Urban Above-Ground Biomass Estimation Using GEDI Laser Data and Optical Remote Sensing Images

Xuedi Zhao; Wenmin Hu; Jiang Han; Wei Wei; Jiaxing Xu

doi:10.3390/rs16071229

Remote Sensing (Mar 2024)

Urban Above-Ground Biomass Estimation Using GEDI Laser Data and Optical Remote Sensing Images

Xuedi Zhao,
Wenmin Hu,
Jiang Han,
Wei Wei,
Jiaxing Xu

Affiliations

Xuedi Zhao: The National Joint Engineering Laboratory of Internet Applied Technology of Mines, China University of Mining and Technology, Xuzhou 221116, China
Wenmin Hu: The National Joint Engineering Laboratory of Internet Applied Technology of Mines, China University of Mining and Technology, Xuzhou 221116, China
Jiang Han: The National Joint Engineering Laboratory of Internet Applied Technology of Mines, China University of Mining and Technology, Xuzhou 221116, China
Wei Wei: The National Joint Engineering Laboratory of Internet Applied Technology of Mines, China University of Mining and Technology, Xuzhou 221116, China
Jiaxing Xu: The National Joint Engineering Laboratory of Internet Applied Technology of Mines, China University of Mining and Technology, Xuzhou 221116, China

DOI: https://doi.org/10.3390/rs16071229
Journal volume & issue: Vol. 16, no. 7
p. 1229

Abstract

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Accurate estimating of above-ground biomass (AGB) of vegetation in urbanized areas is essential for urban ecosystem services. NASA’s Global Ecosystem Dynamics Investigation (GEDI) mission can obtain precise terrestrial vegetation structure, which is very useful for AGB estimation in large forested areas. However, the spatial heterogeneity and sparse distribution of vegetation in urban areas lead to great uncertainty in AGB estimation. This study proposes a method for estimating vegetation heights by fusing GEDI laser observations with features extracted from optical images. GEDI is utilized to extract the accurate vegetation canopy height, and the optical images are used to compensate for the spatial incoherence of GEDI. The correlation between the discrete vegetation heights of GEDI observations and image features is constructed using Random Forest (RF) to obtain the vegetation canopy heights in all vegetated areas, thus estimating the AGB. The results in Xuzhou of China using GEDI observations and image features from Sentinel-2 and Landsat-8 satellites indicate that: (1) The method of combining GEDI laser observation data with optical images is effective in estimating AGB, and its estimation accuracy (R2 = 0.58) is higher than that of using only optical images (R2 = 0.45). (2) The total AGB in the shorter vegetation region is higher than the other two in the broadleaf forest and the coniferous forest, but the AGB per unit area is the lowest in the shorter vegetation area at 33.60 Mg/ha, and it is the highest in the coniferous forest at 46.60 Mg/ha. And the highest average AGB occurs in October–December at 59.55 Mg/ha in Xuzhou. (3) The near-infrared band has a greater influence on inverted AGB, followed by textural features. Although more precise information about vegetation should be considered, this paper provides a new method for the AGB estimation and also a way for the evaluation and utilization of urban vegetation space.

Published in Remote Sensing

ISSN: 2072-4292 (Online)
Publisher: MDPI AG
Country of publisher: Switzerland
LCC subjects: Science
Website: http://www.mdpi.com/journal/remotesensing/

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