This study investigated the influence of topographic variability on land use land cover (LULC) change in Hugumburda national forest priority area. The study was based on three periods of LULC maps derived from satellite imageries: Landsat TM for 1985, Landsat ETM+ for 2000 and Landsat OLI for 2015, and topographic attributes derived from ASTER digital elevation model. Supervised image classification was carried out using Maximum Likelihood classifier algorithm. Changes in LULC vis-a-vis topographic variability (altitude, slope and aspect) were assessed based on overlay analysis in a GIS environment. Six LULC classes were identified with an overall accuracy of 93% and Kappa statistics of 0.90. Shrubland and forest land were the dominant LULC types which respectively accounted for about 36% and 26% in 1985 and 39% and 33% in 2000. Forest land (35%) followed by shrubland (30%) continued to be the dominant LULC types in 2015. Between 1985 and 2015, about 23% of the study area showed changes in LULC which constitute increase in forest cover by about 715 ha mainly at the expense of shrubland. Steep slopes, higher altitudes and Northeast aspect were important topographic attributes where marked increased in forest cover was observed. The increase in forest cover along steep slopes, higher altitudes and Northeast aspect can be used to stimulate further expansion of forest cover along similar topographic conditions. This study demonstrated that topographic variability plays an important role in controlling LULC changes. Detailed investigation of drivers of the increased in forest cover is required to scale up the success in other regions with similar climatic and topographic settings.
|Number of pages||8|
|Journal||Remote Sensing Applications: Society and Environment|
|Publication status||Published - 1 Jan 2019|
Birhane, E., Ashfare, H., Fenta, A. A., Hishe, H., Gebremedhin, M. A., G. Wahed, H., & Solomon, N. (2019). Land use land cover changes along topographic gradients in Hugumburda national forest priority area, Northern Ethiopia. Remote Sensing Applications: Society and Environment, 13, 61-68. https://doi.org/10.1016/j.rsase.2018.10.017