Mapping Functional Riparian Zones in Agricultural Watershed Using LiDAR and Photogrammetry Methods
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Date
2021-10
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University of New Brunswick
Abstract
This thesis reports on a new photogrammetry method to map functional riparian areas across agricultural watersheds, developed near Havelock, New Brunswick. The main objective was to assess the feasibility of using consumer-grade Unmanned Aerial Vehicles (UAV) as means of predicting the extents of this ecotone. To do so, we explored how to optimally set flight parameters, such as image overlap, pre-planned flight grid, flight height, and the camera angle for image reconstruction and successful 3D point extraction.
Near ground aerial images were collected during different seasons over the study site. These images were used to map the variability in the field conditions, including watercourses and riparian vegetation. We also generated a UAV-photogrammetric digital elevation model (DEM), which was compared to Service New Brunswick Geospatial datasets (Coarse resolution with average point spacing of 70 meters and LiDAR-derived DEMs, at 1.2 points/m2 and 6 points/m2).
The coarse resolution DEM was interpolated at a 2 meter cell size, whereas LiDAR and UAV-derived DEMs were interpolated at a 30cm cell size. We used these topographic models to estimate the surface area occupied by stream channels, using the Slope Gradient (SG) raster and the Vertical Slope Position (VSP). Furthermore, we tested the accuracy of these topographic models in predicting the extents of the functional riparian area using the Vertical Distance To Channel Network (VDTCN).
We found that the UAV-derived DEM achieved the best accuracy in predicting the area occupied by stream channels when the SG was used, followed by LiDAR collected at 6 points/m2 (LiDAR 6.0), with mean squared errors (MSE) of 1.81 and 1.91, respectively. We also found that LiDAR and UAV-derived DEMs achieved 63% agreement (as measured in Kappa Coefficient) in predicting the extents of functional riparian areas, even though the UAV provided higher terrain detail. However, coarse resolution DEM did not provide accurate topographic detail for estimating the channel surface area nor the functional riparian zone extents.
The results presented herein proved that UAVs were an accurate and economic means for prediction, management, assessment, and monitoring of riparian ecosystems over agricultural landscapes.