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

Abstract

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.

Communication and Control in Collaborative UAVs: Recent Advances and Future Trends

“…UAVs with integrated multimodal sensors and improved photogrammetry and computer algorithms have also displayed great results for acquiring and processing terrain data. The digital surface models and digital elevation models generated from drones provide essential inputs for topography for the accurate modeling of flood plain hydrodynamics, and overland flow predictions [179]. Moreover, UAVs equipped with multispectral cameras also provide the most accurate metadata, leading to efficient and straightforward imagery data collection for vegetation mapping [180].…”

Using Unmanned Aerial Vehicle and LiDAR-Derived DEMs to Estimate Channels of Small Tributary Streams

“…Additional errors originating from lens distortion, GPS position error, aircraft attitude uncertainty and errors in time domain can lead to decreased accuracy in the relative UAV map geolocation [61]. Therefore, the overall geolocation was corrected using the target coordinates as checkpoints, acquired with handheld GPS as relative accuracy [62]. Six ground control markers (square targets of 1.44 m 2 ) were equally distributed across the site and at the boundaries of the study area, including next to the watercourse at different elevations (Figure 3).…”

“…These results indicate high precision between the measured coordinates (GCPs registered in the field) compared with the software-calculated position. Elevation ranges predicted for each DEM for the study area can be seen in a previous study [62], where the same elevation sources were used.…”

Body-UAV Near-Ground LoRa Links Through a Mediterranean Forest

“…Instead, unmanned aerial vehicles (UAVs) and systems (UASs) are game-changing technologies for SaR missions. They could dramatically increase the survival chances of the targets to be rescued by avoiding terrestrial obstacles and speeding up operations through near-ground flights having altitudes of tens of meters [15], [16]. Thus, body-UAV links, viz., electromagnetic links between a body-worn device and a UAV [17], can maximize the benefits of using wearable LoRa devices [18].…”