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The accuracy of existing forest cover products typically suffers from “rounding” errors arising from classifications that estimate the fractional cover of forest in each pixel, which often exclude the presence of large, isolated trees and small or narrow forest clearings, and is primarily attributable to the moderate resolution of the imagery used to make maps. However, the degree to which such high-resolution imagery can mitigate this problem, and thereby improve large-area forest cover maps, is largely unexplored. Here, we developed an approach to map tropical forest cover at a fine scale using Planet and Sentinel-1 synthetic aperture radar (SAR) imagery in the Google Earth Engine platform and used it to map all of Southeastern Asia’s forest cover. The machine learning approach, based on the Random Forests models and trained and validated using a total of 37,345 labels collected from Planet imagery across the entire region, had an accuracy of 0.937 and an F1 score of 0.942, while a version based only on Planet imagery had an accuracy of 0.908 and F1 of 0.923. We compared the accuracy of our resulting maps with 5 existing forest cover products derived from medium-resolution optical-only or combined optical-SAR approaches at 3,000 randomly selected locations. We found that our approach overall achieved higher accuracy and helped minimize the rounding errors commonly found along small or narrow forest clearings and deforestation frontiers where isolated trees are common. However, the forest area estimates varied depending on topographic location and showed smaller differences in highlands (areas >300 m above sea level) but obvious differences in complex lowland landscapes. Overall, the proposed method shows promise for monitoring forest changes, particularly those caused by deforestation frontiers. Our study also represents one of the most extensive applications of Planet imagery to date, resulting in an open, high-resolution map of forest cover for the entire Southeastern Asia region. © 2023 Feng Yang et al.

Publication Title

Journal of Remote Sensing (United States)

Publication Date









Planet imagery, forest cover, Southeast Asia

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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Geography Commons



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