Preprint Article Version 1 This version is not peer-reviewed

Analysis of Remotely Sensed Data for Mapping Land Cover Types by Applying a Maximum Likelihood Classifier Algorithm, in Al-Ahsaa Oasis, Eastern Region, Saudi Arabia

Version 1 : Received: 25 January 2018 / Approved: 25 January 2018 / Online: 25 January 2018 (15:56:02 CET)

How to cite: Salih, A. Analysis of Remotely Sensed Data for Mapping Land Cover Types by Applying a Maximum Likelihood Classifier Algorithm, in Al-Ahsaa Oasis, Eastern Region, Saudi Arabia. Preprints 2018, 2018010244 (doi: 10.20944/preprints201801.0244.v1). Salih, A. Analysis of Remotely Sensed Data for Mapping Land Cover Types by Applying a Maximum Likelihood Classifier Algorithm, in Al-Ahsaa Oasis, Eastern Region, Saudi Arabia. Preprints 2018, 2018010244 (doi: 10.20944/preprints201801.0244.v1).

Abstract

Accurate, detailed and recent Information about land cover/use is important and much more needed for different aspects of sustainable development and environmental management. As remote sensing datasets are becomes one of the most important and effective tools to generate such information, this study aimed to generating land cover map for sub area in Al-Ahasaa Oasis, Saudi Arabia, by using and classifying a subset of Landsat-ETM+ image of the selected study area, as bases and required input for future studies and researches. Different image preprocessing techniques in addition to a will-known and widely used classification method (i.e., Maximum Likelihood classifier) were applied. To be reliable with the final product, accuracy assessment was carried out with 89% agreement and accepted according to the applied method. Different land cover classes were found in the study area, which includes (Sand dunes, Water bodies, Sabakha, Bare soil, Urban, and Agricultural lands). The study also revealed that the dominant land cover class is sand dunes with approximately ± 70% in area. The study strongly indicated that the area has long been affected by sand movement. Finally, the study suggested that, further researches with more advanced methods rather than traditional methods are needed in the future to support the findings of this study, with high degree of accuracy.

Subject Areas

Remote sensing, classification, Al-Ahsaa, Saudi Arabia, Land cover

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