The Dabus Wetland complex in the highlands of Ethiopia is within the headwaters of the Nile Basin and is home to significant ecological communities and rare or endangered species. Its many interrelated wetland types undergo seasonal and longer-term changes due to weather and climate variations as well as anthropogenic land use such as grazing and burning. Mapping and monitoring of these wetlands has not been previously undertaken due primarily to their relative isolation and lack of resources. This study investigated the potential of remote sensing based classification for mapping the primary vegetation groups in the Dabus Wetlands using a combination of dry and wet season data, including optical (Landsat spectral bands and derived vegetation and wetness indices), radar (ALOS PALSAR L-band backscatter), and elevation (SRTM derived DEM and other terrain metrics) as inputs to the non-parametric Random Forest (RF) classifier. Eight wetland types and three terrestrial/upland classes were mapped using field samples of observed plant community composition and structure groupings as reference information. Various tests to compare results using different RF input parameters and data types were conducted. A combination of multispectral optical, radar and topographic variables provided the best overall classification accuracy, 94.4% and 92.9% for the dry and wet season, respectively. Spectral and topographic data (radar data excluded) performed nearly as well, while accuracies using only radar and topographic data were 82–89%. Relatively homogeneous classes such as Papyrus Swamps, Forested Wetland, and Wet Meadow yielded the highest accuracies while spatially complex classes such as Emergent Marsh were more difficult to accurately classify. The methods and results presented in this paper can serve as a basis for development of long-term mapping and monitoring of these and other non-forested wetlands in Ethiopia and other similar environmental settings.

The usage of land use and land cover change information has significantly risen as a result of the requirement for relevant data for environmental monitoring, modeling, and planning. The main objective of the study is to analyze the trend of land use and land cover dynamics in Sheka biosphere reserve: A Case of Shato Core Area, Southwest Ethiopia. To map the land use and land cover, supervised classifications were used, and an accuracy evaluation was conducted. Information on the trend of land use and land cover change was obtained using the IDRISI software’s land change modeler. Results showed that about 308.29ha (56.7%) of wetland and 3,215.6ha (19.6%) of natural forest were converted to other land use types in the last 30 years. Plantation and rural settlement increased by 2,234.3ha (10.2%) and 1289.6ha (6.6%) respectively from 1991–2021. Sheka biosphere reserve was registered with UNESCO in 2012 and Shato is one of the core areas. It covers 5023.3ha (25.5%) of the study area. However, 1482ha (7.5%) were given to coffee plantations, and currently, only 3,541ha (18%) are left. The main drivers of land cover changes were attributed to large-scale agricultural intensification and its induced expansions of rural settlements in and around the Shato core area. The overall accuracy and kappa statistics for 1991, 2006, and 2021 were 74% (0.74), 81% (0.81), and 81.2% (0.812), respectively. Thus, land use and land cover change trend mapping and analysis play a crucial role in conservation planning and habitat monitoring. The study concluded that wetland and forestland conversions in the study area are decreasing overwhelmingly and need serious intervention mechanisms to tackle the loss of biodiversity in the Shato core area.

Wetland vegetation and ecology of Lake Abaya in the southern Ethiopia was studied to determine floristic composition, plant community type and vegetation ecology. A total of 102 plots were laid along transects that were set up preferentially across areas where there were rapid changes in vegetation or marked environmental gradients to collect data on estimate of percentage aerial cover of plant species and environmental variables. Vegetation data was analyzed by agglomerative hierarchical cluster analysis using similarity ratio as a resemblance index and Ward’s linkage method. Multivariate data analysis was performed using appropriate packages in R version 2.14.0. Canonical Correspondence Analysis (CCA) was used to explore the relationship between the species composition and environmental variables. The environmental data included in the CCA were determined using stepwise backward and forward selection of variables by ANOVA test. Statistical measurement regarding species diversity, richness and evenness of the plant community types was carried out by using Shannon-Wiener diversity indices. A total of 92 plant species belonging to 66 genera and 34 families were identified. Families Poaceae, Asteraceae, Fabaceae, Cyperaceae, Solanaceae, Euphorbiaceae and Amaranthaceae account for about 56.99% of the total proportion. Based on the cluster analysis, five plant community types were identified. The most important factors influencing the plant species composition and pattern of wetland plant communities were water drainage, water depth, land use, slope, altitude, and hydrogeomorphology. Therefore, these factors should be considered in future management and protection under the circumstance of climate change and human activities.