Spatial Scale-Up Modeling of Forest Canopy Water Storage Capacity by Using Multi-Source Remote Sensing Data: A Case Study in Southern Jiangxi Province

Forest canopy water storage capacity is a critical component of ecohydrological research. However, because most current studies focus on the plot or stand scale, upscaling these fine-scale measurements to regional spatial scales remains a major challenge. Taking the forest in southern Jiangxi province as a case study, we integrated water immersion experiments, Handheld Laser Scanning (HLS), Unmanned Aerial Vehicle LiDAR (UAV-LiDAR), and optical remote sensing data to construct a spatial upscaling model. This model aims to quantify regional canopy water storage capacity and delineate its spatial patterns. The results indicate that: (1) the water storage capacity of branches and leaves per unit surface area of coniferous trees was significantly higher than that of broad-leaved trees, and the water storage capacity of branches was 6.0–10.7 times that of leaves. The mean canopy water storage capacities of coniferous forests, mixed coniferous and broad-leaved forests, and broad-leaved forests were 1.41 ± 0.27 mm, 1.30 ± 0.45 mm, and 1.26 ± 0.36 mm, respectively. (2) The canopy water storage capacity was significantly positively correlated with canopy volume (VC) and average canopy area (AC) extracted from UAV-LiDAR data, and vegetation structure factors such as normalized difference vegetation index (NDVI) and vegetation cover (FVC) extracted from optical remote sensing, and significantly negatively correlated with altitude and slope. Among them, canopy closure (C), average canopy area (AC), and altitude were key factors affecting canopy water storage capacity. (3) The upscaling prediction models based on UAV-LiDAR data and optical remote sensing factors, respectively, show reliable prediction performance, with R2 values of 0.884 and 0.815, RMSE of 0.951 and 0.116 mm, respectively. (4) The canopy water storage in the study area ranged from 0 to 1.76 mm, with a prediction uncertainty ranging from 0.12 to 0.49 mm. Canopy water storage is higher in the continuous middle and low mountain and hill areas within the region, while it is relatively lower in the high elevation ridge areas along the western, eastern, and southern margins. The results provide baseline structural information for understanding the spatial patterns of regional forest canopy interception potential.

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Publication Details

Journal
Remote Sensing
Published
2026-04-26
DOI
https://doi.org/10.3390/rs18091325
Primary Topic
Plant Water Relations and Carbon Dynamics
Type
article
Field-Weighted Citation Impact
0.00

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article

Spatial Scale-Up Modeling of Forest Canopy Water Storage Capacity by Using Multi-Source Remote Sensing Data: A Case Study in Southern Jiangxi Province

Chao Huang, Quan Liu, Shengsheng Xiao, Shun Li et al.
Remote Sensing
Plant Water Relations and Carbon Dynamics
article

Spatial Scale-Up Modeling of Forest Canopy Water Storage Capacity by Using Multi-Source Remote Sensing Data: A Case Study in Southern Jiangxi Province

Chao Huang, Quan Liu, Shengsheng Xiao, Shun Li, Lizhi Tao, Zhiwei Wu
article en

Abstract

Forest canopy water storage capacity is a critical component of ecohydrological research. However, because most current studies focus on the plot or stand scale, upscaling these fine-scale measurements to regional spatial scales remains a major challenge. Taking the forest in southern Jiangxi province as a case study, we integrated water immersion experiments, Handheld Laser Scanning (HLS), Unmanned Aerial Vehicle LiDAR (UAV-LiDAR), and optical remote sensing data to construct a spatial upscaling model. This model aims to quantify regional canopy water storage capacity and delineate its spatial patterns. The results indicate that: (1) the water storage capacity of branches and leaves per unit surface area of coniferous trees was significantly higher than that of broad-leaved trees, and the water storage capacity of branches was 6.0–10.7 times that of leaves. The mean canopy water storage capacities of coniferous forests, mixed coniferous and broad-leaved forests, and broad-leaved forests were 1.41 ± 0.27 mm, 1.30 ± 0.45 mm, and 1.26 ± 0.36 mm, respectively. (2) The canopy water storage capacity was significantly positively correlated with canopy volume (VC) and average canopy area (AC) extracted from UAV-LiDAR data, and vegetation structure factors such as normalized difference vegetation index (NDVI) and vegetation cover (FVC) extracted from optical remote sensing, and significantly negatively correlated with altitude and slope. Among them, canopy closure (C), average canopy area (AC), and altitude were key factors affecting canopy water storage capacity. (3) The upscaling prediction models based on UAV-LiDAR data and optical remote sensing factors, respectively, show reliable prediction performance, with R2 values of 0.884 and 0.815, RMSE of 0.951 and 0.116 mm, respectively. (4) The canopy water storage in the study area ranged from 0 to 1.76 mm, with a prediction uncertainty ranging from 0.12 to 0.49 mm. Canopy water storage is higher in the continuous middle and low mountain and hill areas within the region, while it is relatively lower in the high elevation ridge areas along the western, eastern, and southern margins. The results provide baseline structural information for understanding the spatial patterns of regional forest canopy interception potential.

Remote SensingVol. 18(9)
Jiangxi Provincial Institute of Water Sciences (CN), Jiangxi Academy of Environmental Sciences (CN), Jiangxi Normal University (CN), Jiangxi Agricultural University (CN)
National Natural Science Foundation of China, Natural Science Foundation of Jiangxi Province, Major Discipline Academic and Technical Leaders Training Program of Jiangxi Province
Clean water and sanitation
Openalex Percentile: Top 5%
Plant Water Relations and Carbon Dynamics
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