Land use Land cover change detection using supervised imagery classification: Lessons to learn and experiences to share from Hugumburda-Giratkahisu Forest, North Ethiopia

Understanding historic dynamism of land use land cover changes within a delineated forest area could enable to subscribe appropriate management interventions to positively affect the dynamics. The objective of this study was to assess land use land cover change in Hugumburda Girat Kahisu forest. Three sets of remotely sensed data, Landsat imageries of 1972, 1986 and 2015 were used to produce the land use land cover for the respective years. Supervised image classification was carried out using the Maximum likelihood classifier algorithm using ground truth signature file. For the three sets of Landsat images, five land use land cover classes were successfully captured. To point out the main causes of the land use land cover dynamics group discussion was made with a group composed of elders and administration bodies. The classification result showed none vegetated land (cultivated land and bare land covered (53%), grassland (24%) open forest (12%), and Dense forest (8%) were the dominant land use land cover class types in 1972. In 1986, none vegetated land (56%), grassland (21%) open forest (11%), and Dense forest (8%), were the major land use land cover types. In 2015, gradual increase in Dense forest was detected where dense forest and open forests covered almost half (48%) of the forest reserve and the non-vegetated land had only 27% coverage. An overall accuracy of 93% with Kappa statistics of 0.90 was achieved. Therefore, within the last 43 years, the forest area had shown positive dramatic change. The significance of changes were the protection and control of forest area, the experiences and lessons from these forest areas can be upscale to better manage the remaining natural forests in Ethiopia.