Prediction of land cover in continental United States using machine learning techniques
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Land cover is a reliable source for studying changes in the land use patterns at a large scale. With advent of satellite images and remote sensing technologies, land cover classification has become easier and more reliable. In contrast to the conventional land cover classification methods that make use of land and aerial photography, this research uses small scale Digital Elevation Maps and it’s corresponding land cover image obtained from Google Earth Engine. Two machine learning techniques, Boosted Regression Trees and Image Analogy, have been used for classification of land cover regions in continental United States. The topographical features selected for this study include slope, aspect, elevation and topographical index (TI). We assess the efficiency of machine learning techniques in land cover classification using satellite data to establish the topographic-land cover relation. The thesis establishes the topographic-land cover relation, which is crucial for conservation planning, and habitat or species management. The main contribution of the research is its demonstration of the dominance of various topographical attributes and the ability of the techniques used to predict land cover over large regions and to reproduce land cover maps in high resolution. In comparison to traditional remote sensing methods such as, aerial photography, to develop land cover maps, both the methods presented are inexpensive, faster. The need for this research is in synergy with past studies, which show that large-scale data, processing, along with integration and interpretation make automated and accurate methods of change in land cover mapping highly desirable.