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利用ICESat-2 与融合模型生成的青藏高原误差降低数字高程模型。

Error-Reduced Digital Elevation Model of the Qinghai-Tibet Plateau using ICESat-2 and Fusion Model.

机构信息

Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, School of Geography and Ocean Science, Nanjing University, Nanjing, China.

Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, China.

出版信息

Sci Data. 2024 Jun 5;11(1):588. doi: 10.1038/s41597-024-03428-4.

DOI:10.1038/s41597-024-03428-4
PMID:38839786
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11153629/
Abstract

The Qinghai-Tibet Plateau (QTP) holds significance for investigating Earth's surface processes. However, due to rugged terrain, forest canopy, and snow accumulation, open-access Digital Elevation Models (DEMs) exhibit considerable noise, resulting in low accuracy and pronounced data inconsistency. Furthermore, the glacier regions within the QTP undergo substantial changes, necessitating updates. This study employs a fusion of open-access DEMs and high-accuracy photons from the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2). Additionally, snow cover and canopy heights are considered, and an ensemble learning fusion model is presented to harness the complementary information in the multi-sensor elevation observations. This innovative approach results in the creation of HQTP30, the most accurate representation of the 2021 QTP terrain. Comparative analysis with high-resolution imagery, UAV-derived DEMs, control points, and ICESat-2 highlights the advantages of HQTP30. Notably, in non-glacier regions, HQTP30 achieved a Mean Absolute Error (MAE) of 0.71 m, while in glacier regions, it reduced the MAE by 4.35 m compared to the state-of-the-art Copernicus DEM (COPDEM), demonstrating its versatile applicability.

摘要

青藏高原（QTP）对于研究地球表面过程具有重要意义。然而，由于地形崎岖、森林树冠和积雪堆积，开放获取的数字高程模型（DEMs）存在相当大的噪声，导致精度低且数据不一致性明显。此外，QTP 内的冰川区域发生了重大变化，需要更新。本研究采用开放获取的 DEM 和来自冰、云和陆地高度卫星-2（ICESat-2）的高精度光子融合。此外，还考虑了雪盖和树冠高度，并提出了一个集成学习融合模型，以利用多传感器高程观测中的互补信息。这种创新方法产生了 HQTP30，这是 2021 年 QTP 地形的最精确表示。与高分辨率图像、无人机衍生的 DEM、控制点和 ICESat-2 的比较分析突出了 HQTP30 的优势。值得注意的是，在非冰川地区，HQTP30 的平均绝对误差（MAE）为 0.71m，而在冰川地区，与最先进的哥白尼 DEM（COPDEM）相比，MAE 降低了 4.35m，这表明其具有广泛的适用性。

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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2281/11153629/59db3b6bff6a/41597_2024_3428_Fig7_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2281/11153629/0351d8729e1a/41597_2024_3428_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2281/11153629/13a7899a3efe/41597_2024_3428_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2281/11153629/c2359dcaf215/41597_2024_3428_Fig11_HTML.jpg
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