Comparison of Three Algorithms for the Evaluation of TanDEM-X Data for Gully Detection in Krumhuk Farm (Namibia)

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.

This paper explores fully automated satellite-based remote sensing methods with the aim of developing solutions applicable at a regional or even national scale. 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 of the different gully classification methods for one validation plot.

Results show that MPCA is the most reliable method to identify gullies, achieving an overall accuracy of ca. 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.

Find all details in the full paper:

Orti, M.V., Negussie, K., Corral-Pazos-de-Provens, E., Höfle, B. & Bubenzer, O. (2019): Comparison of Three Algorithms for the Evaluation of TanDEM-X Data for Gully Detection in Krumhuk Farm (Namibia). Remote Sensing. Vol. 11 (11), pp. 1-22.

The research of this paper is complementary to the project DEM_HYDR2024, funded by the Deutsches Zentrum für Luft- und Raumfahrt (DLR) for the used TanDEM-X datasets. Fieldwork campaigns needed for this research were funded by Integrated Land Management Institute (ILMI, grant number RY210400) and by the Department of Geo-Spatial Science and Technology at Namibia University of Science and Technology. Financial support was provided by the Deutsche Forschungsgemeinschaft for Open Access Publishing.

Methods of gully detection and monitoring are the core research subject of the PhD project of Miguel Orti on the development of gully identification and measurement methods combining remote sensing and crowdsourcing techniques.

Find out about the 3DGeo group’s work in further geomorphic settings, such as costal environments in the Netherlands, snow cover monitoring at Zugspitze mountain and rock glacier surface changes in the Austrian alps.