Abstract
Namibia is a dry and low populated country highly dependent on agriculture, with many areas experiencing land degradation accelerated by climate change. One of the most obvious and damaging manifestations of these degradation processes are gullies, which lead to great economic losses while accelerating desertification. The development of standardized methods to detect and monitor the evolution of gully-affected areas is crucial to plan prevention and remediation strategies. With the aim of developing solutions applicable at a regional or even national scale, fully automated satellite-based remote sensing methods are explored in this research. For this purpose, three different algorithms are applied to a Digital Elevation Model (DEM) generated from the TanDEM-X satellite mission to extract gullies from their geomorphological characteristics: (i) Inverted Morphological Reconstruction (IMR), (ii) Smoothing Moving Polynomial Fitting (SMPF) and (iii) Multi Profile Curvature Analysis (MPCA). These algorithms are adapted or newly developed to identify gullies at the pixel level (12 m) in our study site in the Krumhuk Farm. The results of the three methods are benchmarked with ground truth; specific scenarios are observed to better understand the performance of each method. Results show that MPCA is the most reliable method to identify gullies, achieving an overall accuracy of approximately 0.80 with values of Cohen Kappa close to 0.35. The performance of these parameters improves when detecting large gullies (>30 m width and >3 m depth) achieving Total Accuracies (TA) near to 0.90, Cohen Kappa above 0.5, and User Accuracy (UA) and Producer Accuracy (PA) over 0.50 for the gully class. Small gullies (<12 m wide and <2 m deep) are usually neglected in the classification results due to spatial resolution constraints within the input DEM. In addition, IMR generates accurate results for UA in the gully class (0.94). The MPCA method developed here is a promising tool for the identification of large gullies considering extensive study areas. Nevertheless, further development is needed to improve the accuracy of the algorithms, as well as to derive geomorphological gully parameters (e.g., perimeter and volume) instead of pixel-level classification.
Highlights
Soil erosion is recognized as the most significant cause of land degradation worldwide and “is often blamed for drastic reduction of soil fertility” [1]
The need for large area gully monitoring and mapping in Namibia is becoming crucial for the planning of regional strategies to tackle land degradation and for providing raw data for exhaustive research in geomorphology processes
After comparing the three algorithms (IMR, Smoothing Moving Polynomial Fitting (SMPF) and Multi-Profile Curvature Analysis (MPCA)), it is evident that MPCA yields the best results and should be prioritized for further development
Summary
Soil erosion is recognized as the most significant cause of land degradation worldwide and “is often blamed for drastic reduction of soil fertility” [1] It refers to the absolute loss of soil, carbon and nutrients from the top layers, generally caused by water, ice, wind, and human activities [2]. The direct impact of gullies in terms of soil loss is often underestimated when considered together with other types of surface erosion (i.e., soil sheets and rills), authors like [4,5,6] have observed that gully erosion is the main source of sedimentation in river basins In this regard, Valentin et al [1] state that the development of gullies tends “to enhance drainage and accelerate aridification processes in semi-arid zones”. The environmental and economic impact of gullies is severe in Namibia, including the loss of water harvesting capacity to dry wells [7]
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