How to Find Accurate Terrain and Canopy Height GEDI Footprints in Temperate Forests and Grasslands?

IF 2.9 3区 地球科学 Q2 ASTRONOMY & ASTROPHYSICS Earth and Space Science Pub Date : 2024-10-25 DOI:10.1029/2024EA003709
Vítězslav Moudrý, Jiří Prošek, Suzanne Marselis, Jana Marešová, Eliška Šárovcová, Kateřina Gdulová, Giorgi Kozhoridze, Michele Torresani, Duccio Rocchini, Anette Eltner, Xiao Liu, Markéta Potůčková, Adéla Šedová, Pablo Crespo-Peremarch, Jesús Torralba, Luis A. Ruiz, Michela Perrone, Olga Špatenková, Jan Wild
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Abstract

Filtering approaches on Global Ecosystem Dynamics Investigation (GEDI) data differ considerably across existing studies and it is yet unclear which method is the most effective. We conducted an in-depth analysis of GEDI's vertical accuracy in mapping terrain and canopy heights across three study sites in temperate forests and grasslands in Spain, California, and New Zealand. We started with unfiltered data (2,081,108 footprints) and describe a workflow for data filtering using Level 2A parameters and for geolocation error mitigation. We found that retaining observations with at least one detected mode eliminates noise more effectively than sensitivity. The accuracy of terrain and canopy height observations depended considerably on the number of modes, beam sensitivity, landcover, and terrain slope. In dense forests, a minimum sensitivity of 0.9 was required, while in areas with sparse vegetation, sensitivity of 0.5 sufficed. Sensitivity greater than 0.9 resulted in an overestimation of canopy height in grasslands, especially on steep slopes, where high sensitivity led to the detection of multiple modes. We suggest excluding observations with more than five modes in grasslands. We found that the most effective strategy for filtering low-quality observations was to combine the quality flag and difference from TanDEM-X, striking an optimal balance between eliminating poor-quality data and preserving a maximum number of high-quality observations. Positional shifts improved the accuracy of GEDI terrain estimates but not of vegetation height estimates. Our findings guide users to an easy way of processing of GEDI footprints, enabling the use of the most accurate data and leading to more reliable applications.

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如何在温带森林和草地中找到准确的地形和树冠高度 GEDI 脚印?
全球生态系统动力学调查(GEDI)数据的过滤方法在现有研究中差异很大,目前还不清楚哪种方法最有效。我们深入分析了 GEDI 在绘制西班牙、加利福尼亚和新西兰温带森林和草地三个研究地点的地形和树冠高度图时的垂直精度。我们从未经过滤的数据(2,081,108 个足迹)入手,描述了使用 2A 级参数进行数据过滤和减轻地理定位误差的工作流程。我们发现,保留至少一种检测模式的观测数据比灵敏度更有效地消除噪声。地形和树冠高度观测的准确性在很大程度上取决于模式数量、波束灵敏度、土地覆盖率和地形坡度。在茂密的森林中,最低灵敏度要求为 0.9,而在植被稀疏的地区,灵敏度为 0.5 即可。灵敏度大于 0.9 会导致高估草地的冠层高度,尤其是在陡坡上,高灵敏度会导致检测到多种模式。我们建议排除草地上超过五种模式的观测数据。我们发现,过滤低质量观测数据的最有效策略是将质量标志和 TanDEM-X 的差值结合起来,在剔除低质量数据和最大限度保留高质量观测数据之间取得最佳平衡。位置移动提高了 GEDI 地形估计的准确性,但没有提高植被高度估计的准确性。我们的研究结果为用户提供了处理 GEDI 脚印的简便方法,使他们能够使用最准确的数据,并带来更可靠的应用。
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来源期刊
Earth and Space Science
Earth and Space Science Earth and Planetary Sciences-General Earth and Planetary Sciences
CiteScore
5.50
自引率
3.20%
发文量
285
审稿时长
19 weeks
期刊介绍: Marking AGU’s second new open access journal in the last 12 months, Earth and Space Science is the only journal that reflects the expansive range of science represented by AGU’s 62,000 members, including all of the Earth, planetary, and space sciences, and related fields in environmental science, geoengineering, space engineering, and biogeochemistry.
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