RT Journal Article
T1 Comparison of Three Algorithms for the Evaluation of TanDEM-X Data for Gully Detection in Krumhuk Farm (Namibia)
A1 Vallejo Orti, Miguel
A1 Negussie, Kaleb
A1 Corral Pazos de Provens, Eva
A1 Höfle, Bernhard
A1 Bubenzer, Olaf
AB Namibia is a dry and low populated country highly dependent on agriculture, with manyareas experiencing land degradation accelerated by climate change. One of the most obvious anddamaging manifestations of these degradation processes are gullies, which lead to great economiclosses while accelerating desertification. The development of standardized methods to detect andmonitor the evolution of gully-a ected areas is crucial to plan prevention and remediation strategies.With the aim of developing solutions applicable at a regional or even national scale, fully automatedsatellite-based remote sensing methods are explored in this research. For this purpose, three di erentalgorithms are applied to a Digital Elevation Model (DEM) generated from the TanDEM-X satellitemission to extract gullies from their geomorphological characteristics: (i) Inverted MorphologicalReconstruction (IMR), (ii) Smoothing Moving Polynomial Fitting (SMPF) and (iii) Multi ProfileCurvature Analysis (MPCA). These algorithms are adapted or newly developed to identify gullies atthe pixel level (12 m) in our study site in the Krumhuk Farm. The results of the three methods arebenchmarked with ground truth; specific scenarios are observed to better understand the performanceof each method. Results show that MPCA is the most reliable method to identify gullies, achieving anoverall accuracy of approximately 0.80 with values of Cohen Kappa close to 0.35. The performance ofthese parameters improves when detecting large gullies (>30 m width and >3 m depth) achievingTotal Accuracies (TA) near to 0.90, Cohen Kappa above 0.5, and User Accuracy (UA) and ProducerAccuracy (PA) over 0.50 for the gully class. Small gullies (<12 m wide and <2 m deep) are usuallyneglected 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 methoddeveloped here is a promising tool for the identification of large gullies considering extensive studyareas. 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 ofpixel-level classification.
PB MDPI
SN 2072-4292
YR 2019
FD 2019-06
LK http://hdl.handle.net/10272/16526
UL http://hdl.handle.net/10272/16526
LA eng
NO Vallejo Orti, M., 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, 11(11), 1327. DOI: https://doi.org/10.3390/rs11111327
NO This research is complementary to the project DEM_HYDR2024, whose donor was the Deutsches Zentrum fur Luft- und Raumfahrt (DLR) for the used TanDEM-Xdatasets. Fieldwork campaigns needed for this research were funded by Integrated Land Management Institute (ILMI) under grant number RY210400 (http://ilmi.nust.na/) and by the Department of Geo-Spatial Science and Technology (http://fnrss.nust.na/?q=department/geo-spatial-technology) at Namibia University of Science and Technology. Financial support was provided by the Deutsche Forschungsgemeinschaft for Open Access Publishing.
DS Repositorio Institucional de la Universidad de Huelva
RD 31 may 2026