University of Khartoum

Categorization and Identification of Acacia Trees Based on Species in El Ain Natural Forest Reserve by Using MultiTemporal Landsat Imagery and Spectral Angel Mapper Classification

Categorization and Identification of Acacia Trees Based on Species in El Ain Natural Forest Reserve by Using MultiTemporal Landsat Imagery and Spectral Angel Mapper Classification

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dc.contributor.author Khiry, Manal Awad
dc.contributor.author Csaplovics, E.
dc.contributor.other Forest Management en_US
dc.date 2016
dc.date.accessioned 2017-03-01T05:53:40Z
dc.date.available 2017-03-01T05:53:40Z
dc.date.submitted 2017
dc.identifier.uri http://khartoumspace.uofk.edu/123456789/24649
dc.description.abstract Identification and classification of Acacia trees cover based on their species in arid and semi-arid areas through remotely sensed images involves various considerations, processes and techniques. Efforts to remotely sense arid land vegetation are often hindered by high reflectance of the soil background, mixtures of green and senescent grasses, and the occurrence of shrubs in grasslands. Elain forest reserve area, in North Kordofan state, Sudan, endures intensive different heterogeneity of Acacia trees cover and is highly sensitive to climate fluctuations and human intervention. Mapping of tree based on their species in these areas needs more input of high advance techniques under condition of appropriate forest management strategies. The main objective of current paper was to determine the applicability of the spatial and spectral resolution of Landsat imagery and sup pixel classification in combination with ancillary data and field sampling to distinguish and discriminate Acacia species distribution at Elain Forest Reserve in North Kordofan State. The multi-temporal landsat imagery (1987,1994 and 2016) , Minimum Noise Fraction (MNF) and Pixel Purity Index (PPI) were applied to extract different spectral signatures of Acacia trees in the study area. The Spectral Angel Mapper Classification (SAM)- was applied using selected endmmbers from PPI results . The paper provides a reliable comparable method of performing Multispectral Processing of landsat data using ENVI for Hyperspectral classification and mapping Acacia trees based on their species in arid regions . en_US
dc.language.iso en en_US
dc.publisher University of Khartoum en_US
dc.subject arid lands en_US
dc.subject Elain forest reserve en_US
dc.subject multi-temporal landsat imagery en_US
dc.subject MNF- en_US
dc.subject PPI and SAM- classification en_US
dc.title Categorization and Identification of Acacia Trees Based on Species in El Ain Natural Forest Reserve by Using MultiTemporal Landsat Imagery and Spectral Angel Mapper Classification en_US
dc.type Publication en_US
dc.Faculty Forestry en_US

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