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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.

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.

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.

This study used in-situ GPS data to validate the accuracy of horizontal coordinates and orientation of linear features of orthophoto and line map for Bahir Dar city. GPS data is processed using GAMIT/GLOBK and Lieca GeoOfice (LGO) in a least square sense with a tie to local and regional GPS reference stations to predict horizontal coordinates at five checkpoints. Real-Time-Kinematic GPS measurement technique is used to collect the coordinates of road centerline to test the accuracy associated with the orientation of the photogrammetric line map. The accuracy of orthophoto was evaluated by comparing with in-situ GPS coordinates and it is in a good agreement with a root mean square error (RMSE) of 12.45 cm in x- and 13.97 cm in y-coordinates, on the other hand, 6.06 cm with 95% confidence level GPS coordinates from GAMIT/GLOBK. Whereas, the horizontal coordinates of the orthophoto are in agreement with in-situ GPS coordinates at an accuracy of 16.71 cm and 18.98 cm in x and y-directions respectively and 11.07 cm with 95% confidence level – GPS data is processed by LGO and a tie to local GPS network. Similarly, the accuracy of linear feature is in a good fit with in-situ GPS measurement. The GPS coordinates of the road centerline deviates from the corresponding coordinates of line map by a mean value of 9.18cm in x- direction and -14.96cm in y-direction. Therefore, it can be concluded that, the accuracy of the orthophoto and line map is within the national standard of error budget (~ 25cm).

This study used in-situ GPS data to validate the accuracy of horizontal coordinates and orientation of linear features of orthophoto and line map for Bahir Dar city. GPS data is processed using GAMIT/GLOBK and Lieca GeoOfice (LGO) in a least square sense with a tie to local and regional GPS reference stations to predict horizontal coordinates at five checkpoints. Real-Time-Kinematic GPS measurement technique is used to collect the coordinates of road centerline to test the accuracy associated with the orientation of the photogrammetric line map. The accuracy of orthophoto was evaluated by comparing with in-situ GPS coordinates and it is in a good agreement with a root mean square error (RMSE) of 12.45 cm in x- and 13.97 cm in y-coordinates, on the other hand, 6.06 cm with 95% confidence level - GPS coordinates from GAMIT/GLOBK. Whereas, the horizontal coordinates of the orthophoto are in agreement with in-situ GPS coordinates at an accuracy of 16.71 cm and 18.98 cm in x and y-directions respectively and 11.07 cm with 95% confidence level – GPS data is processed by LGO and a tie to local GPS network. Similarly, the accuracy of linear feature is in a good fit with in-situ GPS measurement. The GPS coordinates of the road centerline deviates from the corresponding coordinates of line map by a mean value of 9.18cm in x- direction and -14.96cm in ydirection. Therefore, it can be concluded that, the accuracy of the orthophoto and line map is within the national standard of error budget (ℓ 25cm). [ABSTRACT FROM AUTHOR]

The soils of the high-elevation mountains along the East African Rift Valley are poorly understood. Assessing the potential of soils for agriculture, climate change mitigation, and environmental functioning requires insight into how they relate to the factors influencing soil formation. Between 3000 and 4120 m a.s.l., 85 soil profiles of Mount Guna were described and sampled. Standard physicochemical analyses were done on all pedons. Additionally, X-ray diffraction, Alox and Feox content, and P fixation were performed on six selected profiles. Soils on Mount Guna included Andosols, Phaeozems, Leptosols, Regosols, Cambisols, Luvisols, and Vertisols. With increasing elevation, clay content, bulk density, and pH decreased while the C:N ratio remained constant. In contrast, sand, silt, silt-to-clay ratio, SOC, Ntotal, and SOCS increased. With a factor analysis, the soil-forming factors’ elevation/climate could be disentangled from the factor’s parent material as these affect topsoil and subsoil differently. In the ordination based on climate/elevation and parent material, Andosols and Vertisols stood out while other Reference Soil Groups (RSG) showed indistinct patterns. Soil erosion appeared as an additional soil-forming factor not accounted for by the factor analysis. The distribution of the RSG was significantly associated with elevation belts (p < 0.001), lithology (p < 0.001), and landcover (p < 0.003). On the summital ridge, the Andosols were crucial for groundwater storage due to high precipitation. Shallow and stony soils in the mid-elevation belt contributed to runoff generation. Average soil carbon stock ranged from 8.1 to 11 kg C m−2 in the topsoil and from 29.2 to 31.9 kg C m−2 in the upper meter, emphasizing the global importance of high-elevation areas for carbon sequestration.