Remote Sensing Applications and Decision Support

Detection of Olea europaea subsp. cuspidata and Juniperus procera in the dry Afromontane forest of northern Ethiopia using subpixel analysis of Landsat imagery

[+] Author Affiliations
Hadgu Hishe, Kidane Giday

Mekelle University, College of Dryland Agriculture and Natural Resources, Department of Land Resource Management and Environmental Protection, P.O. Box 231, Mekelle, Tigray, Ethiopia

Mulugeta Neka

Bahirdar University, College of Social Sciences, Department of Geography and Environmental Studies, P.O. Box 79, Bahirdar, Amhara, Ethiopia

Teshome Soromessa

Addis Ababa University, Center for Environmental Science, College of Natural Science, P.O. Box 1176, Addis Ababa, Ethiopia

Jos Van Orshoven, Bart Muys

University of Leuven, Department of Earth and Environmental Sciences, Division Forest, Nature, Landscape, Celestijnenlaan 200E, P.O. Box 2411, Leuven 3001, Belgium

J. Appl. Remote Sens. 9(1), 095975 (Dec 24, 2015). doi:10.1117/1.JRS.9.095975
History: Received June 9, 2015; Accepted November 20, 2015
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Abstract.  Comprehensive and less costly forest inventory approaches are required to monitor the spatiotemporal dynamics of key species in forest ecosystems. Subpixel analysis using the earth resources data analysis system imagine subpixel classification procedure was tested to extract Olea europaea subsp. cuspidata and Juniperus procera canopies from Landsat 7 enhanced thematic mapper plus imagery. Control points with various canopy area fractions of the target species were collected to develop signatures for each of the species. With these signatures, 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 trees in pure and mixed pixels. Total of 100 pixels each were field verified for both species. An overall accuracy of 85% was achieved for O. europaea subsp. cuspidata and 89% for J. procera. A high overall accuracy level of detecting species at a natural forest was achieved, which encourages using the algorithm for future species monitoring activities. We recommend that the algorithm has to be validated in similar environment to enrich the knowledge on its capability to ensure its wider usage.

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© 2015 Society of Photo-Optical Instrumentation Engineers

Citation

Hadgu Hishe ; Kidane Giday ; Mulugeta Neka ; Teshome Soromessa ; Jos Van Orshoven, et al.
"Detection of Olea europaea subsp. cuspidata and Juniperus procera in the dry Afromontane forest of northern Ethiopia using subpixel analysis of Landsat imagery", J. Appl. Remote Sens. 9(1), 095975 (Dec 24, 2015). ; http://dx.doi.org/10.1117/1.JRS.9.095975


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