GIS based mapping of land cover changes utilizing multi-temporal remotely sensed image data in Lake Hawassa Watershed, Ethiopia

Abstract

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.

Landscape composition and configuration in the central highlands of Ethiopia

“…Subsequent reduction in forestland and shrubland was observed across the study period. This has been evidenced by many studies in Ethiopia (Fetene et al, 2016;Meshesha, Tsunekawa, Tsubo, Ali, & Haregeweyn, 2014;Reid et al, 2000;Tsegaye et al, 2010;Wondrade et al, 2014) and many tropical countries (Lira, Tambosi, Ewers, & Metzger, 2012;Nahuelhual, Carmona, Aguayo, & Echeverria, 2014;Putz et al, 2014). Such conversion of forestland, shrubland, and grasslands was anthropogenic in nature conditioned by socioeconomic, political, and institutional factors (Echeverria et al, 2008;Temesgen et al, 2013).…”

“…The degree of fragmentation has been described as a function of patch, class, and landscape metrics described in the Methods section because the best way to quantify the relative importance of habitat loss and fragmentation is to conduct comparative analyses at the landscape scale because a combination of both landscape-and patch-scale variables determines structure and function of ecosystems (Laurance, 2008;Santos-Filho, Peres, da Silva, & Sanaiotti, 2012). In Ethiopia, several studies have been conducted to understand LULC particularly with reference to deforestation (Feoli, Vuerich, & Zerihun, 2002;Gebrehiwot, Bewket, Gardenas, & Bishop, 2014;Reid et al, 2000;Tsegaye, Moe, Vedeld, & Aynekulu, 2010;Wondrade, Dick, & Tveite, 2014;Zeleke & Hurni, 2001) but to the best knowledge of the authors, no investigation has been carried out on fragmentation analysis combined with LULC.…”

Land cover and landscape changes in Shaanxi Province during China’s Grain for Green Program (2000–2010)

“…Only a few researchers seem to be aware of this problem (Alo and Pontius 2008;Carr et al 2009;Tsegaye et al 2010;Manandhar et al 2010); few studies have modeled changes over multiple time periods (Aide et al 2013;Nigatu et al 2014). With regard to the GFG, researchers have examined the effectiveness of the policy in promoting environmental health, ecosystem services, and desired land use changes (Cao et al 2010;Shen and Wang 2013) while suffering from the limitations described above.…”

Vegetative growth of grasslands based on hyper-temporal NDVI data from the Modis sensor

“…Through the removal of outliers and the application of the root mean square filter, consistently patterned series were obtained, for which the temporal pattern was approximated while maintaining a balance between low and high frequencies, necessary to evaluate vegetation growth, mitigate atmospheric effects, and detect degradation processes. The hyper-temporal approach can adequately characterize the dynamics of land use, which, despite the existence of traditional areas with pasture crops, also has semi-deciduous arboreal vegetation and climate conditions that support rapid regeneration, which can cause a high degree of spectral-temporal confusion (Jesus & Epiphanio, 2010;Wondrade et al, 2014). Therefore, a first step in the characterization of grassland areas through satellite imaging is the analysis of historical series, whose pixels at the same geographical locations are monitored over time and can indicate permanent or temporary changes in land use.…”