Abstract
In Ethiopia, forest cover changes were registered at local level that adds up to the changes observed at the national level. The rapid advance of deforestation over recent decades has resulted in the conversion of the majority of the Walmara woreda’s forest in isolated patches, endangering not only their continuity but the biodiversity within them. Geographic information system (GIS) techniques and remote sensing (RS) from satellite platforms offer a best way to identify forest cover change.
The main objectives of the study were to examine and map the trends and extents of the forest cover changes and to identify the possible proximate causes during the study periods. To attain the objectives both social survey data and remote sensing and GIS techniques were utilized. Socioeconomic data collection in form of Household and key informant interview was used in disclosing the major driving forces of the change. Quantum GIS and SPSS 16.0 are respectively used for the analysis of the spatial and temporal forest cover change and the socioeconomic data. A supervised image classification technique with Maximum likelihood classification algorism was applied on Landsat 5, 7 and 8 satellite images of 1985, 2000 and 2017. Six main land use land cover classes namely, Agriculture, Forest, Cropland, Grassland, Settlements and Water body were identified. The results showed that the area of Forest, Grass Land, Crop Land, Waterbody, Wet Land and Settlement in year 1985 forest cover of the woreda showed an area augmentation with 1719ha while non-forest experienced with area downfall with 1719ha in period I. In contrast to period I, area boost of non-forest and area shrinkage to forest cover were identified. Due to the area increment of settlement and agricultural land were registered at the outgoings of forest land of the woreda, it is recommended that Implementing effective Strategies to reduce deforestation should be launched in the study area to protect and conserve this forest from further deforestation.
Key words Walmara wereda, Landsat, QGIS, major driving force, supervised classification.