Uncertainty assessment of ephemeral gully identification, characteristics and topographic threshold when using aerial photographs in agricultural settings

Abstract

Manual digitizing on aerial photographs is still commonly used for characterizing gully erosion over large areas. Even when automated detection procedures are implemented, manual digitizing is frequently being resorted to in order to constitute reference datasets used for training and validation. In both cases, manual digitizing entails some subjective decisions on behalf of the operator, which introduces uncertainty into the resulting datasets. To assess the magnitude of this uncertainty, 11 experienced operators were asked to digitize and classify ephemeral gullies (EGs) on cropland following a standardized methodology. The resulting 11 datasets were compared in terms of number, type and location of EGs. Furthermore, for EGs located on a well‐defined runoff flow concentration axis, the slope versus contributing area topographic thresholds required for initiating gully channels were assessed using four thresholding methods, and compared across the 11 datasets. The operators identified 259 different EGs. However, the number (52–139) and sum total length (8.9–23.7 km) of EGs varied widely across operators. Only 34% of the EGs were digitized by more than half of the operators, and 7% were identified by all. Identification of EGs located on a well‐defined flow concentration axis proved least subjective. The longer the EG and the more fields the EG crossed, the larger the number of operators that were able to identify it. EGs were also most easily identified when located in sugar beet fields as compared to other crops. EG classification and topographic threshold lines were also found to be strongly operator‐dependent. Quantile regression appeared to be one of the most robust thresholding methods. Operator subjectivity when digitizing EGs on orthophotographs introduces uncertainty that should be taken into account in future remote sensing‐based studies of EG erosion whenever they rely, in part or in full, on manual photograph interpretation. Copyright © 2014 John Wiley & Sons, Ltd.

Assessment of Gully Erosion Susceptibility Using Multivariate Adaptive Regression Splines and Accounting for Terrain Connectivity

“…Gully erosion is responsible for large soil losses in a wide range of environments. The contribution of gully erosion to the total sediment budget is particularly relevant in agricultural catchments, where it causes severe damage to crops and hampers agricultural traffic (Maugnard et al, 2014;Poesen et al, 2003). Moreover, gullies connect upland areas with the stream network, modifying water and sediment connectivity, especially during intense rainstorms (Torri & Poesen, 2014).…”

Detection of Catchment-Scale Gully-Affected Areas Using Unmanned Aerial Vehicle (UAV) on the Chinese Loess Plateau

“…In the past century, an increasing number of researches focus on the gully erosion due to its ubiquity and severity [7]. Evaluation of gully-affected areas is the basis for control and monitoring of gully erosion; as such, detection of these areas has become a growing interest in gully erosion community [8][9][10].…”

Application of historical and recent aerial imagery in monitoring water erosion occurrences in Czech highlands

“…by Frazier et al 1983;Fulajtár 2001;Maugnard et al 2014) and nowadays also UAV images (d'Oleire-Oltmanns et al 2012;Eltner et al 2015) are the most widely used data sources for monitoring and analyzing single catchments and smaller areas. Erosion pattern assessment and quantification can be done automatically by means of supervised or unsupervised classification (Fulajtár 2001), in some cases using object-oriented analysis techniques (Wang et al 2014).…”