Mapping and quantifying the status of Land use/Land cover (LULC) changes and drivers of change are important for identifying vulnerable areas for change and designing sustainable ecosystem services. This study analyzed the status of LULC changes and key drivers of change for the last 30 years through a combination of remote sensing and GIS with the surveying of the local community understanding of LULC patterns and drivers in the Gubalafto district, Northeastern Ethiopia. Five major LULC types (cultivated and settlement, forest cover, grazing land, bush land and bare land) from Landsat images of 1986, 2000, and 2016 were mapped. The results demonstrated that cultivated and settlement constituted the most extensive type of LULC in the study area and increased by 9% extent. It also revealed that a substantial expansion of bush land and bare land areas during the past 30 years. On the other hand, LULC classes that has high environmental importance such as grazing land and forest cover have reduced drastically through time with expanding cultivated and settlement during the same period. The grazing land in 1986 was about 11.1% of the total study area, and it had decreased to 5.7% in 2016. In contrast, cultivated and settlement increased from 45.6% in 1986 to 49.5% in 2016. Bush land increased from 14.8 to 21% in the same period, while forest cover declined from 8.9 to 2% in the same period. The root causes for LULC changes in this particular area include population growth, land tenure insecurity, and common property rights, persistent poverty, climate change, and lack of public awareness. Therefore, the causes for LULC changes have to be controlled, and sustainable resources use is essential; else, these scarce natural resource bases will soon be lost and will no longer be able to play their contribution in sustainable ecosystem services. Article Highlights Forest cover and grazing lands declined rapidly. Fluctuating trends in cultivated and settlement, bush land and bare land. Population pressure and associated demand are the main causes behind LULC changes in the study area.

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In this study, a support vector regression (SVR) approach based on a radial basis function was used for estimating sugarcane yield in the Wonji-Shoa sugarcane plantation (Ethiopia) combining Landsat 8 (L8) and sentinel 2A (S2A) data. Vegetation Indices(VIs) involving visible, near-infrared, and shortwave infrared bands were calculated from the L8 and S2A sensor observations, and seasonal cumulative values were computed for the period June to October in the 9th month and June to November in the 10th month of the year for 2016/17 to 2018/19 cropping seasons. Sugarcane yield was predicted using the SVR, Multilayer perceptron neural network (MLPNN), and Multiple linear regression (MLR) methods. Then, a tenfold cross-validation approach was implemented for the performance evaluation. The results showed significant correlations between sugarcane yield and cumulative values of VIs computed during the 10th month in the growing season. The results also revealed that the estimation accuracy of sugarcane was better using the combined L8 and S2A (RMSE = 12.95 t/ha, and MAE = 10.14 t/ha) than using the S2A data alone (RMSE = 14.71 t/ha, and MAE = 12.18 t/ha). Comparing SVR results with MLPNN and MLR disclosed that SVR outperforms the other two models in terms of prediction accuracy. Overall, this study demonstrated the successful application of the SVR in developing a model for Sugarcane yield estimation and it may provide a guideline for improving the estimations of sugarcane in the study area.

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