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Abstract
Gullies are responsible for detaching massive volumes of productive soil, dissecting natural landscape and causing damages to infrastructure. Despite existing research, the gravity of the gully erosion problem underscores the urgent need for accurate mapping of gullies, a first but essential step toward sustainable management of soil resources. This study aims to obtain the spatial distribution of gullies through comparing various classifiers: k-dimensional tree K-Nearest Neighbor (k-d tree KNN), Minimum Distance (MD), Maximum Likelihood (ML), and Random Forest (RF). Results indicated that all the classifiers, with the exception of ML, achieved an overall accuracy (OA) of at least 0.85. RF had the highest OA (0.94), although it was outperformed in gully identification by MD (0% commission), but the omission error was 20% (MD). Accordingly, RF was considered as the best algorithm, having 13% error in both adding (commission) and omitting pixels as gullies. Thus, RF ensured a reliable outcome to map the spatial distribution of gullies. RF-derived gully density map reflected the agricultural areas most exposed to gully erosion. Our approach of using satellite imagery has certain limitations, and can be used only in arid or semiarid regions where gullies are not covered by dense vegetation as the vegetation biases the extracted gullies. The approach also provides a solution to the lack of laser scanned data, especially in the context of the study area, providing better accuracy and wider application possibilities.
Highlights
We evaluated the performance of K-Nearest Neighbor (KNN), Minimum Distance (MD), Maximum Likelihood (ML), and Random Forest (RF) at class level, focusing on gully mapping while taking into account different gullies across various parts of the study area
Whereas RF and KNN accounted for the largest proportions of GL (57%) and SV (31%)
Other classifiers recorded the same results in four selected sites with the exception of site #1 where the ML had the highest areal extent of gully erosion (19%), followed by RF and KNN both recording 17%, and MD (12%)
Summary
The onsite problems include, but are not restricted to, reduction in soil fertility, loss of soil and nutrients, and destruction of man-made infrastructure (e.g., buildings, roads, bridges), whereas off-site problems include sedimentation of freshwater bodies, which in turn decreases water quality and quantity, leading to loss of freshwater biodiversity [5,6] These effects further undermine the economic and ecological value brought about by the natural environment to societies. It is reported that almost 800 million people around the world directly depend on steep lands for their sustenance [7] Owing to these concerns, the need for better understanding and assessment of soil erosion has never been so urgent
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