The increasing size of solid waste generated and the lack of proper waste management systems are growing concerns in developing countries, including Ethiopia. This study assessed municipal solid waste reuse practices and identified determining factors in Robe City. To realize this objective, the study used a mixed research approach with a cross-sectional survey design. Data were collected from 391 randomly selected households through a survey questionnaire. Additionally, key informants’ interviews and observations were used to collect qualitative data. Descriptive statistics and binary logistic regression models were used to analyze the reuse of solid wastes and the determining factors. Results revealed that 63.4

Climate change and anthropogenic activities pose significant threats to terrestrial ecosystem functions and processes, which greatly increase ecological risk. Therefore, investigating the spatiotemporal dynamics of ecological risk and its driving factors in the Beshilo River Watershed is essential for evaluating ecological conditions and supporting sustainable ecosystem management. This study integrates the composite ecological risk index (ERI) with machine learning (ML) and ensemble learning (EL) approaches to assess ecological risk. Ten driving factors were analyzed using the Geodetector and Shapley Additive Explanations (SHAP) to explore their influence on ecological risk. The results revealed that high ecological risk was primarily concentrated in the western and central parts of the watershed. Furthermore, the ML and EL models outperform the ERI in predictive accuracy. Among the ensemble models, the Random Forest -Extreme Gradient Boosting (XGBoost) model achieved the best performance (R2 = 0.976, RMSE = 0.0028, MAE = 0.0024). Trend analysis using the Mann-Kendall test and Sen’s slope indicates a statistically significant declining trend in ecological risk, despite noticeable temporal fluctuations during the study period. The normalized vegetation index, leaf area index, elevation, soil and precipitation were identified as the dominant drivers of ecological risk. This study provides valuable insights for developing effective ecological restoration and conservation strategies in the watershed.

Assessing spatial and temporal variations of soil moisture has a great role in different applications, such as natural resource management, flood-risk prediction, irrigation development, hydrology, and climatology. The current study is focusing on investigating the spatial relationship between soil moisture and vegetation cover. The Delta index model was applied using Sentinel-1 images to estimate soil moisture, and the Normalized Difference Vegetation Index was applied using Sentinel-2 images to assess vegetation cover. Besides, regression analysis was conducted to investigate the spatial relationship between soil moisture and vegetation cover. The derived soil moisture ranges from 0% to 48.4811%. Most areas with high elevation had high soil moisture, and low elevation had low soil moisture. In most part of the study area, vegetation cover and soil moisture have a direct relationship. Thus, higher vegetation cover was observed in areas with higher soil moisture; and lower vegetation cover was observed in areas with lower soil moisture.

The current magnitude of flooding in Ethiopia is unprecedented. It is a typical disaster in Ethiopia with the evidence of the recent Dire Dawa and Omo River surroundings, especially during the rainy season. The situation resulted in much human death and destruction of infrastructures in different parts of the country, and the Alawuha watershed is among the typical areas for this problem. The study’s aim was to evaluate the flood risk management practices and map flood-vulnerable areas in the Alawuha catchment. Geographic information system (GIS) multi-criteria analysis and remote sensing with field verification were employed to meet the objective of this study. Slope, elevation, rainfall, drainage density, soil type, and distance to rivers are flood event aggravating factors in this study. These factors were weighted accordingly with their contribution to flood hazards. In addition, land use/land cover (LULC) and population distribution were identified as flood vulnerability factors. The weighted overlay analysis result shows that Sanka, Afrikari, Gedo-ber, Hara, and the surroundings, Woldia, were identified as high flood risk areas of the watershed. To minimize this problem, applying physical and biological measures at the watershed level is recommended.

The land surface temperature (LST) has been increased worldwide from time to time with the rapid increase of impervious surfaces such as built-up areas, parking lots, and asphalt and concrete roads. Several studies have examined the impacts of spatial dynamics of land use land cover (LULC) on the spatial variability of LST. However, there have not been systematic reviews conducted about the relationship between LULC and LST. Therefore, this study was conducted to investigate the relationship between LULC and LST with the main objective of synthesizing the relationship between LULC and LST using remote sensing data. An extensive literature search was conducted from the most familiar electronic databases such as Science Direct, Scopus, Web of Science, and Google Scholar between 27/08/2021 and 28/08/2021. The studies that are focussed on the relationship between LULC and LST and/or the impacts of LULC change on the LST using remote sensing were included for the analysis. Besides, papers conducted over the last 5 years (January/2016 to August/2021) were selected in this systematic study since this study focused on the most recent studies. In this systematic review, 100 studies were included for the study analysis. Based on the analysis of this study, built-up land has the first highest LST from the thirteen LULC types. Besides, bare land has a higher LST next to built-up land. On the other side, snow cover has the lowest LST among the LULC types. Lastly, waterbodies have a lower LST compared to vegetation cover.

Rangeland resources of the Bale lowlands have been degraded due to climate change, human factors, lack of sufficient environmental and rangeland policies, disaster mitigation strategies, and good management. The study identified suitable rangeland for cattle, sheep, goat, and camel production in the Bale lowlands using GIS-Based Multicriteria Decision Analysis and remote sensing techniques. Land-use and land-cover, rainfall, water accessibility, slope, and soil types were used for the suitability analysis. The study showed that an area of 4112, 16311, 6643, and 9820 km2 was highly suitable for cattle, sheep, goats, and camels, respectively. The results of the study also indicated that an area of 40099, 30925, 41981, and 36802 km2 was moderately suitable for cattle, sheep, goats, and camels, respectively. In addition, an area of 7644, 4671, 3630, and 5632 km2 was marginally suitable for cattle, sheep, goats, and camels, respectively. On the other hand, an area of 399 and 346 km2 was not suitable for cattle and sheep, respectively. The study is important for improving livestock production and mitigating the impacts of traditional livestock mobility on local communities. The study can also provide insights for government authorities to formulate environmental and rangeland policies to identify rangeland types and separate the rangeland for each livestock category.

Irrigation plays a vital role to increase agricultural productivity and improve the livelihoods of the communities specifically and the economy of Ethiopia in general. However, there are gaps in development of irrigation schemes and irrigation practice. Therefore, land suitability evaluation is critical to identify the potential lands for surface irrigation. The aim of the study was to identify suitable lands for surface irrigation in Rib–Gumara watershed using remote sensing and GIS-based multicriteria evaluation techniques. The main criteria used in the study were land-use/land-cover (LULC), distance to rivers, slope, and soil physical and chemical properties like soil type, texture, drainage, depth, cation exchange capacity, electrical conductivity (ECe), pHs and exchangeable sodium percentage. The LULC types were extracted from Landsat 8 image using a maximum likelihood classifier. Surface analysis was used to extract slope factor from ASTER DEM, and watershed analysis was also employed to delineate watershed and extract rivers which are drained in the watershed. Likewise, Euclidean distance spatial analysis was also utilized to evaluate the distance of rivers to the potential land. Finally, each criterion was standardized and the analytical hierarchy process was applied to develop suitable irrigation land. The irrigation land suitability was ranked as highly suitable (S1), moderately suitable (S2), marginally suitable (S3) and not suitable (N1). Cross-tabulation was also done to understand the contribution of each factor to suitable irrigation land. The results of study show that 971.37 km2 (27.55%) of the area of the watershed was highly suitable, whereas 2291.28 km2 (64.99%) of the area was moderately suitable, 72.34 km2 (2.05%) was marginally suitable and 190.46 km2 (5.4%) was not suitable. Therefore, the government should intensively work and launch different irrigation scheme projects to utilize these potential irrigation lands of Rib–Gumara watershed.

Rangeland in Bale lowlands has been seriously degraded due to human-induced problems and natural factors. The study was conducted to analyze LULC change and its deriving factors and evaluate the impacts of rangeland dynamics on livestock mobility in Bale lowlands from 1990–2020. Landsat Thematic Mapper (TM) 1990, Enhanced Thematic Mapper Plus (ETM +) 2000, and Operational Land Imager (OLI) 2014 and 2020 were chosen to derive LULC classes using maximum likelihood image classifier. Besides, a household survey was used to understand the major causes of LULC change, as well as the impacts of rangeland dynamics on livestock mobility. The accuracy reports of classified LULC classes of the study were 88.2% (1990), 89.19% (2000), 93.8% (2014), and 95.2% (2020). The result of the study revealed that there was extreme bush encroachment (545.54%), expansion of settlement (19,166%), and farmland (171.27%) while forest cover has slightly decreased (−8.76%) from 1990 to 2020. On the other hand, shrubland (−72.74%) and grassland (−59.2%) have extremely declined. During the study period, rangeland of Bale lowlands was degraded with annual rate of −0.8%. The study also revealed that expansion of farmland, settlement, communal land, and bush encroachment was the main driving factors for LULC change in Bale lowlands. Bale pastoralists are vulnerable to the death of their livestock, and they need to travel long distances because of rapid rangeland degradation. Therefore, suitable land-use and management policies for pastoral communities should be formulated and implemented so as to permanently mitigate the problem.

Land-use and land-cover (LULC) change as a result of rapid urban expansion cause land surface temperature (LST) variations. The study aims to analyze urban LULC change and its impact on the seasonal, spatial and temporal Surface Urban Heat Islands (SUHI) of Addis Ababa city and its surrounding from 1987 to 2019 using Landsat images. The result indicates that the Impervious Surface (IS) of Addis Ababa city and the surroundings have expanded from 81.49 km 2 in 1987 to 591.85 km 2 in 2019 with a 6.2% rate of change. On the other hand, vegetation cover which has a high thermal cooling effect has been degraded from 217.66 km 2 in 1987 to 157.8 km 2 in 2019. The spatial pattern of LST increased from northern highlands towards southern lowlands. The mean temperature for January and February 1987 was 26.22 °C and 27.76 °C, respectively. On 25 January 2002, the study area exhibited the mean LST of 28.25 °C, whereas on 26 February 2002, the mean temperature was increased to 31.26 °C. On 16 January 2019, the mean LST was 31.14 °C, whereas on 1 February declined to 30.62 °C. The study area exhibited a high mean LST on 21 March 2019 which was 36.1 °C, whereas on 15 October 2019 the mean LST was 25.41 °C. The study shows that very high LST exhibited on fallow land (27.78, 30.39 and 33.38 °C), crop (26.5, 28.66 and 30.83 °C), grassland (26.52, 28.53 and 31.15 °C) and IS (26.58, 28.38 and 31.39 °C) while low LST found on vegetation cover (22.76, 21.64 and 24.44 °C in 1987, 2002 and 2019, respectively). The mean LST has a positive correlation with fraction of IS (R 2  = 0.5152, 0.5855, 0.7184), CL (R 2  = 0.716, 0.6294, 0.7089), FL (R 2  = 0.6373, 0.6138, 0.8667) and GL (R 2  = 0.6513, 0.6073, 0.6442) while negative with VC (R 2  = 0.6295, 0.5601, 0.6357 in 1987, 2002 and 2019, respectively). The mean LST that exhibited in Z1 and Z2 was 25.47 and 26.91 °C, 26.06 and 28.88 °C, and 29.43 and 32.33 °C in 1987, 2002, and 2019, respectively. The IS declined when moving from the center to the peripheral area and the mean LST increased towards the rural area along urban–rural zones (URZs). In the north–south direction along URZs, the minimum and maximum mean LST in January increased from 15.83 °C in 1987 to 18.57 °C in 2019 and 33.64 °C in 1987 to 35.58 °C in 2019, respectively. Besides, the minimum and maximum mean LST for February in 1987 and 2019 was 17.57 °C and 18.97 °C and 32.82 °C and 35.21 °C, respectively. The minimum mean temperature also increased from 18.24 °C to 23.4 °C, and maximum mean LST from 32.84 °C to 41.9 °C from October to March 2019. Along east–west direction, the minimum and maximum mean LST in January was found 18.48 °C and 30.8 °C in 1987 while it was increased to 23.66 °C and 35.69 °C in 2019, respectively. The minimum and maximum mean LST in February was also increased from 20.17 to 23.57 °C and 30.81 to 34.91 °C from 1987 to 2019, respectively. Along northeast–southwest direction, the minimum mean LST in January 1987 was 16.17 °C and increased to 18.16 °C in 2019, whereas the maximum mean temperature increased from 32.1 °C in 1987 to 36.06 °C in 2019. The minimum mean LST in February 1987 was 19.43 °C and decreased to 18.5 ℃ in 2019, whereas the maximum value raised from 32.07 °C in 1987 to 36.85 °C in 2019. The pattern of LST decreased when moving to URZ60 to URZ90 in the northwest direction of the city center and increased city center to URZ47 and city center to URZ165 in the southeast direction. The study revealed that SUHII was highly concentrated in the urban area than the peripheral area. Therefore, to reduce SUHI and to have a sound environment, vegetation cover with dense trees canopy and greenery areas covered with grasses and trees are very important for the city and the surrounding.

Drought is a natural hazard that results from a deficiency of precipitation and water availability from expected or normal amounts, usually extended over a season or longer period. Drought can be hydrological, meteorological, agricultural and socio-economical. It affects the ecology, biodiversity, hydrology and climate and economy and the wellbeing of the societies at local, regional and global levels. Drought causes for significant environmental and economic problems, which in turn affect the balance of food supply and demand leads to poverty. Therefore, drought monitoring and prediction and warning system is a very essential component to minimize vulnerabilities and risks. In this regard, drought indices play a great role. The objective of this review is to show different available drought indices used for monitoring drought events. For investigating drought using a single index is not providing better results, therefore, integrating different indices is recommended because the environmental variable is spatially different and the indices do not use the same model and there are gaps in the model. Thus, by integrating different indices it is possible to achieve better drought results. Keywords: Drought; drought indices; drought monitoring DOI: 10.7176/CER/13-5-01 Publication date: August 31 st 2021

Extreme land-use and land-cover (LULC) as the result of rapid urbanization has been raising land surface temperature of core city areas and its surrounding. Therefore, investigation on surface temperature is very vital to analyze temperature variations and minimize its effect. This research aims to analyze the impacts of LULC changes on LST in Bangui city, Central African Republic using combined techniques of remote sensing and GIS. The result of this study indicates that there was a significant change in LULC between 1986 and 2017 particularly expanded in vegetation and built-up areas and declined in bare soil. For instance, built-up increased by + 130.29 % with a rate of 137.06; and vegetation increased by 8.44% or a rate of 17.2. Whereas bare soil was sharply declined by −35.33% for a rate of −155.83. The mean LST of the city firstly decreased from 26.24 °C in 1986 to 23.37 °C in 1999 and increased to 27.23 °C in 2017. The study also stated that the mean LST of built-up areas increased from 26.21 °C in 1986 to 27.59 °C in 2017. Besides, the mean LST of bare soil raised from 26.51 °C to 27.33 °C in 1986 and 2017 respectively. These indicate that built up and bare soil experienced high LST than vegetation and water body. The study found a positive correlation between NDBI and LST whereas negative correlations of LST with NDVI and NDLI. City planners should be implemented urban green belts and green roof to mitigate the effect of surface urban heat islands (SUHI) in the city and its surooundings.

Land surface temperature (LST) is the burning issue in the world since it affects climate and environment at local, regional and global level. Mainly it resulted from urbanization and its associated extreme Land-use and Land-cover (LULC) changes. Therefore, monitoring of LULC alteration is a significant component in examining of LST variation and applying sustainable mitigation measures. The research has the objective to analyze the spatial-temporal patterns of LST and its variation with LULC composition in Bahir Dar city and its surrounding from 1987 to 2017 with fifteen-year intervals using Landsat images. Image preprocessing was done to retrieve NDVI, NDBI, LST and LULC, and urban-rural gradient analysis and LST Intensity (LSTI) were computed. As the result shows that urban areas of Bahir Dar city rapidly expanded since 1987. The mean LST values increased to 34.5 °C in 1987 to 37.57 °C in 2002 and decreased to 34.57 °C in 2017. Paved surface and agricultural land exhibited higher LST values whereas waterbody and vegetation experienced lower LST. The result also shows that LST has a positive relationship with NDBI (1987: R2 = 0.51; 2002: R2 = 0.48; and 2017: R2 = 0.4) and negative relationship with NDVI (1987: R2 = 0.24; 2002: R2 = 0.25; and 2017: R2 = 0.33). The LST increased towards sub-urban areas and LSTI was higher in the distance between 4 and 10 km. The results of this study are significant for urban planners to implement sustainable LST mitigation strategies and create a conducive living environment in Bahir Dar city.

Solid waste management is a serious problem in most cities of the world due to rapid urban expansion and it causes increasing solid waste generation. The practice of solid waste management in Robe town was very poor and it is one of the chronic problems of the town. Therefore, the town needs a suitable landfill site to properly manage solid wastes and mitigate its impacts on public health and environment. The purpose of the study is to identify suitable landfill sites in Robe town, Ethiopia, that is socially and environmentally acceptable, and economically feasible by applying geographic information system and multi-criteria decision analysis and evaluation techniques. This study was based on factor criteria thematic layers of land-use and land-cover types, groundwater depth, lineament, soil permeability, river, water pipelines, slope, main and secondary roads, and constraints thematic maps of boreholes, built-up areas, and green areas. The analytical hierarchy process pair-wise comparison model was used to compute the weight of criteria. The weighted linear combination model was also used to combine different criteria weight and produce a suitable landfill site map. Landfill site suitability map was prepared by overlaying different criteria and suitability ranks were assigned as unsuitable, low suitable, moderately suitable, highly suitable and very highly suitable. The result of the study shows that 41.02 km2 (651.12%) of the area was unsuitable, 16.27 km2 (20.28%) was low suitable, 10.53 km2 (13.12%) was moderately suitable, 7.54 km2 (9.40%) was highly suitable and 4.88 km2 (6.08) was very highly suitable. From highly and very highly suitable sites, 7 candidate landfill sites were selected and evaluated in terms of area of the site, distance to nearby boreholes, built-up areas, green areas and distance from the center of the town to choose the most suitable site. According to the result of the study, landfill site 6 was the most suitable followed by landfill site 5 while landfill site 2 was the least suitable. The result also shows that selected suitable sites are expected to be friendly to the environment and the societies. Therefore, to have a sound environment and improve public safety, the town should need a landfill site and implement integrated solid waste management.

The amount of solid waste generated in developing countries is rising over time due to economic growth, change inconsumer behavior, and lifestyles of people. But it is hard to manage and handle the increase of solid waste with existing waste management infrastructure. Thus, the management system of solid waste is very poor and has become a serious problem. The main purpose of this study is to quantify the volume of solid waste generated and investigate factors affecting generation and disposal of wastes in the study area. The result of this study indicated that total waste generated from households was about 97.092 kg/day. Furthermore, the study reveals that the solid waste generation rate of the town is 0.261 kg/person/day. About 57.5% of solid waste is properly disposed of to a landfill site, whereas the remaining 42.5% is illegally dumped at the roadsides and open fields. Implications: Nowadays, in developing countries there is a high concentration of people in urban areas, causing the generation of an enormous concentration of municipal waste in urban areas. Therefore this study's findings will be important for various policymakers and town planners. This may also serve as a benchmark for the municipal authorities of the town for whom the problem is still invisible and negligible and can push environmental protection authorities to reexamine the implementation of their policies and strategies with regard to the broader issues of human and environmental health conditions of town dwellers.