Classifying multi-temporal image data to produce thematic maps and quantify land cover changes is one of the most common applications of remote sensing. Mapping land cover changes at the regional level is essential for a wide range of applications including land use planning, decision making, land cover database generation, and as a source of information for sustainable management of natural resources. Land cover changes in Lake Hawassa Watershed, Southern Ethiopia, were investigated using Landsat MSS image data of 1973, and Landsat TM images of 1985, 1995, and 2011, covering a period of nearly four decades. Each image was partitioned in a GIS environment, and classified using an unsupervised algorithm followed by a supervised classification method. A hybrid approach was employed in order to reduce spectral confusion due to high variability of land cover. Classification of satellite image data was performed integrating field data, aerial photographs, topographical maps, medium resolution satellite image (SPOT 20 m), and visual image interpretation. The image data were classified into nine land cover types: water, built-up, cropland, woody vegetation, forest, grassland, swamp, bare land, and scrub. The overall accuracy of the LULC maps ranged from 82.5 to 85.0 %. The achieved accuracies were reasonable, and the observed classification errors were attributable to coarse spatial resolution and pixels containing a mixture of cover types. Land cover change statistics were extracted and tabulated using the ERDAS Imagine software. The results indicated an increase in built-up area, cropland, and bare land areas, and a reduction in the six other land cover classes. Predominant land cover is cropland changing from 43.6 % in 1973 to 56.4 % in 2011. A significant portion of land cover was converted into cropland. Woody vegetation and forest cover which occupied 21.0 and 10.3 % in 1973, respectively, diminished to 13.6 and 5.6 % in 2011. The change in water body was very peculiar in that the area of Lake Hawassa increased from 91.9 km(2) in 1973 to 95.2 km(2) in 2011, while that of Lake Cheleleka whose area was 11.3 km(2) in 1973 totally vanished in 2011 and transformed into mud-flat and grass dominated swamp. The "change and no change" analysis revealed that more than one third (548.0 km(2)) of the total area was exposed to change between 1973 and 2011. This study was useful in identifying the major land cover changes, and the analysis pursued provided a valuable insight into the ongoing changes in the area under investigation.

In the rapidly urbanizing African continent, monitoring, mapping and modeling of urban growth is an indispensable task to understand the magnitude and rate of the ongoing changes. Remote sensing data have been found useful in mapping urban areas and as a source of data for modeling and analysis of spatio-temporal trajectories of cities. In this study, remote sensing data from different sensors extending over a period of 24 years (1987-2011) were used to classify and extract areas of the established land use land cover classes in Hawassa City, Ethiopia. Among those classes, built-up areas were used to quantify urban growth and sprawl. The result of the mapping indicated that the built-up area had increased by 234.5% between 1987 and 2011. The area under investigation was subdivided into eight equal zones within a circle to apply analytical models. To analyze the pattern, process and overall growth status of Hawassa City, Pearson's Chi-square statistics, Shannon's entropy, and degree-of-goodness models were employed. The result revealed that the degree-of-freedom was high indicating the disparity between observed and expected urban growth. The entropy values both in temporal intervals and zones were found higher than halfway mark of respectively showing the tendency of sprawl. Except the South Zone, the city had not experienced "goodness" during the entire study period. This study has provided new evidence about the urban growth in the study area that could be used by city managers.

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Soil erosion is the most challenging and continuous environmental problems resulting in both on-site and off-site effects in the world particularly in Ethiopia. Karesa watershed is one of the most erosionprone watersheds which received little soil conservation attention. This study was conducted to estimate average annual soil loss rate using Geographic Information System and Universal Soil Loss Equation Model adapted to Ethiopian condition. The following datasets were obtained from different sources for estimating annual soil loss such as 15 years mean annual rainfall data for estimating erosivity factor, digital soil map for estimating soil erodibility factor, 30 m × 30 m resolution Digital Elevation Model for estimating slope length and slope steepness (LS) factor, Landsat6ETM+ images with 30 m × 30 m resolution for detecting vegetation cover and conservation practice factor. The result reveals that 42,413.72 ton per year soil loss from 9939 ha entire watershed or 4.27 tons per hectare per year average annual soil loss rate was observed. The mean annual soil loss rate was classified into four erosion severity classes as very less, less, moderate and high. The result also implies that 94.4% (9383.07 ha) of the watershed areas contributes 81.13% of the total soil loss which were observed from two slope classes (0-15% and 15-30%) and categorized under very less to less soil loss (0-6.25 tons ha-1 yr-1). On the other hand, moderate to high soil loss (6.25-25 tons ha-1 yr-1) was obtained on slope classes of >30% which covers 555.93 ha (5.6%) of the watershed areas and contributes 18.82% of the total soil loss indicating the maximum share of slope mainly due to cultivation of marginal land,intensive cultivation, poor vegetation cover during critical rainfall period. Moreover, about 2,184.93 ha of the watershed area requires integrated soil and water conservation measures.

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