A GIS focal approach for characterizing gully geometry

Abstract

In this work we examined the explanatory factors of gully geometry in the Campiña landscape in southern Spain using a geographic information system (GIS) focal approach with regional topographic products (1.5 m LiDAR [light detecton and ranging] digital elevation model [DEM] for elevation data and 0.5 m orthophotography for gully limit delineation). We evaluated the performance of a focal approach (based on neighbourhood calculations) using topographic factors for characterizing the gully linear extent and cross‐section (XS) morphology (i.e., depth, D, width, W, mean slope, S and width‐to‐depth ratio, WDR). As for the longitudinal analysis, focal variables (e.g., using the 11 × 11 mean thalweg slope and 75‐m downstream mean slope) outperformed the pixel‐based approach, with accurate spatial predictions for gully headcuts and mouth locations. The threshold between gullied and non‐gullied areas in a slope‐area diagram took the form of a break‐line rather than a straight limit. For estimating the main XS dimensions (D and W), the Half‐width method was applied by selecting the closest point to the thalweg at the gully limits. The tendency to underestimate XS depths was corrected by applying the equations derived from an assessment of the sampling errors in DEMs datasets. The 50 m adjacent slope to the gully was the best explanatory descriptor of both XS dimensions (due to energetic and geometric reasons) along with the channel density, drainage area and total gully length. The XS mean slope and WDR were controlled by the resistance of the materials (investigated through geotechnical and vane‐test surveys) where incision processes were dominant (V‐shaped XSs in upstream and middle reaches). As sedimentation started to become significant, both morphological parameters changed abruptly and XSs tended to show a trapezoid shape. An adaptative focal strategy with varying window sizes produced better results than the pixel‐based approach, requiring larger calculation scopes in areas of greater DEM uncertainty.

Mapping rill soil erosion in agricultural fields with UAV‐borne remote sensing data

“…A very important aspect of our UAV image classification is that it is based on 3D data that can readily be acquired for the whole eroding area within fields. It enables the measurement of important rill parameters such as width, depth, length, and cross‐section at any section of the rill channel, thus providing its full morphology (Castillo et al, 2021). These rill parameters are, therefore, a true representation of the rill morphology.…”

“…Conventionally, rill erosion surveys involve the time‐consuming measurement of rill length, width, and depth in transects with a tape measure for estimating erosion volume (Casalí et al, 2006; Di Stefano et al, 2017; Fiorucci et al, 2015; Gessesse et al, 2010). In contrast, remote sensing techniques enable fast, inexpensive, non‐invasive, systematic, and larger‐scale erosion surveying (Carollo et al, 2015; Castillo et al, 2021; Fiorucci et al, 2015; Gessesse et al, 2010; Wells et al, 2016). While mapping and monitoring gully erosion with remote sensing has attracted considerable attention in recent years (Marzolff & Poesen, 2009; Oleire‐Oltmanns et al, 2014; Perroy et al, 2010; Sepuru & Dube, 2018; Shruthi et al, 2011), only a few studies have applied this technology to the quantitative analysis of rill erosion.…”

“…Additionally, in many cases, the analysis of rill erosion by remote sensing comprised only the delineation of rill networks using the theory of drop analysis and flow accumulation or manual digitization (Carollo et al, 2015; Gessesse et al, 2010; Lu et al, 2017; Vinci et al, 2015; Wells et al, 2016; Zhang et al, 2016) without analysing the full 3D morphology of rill incisions that was addressed only in selected studies related to gully mapping (e.g. Castillo et al, 2021). Semi‐automated methods have been introduced for rill detection and rill parameter extraction from sub‐centimetre‐resolution DEMs (Bazzoffi, 2015; Eltner et al, 2015).…”

Achieving change through gully erosion research

“…Computational mapping of gully extent and geometry from digital elevation models (DEMs) such as those derived from airborne LiDAR (light detection and ranging) is developing in accuracy and scale (Castillo et al, 2021;Vanmaercke et al, 2021;Walker et al, 2020).…”

“…Collaborating with other researchers can help to transfer methods and findings between regions, that is to make it more useable and more widely used. Facilitating fruitful international research collaborations has been a benefit of the ISGE conference series, including but not limited to those that have resulted in joint publications (e.g., Ayele et al, 2018; Castillo et al, 2021; Liu et al, 2019; Luquin et al, 2021; Marzolff & Pani, 2018). Multi‐disciplinary research is a promising approach to influencing land management, such as with researchers addressing agricultural water management, soil science, or adapting agriculture to a warmer and more variable climate (Lal et al, 2011).…”