Abstract
<strong class="journal-contentHeaderColor">Abstract.</strong> High-resolution mapping of tree cover is indispensable for effectively addressing tropical forest carbon loss, climate warming, biodiversity conservation, and sustainable development. However, the availability of precise high-resolution tree cover map products remains inadequate due to the inherent limitations of mapping techniques utilizing medium-to-coarse resolution satellite imagery, such as Landsat and Sentinel-2 imagery. In this study, we have generated an annual tree cover map product at a resolution of 4.77 m for Southeast Asia (SEA) for the years 2016–2021 by integrating Planet-Norway’s International Climate & Forests Initiative (NICFI) imagery and Sentinel-1 Synthetic Aperture Radar data. we have also collected annual samples to assess the accuracy of our Planet-NICFI tree cover map products. The results show that our Planet-NICFI tree cover map products during 2016–2021 achieve high accuracy, with an overall accuracy of <span class="ILfuVd" lang="de"><span class="hgKElc">≥</span></span> 0.867 ± 0.017 and a mean F1 score of 0.921, respectively. Furthermore, our tree cover map products exhibit high temporal consistency from 2016 to 2021. Compared to existing map products (FROM-GLC10, ESA WorldCover 2020 and 2021), our tree cover map products exhibit better performance, both statistically and visually. Yet, the imagery obtained from Planet-NICFI performs less in mapping tree cover in areas with diverse vegetation or complex landscapes due to insufficient spectral information. Nevertheless, we highlight the capability of Planet-NICFI datasets in providing quick and fine-scale tree cover mapping to a large extent. The consistent characterization of tree cover dynamics in SEA's tropical forests can be further applied in various disciplines. The annual Planet-NICFI V1.0 tree cover map products from 2016 to 2021 at 4.77 m resolution are publicly available at <a href="https://cstr.cn/31253.11.sciencedb.07173" target="_blank" rel="noopener">https://cstr.cn/31253.11.sciencedb.07173</a> (Yang and Zeng, 2023).
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