Increased flooding and newly formed rills and gullies were observed in the Cheleleka wetland watershed, over the past three to five years. These events are due to problems related to land use changes and are adversely affecting land productivity. This study was conducted to quantify, analyze and map soil erosion risk areas using the Revised Universal Soil Loss Equation.

Application of unmanned aerial systems was limited to the military until the last decade when we see dramatic growth of interest by civilian users. Among the many fields of application of unmanned aerial vehicles (UAVs), forestry has diverse uses ranging from forest cover assessment to species classification and real-time forest fire monitoring. Inspired by the potential uses of the technology, this study is a review of literature on the types and uses of UAVs, the challenges and opportunities, current experiences and the future prospects of using UAVs for forest resources monitoring in Ethiopia. The study has identified potential uses of UAVs for forestry applications. It has also shown that there is perceived need for accurate, demand-based and cost-effective tools for forest resources monitoring in developing countries including Ethiopia. Hence, the use of small UAVs in the forestry sector in Ethiopia is believed to be a supplementary method to the existing methods of spatial data capture for filling the gap of information and improving the quality of forest information that is needed to comply with international standards. The results of this study indicate that Ethiopia can make use of the technology and improve its forest information system. However, while doing so, rules and regulations must be put in place to avoid the challenges that come along with introducing the technology. If properly used, the technology will enhance the forest management decision-support system of the country.

Spatial variabilities and drivers of land use and land cover (LULC) change over time and are crucial for determining the region’s economic viability and ecological functionality. The North-Western Himalayan (NWH) regions have witnessed drastic changes in LULC over the last 50 years, as a result of which their ecological diversity has been under significant threat. There is a need to understand how LULC change has taken place so that appropriate conservation measures can be taken well in advance to understand the implications of the current trends of changing LULC. This study has been carried out in the Baramulla district of the North-Western Himalayas to assess its current and future LULC changes and determine the drivers responsible for future policy decisions. Using Landsat 2000, 2010, and 2020 satellite imagery, we performed LULC classification of the study area using the maximum likelihood supervised classification. The land-use transition matrix, Markov chain model, and CA-Markov model were used to determine the spatial patterns and temporal variation of LULC for 2030. The CA-Markov model was first used to predict the land cover for 2020, which was then verified by the actual land cover of 2020 (Kappa coefficient of 0.81) for the model’s validation. After calibration and validation of the model, LULC was predicted for the year 2030. Between the years 2000 and 2020, it was found that horticulture, urbanization, and built-up areas increased, while snow cover, forest cover, agricultural land, and water bodies all decreased. The significant drivers of LULC changes were economic compulsions, climate variability, and increased human population. The analysis finding of the study highlighted that technical, financial, policy, or legislative initiatives are required to restore fragile NWH regions experiencing comparable consequences.

Woodlands, which are part of the landscape and an important source of livelihood for smallholders living in the environmentally vulnerable Central Rift Valley (CRV) of Ethiopia, are experiencing rapid changes. Detecting and monitoring these changes is essential for better management of the resources and the benefits they provide to people. The study used a combination of both quantitative and qualitative methods to analyze the extent and pattern of woodland cover changes from 1973 to 2013. Pixel-based supervised image classification with maximum likelihood classification algorithm was used for land cover classification and change detection analyses. Local peoples’ perceptions were used to explain the patterns of change and their possible reasons. Four major land cover classes were identified, with an overall accuracy of 88.3% and a Kappa statistic of 0.81 for the latest image. The analysis revealed a major land cover reversal, where woodland (92.4%) was the dominant land cover in 1973, while it was agriculture (44.7%) in 2013. A rapid reduction in woodland (54%) and forest (99%) covers took place between 1973 and 2013, with the majority of the conversions being made during the government transition period (1973 to 1986). Agriculture (3878%) and grassland (11,117%) increased tremendously during the 40-year period at the expense of woodlands and forests. Bare land increased moderately (40%). Thus, woodlands are under increasing pressure from other land uses, particularly agriculture, and declining faster. If the current trends of land cover change remain unabated it is likely that woodlands will disappear from the landscape of the area in the near future. Therefore, better forest policy and implementation tools, as well as better woodland management strategies and practices, need to be in place for woodlands to continue providing vital ecosystem goods and services to the local people, as well as to the environment.