A Probabilistic Assessment of Soil Erosion Susceptibility in a Head Catchment of the Jemma Basin, Ethiopian Highlands

Soil erosion represents one of the most important global issues with serious effects on agriculture and water quality, especially in developing countries, such as Ethiopia, where rapid population growth and climatic changes affect widely mountainous areas. The Meskay catchment is a head catchment of the Jemma Basin draining into the Blue Nile (Central Ethiopia) and is characterized by high relief energy. Thus, it is exposed to high degradation dynamics, especially in the lower parts of the catchment. In this study, we aim at the geomorphological assessment of soil erosion susceptibilities. First, a geomorphological map was generated based on remote sensing observations. In particular, we mapped three categories of landforms related to (i) sheet erosion, (ii) gully erosion, and (iii) badlands using a high-resolution digital elevation model (DEM). The map was validated by a detailed field survey. Subsequently, we used the three categories as dependent variables in a probabilistic modelling approach to derive the spatial distribution of the specific process susceptibilities. In this study we applied the maximum entropy model (MaxEnt). The independent variables were derived from a set of spatial attributes describing the lithology, terrain, and land cover based on remote sensing data and DEMs. As a result, we produced three separate susceptibility maps for sheet and gully erosion as well as badlands. The resulting susceptibility maps showed good to excellent prediction performance. Moreover, to explore the mutual overlap of the three susceptibility maps, we generated a combined map as a color composite where each color represents one component of water erosion. The latter map yields useful information for land-use managers and planning purposes

Geomorphological Mapping and Erosion of Abandoned Tailings in the Hiendelaencina Mining District (Spain) from Aerial Imagery and LiDAR Data

The Hiendelaencina district in Spain was the most important silver producer in Europe during 1844–1925. At the end of the 20th century, with mines having closed, some waste rock dumps were reprocessed, and the sludge from the flotation process was stored in two tailings ponds. When this activity ceased, the residues began to be eroded and disperse. In this study, the state of degradation of both deposits was evaluated using historical mapping and light detection and ranging (LiDAR) data, incorporated into a Geographic Information System. In the aerial images (1946–2018), mine tailings and their main erosive and sedimentary forms were mapped. Geoforms linked to hydrological (channels, gullies, alluvial cones), wind (eolian mantles), hydric–gravitational (colluvium) and anthropic (motorbike tracks) processes which move sludge into the surrounding areas were identified. A net loss of 8849 m3 of sludge, a release of 10.3 t of potentially polluting substances and a high erosion rate of 346 t/ha*year were calculated based on LiDAR data from 2009 and 2014. The ponds show a current high degree of erosion that could increase due to both human activity and the growing frequency of drought and torrential rain periods if stabilization measures are not undertaken

Catching geomorphological response to volcanic activity on steep slope volcanoes using multi-platform remote sensing

The geomorphological evolution of the volcanic Island of Stromboli (Italy) between July 2010 and June 2019 has been reconstructed by using multi-temporal, multi-platform remote sensing data. Digital elevation models (DEMs) from PLÉIADES-1 tri-stereo images and from Light Detection and Ranging (LiDAR) acquisitions allowed for topographic changes estimation. Data were comprised of high-spatial-resolution (QUICKBIRD) and moderate spatial resolution (SENTINEL-2) satellite images that allowed for the mapping of areas that were affected by major lithological and morphological changes. PLÉIADES tri-stereo and LiDAR DEMs have been quantitatively and qualitatively compared and, although there are artefacts in the smaller structures (e.g., ridges and valleys), there is still a clear consistency between the two DEMs for the larger structures (as the main valleys and ridges). The period between July 2010 and May 2012 showed only minor changes consisting of volcanoclastic sedimentation and some overflows outside the crater. Otherwise, between May 2012 and May 2017, large topographic changes occurred that were related to the emplacement of the 2014 lava flow in the NE part of the Sciara del Fuoco and to the accumulation of a volcaniclastic wedge in the central part of the Sciara del Fuoco. Between 2017 and 2019, minor changes were again detected due to small accumulation next to the crater terrace and the erosion in lower Sciara del Fuoco.Publishedid 4385V. Processi eruttivi e post-eruttiviJCR Journa

Measuring, modelling and managing gully erosion at large scales: A state of the art

Soil erosion is generally recognized as the dominant process of land degradation. The formation and expansion of gullies is often a highly significant process of soil erosion. However, our ability to assess and simulate gully erosion and its impacts remains very limited. This is especially so at regional to continental scales. As a result, gullying is often overlooked in policies and land and catchment management strategies. Nevertheless, significant progress has been made over the past decades. Based on a review of >590 scientific articles and policy documents, we provide a state-of-the-art on our ability to monitor, model and manage gully erosion at regional to continental scales. In this review we discuss the relevance and need of assessing gully erosion at regional to continental scales (Section 1); current methods to monitor gully erosion as well as pitfalls and opportunities to apply them at larger scales (section 2); field-based gully erosion research conducted in Europe and European Russia (section 3); model approaches to simulate gully erosion and its contribution to catchment sediment yields at large scales (section 4); data products that can be used for such simulations (section 5); and currently existing policy tools and needs to address the problem of gully erosion (section 6). Section 7 formulates a series of recommendations for further research and policy development, based on this review. While several of these sections have a strong focus on Europe, most of our findings and recommendations are of global significance.info:eu-repo/semantics/publishedVersio

Semantic array programming in data-poor environments: assessing the interactions of shallow landslides and soil erosion

This research was conducted with the main objective to better integrate and quantify the role of water-induced shallow landslides within soil erosion processes, with a particular focus on data-poor conditions. To fulfil the objectives, catchment-scale studies on soil erosion by water and shallow landslides were conducted. A semi-quantitative method that combines heuristic, deterministic and probabilistic approaches is here proposed for a robust catchment-scale assessment of landslide susceptibility when available data are scarce. A set of different susceptibility-zonation maps was aggregated exploiting a modelling ensemble. Each susceptibility zonation has been obtained by applying heterogeneous statistical techniques such as logistic regression (LR), relative distance similarity (RDS), artificial neural network (ANN), and two different landslide-susceptibility techniques based on the infinite slope stability model. The good performance of the ensemble model, when compared with the single techniques, make this method suitable to be applied in data-poor areas where the lack of proper calibration and validation data can affect the application of physically based or conceptual models. A new modelling architecture to support the integrated assessment of soil erosion, by incorporating rainfall induced shallow landslides processes in data-poor conditions, was developed and tested in the study area. This proposed methodology is based on the geospatial semantic array programming paradigm. The integrated data-transformation model relies on a modular architecture, where the information flow among modules is constrained by semantic checks. By analysing modelling results within the study catchment, each year, on average, mass movements are responsible for a mean increase in the total soil erosion rate between 22 and 26% over the pre-failure estimate. The post-failure soil erosion rate in areas where landslides occurred is, on average, around 3.5 times the pre-failure value. These results confirm the importance to integrate landslide contribution into soil erosion modelling. Because the estimation of the changes in soil erosion from landslide activity is largely dependent on the quality of available datasets, this methodology broadens the possibility of a quantitative assessment of these effects in data-poor regions

Utility of Spatial Filtering Techniques in the Remote Sensing of Soil Erosion in the Sefid-Rud Reservoir Catchment in Iran

The objective of this study is to investigate the applicability of Landsat Thematic Mapper digital images assisted by computer analysis to the study of soil erosion. The study aims to identify the sources of sediment and areas of dissected land in the catchment basin of the Sefid Rud reservoir in northern Iran. First, histogram equalization is deliberately applied to the original band 3 to reduce the noise and unv/anted edges and lines in the dark tail of the histogram, mainly vegetation, and the light tail, the non-eroded areas, and also to improve the visual appearance of edges and lines on the processed image. The next step is high pass filtering, unlike the conventional edge detection technique in which the first step is low pass filtering. In this instance, the result of low pass filtering was that faint edges, evidence of the gullies, were removed and highly eroded areas appeared as non eroded areas. Therefore low pass filtering was replaced with high pass filtering, which highlighted faint edges and lines. The next step is detecting the edges and lines. When using the edge and line detecting technique for detecting dissected lands one needs to take into account that a gully might appear as two or three edges if its width is more than one pixel or as one line if it is just one pixel or less than one pixel in width on the Thematic Mapper image. Therefore an algorithm should be chosen which has the ability to detect both edges and lines. The existina edge and line detecting filters such as the Sobel , the Robert, compass, the Laplacian convolution masks and the directional line detecting technique were evaluated. The Sobel and the Robert operators were found to be powerful edge detecting techniques, but the Laplacian convolution mask was found to be the best for detecting the badland and gullied areas because it has the ability to detect faint edges as well as coarse edges. Not only does it detect both edges and lines, but it also gives stronger weight to the lines than the edges. Only edges and lines in gullied areas were of interest for detecting the dissected lands, but all other artificial and natural lines and edges were also detected. The result of applying the Laplacian function appears on the screen as black, white and gray pixels. The black pixels are non-eroded land, white pixels are eroded and gray pixels are transitional between eroded and non eroded. To change the transitional pixels to either eroded or non eroded and also for printing the image as hardcopy the thresholding function of IAX was applied to the edge detected image. In order to mask out the noise within the vegetated areas caused by edges of plots of different crops the vegetation index (VI) was added to the detected image. In the derived image black pixels are evidence of gullies and white pixels are non dissected lands. In this image it is possible to find out the relative proportion of dissected and non dissected land globally and / or within the regions of interest. Although it is possible to measure the proportions of dissected and non dissected land and they are also visually distinguishable, they have not been categorised so far. To provide a map with categories of dissection, the first step is to smooth the image. To obtain the smooth image a low pass filter was used. Two ways were tested for producing the map of dissected lands from the smoothed image. In the first method one of the strongest edge detecting techniques, the Sobel operator was used on the smoothed image of dissected lands. In the result boundaries were detected and eroded and non eroded areas outlined. In the second method for categorising the smoothed image, the density slicing function of IAX was used to split the dissected land into different levels of severity. We concluded that the second method gives a better result. It was found in previous work that among erosion features gullies are recognizable on Thematic Mapper data. Detection of gullies and gullied areas by means of classification, whether supervised or unsupervised, was not successful in this study area. We came to the conclusion that the application of a Laplacian mask on the enhanced band 3 image could detect dissected lands. When aerial photographs and Thematic Mapper data are compared, the advantage of aerial photographs was that gullies actively cutting headwards were detectable, but on the Thematic Mapper data distiguishing between active and non active gullies was impossible. Aerial photographs are a very good tool to detect all kinds of erosion features (sheet, rill, and gully), but in my study area applying this new method (DLDT) on Thematic Mapper data can provide as much detail of soil erosion as is included in previous soil erosion maps made from aerial photographs. The Sobel and the Robert operators were found to be very strong edge detectors, but the ability of the Laplacian convolution mask for detecting gullies was greater. (Abstract shortened by ProQuest.)

Gully development and its spatiotemporal variability since the late 19th century in the northern Ethiopian Highlands

This study validates previous research and indicates important land degradation by gullying in the second half of the 20th century in the Northern Ethiopian Highlands. In recent decades, local communities have however proven that with proper land management, this trend can be reversed. At a regional scale, gully networks are increasingly being stabilized and the landscape is greening. These developments have to be understood within a socio-economic environment of strong population growth and a low level of technological development, where most people rely on land resources for their livelihood, and where the fragility of the country’s economy is frequently being emphasized, for example when climatic shocks such as drought cause severe food shortages and famine. Socio-economical developments and their relation to land degradation should therefore be monitored closely. With an annual population growth rate of 2.37% (period 2000-2010, CSA, 2008) and a population size which is likely to double by 2050, the country faces immense challenges. Key is to rehabilitate land as a resource base for food security and ecosystem services, and to strengthen and diversify the rural economy in order to make local communities less dependent on land resources. Such challenges are embraced by many local, national and international programs, and should remain high on the agenda. As to other dryland environments, this study emphasizes that fast land degradation may occur when improper land management is applied. Most dramatic is the development of extensive and deep gully networks, which export large quantities of sediment through the ephemeral gully and river system and therefore jeopardize in situ agricultural production. Moreover, decreased agricultural production in the proximity of gullies can be expected as a result of the depressed water tables. With fast network expansion occurring, infrastructures may be damaged and costs related to future planning may be much higher than originally budgeted for. Downstream effects are also important. Water pollution caused by sediment and urban wastewater threatens human health and decreases agricultural production. As a result of a stronger ash ood regime, rivers – even those that are located many km downstream of the gullies – may respond strongly and geomorphic changes may cause infrastructures to be damaged (e.g., Billi, 2008)

Gully Mapping using Remote Sensing: Case Study in Kwazulu-Natal, South Africa

At present one of the challenges of soil erosion research in South Africa is the limited information on the location of gullies. This is because traditional techniques for mapping erosion which consists of the manual digitization of gullies from air photos or satellite imagery, is limited to expert knowledge and is very time consuming and costly at a regional scale (50-10000km²). Developing a robust, reliable and accurate means of mapping gullies is a current focus for the Institute for Soil, Climate and Water Conservation (ISCW) of the Agricultural Research Council (ARC) of South Africa. The following thesis attempted to answer the question whether “medium resolution multi-spectral satellite observations, such as Landsat TM, combined with information extraction techniques, such as Vegetation Indices and multispectral classification algorithms, can provide a semi-automatic method of mapping gullies and to what level of accuracy?”. More specifically, this thesis investigated the utility of three Landsat TM-derived Vegetation Index (VI) techniques and three classification techniques based on their level of accuracy compared to traditional gully mapping methods applied to SPOT 5 panchromatic imagery at selected scales. The chosen study area was located in the province of KwaZulu-Natal (KZN) South Africa, which is considered to be the province most vulnerable to considerable levels of water erosion, mainly gully erosion. Analysis of the vegetation indices found that Normalized Difference Vegetation Index (NDVI) produced the highest accuracy for mapping gullies at the sub-catchment level while Transformed Soil Adjusted Vegetation Index (TSAVI) was successful at mapping gullies at the continuous gully level. Mapping of gullies using classification algorithms highlighted the spectral complexity of gullies and the challenges faced when trying to identify them from the surrounding areas. The Support Vector Machine (SVM) classification algorithm produced the highest accuracy for mapping gullies in all the tested scales and was the recommended approach to gully mapping using remote sensin