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.

Gullies are landforms with specific patterns of shape, topography, hydrology, vegetation, and soil characteristics. Remote sensing products (TanDEM-X, Sentinel-1 and Sentinel-2) serve as inputs into an iterative algorithm, initialized using a micro-mapping simulation as training data, to map gullies in the northwestern of Namibia. A Random Forest Classifier examines pixels with similar characteristics in a pool of unlabeled data and gully objects are detected where high densities of gully pixels are enclosed by an alpha shape. Gully objects are used in subsequent iterations following a mechanism where the algorithm uses the most reliable pixels as gully training samples. The gully class continuously grows until an optimal scenario in terms of accuracy is achieved. Results are benchmarked with manually tagged gullies (initial gully labelled area <0.3% of the total study area) in two different watersheds (408 km 2 and 302 km 2 , respectively) yielding total accuracies of >98%, with 60% in the gully class, Cohen Kappa>0.5, Matthews Correlation Coefficient>0.5, and ROC Area Under the Curve>0.89. Hence, our method outlines gullies keeping low false-positive rates while the classification quality has a good balance for the two classes (Gully/No Gully). Results show the most significant gully descriptors as the high temporal radar signal coherence (22.4%) and the low temporal variability in Normalized Difference Vegetation Index (NDVI) (21.8%). This research builds on previous studies to face the challenge of identifying and outlining gully-affected areas with a shortage of training data using global datasets, which are then transferable to other large (semi-) arid regions.

The K‐factor of the universal soil loss equation is a core component in many erosion models, as a measure of soil erodibility. It can be estimated by a nomograph, where the summed fractions of silt and very fine sand (VFS) are basic inputs. Frequently, only the three broad particle‐size classes of sand, silt, and clay are measured in laboratories; thus, the VFS fraction must be estimated. Three models are currently available for this estimation, namely, (a) the Revised Universal Soil Loss Equation formula, (b) the European Soil Data Centre method, and (c) the Shirazi–Boersma theory, all three use just the sand fraction as explanatory variable. Nevertheless, their accuracy has never been assessed, and this is the main purpose of this study. The data used to test the VFS estimation methods were drawn from the National Cooperative Soil Survey Soil Characterization Database, incorporating data from more than 300,000 soil horizon samples. The test results show a poor performance of the models, all of which were found to be unsuitable for 31.1% of the textural triangle, accounting for 32.3% of the soil samples. Moreover, it is demonstrated that any conceivable model based solely on the broad particle‐size classes would suffer from a high degree of uncertainty. Consequently, the number of explanatory variables should be increased in order to improve the performance of models. An alternative prediction chart is provided for the first approximation of K‐factor, based on the textural triangle.

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Los cultivos de frutos rojos o berries ocupan una gran superficie en el tercio sur de la provincia de Huelva. Se caracterizan por una agricultura tecnificada, intensiva, que se vale de invernaderos en túnel para la obtención de un producto de alto valor añadido. Este valor induce una ocupación casi total del territorio, con grandes movimientos de tierras, rectificaciones y canalizaciones de arroyos, ocupaciones de dominios públicos hidráulico, viario y otros. Así mismo, esta actividad genera importantes cantidades de residuos cuya manipulación es a menudo inadecuada. Este conjunto de circunstancias produce impactos paisajísticos significativos. En el presente trabajo se identifican y describen los problemas señalados, y se plantean posibles estrategias de integración paisajística. Se definen tres tipos de medidas: i) aquellas que deben responder a situaciones ilegales que deben ser corregidas por los propietarios; ii) aquellas que identifiquen usos territoriales irregulares no sancionables cuya corrección puede desarrollarse instando al propietario a su resolución, incluso incentivándole; iii) medidas de mejora y creación de hábitats naturales y de la imagen visual general cuyo desarrollo puede plantearse mediante incentivos y formación a los propietarios sobre las posibilidades de dar un uso terciario a sus explotaciones. La integración paisajística de las explotaciones de frutos rojos es una necesidad importante para el desarrollo turístico de Huelva. Además, puede constituirse en una oportunidad para aquellos agricultores que se planteen generar espacios visitables que proyecten aún más hacia el exterior un producto y una denominación bien conocidos a escala mundial, pero que necesitan consolidar su imagen.