Improved Accuracy of Riparian Zone Mapping Using Near Ground Unmanned Aerial Vehicle and Photogrammetry Method

• Main Authors: Joan Grau, Kang Liang, Jae Ogilvie, Paul Arp, Sheng Li, Bonnie Robertson, Fan-Rui Meng
• Format: Article
• Language: English
• Published: MDPI AG 2021-05-01
• Series: Remote Sensing
• Subjects: riparian zone; mapping; DEM; UAV; LiDAR; VDTCN
• Online Access: https://www.mdpi.com/2072-4292/13/10/1997

In agriculture-dominant watersheds, riparian ecosystems provide a wide array of benefits such as reducing soil erosion, filtering chemical compounds, and retaining sediments. Traditionally, the boundaries of riparian zones could be estimated from Digital Elevation Models (DEMs) or field surveys. In this study, we used an Unmanned Aerial Vehicle (UAV) and photogrammetry method to map the boundaries of riparian zones. We first obtained the 3D digital surface model with a UAV. We applied the Vertical Distance to Channel Network (VDTCN) as a classifier to delineate the boundaries of the riparian area in an agricultural watershed. The same method was also used with a low-resolution DEM obtained with traditional photogrammetry and two more LiDAR-derived DEMs, and the results of different methods were compared. Results indicated that higher resolution UAV-derived DEM achieved a high agreement with the field-measured riparian zone. The accuracy achieved (Kappa Coefficient, KC = 63%) with the UAV-derived DEM was comparable with high-resolution LiDAR-derived DEMs and significantly higher than the prediction accuracy based on traditional low-resolution DEMs obtained with high altitude aerial photos (KC = 25%). We also found that the presence of a dense herbaceous layer on the ground could cause errors in riparian zone delineation with VDTCN for both low altitude UAV and LiDAR data. Nevertheless, the study indicated that using the VDTCN as a classifier combined with a UAV-derived DEM is a suitable approach for mapping riparian zones and can be used for precision agriculture and environmental protection over agricultural landscapes.