UAV Photogrammetry-Based Leaf Area Index for Above-Ground Biomass Estimation in Wetlands

Wetlands are highly productive and biologically diverse environments that provide numerous ecosystem services. Effective monitoring methods are critical to ensure their preservation and management. This paper used UAV-derived RGB images and SfM photogrammetry to create point clouds. Primary tie points were utilized as they are the more suitable representation of the raw 3D information of the common features from the overlapping images. A Leaf Area Index (LAI) estimation workflow for LiDAR was then tested on the UAV-SfM point cloud of two wetland types – alpine peatland and coastal salt marsh- to assess its applicability. The LAI and AGB values for the peatland were 0.75 – 2.58 and 0 – 318.8 g/m2 respectively, with R2 of 0.67 and Pearson's coefficient of 0.82. For the salt marsh, the values range from 0.02 – 2.04 for LAI, and 0 – 2378 g/m2 for AGB, with R2 of 0.84 and Pearson's coefficient of 0.86. Overall, the method is sensitive to vegetation density/ biomass and works well in sparser and shorter vegetation as in the case of the salt marshes in the Mediterranean compared to densely vegetated wetlands such as alpine peatlands. The flexibility and low-cost nature of UAV-SfM, and its ability to produce quality information rapidly should be further maximized in vegetation monitoring and assessment even in other environments such as agricultural lands and urban green spaces.