Annual detection of forest cover loss using time series satellite measurements of percent tree cover

Xiao Peng Song, Chengquan Huang, Joseph O. Sexton, Saurabh Channan, John R. Townshend

Research output: Contribution to journalArticle

28 Scopus citations

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 ~65% at 250-m, annual resolution and increases to >85% when temporal resolution is relaxed to ±1 yr. The r2 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.

Original languageEnglish
Pages (from-to)8878-8903
Number of pages26
JournalRemote Sensing
Volume6
Issue number9
DOIs
StatePublished - 2014

Keywords

  • Change detection
  • Forest
  • Land cover
  • Landsat
  • MODIS
  • Time series
  • Vegetation continuous fields

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