Spatial Scale-Up Modeling of Forest Canopy Water Storage Capacity by Using Multi-Source Remote Sensing Data: A Case Study in Southern Jiangxi Province.
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| Title: | Spatial Scale-Up Modeling of Forest Canopy Water Storage Capacity by Using Multi-Source Remote Sensing Data: A Case Study in Southern Jiangxi Province. |
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| Authors: | Liu, Quan1,2 (AUTHOR), Xiao, Shengsheng1,2 (AUTHOR), Huang, Chao3 (AUTHOR) heipichao85@hotmail.com, Li, Shun1,2 (AUTHOR), Wu, Zhiwei2 (AUTHOR), Tao, Lizhi2,3 (AUTHOR) |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 9, p1325. 25p. |
| Subjects: | LIDAR, Optical remote sensing, Remote sensing, Ecohydrology, Prediction models |
| Geographic Terms: | Jiangxi Sheng (China) |
| Abstract: | Highlights: What are the main findings? Coniferous branches and leaves exhibit a greater canopy water storage capacity per unit area compared to those of broad-leaved trees. Average canopy area, canopy closure, and altitude dominate canopy water storage capacity. The combined use of multi-source remote sensing enables the reliable spatial upscaling of canopy water storage capacity. Integrated LiDAR and optical data offer a novel perspective for regional water conservation assessment. What are the implications of the main findings? The proposed spatial upscaling model provides a structural baseline for evaluating regional canopy ecohydrological potential. The spatial distribution map of canopy water-holding capacity in southern Jiangxi offers structural reference data for understanding forest canopy interception characteristics. 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 ( V C ) and average canopy area ( A C ) 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 ( A C ), 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. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? Coniferous branches and leaves exhibit a greater canopy water storage capacity per unit area compared to those of broad-leaved trees. Average canopy area, canopy closure, and altitude dominate canopy water storage capacity. The combined use of multi-source remote sensing enables the reliable spatial upscaling of canopy water storage capacity. Integrated LiDAR and optical data offer a novel perspective for regional water conservation assessment. What are the implications of the main findings? The proposed spatial upscaling model provides a structural baseline for evaluating regional canopy ecohydrological potential. The spatial distribution map of canopy water-holding capacity in southern Jiangxi offers structural reference data for understanding forest canopy interception characteristics. 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 ( V C ) and average canopy area ( A C ) 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 ( A C ), 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. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18091325 |
