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Remote Sens. 2017, 9(4), 320; doi:10.3390/rs9040320

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

1
East-West Center, 1601 East-West Road, Honolulu, HI 96848, USA
2
Center for Sustainability and the Global Environment, Nelson Institute for Environmental Studies, University of Wisconsin–Madison, 1710 University Avenue, Madison, WI 53726, USA
3
Department of Geography, University of Wisconsin–Madison, 550 North Park Street, Madison, WI 53706, USA
4
Centre for Development and Environment, University of Bern, Hallerstr. 10, CH-3012 Bern, Switzerland
5
Institute of Geography, University of Bern, Hallerstr. 12, CH-3012 Bern, Switzerland
6
Faculty of Environment, Vietnam National University of Agriculture, Trau Quy, Gia Lam, Hanoi 131004, Vietnam
*
Author to whom correspondence should be addressed.
Academic Editors: Krishna Prasad Vadrevu, Rama Nemani, Chris Justice, Garik Gutman, Clement Atzberger and Prasad S. Thenkabail
Received: 23 December 2016 / Revised: 10 March 2017 / Accepted: 24 March 2017 / Published: 29 March 2017
View Full-Text   |   Download PDF [24021 KB, uploaded 30 March 2017]   |  

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. View Full-Text
Keywords: Landsat; time series; SVM; land cover change; boom crops; remote sensing; Southeast Asia Landsat; time series; SVM; land cover change; boom crops; remote sensing; Southeast Asia
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Hurni, K.; Schneider, A.; Heinimann, A.; Nong, D.H.; Fox, J. Mapping the Expansion of Boom Crops in Mainland Southeast Asia Using Dense Time Stacks of Landsat Data. Remote Sens. 2017, 9, 320.

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