Synergistic mapping of urban tree canopy height using ICESat-2 data and GF-2 imagery

Synergistic mapping of urban tree canopy height using ICESat-2 data and GF-2 imagery

Mapping urban top of canopy height (UTCH) is essential for quantifying urban vegetation carbon storage and developing effective vegetation management strategies. However, the scarcity and uneven distribution of urban measurement samples pose significant challenges to accurately estimating UTCH on a...

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Bibliographic Details
Main Authors: Xiaodi Xu, Ya Zhang, Peng Fu, Chaoya Dang, Bowen Cai, Qingwei Zhuang, Zhenfeng Shao, Deren Li, Qing Ding
Format: Article
Language:English
Published: Elsevier 2025-02-01
Series:International Journal of Applied Earth Observations and Geoinformation
Subjects:
Canopy height
Urban
Remote Sensing
Machine Learning
Feature Selection
Online Access:http://www.sciencedirect.com/science/article/pii/S1569843224007064
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Summary:Mapping urban top of canopy height (UTCH) is essential for quantifying urban vegetation carbon storage and developing effective vegetation management strategies. However, the scarcity and uneven distribution of urban measurement samples pose significant challenges to accurately estimating UTCH on a large scale in complex urban environments. To address this issue, this study utilized ICESat-2 photon spot height data as reference samples, in conjunction with high-resolution GF-2 remote sensing data, to estimate UTCH. To achieve UTCH mapping at a resolution of 4 m, a synergistic model integrating data from the GF-2 and ICESat-2 grid-based canopy height was constructed using the Random Forest technique. The model’s performance was evaluated using 111 urban tree canopy height samples collected across different urban areas. The experimental results demonstrated a moderate correlation between estimated and actual canopy heights, with a coefficient of determination (R) = 0.53, root mean square error (RMSE) = 2.9 m, and mean absolute error (MAE) = 2.04 m. Texture information, the red band, and MNDVI are key indicators for determining UTCH, with contribution percentages of 25.29 %, 13.7 %, and 25.75 %, respectively. As a result, the UTCH model created by fusing remote sensing spectral data with satellite-based lidar data can accurately estimate UTCH and offer a practical solution for predicting UTCH on a regional or even global scale.
ISSN:1569-8432

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