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Development of Landsat-based annual US forest disturbance history maps (1986–2010) in support of the North American Carbon Program (NACP)

Authors :
Louis Keddell
Chengquan Huang
Samuel N. Goward
Feng Zhao
Elaine Denning
Mary A. Lindsey
Zhiqiang Yang
Karen Schleeweis
Jennifer L. Dungan
Andrew Michaelis
Khaldoun Rishmawi
Warren B. Cohen
Source :
Remote Sensing of Environment. 209:312-326
Publication Year :
2018
Publisher :
Elsevier BV, 2018.

Abstract

In Phase III of the North American Forest Dynamics (NAFD) study an automatic workflow has been developed for evaluating forest disturbance history using Landsat observations. It has four major components: an automated approach for image selection and preprocessing, the vegetation change tracker (VCT) forest disturbance analysis, postprocessing, and validation. This approach has been applied to the conterminous US (CONUS) to produce a comprehensive analysis of US forest disturbance history using the NASA Earth Exchange (NEX) cloud computing system. The resultant NAFD-NEX product includes 25 annual forest disturbance maps for 1986–2010 and two time-integrated maps to provide spatial-temporal synoptic view of disturbances over this time period. These maps were derived based on 24,000+ scenes selected from 350,000+ available Landsat images at 30-m resolution, and were validated using a visual assessment of Landsat time-series images in combination with high-resolution and other ancillary data sources over samples selected using a probability based sampling method. The validation revealed no major biases in the NAFD-NEX maps for disturbance events that resulted in at least 20% canopy cover loss. The average user's and producer's accuracies for the disturbance class were 53.6% and 53.3%, respectively, with the individual year's user's accuracy varying from 42.8% to 73.6% and producer's accuracy from 39.0% to 84.8% over the 25-year period. The NAFD-NEX disturbance maps are available from a web portal of the Oak Ridge National Laboratory Distributed Active Archive Center (ORNL-DAAC) at https://doi.org/10.3334/ORNLDAAC/1290 .

Details

ISSN :
00344257
Volume :
209
Database :
OpenAIRE
Journal :
Remote Sensing of Environment
Accession number :
edsair.doi...........bbbe6a8b1bf94174540fb206298e808e
Full Text :
https://doi.org/10.1016/j.rse.2018.02.035