A high-resolution inland surface water body dataset for the tundra and boreal forests of North America

[摘要] Inland surface waters are abundant in the tundra and borealforests of North America, essential to environments and human societies butvulnerable to climate changes. These high-latitude water bodies differgreatly in their morphological and topological characteristics related tothe formation, type, and vulnerability. In this paper, we present a waterbody dataset for the North American high latitudes (WBD-NAHL). Nearly 6.5million water bodies were identified, with approximately 6 million( ∼90  %) of them smaller than 0.1  km 2 . The datasetprovides area and morphological attributes for every water body. During thisstudy, we developed an automated approach for detecting surface water extentand identifying water bodies in the 10 m resolution Sentinel-2 multispectralsatellite data to enhance the capability of delineating small water bodiesand their morphological attributes. The approach was applied to theSentinel-2 data acquired in 2019 to produce the water body dataset for theentire tundra and boreal forests in North America. The dataset provided amore complete representation of the region than existing regional datasetsfor North America, e.g., Permafrost Region Pond and Lake (PeRL). The totalaccuracy of the detected water extent by the WBD-NAHL dataset was 96.36 %through comparison to interpreted data for locations randomly sampled across theregion. Compared to the 30 m or coarser-resolution water datasets, e.g., JRCGSW yearly water history, HydroLakes, and Global Lakes and Wetlands Database(GLWD), the WBD-NAHL provided an improved ability on delineating waterbodies and reported higher accuracies in the size, number, and perimeterattributes of water body by comparing to PeRL and interpreted regionaldataset. This dataset is available from the National Tibetan Plateau/ThirdPole Environment Data Center (TPDC; http://data.tpdc.ac.cn , last access: 6 June 2022): https://doi.org/10.11888/Hydro.tpdc.271021 (Feng and Sui, 2020).

[学科分类] 眼科学