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Quantifying the hydrologic response of land use/land cover change (LULCC) is of paramount importance to improve land management. This study was carried out to analyze the effect of LULCC on water quality and quantity. LULCC of the watershed in 1986, 1999 and 2011 was analyzed from Landsat satellite images using supervised classification. Time series and point data were collected from the upper and lower sections of Wedesa, Wesha and Hallo Rivers. Water quality parameters (turbidity, suspended solid (SS), total dissolved solid (TDS), pH, electric conductivity (EC), total organic carbon (TOC), ammonia, nitrate and phosphate) were analyzed in the laboratory. A considerable decline in forest and an increase in woodland were observed in the watershed during the indicated periods. Turbidity, SS, TDS and EC were significantly higher (P < 0.05) in the lower section of the rivers compared to the upper ones. Ammonia, nitrate and phosphate were higher in the lower section of some rivers compared to the upper ones. In general, water quality in the upper watershed of the three rivers was better than the lower one with respect to considered parameters, which might be related to the observed LULCC. Most water quality parameters varied (P < 0.05) seasonally in both the upper and lower sections of the rivers. Despite the irregular rainfall pattern and increased water consumption from the catchment, the annual discharge of the Tikur-Wuha River to Lake Hawassa shows an increasing trend. We concluded that the discharge is not only related to the upstream LULCC but also to the management of the Cheleleka wetland. However, further investigation is required to determine the dominant factors affecting inflow discharge to Lake Hawassa.

Accurate information on land use and land cover change (LULCC) is critical for understanding the causes of change and for developing effective policies and strategies to slow and reverse land degradation. In Ethiopia, the speed and scale of LULCC has been accelerated in the last 3-4 decades of the 21 st century. The objectives of this study were to assess: (i) the extent of LULCC and normalized difference vegetation index (NDVI) and the link to land degradation; (ii) the causes of LULCC and implication for climate change adaptation. Satellite images analysis was used to detect the change in area and vegetation index, and farmers' perception to see the magnitude of LULCC dynamics and causes of deforestation. Correlations were made between vegetation index with dry season rainfall and temperature. The analysis of confusion matrix of LULC classification showed 87% accuracy with Kappa coefficient of 0.84. In the period 1986-2016, agriculture and settlement areas have increased by 250% and 618%, respectively. On the other hand, forests and woodlands have decreased by 72% and 84%, respectively. These were also validated with the farmers' quantification results with similar trends. Different causes have played roles in the dynamics of LULCC. The results showed that vegetation dynamics vary both spatially and temporally against precipitation and temperature. This study informs the need to focus on halting deforestation and development of alternative energy sources. It further helps to design future land management directions, landscape based adaptation and rehabilitation strategies to be considered by policy makers.

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.

Forests, particularly in the tropics, are suffering from deforestation and forest degradations. The estimation of forest area and canopy cover is an essential part of the establishment of a measurement, reporting, and verification (MRV) system that is needed for monitoring carbon stocks and the associated greenhouse gas emissions and removals. Information about forest area and canopy cover might be obtained by visual image interpretation as an alternative to expensive fieldwork. The objectives of this study were to evaluate different types of satellite images for forest area and canopy cover estimation though visual image interpretation, and assess the influence of sample sizes on the estimates. Seven sites in Ethiopia with different vegetation systems were subjectively identified, and visual interpretations were carried out in a systematical design. Bootstrapping was applied to evaluate the effects of sample sizes. The results showed that high-resolution satellite images (≤5 m) (PlanetScope and RapidEye) images produced very similar estimates, while coarser resolution imagery (10 m, Sentinel-2) estimates were dependent on forest conditions. Estimates based on Sentinel-2 images varied significantly from the two other types of images in sites with denser forest cover. The estimates from PlanetScope and RapidEye were less sensitive to changes in sample size.Land 2018, 7, 92 2 of 17 the forest cover of Ethiopia was reported to be more than 30-40% of the area [10,11]. However, the origin of this number is uncertain, and the amount itself is questionable [12]. Either way, there has been severe deforestation and forest degradation in the country the last century [11][12][13][14]. The pressure to convert forests into land for food production to support the increasing human population and provide socio-economic benefits to the nature-based livelihoods of the majority of the people has been huge. The use of wood for fuel has also aggravated the rate of deforestation [10,11].Ethiopia is currently in the process of implementing REDD+, and one of the prerequisites for implementing the REDD+ mechanism is to develop a robust measuring, reporting, and verification (MRV) system following the Intergovernmental Panel on Climate Change (IPCC) Good Practice Guidelines [15]. Information about forest area and canopy cover are required for a MRV system in the REDD+ process and sustainable forest management practices. Forest area is the proportion of an area that is covered with trees and other perennial components of a forest land. The definition of forest varies among countries, the contexts of institutions, and of course the purpose [16]. The Food and Agriculture Organization (FAO) of the United Nations defines forest as land spanning more than 0.5 hectares with trees higher than 5 m and a canopy cover of more than 10%, or trees that are able to reach these thresholds in situ [17]. In Ethiopia, the working definition of forest describes it as any land spanning at least 0.5 hectares covered by trees (including bamboo) attaining a heig...