Influence of Topography on UAV LiDAR-Based LAI Estimation in Subtropical Mountainous Secondary Broadleaf Forests

• Published in: Forests Vol. 15; no. 1; p. 17
• Main Authors: Li, Yunfei, Zeng, Hongda, Xiong, Jingfeng, Miao, Guofang
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2024
• Subjects: Accuracy; Aerial surveying; Canopies; Correlation coefficient; Correlation coefficients; Data acquisition; Data collection; Density; Drone aircraft; Environmental aspects; Flight; Flight paths; Forests; Heterogeneity; LAI; Land area; Lasers; Leaf area; Leaf area index; Leaves; Lidar; Measurement; Montane environments; montane forests; Mountain forests; Nutrient balance; Nutrient dynamics; Nutrient utilization; Optical radar; Parameter estimation; Plant canopies; point cloud attributes; Remote sensing; Slopes; Software; Spatial heterogeneity; Terrain; Topographical drawing; Topography; UAV LiDAR; Unmanned aerial vehicles; Vegetation; Water consumption; China; United States > US
• ISSN: 1999-4907; 1999-4907
• DOI: 10.3390/f15010017

The leaf area index (LAI) serves as a crucial metric in quantifying the structure and density of vegetation canopies, playing an instrumental role in determining vegetation productivity, nutrient and water utilization, and carbon balance dynamics. In subtropical montane forests, the pronounced spatial heterogeneity combined with undulating terrain introduces significant challenges for the optical remote sensing inversion accuracy of LAI, thereby complicating the process of ground validation data collection. The emergence of UAV LiDAR offers an innovative monitoring methodology for canopy LAI inversion in these terrains. This study assesses the implications of altitudinal variations on the attributes of UAV LiDAR point clouds, such as point density, beam footprint, and off-nadir scan angle, and their subsequent ramifications for LAI estimation accuracy. Our findings underscore that with increased altitude, both the average off-nadir scan angle and point density exhibit an ascending trend, while the beam footprint showcases a distinct negative correlation, with a correlation coefficient (R) reaching 0.7. In contrast to parallel flight paths, LAI estimates derived from intersecting flight paths demonstrate superior precision, denoted by R2 = 0.70, RMSE = 0.75, and bias = 0.42. Notably, LAI estimation discrepancies intensify from upper slope positions to middle positions and further to lower ones, amplifying with the steepness of the gradient. Alterations in point cloud attributes induced by the terrain, particularly the off-nadir scan angle and beam footprint, emerge as critical influencers on the precision of LAI estimations. Strategies encompassing refined flight path intervals or multi-directional point cloud data acquisition are proposed to bolster the accuracy of canopy structural parameter estimations in montane landscapes.