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

Awad Alharthi; Mohamed A El-Sheikh; Mohamed Elhag; Abdulrahman A Alatar; Ghanim A Abbadi; Eslam M Abdel-Salam; Ibrahim A Arif; Ariej A Baeshen; Ebrahem M Eid

doi:10.1016/j.sjbs.2020.07.021

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

Saudi J Biol Sci. 2020 Nov;27(11):3169-3179. doi: 10.1016/j.sjbs.2020.07.021. Epub 2020 Jul 23.

Authors

Awad Alharthi¹, Mohamed A El-Sheikh^{1

2}, Mohamed Elhag³, Abdulrahman A Alatar¹, Ghanim A Abbadi⁴, Eslam M Abdel-Salam¹, Ibrahim A Arif¹, Ariej A Baeshen⁵, Ebrahem M Eid⁶

Affiliations

¹ Botany & Microbiology Department, College of Science, King Saud University, P.O. Box. 2455, Riyadh 11451, Saudi Arabia.
² Botany Department, Faculty of Science, Damanhour University, Damanhour, Egypt.
³ Department of Hydrology and Water Resources Management, Faculty of Meteorology, Environment & Arid Land Agriculture, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
⁴ Department of Biological Sciences, Faculty of Science, Kuwait University, P.O. Box 5969, Safat 13060, Kuwait.
⁵ Biological Sciences Department, Faculty of Science, University of Jeddah, Saudi Arabia.
⁶ Biology Department, College of Science, King Khalid University, Abha 61321, P.O. Box 9004, Saudi Arabia.

Abstract

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: Acacia tortilis-Acacia ehrenbergiana, VG II: Acacia tortilis-Stipagrostis plumosa, VG III: Zygophyllum coccineum-Zygophyllum simplex, VG IV: Acacia raddiana-Lycium shawii-Anabasis setifera, VG V: Tamarix aucheriana-Juncus rigidus, VG VI: Capparis decidua-Zygophyllum simplex and VG VII: Avicennia marina-Aristida adscensionis) and ranged between halophytic vegetation on the coast to xerophytic vegetation with scattered Acacia 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.

Keywords: Accuracy assessment; Land use/land cover; Support vector machine; Thematic change detections.