No abstract

Uncontrolled change in land use and land cover (LULC) enhances the concentrations of greenhouse gases in the atmosphere. This study, therefore, is aimed at the spatiotemporal analysis of LULC dynamics and their implications for the greenhouse gas emissions of the Adama district of Ethiopia. The dry season Landsat image Thematic Mapper (TM) of 1986, Enhanced Thematic Mapper (ETM) of 2000, and Enhanced Thematic Mapper Plus (ETM+) of 2014 were downloaded from the United States Geological Survey Global Visualization Viewer Website and employed. The hybrid classification approach was performed after the preprocessing of the image. Moreover, observations, key informant interviews, and focus group discussions were used. The analysis was carried out using the image data and survey data. The result indicates that agricultural land and shrub and bush lands covered 80.98%, 76.75%, and 74.42% of the study area during 1986, 2000, and 2014, respectively. Although there were differences in the magnitudes and rates of change during the considered years, the LULC classification results of this study indicated that most natural environments are converted to human‐dominated environments, which can be attributed to human‐induced activities. Due to this conversion, environmental degradation is aggravated, which again paves the way for the increased concentration of greenhouse gases in the atmosphere. The study concludes that, largely as a result of interventions from the communities living in the area, the study area is being transformed from the natural ecosystem to a managed environment. Hence, the practices of smallholder farmers with respect to protected areas, afforestation, and reforestation must be strengthened and supported by an integrated policy framework. Integr Environ Assess Manag 2019;00:1–13. © 2019 SETAC

Microwave remote sensing instrument like Soil Moisture Active Passive ranging from 1 cm to 1 m has provided spatial soil moisture information over the entire globe. However, Soil Moisture Active Passive satellite soil moisture products have a coarse spatial resolution (36km x 36km), limiting its application at the basin scale. This research, subsequently plans to; (1) Evaluate the capability of SAR for the retrieval of surface roughness variables in the Awash River basin; (2) Measure the performance of Random Forest (RF) regression model to downscale SMAP satellite soil moisture over the Awash River basin; (3) validate downscaled soil moisture data with In-situ measurements in the river basin. Random Forest (RF) based downscaling approach was applied to downscale satellite-based soil moisture product (36km x 36km) to fine resolution (1km x 1km). Fine spatial resolution (1km) soil moisture data for the Awash River basin was generated. The downscaled soil moisture product also has a strong spatial correlation with the original one, allowing it to deliver more soil moisture information than the original one. In-situ soil moisture and downscaled soil moisture had a 0.69 Pearson correlation value, compared to a 0.53 correlation between the original and In-situ soil moisture. In-situ soil moisture measurements were obtained from the Middle and Upper Awash sub-basins for validation purposes. In the case of Upper Awash, downscaled soil moisture shows a variation of 0.07 cm3 /cm3, -0.036 cm3 /cm3, and 0.112 cm3 /cm3 with Root Mean Square Error, Bias error, and Unbiased Root Mean Square Error respectively. Following that, the accuracy of downscaled soil moisture against the Middle Awash Sub-basin reveals a variance of 0.1320 cm3 /cm3, -0.033 cm3 /cm3, and 0.148 cm3 /cm3 with Root Mean Square Error, Bias error, and Unbiased Root Mean Square Error respectively. Future studies should take into account the temporal domain of Soil Moisture Active Passive satellite soil moisture product downscaling over the study region.