Mapping of

Land cover map of North Sinai was produced based on the FAO-Land Cover Classification System (LCCS) of 2004. The standard FAO classification scheme provides a standardized system of classification that can be used to analyze spatial and temporal land cover variability in the study area. This approac...

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Bibliographic Details
Main Authors: Sayed M. Arafat, Nasser S. Saleh, Mohamed Aboelghar, Mohamed Elshrkawy
Format: Article
Language:English
Published: Elsevier 2014-06-01
Series:Egyptian Journal of Remote Sensing and Space Sciences
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S1110982314000076
Description
Summary:Land cover map of North Sinai was produced based on the FAO-Land Cover Classification System (LCCS) of 2004. The standard FAO classification scheme provides a standardized system of classification that can be used to analyze spatial and temporal land cover variability in the study area. This approach also has the advantage of facilitating the integration of Sinai land cover mapping products to be included with the regional and global land cover datasets. The total study area is covering a total area of 20,310.4 km2 (203,104 hectare). The landscape classification was based on SPOT4 data acquired in 2011 using combined multispectral bands of 20 m spatial resolution. Geographic Information System (GIS) was used to manipulate the attributed layers of classification in order to reach the maximum possible accuracy. GIS was also used to include all necessary information. The identified vegetative land cover classes of the study area are irrigated herbaceous crops, irrigated tree crops and rain fed tree crops. The non-vegetated land covers in the study area include bare rock, bare soils (stony, very stony and salt crusts), loose and shifting sands and sand dunes. The water bodies were classified as artificial perennial water bodies (fish ponds and irrigated canals) and natural perennial water bodies as lakes (standing). The artificial surfaces include linear and non-linear features.
ISSN:1110-9823