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 approachwith existing datasets developed through visual interpretation. Based on randomsampling and comparisonwith high-resolution images, the user's accuracy and producer's accuracy of our generated map are around 85% and 80% in our study region.
Bibliographical noteFunding Information:
Acknowledgments: X.J., A.K., and V.K. were funded by the NSF Awards 1838159 and 1029711. J.S.G. and P.C.W. were supported by the Belmont Forum/FACCE-JPI funded DEVIL project (Delivering Food Security from Limited Land) (NE/ M021327/1). K.M.C. was funded by the NASA New (Early Career) Investigator Program in Earth Science (NNX16AI20G) and the US Department of Agriculture’s National Institute of Food and Agriculture, including Hatch Project HAW01136-H and McIntire Stennis Project HAW01146-M, managed by the College of Tropical Agriculture and Human Resources. Access to computing facilities was provided by NASA Earth Exchange and Minnesota Supercomputing Institute.
Funding: National Science Foundation: 1029711.
© 2020 by the author.
- Ensemble learning
- Land cover change
- Remote sensing