Arrogant practices of land use including expansion of agricultural land and infrastructural development are resulting in deforestation that goes to climate change. Cellular Automata (CA)-Markov chain combines the advantages of cellular and Markov chain analysis to simulate and predict future land use/cover trends depending on the Land Use Land Cover (LULC) changes in the past. First, spatial distribution of LULC and area changed were calculated using IDRISI software and GIS technology, and then the forest land cover conversion to other LULC was evaluated to obtain rate of deforestation during a period of 1980-2018. Second, using transition potential matrices of 1999-2018, CA-Markov chain was executed to simulate spatial distribution of land use/cover in 2018. Based on the simulated LULC map for 2018 and the actual LULC map of 2018 CA-Markov Model was validated with a kappa index of 1. Finally, future land use/cover transformed during the periods of 2018-2037 and 2037-2056 were predicted using CA-Markov Chain Model. The results revealed that decreasing of forest land and increasing of agricultural land in the study period. Forest land was decreased by 52,156.71 hectares from 1980 to 2018, while agricultural land increased by 78,021.35 hectares during 1980-2018. Rate of deforestation between 1980 and 2018 was 1,372.54 hectares per year. Therefore, the predicted results of 2037 year suggests that forest cover would decreases by 30,204.65 hectares within 19 years and also agricultural land would be increases by 30,693.91 hectares between 2018 and 2037. It approved helping concerned bodies that work on the forest better understand and address a tough land use system, and develop the ameliorated land use management strategies that can better protect forest degradation.

Arrogant practices of land use including expansion of agricultural land and infrastructural development are resulting in deforestation that goes to climate change. Cellular Automata (CA)-Markov chain combines the advantages of cellular and Markov chain analysis to simulate and predict future land use/cover trends depending on the Land Use Land Cover (LULC) changes in the past. First, spatial distribution of LULC and area changed were calculated using IDRISI software and GIS technology, and then the forest land cover conversion to other LULC was evaluated to obtain rate of deforestation during a period of 1980–2018. Second, using transition potential matrices of 1999–2018, CA-Markov chain was executed to simulate spatial distribution of land use/cover in 2018. Based on the simulated LULC map for 2018 and the actual LULC map of 2018 CA-Markov Model was validated with a kappa index of 1. Finally, future land use/cover transformed during the periods of 2018–2037 and 2037–2056 were predicted using CA-Markov Chain Model. The results revealed that decreasing of forest land and increasing of agricultural land in the study period. Forest land was decreased by 52,156.71 hectares from 1980 to 2018, while agricultural land increased by 78,021.35 hectares during 1980–2018. Rate of deforestation between 1980 and 2018 was 1,372.54 hectares per year. Therefore, the predicted results of 2037 year suggests that forest cover would decreases by 30,204.65 hectares within 19 years and also agricultural land would be increases by 30,693.91 hectares between 2018 and 2037. It approved helping concerned bodies that work on the forest better understand and address a tough land use system, and develop.

Geographic Information System and Remote Sensing played an important role in analyzing environmental and socio-economic drivers that created favorable condition for malaria breeding as well as in identifying hazard and risk areas. This study gives great emphasis on mapping malaria hazard and risk areas in Gedio zone of SNNPs using geospatial technology. The study identifies two major drivers like Environmental (physical) factors: which provide for the endurance of mosquitoes and Socio-economic factors. The above data were presented and analyzed quantitatively. The content analysis shows that Malaria hazard prevalence areas were mapped based on the environmental factors which are potential of providing good environmental conditions for mosquito breeding. The hazard map was produced using elevation, slope, proximity to breeding sites, and soil type as the factors for breeding mosquitoes. The malaria hazard analysis of the Gedio zone revealed that from the total area, 9.83%, 35.29% is mapped as a very high and high-risk area, whereas, the remaining 38.73%, a 16.14%, and 0.01% were mapped as moderate, low, very low level of malaria hazard respectively. The total area of the study area more than 1/3rd of the area is identified as a very high and high malaria risk area while the rest 2/3rd of an area is considered as a moderate to very low hazard risk zone. Accordingly, very high malaria risk area is found around towns because of population density. Finally, I recommend that the concerned body should have to expand health center, creating awareness of society, especially around populated areas where the risk is high and environmental and individual sanitation can reduce the risk of malaria.

This study specifies flood hazard areas as well as flood risk areas of Gelana River Watershed. It specifically aims to investigate factors that create good conditions for flood hazard, generate flood hazard and risk areas from environmental and socio-economic factors using integration of Multi-Criteria Evaluation (MCE) and Geospatial Techniques. The research was conducted using quantitative research approach. Therefore, slope, elevation, soil type, land use land cover (LULC), and drainage density were the environmental factors developed for the generation of flood hazard. In addition, flood hazard, LULC and population data factors were developed to generate flood risk areas of Gelana river watershed. As the result, flood hazard map reveals 64.68, 1769.48, 1345.38, 244.37, 10.73 square kilometers of Gelana river watershed, is subjected to very high, high, moderate, low and very low flood hazardous respectively. It is revealed that 46.52% of the watershed has very high to high flood risk. The rest 47.20%, 6.24%, and 0.05% of the study area has medium, low and very low flood risk respectively. Therefore, the area incorporated under very high and high hazardous and risk areas are located around the Main River and lower course of the watershed. Generally, to overcome the problem, different concerned bodies of governmental offices and Non-Governmental Organizations (NGOs) should give attention especially for those areas put under very high and high hazardous and risk areas of the watershed.