@article{sextonGlobal30mResolution2013,
  title = {Global, 30-m Resolution Continuous Fields of Tree Cover: {{Landsat}}-Based Rescaling of {{MODIS}} Vegetation Continuous Fields with Lidar-Based Estimates of Error},
  author = {Sexton, Joseph O. and Song, Xiao-Peng and Feng, Min and Noojipady, Praveen and Anand, Anupam and Huang, Chengquan and Kim, Do-Hyung and Collins, Kathrine M. and Channan, Saurabh and DiMiceli, Charlene and Townshend, John R.},
  date = {2013-09},
  journaltitle = {International Journal of Digital Earth},
  volume = {6},
  pages = {427--448},
  issn = {1753-8947},
  doi = {10.1080/17538947.2013.786146},
  url = {http://mfkp.org/INRMM/article/12286079},
  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 = 8.6\,\% in 2000 and 11.9\,\% 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 = 16.8\,\% for MODIS-based vs. 17.4\,\% for Landsat-based estimates), but RMSE of Landsat estimates was 3.3 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 of errors through calibration to lidar, with post-calibration RMSE of 9.4\,\% compared to 13.5\,\% in MODIS estimates. Provided for free download at the Global Land Cover Facility (GLCF) website (www.landcover.org), the 30-m resolution GLCF tree cover dataset is the highest-resolution multi-temporal depiction of Earth's tree cover available to the Earth science community.},
  keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-12286079,~to-add-doi-URL,forest-resources,global-scale,land-cover,lidar,modis,open-data,proportion,remote-sensing},
  number = {5}
}

{"_id":"bQSECXNhzxeBjTcCz","bibbaseid":"sexton-song-feng-noojipady-anand-huang-kim-collins-etal-global30mresolutioncontinuousfieldsoftreecoverlandsatbasedrescalingofmodisvegetationcontinuousfieldswithlidarbasedestimatesoferror","authorIDs":[],"author_short":["Sexton, J. O.","Song, X.","Feng, M.","Noojipady, P.","Anand, A.","Huang, C.","Kim, D.","Collins, K. M.","Channan, S.","DiMiceli, C.","Townshend, J. R."],"bibdata":{"bibtype":"article","type":"article","title":"Global, 30-m Resolution Continuous Fields of Tree Cover: Landsat-Based Rescaling of MODIS Vegetation Continuous Fields with Lidar-Based Estimates of Error","author":[{"propositions":[],"lastnames":["Sexton"],"firstnames":["Joseph","O."],"suffixes":[]},{"propositions":[],"lastnames":["Song"],"firstnames":["Xiao-Peng"],"suffixes":[]},{"propositions":[],"lastnames":["Feng"],"firstnames":["Min"],"suffixes":[]},{"propositions":[],"lastnames":["Noojipady"],"firstnames":["Praveen"],"suffixes":[]},{"propositions":[],"lastnames":["Anand"],"firstnames":["Anupam"],"suffixes":[]},{"propositions":[],"lastnames":["Huang"],"firstnames":["Chengquan"],"suffixes":[]},{"propositions":[],"lastnames":["Kim"],"firstnames":["Do-Hyung"],"suffixes":[]},{"propositions":[],"lastnames":["Collins"],"firstnames":["Kathrine","M."],"suffixes":[]},{"propositions":[],"lastnames":["Channan"],"firstnames":["Saurabh"],"suffixes":[]},{"propositions":[],"lastnames":["DiMiceli"],"firstnames":["Charlene"],"suffixes":[]},{"propositions":[],"lastnames":["Townshend"],"firstnames":["John","R."],"suffixes":[]}],"date":"2013-09","journaltitle":"International Journal of Digital Earth","volume":"6","pages":"427–448","issn":"1753-8947","doi":"10.1080/17538947.2013.786146","url":"http://mfkp.org/INRMM/article/12286079","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 = 8.6\\,% in 2000 and 11.9\\,% 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 = 16.8\\,% for MODIS-based vs. 17.4\\,% for Landsat-based estimates), but RMSE of Landsat estimates was 3.3 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 of errors through calibration to lidar, with post-calibration RMSE of 9.4\\,% compared to 13.5\\,% in MODIS estimates. Provided for free download at the Global Land Cover Facility (GLCF) website (www.landcover.org), the 30-m resolution GLCF tree cover dataset is the highest-resolution multi-temporal depiction of Earth's tree cover available to the Earth science community.","keywords":"*imported-from-citeulike-INRMM,~INRMM-MiD:c-12286079,~to-add-doi-URL,forest-resources,global-scale,land-cover,lidar,modis,open-data,proportion,remote-sensing","number":"5","bibtex":"@article{sextonGlobal30mResolution2013,\n title = {Global, 30-m Resolution Continuous Fields of Tree Cover: {{Landsat}}-Based Rescaling of {{MODIS}} Vegetation Continuous Fields with Lidar-Based Estimates of Error},\n author = {Sexton, Joseph O. and Song, Xiao-Peng and Feng, Min and Noojipady, Praveen and Anand, Anupam and Huang, Chengquan and Kim, Do-Hyung and Collins, Kathrine M. and Channan, Saurabh and DiMiceli, Charlene and Townshend, John R.},\n date = {2013-09},\n journaltitle = {International Journal of Digital Earth},\n volume = {6},\n pages = {427--448},\n issn = {1753-8947},\n doi = {10.1080/17538947.2013.786146},\n url = {http://mfkp.org/INRMM/article/12286079},\n 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 = 8.6\\,\\% in 2000 and 11.9\\,\\% 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 = 16.8\\,\\% for MODIS-based vs. 17.4\\,\\% for Landsat-based estimates), but RMSE of Landsat estimates was 3.3 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 of errors through calibration to lidar, with post-calibration RMSE of 9.4\\,\\% compared to 13.5\\,\\% in MODIS estimates. Provided for free download at the Global Land Cover Facility (GLCF) website (www.landcover.org), the 30-m resolution GLCF tree cover dataset is the highest-resolution multi-temporal depiction of Earth's tree cover available to the Earth science community.},\n keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-12286079,~to-add-doi-URL,forest-resources,global-scale,land-cover,lidar,modis,open-data,proportion,remote-sensing},\n number = {5}\n}\n\n","author_short":["Sexton, J. O.","Song, X.","Feng, M.","Noojipady, P.","Anand, A.","Huang, C.","Kim, D.","Collins, K. M.","Channan, S.","DiMiceli, C.","Townshend, J. 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