Abstract
We performed a multi-date composite change detection technique using a dense-time stack of Landsat data to map land-use and land-cover change (LCLUC) in Mainland Southeast Asia (MSEA) with a focus on the expansion of boom crops, primarily tree crops. The supervised classification was performed using Support Vector Machines (SVM), which are supervised non-parametric statistical learning techniques. To select the most suitable SMV classifier and the related parameter settings, we used the training data and performed a two-dimensional grid search with a three-fold internal cross-validation. We worked in seven Landsat footprints and found the linear kernel to be the most suitable for all footprints, but the most suitable regularization parameter C varied across the footprints. We distinguished a total of 41 LCLUCs (13 to 31 classes per footprint) in very dynamic and heterogeneous landscapes. The approach proved useful for distinguishing subtle changes over time and to map a variety of land covers, tree crops, and transformations as long as sufficient training points could be collected for each class. While to date, this approach has only been applied to mapping urban extent and expansion, this study shows that it is also useful for mapping change in rural settings, especially when images from phenologically relevant acquisition dates are included.
Highlights
Over the past half century, the five countries of Mainland Southeast Asia (MSEA)—Cambodia, land mosaic eucalyptus (Laos), Myanmar, Thailand, and Vietnam—have witnessed major shifts from predominantly subsistence agrarian economies to increasingly commercialized agriculture and, in the case of Thailand and Vietnam, to industrialized societies
For all seven Landsat footprints, the linear kernel (g = 0) was preferred over the radial basis function (RBF) kernel based on the averaged F1 measure (Table 3)
130047), shows low user’s accuracy (UA) and producer’s accuracy (PA) across almost all footprints. These results suggest that the accuracies of accuracies of the Land-Cover and Land-Use Change Program (LCLUC) classes are affected by class extent (larger classes tend to show the LCLUC classes are affected by class extent higher accuracies) and class heterogeneity
Summary
Over the past half century, the five countries of Mainland Southeast Asia (MSEA)—Cambodia, Laos, Myanmar, Thailand, and Vietnam—have witnessed major shifts from predominantly subsistence agrarian economies to increasingly commercialized agriculture and, in the case of Thailand and Vietnam, to industrialized societies. Major drivers of change include policy initiatives that fostered regional economic integration and promoted large-scale infrastructure development, including extensive road-building, rapid expansion of boom crop plantations (e.g., rubber, coffee, etc.), and Remote Sens. 2017, 9, 320 large-scale hydropower dam construction [1,2,3] These policy initiatives have led to shifts in smallholder livelihood strategies and natural resource use practices over the past two decades, including agricultural intensification and the linking of smallholder production systems to land, labor, and commodity markets. In a recent survey of agrarian change, Hall et al [4] identified the three major land-cover transitions in Southeast Asia as being: urbanization, forest loss and gain, and agricultural intensification, including the massive expansion of boom crops. Between 2000 and 2010, the extent of the landscape occupied by tree crops grew from 54,860 km to 83,710 km , representing an annual rate of change of
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