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Remote Sens. 2016, 8(3), 236; doi:10.3390/rs8030236

Regional Mapping of Plantation Extent Using Multisensor Imagery

1
Applied Geosolutions, Newmarket, NH 03857, USA
2
Department of Mathematics and Statistics, University of New Hampshire, Durham, NH 03824, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Xiangming Xiao, Jinwei Dong, Randolph H. Wynne and Prasad S. Thenkabail
Received: 31 December 2015 / Revised: 15 February 2016 / Accepted: 4 March 2016 / Published: 14 March 2016
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Abstract

Industrial forest plantations are expanding rapidly across Monsoon Asia and monitoring extent is critical for understanding environmental and socioeconomic impacts. In this study, new, multisensor imagery were evaluated and integrated to extract the strengths of each sensor for mapping plantation extent at regional scales. Two distinctly different landscapes with multiple plantation types were chosen to consider scalability and transferability. These were Tanintharyi, Myanmar and West Kalimantan, Indonesia. Landsat-8 Operational Land Imager (OLI), Phased Array L-band Synthetic Aperture Radar-2 (PALSAR-2), and Sentinel-1A images were fused within a Classification and Regression Tree (CART) framework using random forest and high-resolution surveys. Multi-criteria evaluations showed both L-and C-band gamma nought γ° backscatter decibel (dB), Landsat reflectance ρλ, and texture indices were useful for distinguishing oil palm and rubber plantations from other land types. The classification approach identified 750,822 ha or 23% of the Taninathryi, Myanmar, and 216,086 ha or 25% of western West Kalimantan as plantation with very high cross validation accuracy. The mapping approach was scalable and transferred well across the different geographies and plantation types. As archives for Sentinel-1, Landsat-8, and PALSAR-2 continue to grow, mapping plantation extent and dynamics at moderate resolution over large regions should be feasible. View Full-Text
Keywords: plantations; rubber; oil palm; PALSAR-2; Sentinel-1; random forest; classification; data fusion; Myanmar; Kalimantan plantations; rubber; oil palm; PALSAR-2; Sentinel-1; random forest; classification; data fusion; Myanmar; Kalimantan
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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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MDPI and ACS Style

Torbick, N.; Ledoux, L.; Salas, W.; Zhao, M. Regional Mapping of Plantation Extent Using Multisensor Imagery. Remote Sens. 2016, 8, 236.

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