Automated Plantation Mapping in Southeast Asia Using Remote Sensing Data

Abstract

Plantation mapping is critical for understanding and addressing deforestation, a key driver of climate change and ecosystem degradation. Unfortunately, most plantation maps are limited to small areas for specific years because they rely on visual inspection of imagery. Here we propose an automated approach for annual mapping of plantations using remote sensing data. Due to the heterogeneity of land cover classes, we propose a novel ensemble learning method that simultaneously uses 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. With the experiments on MODIS data, we demonstrate the superiority of the proposed method over multiple baselines. In addition, we conduct extensive validation by comparing the detected plantation by our approach with the existing datasets developed through visual interpretation by expert observers. Based on the random sampling and the comparison with high-resolution images, the precision (i.e. user’s accuracy) and recall (i.e. producer’s accuracy) of our generated map are around 85.53% and 81.51%, respectively, and the overall accuracy is 95.20%.