Next Article in Journal
Dimensionality Reduction of Hyperspectral Image with Graph-Based Discriminant Analysis Considering Spectral Similarity
Next Article in Special Issue
Mapping 2000–2010 Impervious Surface Change in India Using Global Land Survey Landsat Data
Previous Article in Journal
Spectroscopic Estimation of Biomass in Canopy Components of Paddy Rice Using Dry Matter and Chlorophyll Indices
Previous Article in Special Issue
Monitoring Rice Agriculture across Myanmar Using Time Series Sentinel-1 Assisted by Landsat-8 and PALSAR-2
Article Menu
Issue 4 (April) cover image

Export Article

Open AccessArticle

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

East-West Center, 1601 East-West Road, Honolulu, HI 96848, USA
Center for Sustainability and the Global Environment, Nelson Institute for Environmental Studies, University of Wisconsin–Madison, 1710 University Avenue, Madison, WI 53726, USA
Department of Geography, University of Wisconsin–Madison, 550 North Park Street, Madison, WI 53706, USA
Centre for Development and Environment, University of Bern, Hallerstr. 10, CH-3012 Bern, Switzerland
Institute of Geography, University of Bern, Hallerstr. 12, CH-3012 Bern, Switzerland
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
Remote Sens. 2017, 9(4), 320;
Received: 23 December 2016 / Revised: 10 March 2017 / Accepted: 24 March 2017 / Published: 29 March 2017
PDF [24021 KB, uploaded 30 March 2017]


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

Graphical abstract

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).

Supplementary materials


Share & Cite This Article

MDPI and ACS Style

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top