Soil loss estimation using GIS and Remote sensing techniques: A case of Koga watershed, Northwestern Ethiopia

AbstractSoil loss by runoff is a severe and continuous ecological problem in Koga watershed. Deforestation, improper cultivation and uncontrolled grazing have resulted in accelerated soil erosion. Information on soil loss is essential to support agricultural productivity and natural resource management. Thus, this study was aimed to estimate and map the mean annual soil loss by using GIS and Remote sensing techniques. The soil loss was estimated by using Revised Universal Soil Equation (RUSLE) model. Topographic map of 1:50,000 scale, Aster Digital Elevation Model (DEM) of 20m spatial resolution, digital soil map of 1:250,000 scale, thirteen years rainfall records of four stations, and land sat imagery (TM) with spatial resolution of 30m was used to derive RUSLE's soil loss variables. The RUSLE parameters were analyzed and integrated using raster calculator in the geo-processing tools in ArcGIS 10.1 environment to estimate and map the annual soil loss of the study area. The result revealed that the annual soil loss of the watershed extends from none in the lower and middle part of the watershed to 265tha−1year−1 in the steeper slope part of the watershed with a mean annual soil loss of 47t ha−1year−1. The total annual soil loss in the watershed was 255283t, of these, 181801 (71%) tones cover about 6691 (24%) hectare of land. Most of these soil erosion affected areas are spatially situated in the upper steepest slope part (inlet) of the watershed. These are areas where Nitosols and Alisols with higher soil erodibility character (0.25) values are dominant. Hence, Slope gradient and length followed by soil erodibility factors were found to be the main factors of soil erosion. Thus, sustainable soil and water conservation practices should be adopted in steepest upper part of the study area by respecting and recognizing watershed logic, people and watershed potentials

Mapping malaria risk using geographic information systems and remote sensing: The case of Bahir Dar City, Ethiopia

The main objective of this study was to develop a malaria risk map for Bahir Dar City, Amhara, which is situated south of Lake Tana on the Ethiopian plateau. Rainfall, temperature, altitude, slope and land use/land cover (LULC), as well as proximity measures to lake, river and health facilities, were investigated using remote sensing and geographical information systems. The LULC variable was derived from a 2012 SPOT satellite image by supervised classification, while 30-m spatial resolution measurements of altitude and slope came from the Shuttle Radar Topography Mission. Metrological data were collected from the National Meteorological Agency, Bahir Dar branch. These separate datasets, represented as layers in the computer, were combined using weighted, multi-criteria evaluations. The outcome shows that rainfall, temperature, slope, elevation, distance from the lake and distance from the river influenced the malaria hazard the study area by 35%, 15%, 10%, 7%, 5% and 3%, respectively, resulting in a map showing five areas with different levels of malaria hazard: very high (11.2%); high (14.5%); moderate (63.3%); low (6%); and none (5%). The malaria risk map, based on this hazard map plus additional information on proximity to health facilities and current LULC conditions, shows that Bahir Dar City has areas with very high (15%); high (65%); moderate (8%); and low (5%) levels of malaria risk, with only 2% of the land completely riskfree. Such risk maps are essential for planning, implementing, monitoring and evaluating disease control as well as for contemplating prevention and elimination of epidemiological hazards from endemic areas

geographic information system (GIS) and remote

Solid waste dumping site suitability analysis usin

Land‑use and land‑cover dynamics nexus to local climate variability in Suha watershed, upper Blue Nile basin, Northwest Ethiopia

ABSTRACTThis study examined the nexus between land use, land cover dynamics, and climate variability and change in the Suha sub-watershed of the upper Blue Nile basin (1990–2020). Data sources such as Landsat images (LULC, NDVI, and LST) and NMAE/KNMI (rainfall) were used and analyzed using ArcGIS 10.7.1, QGIS 2.8.3, and XLSTAT 19. The relationship between NDVI and climate variables was determined using Pearson’s correlation coefficient, while the cellular automata-artificial neural network technique was used to predict future LULC change. Results showed that among the six land use classes, cultivated land gained more than 30%, while grassland lost more than 20% in each decade. The LULC dynamic in the future also showed that bare land and the built-up area had the highest increments, while bush-shrub land had the highest diminishing trends. The NDVI values of each land use class were between −0.14 and +0.74 in 1990 and −0.09 and 0.68 in 2000, respectively. In 2013, the NDVI value ranged from −0.04 to +0.46, and in 2020, it was from −0.08 to 0.55, respectively. The NDVI value of the different land uses showed a decreasing trend. However, LST and rainfall in the watershed showed an increasing and decreasing trend, respectively, which is associated with the LULC daynamics. The correlation between NDVI and LST was found to be negative, whereas the relationship between NDVI and rainfall was positive. Hence, an appropriate use of land is an undeniable fact to minimize the undesirable influence of LULC change on climate variability in the area

Changing farming practices as integral to sustenance and cropland-use loss in the context of urban expansion: The case of Jimma City, Southwest Ethiopia

Urbanization can cause changes in farming practices, which in turn bring cropland-use loss. However, this issue is an under-researched topic. This study examines changing farming practices from cropland-use to woodland (Eucalyptus and clay brick production) using panel and cross-sectional data. Landsat imageries were used as longitudinal panel data to assess cropland-use changes from 2001 to 2021. A cross-sectional survey was designed to collect data from randomly selected 300 farmers in the peri-urban areas of Jimma City. A questionnaire was conducted with farmers to identify factors and reasons leading to changes in farming practices, while interviews and group discussions were organized with key informants to gather their experiences of changing farming practices. A two-way cross-matrix was used to estimate the cropland-use change from 2001 to 2021. A spatial regression model was used to determine significant factors of changing farming practices, while the qualitative data was described along with the model results. The results show that woodland increased by 44% from 2013 to 2021 due to changing farming practices from cropland-use to woodland. Cropland and pastureland were converted to built-up areas at 4.4% and 2.8% per year, respectively, while woodland has been converted at 1.5%, implying that it is more resistant to built-up area expansion. The spatial regression model reveals that access to markets, income, institutional barriers, farm size, and family size were the most significant determinants of changing farming practices. Farmers used Eucalyptus as a coping strategy to minimize pressure on natural forests, secure land for lifestyle purposes, and alleviate poverty in the face of rapid urbanization and cropland-use loss. The results of this study indicate that the government should enhance farmers' perceptions; educate them to adopt more environmentally friendly Eucalyptus tree species instead of discouraging Eucalyptus expansion on cropland and establish structures that facilitate farmers’ access to inputs to improve cropland productivity

Modeling the impact of urbanization on land-use change in Bahir Dar City, Ethiopia : an integrated cellular automata-Markov chain approach

The fast-paced urbanization of recent decades entails that many regions are facing seemingly uncontrolled land-use changes (LUCs) that go hand in hand with a range of environmental and socio-economic challenges. In this paper, we use an integrated cellular automata–Markov chain (CA–MC) model to analyze and predict the urban expansion of and its impact on LUC in the city of Bahir Dar, Ethiopia. To this end, the research marshals high-resolution Landsat images of 1991, 2002, 2011, and 2018. An analytical hierarchy process (AHP) method is then used to identify the biophysical and socioeconomic factors underlying the expansion in the research area. It is shown that, during the period of study, built-up areas are rapidly expanding in the face of an overall decline of the farmland and vegetation cover. Drawing on a model calibration for 2018, the research predicts the possible geographies of LUC in the Bahir Dar area for 2025, 2034, and 2045. It is predicted that the conversions of other land-use types into built-up areas will persist in the southern, southwestern, and northeastern areas of the sprawling city, which can mainly be traced back to the uneven geographies of road accessibility, proximity to the city center, and slope variables. We reflect on how our findings can be used to facilitate sustainable urban development and land-use policies in the Bahir Dar area