Mapping the Expansion of Boom Crops in Mainland Southeast Asia Using Dense Time Stacks of Landsat Data

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...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2017-04, Vol.9 (4), p.320
Main Authors: Hurni, Kaspar, Schneider, Annemarie, Heinimann, Andreas, Nong, Duong, Fox, Jefferson
Format: Article
Language:English
Subjects:
Aversion learning
Boom
boom crops
Change detection
Classification
Crops
Footprints
Image acquisition
Land cover
land cover change
Land use
Landsat
Landsat satellites
Learning
Mapping
Regularization
Remote sensing
Searching
Serial learning
Southeast Asia
Stacks
Statistical analysis
Support vector machines
SVM
time series
Training
Transformations (mathematics)
Tree crops
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Summary: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.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs9040320