Downloading: Modeling Aromatic Resins and Gum Arabic Resources from a Combination of High and Medium Spatial Resolution Satellite Imagery - A Case Study of Burco District, Northern Somalia
International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
www.ijsr.net | Open Access | Fully Refereed | Peer Reviewed International Journal

ISSN: 2319-7064



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Modeling Aromatic Resins and Gum Arabic Resources from a Combination of High and Medium Spatial Resolution Satellite Imagery - A Case Study of Burco District, Northern Somalia

Gicheha R. W, Ngigi T. G, Kuria A. G

Abstract: Northern Somalia has high potential for gums and resins production due to its suitable climatic conditions and soil type. These resources are found within the dry lands and do well in areas with sandy soils. Gum Arabic is produced by Acacia Senegal while Gum Resins is produced by Commiphora Myrrh. These resources have great value in that they are used as stabilizers in the food industry, used as medicine, in perfume industry and for raw materials. Northern Somalia being a dry land and where people are nomadic with few options for alternative sources of livelihood due to the difficult environmental conditions resulting from scanty and erratic rainfall and poor soils, this research will be used to assess the availability of these resources within Burco area. Remotely sensed satellite images both high spatial resolution World view images (0.5m resolution) and medium spatial resolution landsat images (30m resolution) were used to map the 2 resources. Two sites were identified (Site A and B), using one site i.e. Site A, the two resources were carefully mapped using the high spatial resolution Worldview image. Using Python scripting, resource percentages per grid cell was computed for the 2 resources plus Other resources. Linear regression models were developed that compared the relationship between the medium resolution components i.e. landsat reflectance bands, PCT and NDVI with the resource as mapped using the high resolution. Resource percentage for site B was computed using the linear regression models and the medium resolution components i.e. landsat reflectance bands, PCT and NDVI. The percentage resource mapped using the high spatial resolution image was compared with the one computed using regression models and the relationship analyzed using SPSS, a statistics software which showed a significant relationship.

Keywords: Remote sensing, Gums and Resins, GIS, Python programming



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