Overland Flow Analysis Using Time Series of Suas-Derived Elevation Models

Abstract

ABSTRACT:With the advent of the innovative techniques for generating high temporal and spatial resolution terrain models from Unmanned Aerial Systems (UAS) imagery, it has become possible to precisely map overland flow patterns. Furthermore, the process has become more affordable and efficient through the coupling of small UAS (sUAS) that are easily deployed with Structure from Motion (SfM) algorithms that can efficiently derive 3D data from RGB imagery captured with consumer grade cameras. We propose applying the robust overland flow algorithm based on the path sampling technique for mapping flow paths in the arable land on a small test site in Raleigh, North Carolina. By comparing a time series of five flights in 2015 with the results of a simulation based on the most recent lidar derived DEM (2013), we show that the sUAS based data is suitable for overland flow predictions and has several advantages over the lidar data. The sUAS based data captures preferential flow along tillage and more accurately represents gullies. Furthermore the simulated water flow patterns over the sUAS based terrain models are consistent throughout the year. When terrain models are reconstructed only from sUAS captured RGB imagery, however, water flow modeling is only appropriate in areas with sparse or no vegetation cover.

r.sim.terrain 1.0: a landscape evolution model with dynamic hydrology

“…Gully erosion rates and evolution can be monitored in the field or modeled on the computer. Field methods include dendrogeomorphology (Malik, 2008) and permanent monitoring stakes for recording erosion rates, extensometers for recording mass wasting events, weirs for recording water and suspended sediment discharge rates, and time series of surveys using total station theodolites (Thomas et al, 2004), unmanned aerial systems (UASs) (Jeziorska et al, 2016;Kasprak et al, 2019;Yang et al, 2019), airborne lidar (Perroy et al, 2010;Starek et al, 2011), and terrestrial lidar (Starek et al, 2011;Bechet et al, 2016;Goodwin et al, 2016;Telling et al, 2017). With terrestrial lidar, airborne lidar, and UAS photogrammetry, there are now sufficient-resolution topographic data to morphometrically analyze and numerically model fine-scale landscape evolution in GIS, including processes such as gully formation and the development of microtopography.…”

Fusion of high-resolution DEMs for water flow modeling

“…We address the first issue by reconstructing bare earth from two UAS surveys acquired in The UAS data were interpolated from the point clouds to 0.3 meter resolution rasters. The details of the data acquisition and processing of the UAS-based DEMs are provided by Jeziorska et al [18]. Lidar data used in this study were collected by the North Carolina Floodplain Mapping Program [19] in 2015 as part of a statewide survey, with average point density of 3 points per square meter and multiple return classified points.…”

Modeling Streamflow and Sediment Loads with a Photogrammetrically Derived UAS Digital Terrain Model: Empirical Evaluation from a Fluvial Aggregate Excavation Operation

“…As a result of the model calibration, it can be subsequently concluded that a photogrammetrically derived DTM from a UAS point cloud is effective in modeling flow. Jeziorska et al, [30] reported that a UAS derived terrain model is more effective in accounting for flow morphology and patterns over a lidar derived DTM in areas not covered by vegetation because of its increased spatial resolution. We attribute the slightly lower values of R, E, and d in the UAS derived terrain model to the uncertainty in interpolated terrain beneath the few areas within the watershed that are covered by trees and shrub and also to the single flow (D-8) algorithm used by SWAT.…”

“…Quite recently, photogrammetrically derived DSM and DTM from high resolution UAS imagery has been emerging in the hydrologic literature [18,[26][27][28]. Photogrammetrically derived UAS data has been used to model surface flow within and outside urban areas [17,29,30], spatial and temporal variability of riverbed hydraulic conductivity [31], channel morphology [32], streambank topography [33], streambank erosion [27], and gully erosion in agricultural and urban watersheds [34,35]. Stocker [34] demonstrated that the photogrammetrically derived data from UAS can measure gully erosion in farmland in a way that LiDAR technology could not due to the increased spatial and temporal resolution that UAS models provide.…”