APPLICATION OF GOOGLE EARTH FOR THE DEVELOPMENT OF BASE MAP IN THE CASE OF GISH ABBAY SEKELA, AMHARA STATE, ETHIOPIA (Published)
Google Earth is a virtual globe, map and Geographical Information Program that was originally called Earth Viewer 3D which is important to maps the Earth by the superimposition of images obtained from satellite imagery, aerial photography and Geographic Information System (GIS) 3D globe. Google Earth is useful for teachers are adopting Google Earth in the classroom for lesson planning; used to map homes and select a random sample for base map development for research. Classification of land use/land cover mapping (LULC) in scales such as urban district through high spatial resolution datasets is too expensive for many pilot projects mainly due to the cost of purchasing raw satellite images. Images from GE with high spatial resolution are free for public and can be used directly in LULC mapping in small geographical extend for mapping of green areas (forests, grasses) and buildings of a cities. Therefore, this study explores the possibility of mapping of green areas (forests, grasses) and buildings in Gish Abbay Sekela through images from Google Earth. After images are saved, georeferencing is taken and Maximum Likelihood Classification was used to develop base map. Under land use and land cover categories 5 (five) major land use land cover types are identified and Classified from the image. These are Agriculture land, Green area (Forest) land, Grazing Land, Building (Settlements) land others (like Market place, Road, Bare land…). The result showed that majority of the study area was covered by Grazing Land 221.534282 (ha) contributes 31.51600547% of the total area. Agriculture and green area/forest land cover an aerial size of 205.296619 ha (29.20599606 %) and 138.965081 ha (19.76951022 %) respectively, whereas the aerial coverage of Building/Settlement and other land use land cover is 6.699674908 ha (6.699674908 %) and 90.036514 ha (12.80881334 %) from the total area of the Gish Abbay town.
Keywords: : GIS, Georeferencing, Google Earth, LULC, Maximum Likelihood Classification