Remote sensing of 10 years changes in the vegetation cover of the northwestern coastal land of Red Sea, Saudi Arabia.

Accurate and up to date land use and land cover (LU/LC) changes information is the main source to understanding and assessing the environmental outcomes of such changes and is important for development plans. Thus, this study quantified the outlines of land cover variation of 10-years in the northwestern costal land of the Red Sea, Saudi Arabia. Two different supervised classification algorithms are visualized and evaluated to preparing a policy recommendation for the proper improvements towards better determining the tendency and the proportion of the vegetation cover changes. Firstly, to determine present vegetation structure of study area, 78 stands with a size of 50 × 50 m were analysed. Secondly, to obtain the vegetation dynamics in this area, two satellite images of temporal data sets were used; therefore, SPOT-5 images were obtained in 2004 and 2013. For each data set, four SPOT-5 scenes were placed into approximately 250-km intervals to cover the northwestern coastal land of the Red Sea. Both supervised and non-supervised cataloguing methods were attained towards organise the study area in 4-major land cover classes through using 5 various organizations algorithms. Approximately 900 points were evenly distributed within each SPOT-5 image and used for assessment accuracy. The floristic composition exhibits high diversity with 142 species and seven vegetation types were identified after multivariate analysis (VG I: -, VG II: - VG III: -, VG IV: , VG V: , VG VI: - and VG VII: -) and ranged between halophytic vegetation on the coast to xerophytic vegetation with scattered trees inland. The dynamic results showed rapid, imbalanced variations arises between 3-land cover classes (areas as urban, vegetation and desert). However, these findings shall serve as the baseline data for the design of rehabilitation programs that conserve biodiversity in arid regions and form treasured resources for an urban planner and decision makers to device bearable usage of land and environmental planning.