0601030 - BÚ 2025 RIV US eng J - Článek v odborném periodiku
Moudrý, Vítězslav - Prošek, Jiří - Marselis, S. - Marešová, J. - Šárovcová, E. - Gdulová, K. - Kozhoridze, G. - Torresani, M. - Rocchini, D. - Eltner, A. - Liu, X. - Potůčková, M. - Šedová, A. - Crespo-Peremarch, P. - Torralba, J. - Ruiz, L. A. - Perrone, M. - Špatenková, O. - Wild, Jan
How to Find Accurate Terrain and Canopy Height GEDI Footprints in Temperate Forests and Grasslands?
Earth and Space Science. Roč. 11, č. 10 (2024), č. článku e2024EA003709. ISSN 2333-5084. E-ISSN 2333-5084
Grant CEP: GA TA ČR(CZ) SS02030018
Institucionální podpora: RVO:67985939
Klíčová slova: error * terrain * vegetation
Obor OECD: Environmental sciences (social aspects to be 5.7)
Impakt faktor: 2.9, rok: 2023 ; AIS: 1.141, rok: 2023
Způsob publikování: Open access
Web výsledku:
https://doi.org/10.1029/2024EA003709DOI: https://doi.org/10.1029/2024EA003709

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.
Trvalý link: https://hdl.handle.net/11104/0360812