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

Paul C. West,Ankush Khandelwal,Leah H. Samberg,Xiaowei Jia,Kimberly M. Carlson,James S. Gerber,Vipin Kumar

doi:10.3390/rs12040636

Abstract

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.

Highlights

In Southeast Asia, expansion of tree crops and managed forests that help meet demand for global commodities has resulted in substantial tropical deforestation [1,2,3]
In 2001, southern and eastern Kalimantan and southern Sumatra contained around 81 × 103 km2 of tree plantations, which include oil palm, pulp and paper, rubber, and coconut palm plantations
Our study evaluated the accuracy of an automated plantation mapping approach (PALM) using Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data in Southeast Asia

Summary

Introduction

In Southeast Asia, expansion of tree crops and managed forests that help meet demand for global commodities has resulted in substantial tropical deforestation [1,2,3]. Replacement of natural forest cover with tree plantations, including tree crops for food (e.g., oil palm), fiber (e.g., pulp and paper), and materials (e.g., rubber), leads to net greenhouse gas (GHG) emissions to the atmosphere [3,4] and negatively impacts local environments, including degradation of biodiversity [5] and water quality [6]. These tree crops are frequently grown at the industrial plantation scale across large contiguous patches by capitalized companies [3], but may be produced by smallholder farmers in small patchy areas [7]. Scalable and timely monitoring of these land uses is essential for understanding whether programs and policies are meeting their stated goals [16,17]

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