On Satellite Imagery of Land Cover Classification for Agricultural Development

Abstract

Distribution of chronological land cover modifications has attained a vibrant concern in contemporary sustainability research. Information delivered by satellite remote sensing imagery plays momentous role in enumerating and discovering the expected land cover for vegetation. Fuzzy clustering has been found successful in implementing a significant number of optimization problems associated with machine learning due to its fractional membership degrees in several neighbouring constellations. This research establishes a framework on land cover classification for agricultural development. The approach is focused on object-oriented classification and is organized with a Fuzzy c-means clustering over segmentation on CIE L*a*b* colour scheme which provides analysis of vegetation coverage and enhances land planning for sustainable developments. This research investigates the land cover variations of the eastern province of Saudi Arabia throughout an elongated span of period from 1984 to 2018 to recognize the possible roles of the land cover alterations on farming. The Landsat satellite imagery and Geographical Information System (GIS), in tandem with Google Earth chronological imagery are employed for land use variation analysis. Experimental results exhibit a reasonable spread in the cultivated zones and reveal that this Colour Segmented Fuzzy Clustering (CSFC) strategy achieves better than the relevant counterpart approaches considering classification accuracy.