Detection ofOlea europaeasubsp.cuspidataandJuniperus procerain the dry Afromontane forest of northern Ethiopia using subpixel analysis of Landsat imagery

Comprehensive and less costly forest inventory approaches are required to monitorthe spatiotemporal dynamics of key species in forest ecosystems. Subpixel analysis using theearth resources data analysis system imagine subpixel classification procedure was tested toextract Olea europaea subsp. cuspidata and Juniperus procera canopies from Landsat 7enhanced thematic mapper plus imagery. Control points with various canopy area fractionsof the target species were collected to develop signatures for each of the species. With thesesignatures, the imagine subpixel classification procedure was run for each species independently.The subpixel process enabled the detection of O. europaea subsp. cuspidata and J. procera treesin pure and mixed pixels. Total of 100 pixels each were field verified for both species. An overallaccuracy of 85% was achieved for O. europaea subsp. cuspidata and 89% for J. procera. A highoverall accuracy level of detecting species at a natural forest was achieved, which encouragesusing the algorithm for future species monitoring activities. We recommend that the algorithmhas to be validated in similar environment to enrich the knowledge on its capability to ensure itswider usage.status: publishe