetation continuous fields with lidar-based estimates of error
Joseph O. Sexton,Xiao-Peng Song,Min Feng,Praveen Noojipady,Anupam Anand,Chengquan Huang,Do-Hyung Kim,K. M. Collins,Saurabh Channan,C. Dimiceli,John R. Townshend +10 more
TL;DR: A global, 30-m resolution dataset of percent tree cover by rescaling the 250-m MOderate-resolution Imaging Spectroradiometer (MODIS) Vegetation Continuous Fields (VCF) Tree Cover layer using circa- 2000 and 2005 Landsat images, incorporating the MODIS Cropland Layer to improve accuracy in agricultural areas.
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Abstract: We developed a global, 30-m resolution dataset of percent tree cover by rescaling the 250-m MOderate-resolution Imaging Spectroradiometer (MODIS) Vegetation Continuous Fields (VCF) Tree Cover layer using circa- 2000 and 2005 Landsat images, incorporating the MODIS Cropland Layer to improve accuracy in agricultural areas Resulting Landsat-based estimates maintained consistency with the MODIS VCF in both epochs (RMSE =86% in 2000 and 119% in 2005), but showed improved accuracy in agricultural areas and increased discrimination of small forest patches Against lidar measurements, the Landsat-based estimates exhibited accuracy slightly less than that of the MODIS VCF (RMSE=168% for MODIS-based vs 174% for Landsat-based estimates), but RMSE of Landsat estimates was 33 percentage points lower than that of the MODIS data in an agricultural region The Landsat data retained the saturation artifact of the MODIS VCF at greater than or equal to 80% tree cover but showed greater potential for removal
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Reply on RC2
Laura Schild
- 28 May 2024
TL;DR: Improved quantitative vegetation cover reconstruction from a global sedimentary pollen data set using REVEALS
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