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Open AccessArticle

Automated Plantation Mapping in Southeast Asia Using MODIS Data and Imperfect Visual Annotations

1
Department of Computer Science, University of Minnesota, MN 55455, Minneapolis , USA
2
Dept. of Natural Resources and Environmental Management, University of Hawai’i at Ma¯noa, HI 96822, Honolulu, USA;
3
Department of Environmental Studies, New York University, NY 10003, New York, USA
4
Institute on the Environment, University of Minnesota, MN 55108, St. Paul, USA
5
Rainforest Alliance, NY 10004, New York, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(4), 636; https://doi.org/10.3390/rs12040636 (registering DOI)
Received: 13 January 2020 / Revised: 9 February 2020 / Accepted: 12 February 2020 / Published: 14 February 2020
(This article belongs to the Special Issue Global Biospheric Monitoring with Remote Sensing)
Expansion of large-scale tree plantations for commodity crop and timber production is a leading cause of tropical deforestation. While automated detection of plantations across large spatial scales and with high temporal resolution is critical to inform policies to reduce deforestation, such mapping is technically challenging. Thus, most available plantation maps rely on visual inspection of imagery, and many of them are limited to small areas for specific years. Here, we present an automated approach, which we call Plantation Analysis by Learning from Multiple Classes (PALM), for mapping plantations on an annual basis using satellite remote sensing data. Due to the heterogeneity of land cover classes, PALM utilizes ensemble learning to simultaneously incorporate training samples from multiple land cover classes over different years. After the ensemble learning, we further improve the performance by post-processing using a Hidden Markov Model. We implement the proposed automated approach using MODIS data in Sumatra and Indonesian Borneo (Kalimantan). To validate the classification, we compare plantations detected using our approach with existing datasets developed through visual interpretation. Based on random sampling and comparison with high-resolution images, the user’s accuracy and producer’s accuracy of our generated map are around 85% and 80% in our study region.
Keywords: remote sensing; land cover change; plantations; ensemble learning; deforestation; tropical; MODIS remote sensing; land cover change; plantations; ensemble learning; deforestation; tropical; MODIS
MDPI and ACS Style

Jia, X.; Khandelwal, A.; Carlson, K.M.; Gerber, J.S.; West, P.C.; Samberg, L.H.; Kumar, A.V. Automated Plantation Mapping in Southeast Asia Using MODIS Data and Imperfect Visual Annotations. Remote Sens. 2020, 12, 636.

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