Assessing Canopy Shade in Tropical Headwater Streams

Forests play a vital role in maintaining healthy riparian ecosystems, influencing hydrological processes and water quality. This study aims to characterize the forest canopy structure using drone-based LiDAR technology to model shading in headwater streams. Specifically, we evaluate the effectiveness of fractional canopy cover metrics derived from high-resolution 3D point clouds in predicting solar radiation interception (fPAR) by riparian forests.

Ecological Context:
By linking forest fractional cover derived from LiDAR with light interception data, the aim was to better understand the relation of canopy cover with light interception. This approach would enhance studies that consider light interception in ecological processes, such as gas exchange and biomass productivity in tropical riparian forests.

This project involves two interconnected analyses:
(1) Forest Cover Extraction from LiDAR Data.
(2) Correlate to the fractions of light intercepted by riparian canopy, using the fractional canopy cover (fcover) across study sites, providing spatially explicit forest structure data.

Modeling and spatial prediction of light interception (fPAR):
Using environmental and forest variables, a predictive model for fPAR was developed based on field measurements and rasterized predictor data, thereby enabling the spatial mapping of the potential light interception.

High-resolution drone LiDAR data were collected from five riparian zones of low-order headwater streams. Simultaneous measurements of solar radiation in open areas and under forest canopy allowed us to quantify the fraction of light intercepted by vegetation (fPAR). Fractional canopy cover was calculated following the methodology by Hopkinson and Chasmer (2009). A Random Forest model calibrated with field measurements assessed the relationship between fractional cover and solar radiation interception, with model performance evaluated using leave-one-out cross-validation, showing strong predictive capacity (R² = 0.79, RMSE ≈ 0.061, MAE ≈ 0.045). This approach demonstrates the potential of integrating high-resolution LiDAR and ground-based data for ecological research and forest management. In the particular case for understanding canopy effects on aquatic ecosystems in tropical regions.

森林在维持健康河岸生态系统、调控水文过程与水质方面发挥着至关重要的作用。本研究旨在借助无人机搭载的激光雷达（LiDAR）技术表征森林冠层结构，以模拟源头溪流的冠层遮荫效应。具体而言，本研究评估了由高分辨率三维点云提取的冠层覆盖度指标，在预测河岸森林对光合有效辐射截获量（fPAR）方面的有效性。

生态背景：
通过将激光雷达（LiDAR）提取的森林冠层覆盖度与光截获数据相结合，本研究旨在更深入地解析冠层覆盖度与光截获之间的关联。该方法将助力于涉及生态过程中光截获的相关研究，例如热带河岸森林的气体交换与生物量生产力研究。

本项目包含两项相互关联的分析内容：
（1）基于激光雷达（LiDAR）数据提取森林覆盖度
（2）以研究区域内的冠层覆盖度（fcover）为指标，关联河岸冠层截获的光分数，从而提供空间显性的森林结构数据。

光截获量（fPAR）的建模与空间预测：
本研究结合环境与森林变量，基于野外实测数据与栅格化预测因子数据构建了fPAR的预测模型，进而实现潜在光截获量的空间制图。

研究团队从5处低级别源头溪流的河岸带采集了高分辨率无人机激光雷达（LiDAR）数据。通过同步测定开阔区域与林冠下的太阳辐射，本研究得以量化植被截获的光合有效辐射分数（fPAR）。冠层覆盖度的计算遵循Hopkinson与Chasmer于2009年提出的方法。本研究采用野外实测数据校准随机森林模型，以评估冠层覆盖度与太阳辐射截获量之间的关联；模型性能通过留一交叉验证进行评估，结果显示其具备优异的预测能力（决定系数R²=0.79，均方根误差RMSE≈0.061，平均绝对误差MAE≈0.045）。该方法证明了将高分辨率激光雷达（LiDAR）与地面实测数据相结合的潜力，可应用于生态研究与森林管理领域，尤其适用于解析热带区域冠层对水生生态系统的影响。