Mathematics in Utilizing Remote Sensing Data for Investigating and Modelling Environmental Problems
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Remote sensing data have already proven useful for environmental monitoring in a timely, detailed, and cost-effective manner to assist various planning and management activities. Remotely sensed data collected over a span of years can be used to identify and characterize both natural and anthropogenic changes over large areas of land at a variety of spatial and temporal scales [1–3]. As climate change and population growth place increasing pressures on many parts of the world, improved methods for monitoring urban growth across a range of spatial and temporal scales will be vital for understanding and addressing the impacts of urbanization on our natural resources [4, 5]. With the advance of machine learning algorithms and computing facilities, many investigations on their real applications are taking place. Combining remote sensing data and mathematics techniques to quantitatively analyze environmental change is a topic growing in importance . The meaningful interpretation of remote sensing data and in situ observations require implementation and analysis using advanced mathematics and statistical techniques. The objective of this special issue is to provide a snapshot of status, potentials, challenges, and achievements of mathematical application in using remote sensing data to address environmental issues. This special issue includes thirteen papers that cover four major topics: image processing methods, land use/land cover change analysis, land degradation, urbanization, and vegetation cover. A brief description of these 13 works is detailed below.
Copyright © 2017 Hasi Bagan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.