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Land use/land cover (LULC) changes driven by human activities are major environmental challenges in many developing regions. This study assessed local community perceptions and household-level drivers of LULC change in northwest Ethiopia. The study combined remote sensing analysis of LULC change with household surveys to understand local perspectives and the socioeconomic, biophysical, and institutional factors influencing household engagement in deforestation activities. The household survey data was collected from 384 randomly selected household heads, as well as from focus group discussions and key informant participants. The remote sensing component used ERDAS IMAGINE 2014 software to classify satellite images and assess LULC changes over time. The quantitative socio-economic data was analyzed using descriptive statistics and econometric methods of the multivariate probit (MVP) model. The study revealed that population growth, poverty, and food insecurity were the major proximate driving factors, while agricultural land expansion, settlement growth, fuelwood collection, overgrazing, and forest fires were the major underlying causes of LULC change. The MVP model result indicated that gender, off-farm income, access to training, family size, educational level, and agroecology were key determinants of households’ involvement in deforestation drivers such as forestland clearing for agriculture, fuelwood collection, timber harvesting, and overgrazing. These findings highlight the need for integrated land use policies and programs that address the socioeconomic and biophysical root causes of LULC change. This study recommends supporting alternative livelihoods, promoting fuel-efficient technologies, and tailoring interventions to different agroecological contexts of sustainable land use planning and natural resource management in the study site. Future studies should compare perceptions and drivers of land use change across different regions to identify common patterns and unique challenges. This approach will enhance understanding and inform targeted interventions for sustainable land management.

Wastewater management in higher educational institutions is an existing environmental challenge in Ethiopia. Thus, this study aimed to evaluate and characterize university of Gondar wastewater and its impact on the downstream community. The University has five campuses and one community school and staff residential areas. Wastewater samples were collected and analyzed for the physic-chemical, nutrients and heavy metals from eight sampling sites in 2020. The results of the study were ranged as temperature (20.60 °C–25.02 °C), pH (6.9–8.2), Electrical conductivity (871.5to3630), turbidity (4.11to330NTUs), total dissolved solids (360.3–1824), dissolved oxygen (3.4–8.5), biological oxygen demand(1.0–4.1 mg/L), salinity (0.34–1.9), nitrate (4.2–33.0 mg/L, nitrite (0.33–10.6 mg/L with mean value of 2.4 mg/L), NH3–N (0.08–3.10 mg/L with mean value of 0.83 mg/L), phosphate (8.38–270.00 mg/L with mean value of 52.53 mg/L), sulfate (2.00–195.00 mg/L with mean value of 79.4 mg/L). The mean value of phosphate was within the permissible limit of FAO and WHO but above the limit of USEPA, and mean concentration of nitrate was found to be above the maximum permissible limit set by FAO and USEPA. Similarly, the values of sulfate was above the acceptable range for wastewater discharged limit set by FAO and WHO and that of ammonia was above the limit of WHO. Regarding heavy metal analyses, only lead was above the permissible limit. Thus, the result showed that the wastewater released from University of Gondar pose a risk to the surrounding environment and human health unless a proper waste management system is implemented. Moreover, the university should implement recycling of wastes in order to apply circular economy.

Land use and cover changes alter the functions and structures of ecosystem, resulting in variations in Ecosystem Service Values (ESVs). Thus, we examined the impacts of land use/land cover (LULC) changes on ESVs from 1992 to 2052 using geospatial technologies. The Landsat images were classified using the supervised maximum likelihood classification technique, and future changes in LULC were predicted using the CA-Markov model. Ecosystem Service Values coefficients were adopted from empirical studies and ESVs changes were evaluated based on the benefit transfer method using LULC data for the study periods, with their corresponding modified ESVs coefficients. The results revealed that, the proportions of grassland, forestland and shrubland declined by 58.5 %, 48.15 % and 33.48 %, respectively, from 1992 to 2022. Simultaneously, the highest rate of expansions of waterbodies (34 times), farmland and settlement threefold as well as bareland (60.2 %) from 1992 to 2022 was noticed. As a result, decreasing trends were experienced in the total ESVs of the district from US$33.6 million in 1992 to US$27.79 million in 2022, and are anticipated to further decline to US$25.94 million in 2052. The ESVs of forestland, shrubland and grassland shrank from 53.1 %, 40 % and 2.78 % in 1992 to 33.28 %, 33.16 % and 1.4 % in 2022 these changes are anticipated to continue for the next three decades, except trend for the increase in grassland ecosystem service value. Therefore, the government should redesign effective land management strategies to alleviate the negative consequences of LULC changes, facilitate payment for ecosystem services, and design ecotourism to boost the income of residents for major land use management-based production systems to increase the ESV in the district.

Evaluating the impacts of land use/land cover (LULC) change on ecosystem service values (ESVs) is critical for understanding the implications of land use change on human well-being, identifying trade-offs and synergies between different ecosystem services, monitoring changes in ecosystem health, and informing decisions for land use planning and conservation efforts. As a result, this study aimed to examine the effects of these changes on ESVs during the last four decades (1984–2022) and predicted periods of 2040 and 2060 by utilizing a combination of geoinformation, and economic evaluation approaches. The modified ecosystem service value (ESV) coefficients were used to estimate the influence of LULC change on ESVs. The result revealed that overall ESV has declined from US$390 × 106 in 1984 to US$273.21 × 106 in 2060. However, the effects on various ecosystem services varied, with certain services, such as crop production, pollination, and biological control, increasing in value. These findings also emphasized the necessity of evaluating the implications of land use decisions on ecosystem services and it depicted that it may be possible to retain or improve certain services even in the face of overall declines in total ESVs. Subsequently, the findings of this study, therefore, give important insights for policymakers, land managers, and local people for devising appropriate land use policies and land use plans as well as realizing, and implementing sustainable land use practices that ensure the provision of essential ecosystem services for future generations.

Abstract Background Soil erosion in Ethiopian highlands is highly consistent with land use/land cover (LULC) changes that are associated with deforestation and a decline in biodiversity. However, identifying soil erosion-prone areas and quantifying soil loss in rugged terrains and various agroecologies due to LULC changes have not been dedicated to scientific studies. Therefore, we quantified the impacts of spatiotemporal LULC changes on soil loss across agroecologies and slope categories using GIS and RUSLE model from 1985 to 2021 in Zoa watershed, southwest Ethiopia. Moreover, prioritizing erosion severity risks at sub-watersheds and quantifying temporal sediment yield is essential for better conservation planning. Landsat images, rainfall, Digital Elevation Model, and soil data were obtained from field observations and secondary sources. Results Bareland and farmland have been expanding at the expense of other land use types. The annual soil loss in the watershed ranged from 0 to 113.21 t ha−1 year−1, 0 to 163.16 t ha−1 year−1, and 0 to 194.58 $$\mathrm{t}\,{\mathrm{ha}}^{-1}\,{\mathrm{year}}^{-1}$$ t ha - 1 year - 1 with a mean annual soil loss of 21.07, 29.35 and 40.93 t ha−1 year−1 in 1985, 2000, and 2021, respectively. Among LULC classes, the highest soil loss was generated from bareland (31.73 $$\mathrm{t }\,{\mathrm{ha}}^{-1}\,{\mathrm{year}}^{-1}$$ t ha - 1 year - 1 ) and farmland (27.08 t ha−1 year−1 ) in 1985 later upsurged to 35.52 $$\mathrm{t }\,{\mathrm{ha}}^{-1}\,{\mathrm{year}}^{-1}$$ t ha - 1 year - 1 and 59.91 t ha−1 year−1 in 2021, respectively, due to the maximum susceptibility of soil erosion risks from unprotected surfaces. The results also revealed that the lowland agroecology generated the highest mean soil loss of 24.05 $$\mathrm{t }\,{\mathrm{ha}}^{-1}\,{\mathrm{year}}^{-1}$$ t ha - 1 year - 1 in 1985, 39.74 t ha−1 year−1 in 2000, and increased to 57.55 $$\mathrm{t }\,{\mathrm{ha}}^{-1}\,{\mathrm{year}}^{-1}$$ t ha - 1 year - 1 in 2021. Considering the slope categories, the highest and most excruciating soil loss was engendered from steep (35.55–60.78 t ha– 1 year– 1) and very steep (52.48–72.69 $$\mathrm{t }\,{\mathrm{ha}}^{-1}\,{\mathrm{year}}^{-1}$$ t ha - 1 year - 1 ) slope terrains during 1985–2021. The northwestern part of the watershed is the most erosion-prone area which is now expanding to the central and western parts of the watershed. The sediment yield increased at the fastest rate at the watershed outlet, from 39.3% in 1985 to 94.26% in 2021. Conclusions The results of this study indicated that the conversion of other LULC categories into farmland was the most detrimental to a watershed in terms of soil loss, which necessitates the implementation of appropriate soil and water conservation measures with effective design by considering spatial variability to reduce soil erosion hazards.

Land Use/Land Cover (LULC) change has inhibited sustainable development for the last millennia by affecting climate, biological cycles, and ecosystem services and functions. In this regard, understanding the historical and future patterns of LULC change plays a crucial role in implementing effective natural resource management. This study aimed to model and characterize the spatiotemporal trajectories of landscape change between the 1984 and 2060 periods. The satellite image spectral information was segmented into seven LULC classes using a hybrid approach of image spectral recognition. The supervised classification technique of Support Vector Machine (SVM) was used to classify the satellite images, whilst the Land Change Modeler (LCM) Module in TerrSet software was used to assess the historical trend and future simulation of LULC dynamics. To predict future landscape changes, transition potential maps were generated using a Multi-layer Perceptron (MLP) neural network algorithm. The findings of the study demonstrated that the Goang Watershed has experienced significant LULC change since 1984. During the 1984–2001, 2001–2022, and 1984–2022 periods, farmland showed a dramatic increasing trend with 7.5 km2/yr−1, 110.3 km2/yr−1, and 64.3 km2/yr−1, respectively. A similar trend was also observed in built-up areas with 0.5 km2/yr−1, 3.2 km2/yr−1, and 2 km2/yr−1. The expansion of farmland and built-up area was at the expense of forest, shrubland, and grasslands. With a business-as-usual scenario, the extent of farmland will continue to increase between 2022 and 2060 while rapid reduction is expected by forest, shrubland, and grasslands. The alarming rate of farmland and built-up area expansion will put significant pressure on biodiversity and ecosystem services in the area. As a result, eco-friendly conservation approaches should be implemented as soon as possible to maintain ecosystem health and encourage sustainable development.

Environmental phenomena are always changing elsewhere in various scales depending on both natural phenomenon and human interference. Land use/over change (LULC) is related to site specific factors such as inappropriate land use planning and the expansion of traditional agricultural practices in steep gradients have led to soil erosion and consequent ecological changes. Thus, it is crucial to determine the trend, pattern, and drivers of land use/land cover dynamics for sustainable natural resource management in Ethiopia. Therefore, we evaluated the spatio-temporal LULC dynamics in different agroeclogies and slope gradients, and their drivers between 1985 and 2021 in the Zoa watershed of Omo-Gibe basin, Southwest Ethiopia. Landsat imageries, focus group discussants, key informants, and field observations were used as source of data to analyze the spatio-temporal LULC trajectories and their drivers. With total accuracies ranging from 87.55% to 91.14%, supervised image classification using the Maximum Likelihood classifier technique was used to categorize five key LULC classes: bareland, farmland, forestland, grassland, and shrubland. The results revealed that shrubland (41.87%) had the largest share in 1985, but later declined to 23.98% in 2000, and 12.6% in 2021. Grassland has declined as well, from 17.15% in 1985 to 2.09% in 2021. In contrast, farmland increased at the fastest rate, from 29.09% in 1985 to 71.12% in 2021. The proportion of farmland exhibited an increasing trend in all agro-ecologies, while forestland has increased only in highland agro-ecologies. Between 1985 and 2021, an extensive area of shrubland and grassland were converted into farmland with a conversation rate of 1.05% and 0.58% per annum, respectively. The expansion of farmland was observed towards moderately and steep rolling slopes which might exacerbate soil degradation. This is due to rapid population increase and ongoing demand for agricultural land. The result of key informant interviews and focus group discussions also revealed that expansion of farmland and settlement are the major drivers of LULC dynamics due to rapid human population growth. Therefore, the regional government and various stakeholders should work on redesigning effective management strategies through appropriate land use planning to address the adverse effects of LULC dynamics.

Dry woodland ecosystems play an important role in the global carbon balance and these need accurate biomass predictions that can be obtained from tree allometry. However, information on allometric models is limited for Combretum-Terminalia woodlands of the Amhara Region in Northwestern Ethiopia. The objective of the study was to develop an allometric model for the aboveground biomass (AGBind) of the tree species in the study areas. A total of 149 trees were considered to predict the AGBind (stems, branches and leaves) of the selected species of which 101 samples were collected from nine tree species in Metema district, and 48 sample trees were obtained from four species in Quara district. A new model was developed using log-transformed data with linear regression, and their performance was assessed with the mean absolute prediction error (MAPE%) and the mean prediction error (MPE%). The results of the study indicated that Diameter at breast height (DBH) was the best predictor variable of biomass components for all species. For site-specific data, combined DBH, tree height and wood density best explained the stem and AGBind with the MAPE ranged 18.6–19.2% compared with the DBH-alone model (MAPE ranging from 29.8 to 30.2%). A positive validation was observed on all model forms, but M3 and M4 provided highly precise predictions of AGBind for independent data. However, a large bias was observed in the branch and leaf biomass model for site-specific data with the MAPE ranged 66.6–72.8%. A little improvement was observed in the model performance of plant functional type, while general models developed from the compiled data performed well. For a given model form, a comparable performance was found between the site-specific, tree functional type and general model. Generally, the general model with DBH and wood density together followed by the one which includes three predictors was a better AGBind predictor over the site and tree functional type. The species and site-specific models are thus accurate for site-based prediction whereas the general model contributes to the reliable prediction of AGBind in the studied Combretum-Terminalia woodlands. The comparison with the published models showed that most models produced large errors with MPE ranging from 16.10 to 36.60%, but the best model of tropical dry forests performed much better with MPE ranging from -2.24 to 0.34% and these models can probably be used for Combretum-Terminalia woodland.