Automated Plantation Mapping in Southeast Asia Using MODIS Data and Imperfect Visual Annotations
Automated Plantation Mapping in Southeast Asia Using MODIS Data and Imperfect Visual Annotations
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DOI：
10.3390/rs12040636
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发表时间：
2020-02
期刊：
影响因子：
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通讯作者：
X. Jia;A. Khandelwal;K. Carlson;J. Gerber;P. West;Leah H. Samberg;Vipin Kumar
中科院分区：
文献类型：
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作者：
X. Jia;A. Khandelwal;K. Carlson;J. Gerber;P. West;Leah H. Samberg;Vipin Kumar
大规模的商品农作物和木材生产的扩展是热带森林砍伐的主要原因，而在大型空间尺度上对植物的自动检测对于减少森林砍伐的政策至关重要。通过从多个类别中学习的种植术分析，用于每年使用卫星遥感数据来绘制植物。在苏门答腊和印度尼西亚婆罗洲（Kalimantan）中使用MODIS数据的自动化方法，我们将检测到的植物与通过视觉解释开发的现有数据集进行了比较。
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.