Nurhussen Ahmed Mohammed

By Nurhussen Ahmed Mohammed and Desale Kidane Asmamaw

The severity of soil loss in the Ethiopian highlands has been increased from time to time. Hence, the assessment of soil erosion using models is very important for planning successful and sustainable soil management. This study was conducted in Bahir Dar Zuria district, Ethiopia with aiming to quantify the amount of soil loss using the GIS-based RUSLE (Revised Universal Soil Loss Equation) model. Based on the study, the most pronounced RUSLE factor that increases soil erosion was the slope length (L) and slope steepness (S). Compared with other land uses, bare land and cropland in the higher slopes were more vulnerable to erosion. As expected slope and soil losses have a direct relationship. About 80% of the study area experienced annual soil loss of less than 1.2 ton/ha/yr. Conversely, soil loss was very high for slopes greater than 30%. This indicated that slope has a great impact on regulating soil loss. The annual soil loss for cropland, vegetation, grassland, and degraded land was 19.05, 8.78, 8.82, and 71.16 ton/ha/yr., respectively. This is to means that land use land cover have a strong relationship with the amount of soil loss. The same land cover with different slopes have different soil loss amount. It was found that lack of vegetative cover during the critical period of rainfall, expansion of croplands, and absence of support practices increase soil erosion. Thus, the application of stone lines, contour tillage, terraces, and grass strip barriers are suggested to break the slope length into shorter distances, reducing overland flow velocity and soil erosion. Moreover, improving the awareness of society to reduce the illegal cutting of trees and apply conservation practices to reduce soil erosion in their farmland is very essential.

Part of the book: Soil Erosion

By Nurhussen Ahmed and Worku Zewdie

Ethiopia successfully launched its first earth-observing satellite sensor in December 2019 for the purpose to manage natural resources and enhance agriculture. This study aimed at evaluating the potential of Ethiopian Remote Sensing Satellite 1 (ETRSS-1), for the first time, for detecting and mapping Prosopis juliflora distribution. To better test its potential, a comparison was made against the novel Sentinel-2 Multispectral Instrument and Landsat-8 Operational Land Manager datasets. Radiometric indices (Scenario-1) and spectral bands (Scenario-2) derived from these sensors were used to model the distribution of Prosopis juliflora using the random forest modeling approach. A total of 241 georeferenced field data on species presence and absence data were used to train and validate datasets in both scenarios. True skill statistics (TSS), area under the curve (AUC), correlation, sensitivity, and specificity were used to evaluate their performance. Our results described that the ETRSS-1-derived variables can be sufficient for modeling and mapping of P. juliflora distribution in such settings. However, higher performance was found from Sentinel-2 with AUC > 0.97 and TSS > 0.89, and followed by Landsat-8 with AUC > 0.93 and TSS > 0.77 and ETRSS-1 with AUC > 0.81 and TSS > 0.57. The lower performance of ETRSS-1 compared to Landsat-8 and Sentinel-2 datasets, however, is partly due to its coarse spectral resolution. Hence, improving the spectral resolution of ETRSS-1 might increase its accuracy.

Part of the book: Applications of Remote Sensing