Historical baselines of forest cover are needed to understand the causes and consequences of recent changes and to assess the effectiveness of land-use policies. However, historical assessment of the global distribution of forest cover and change has been lacking due to obstacles in image acquisition, computational demands, and lack of retrospective reference data for image classification. As limitations of access to imagery and computational
power are overcome, the possibility is increased of an automated retrospective classification of forest cover. We used locally fit classification trees to relate hind-cast observations of “stable pixels” of forest and nonforest cover from circa-2000 to Landsat spectral measurements taken from the circa-1990 epoch of the Global Land Survey collection of Landsat images. Based on analysis of nearly 30,000 Landsat images, forest-cover change
between 1990 and 2000 epochs was detected based on joint probabilities of cover in the two epochs. Assessed across a sample of areas with coincident reference data in the conterminous United States, the resulting maps achieved 93% accuracy for forest cover and 84% for forest-cover change—comparable or even higher than many previous national efforts. Global accuracy assessment likewise showed accuracy of 88% for forest-cover
change. The maps depict the global distribution of gross gains and losses in forest cover, as well as their net change. The initial analysis showed strong effects of extant land use in temperate regions and land-use change in the tropics over the period, while wildfire dominated in the boreal zone. Regions of high net forest loss (e.g., Amazonia) were associated with land-use changes into agriculture, and regions of high gross gains and losses (e.g., southeastern US, Sweden)were associatedwith intensive forestry. These results, including the global forest cover and forest cover change datasets, will be a basis for the estimation of the efficacy of policies and analyzing correlation between forest cover change and socio-economic factors.
Kim, D.-H., et al., Remote Sensing of Environment (2014),
|2014 AGO Global Landsat-based forest-cover change from 1990 to 2000(3).pdf||6.81 MB|
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