Paper Info
Title
Annual Detection of Forest Cover Loss Using Time Series Satellite Measurements of Percent Tree Cover
Abstract
We introduce and test a new method to detect annual forest cover loss from time series estimates of percent tree cover. Our approach is founded on two realistic assumptions: (1) land cover disturbances are rare events over large geographic areas that occur within a short time frame; and (2) spatially discrete land cover disturbances are continuous processes over time. Applying statistically rigorous algorithms, we first detect disturbance pixels as outliers of an underlying chi-square distribution. Then, we fit nonlinear, logistic curves for each identified change pixel to simultaneously characterize the magnitude and timing of the disturbance. Our method is applied using the yearly Vegetation Continuous Fields (VCF) tree cover product from Moderate Resolution Imaging Spectroradiometer (MODIS), and the resulting disturbance-year estimates are evaluated using a large sample of Landsat-based forest disturbance data. Temporal accuracy is similar to 65% at 250-m, annual resolution and increases to >85% when temporal resolution is relaxed to +/- 1 yr. The r(2) of MODIS VCF-based disturbance rates against Landsat ranges from 0.7 to 0.9 at 5-km spatial resolution. The general approach developed in this study can be potentially applied at a global scale and to other land cover types characterized as continuous variables from satellite data.
Year
DOI
Venue
2014
10.3390/rs6098878
REMOTE SENSING
Keywords
Field
DocType
MODIS,Landsat,time series,Vegetation Continuous Fields,change detection,forest,land cover
Vegetation,Moderate-resolution imaging spectroradiometer,Satellite,Change detection,Remote sensing,Pixel,Geology,Image resolution,Temporal resolution,Land cover
Journal
Volume
Issue
Citations 
6
9
3
PageRank 
References 
Authors
0.58
11
5
Name
Order
Citations
PageRank
Xiao-Peng Song19711.26
Chengquan Huang222636.44
Joseph O. Sexton312213.35
Saurabh Channan413614.96
John R. Townshend537367.56