Abstract
This Article is set to track and monitor changes through spatial dependence of remote sensing data and GIS analysis, the suggested working method in this research is by sub pixel classification techniques. Change detection is a central task for land cover monitoring by remote sensing. It uses multi temporal image data sets in order to detect land cover changes from spectral discrepancies [1] (Rafael, et al.). It discusses the study perception of the situation in the past as well as the current and finally the future status of changes that land uses in Eastern Qena meander specifically in places of estuary floods, and the most important estuary leading to Qena, and whether these changes in land are used in direction of the mouth of the stream or not, especially that when it happened before, it caused destruction of both activities, urban & agricultural land. It will rely on Landsat images in years of (1972-2012), conduct analysis, different classifications integration with geographic information systems (GIS), and field as well as samples for the accuracy assessment.
Highlights
The idea monitors spatial changes to observe changes between two different periods
This Article is set to track and monitor changes through spatial dependence of remote sensing data and geographic information systems (GIS) analysis, the suggested working method in this research is by sub pixel classification techniques
Change detection is a central task for land cover monitoring by remote sensing
Summary
The idea monitors spatial changes to observe changes between two different periods. In this study, it will be relying on visuals Landsat in different years starting from 1972, and visualization with sensor Mss. until 2012 and sensor ETM+, that are used in many periods of time to clarify what is the direction of spatial changes that have occurred in Eastern pagan Qena, especially since that region has been exposed to continuous flash flood before. Sub pixel Classifier is an advanced image exploitation tool designed to detect materials that are smaller than an image pixel, using multispectral imagery It is useful for detecting materials that cover larger areas mixed with other materials which complicate accurate classification. It is considered as a powerful, low cost alternative to ground surveys, field sampling, and high-resolution imagery. It addresses the “mixed pixel problem” by successfully distinguishing a specific material when there are materials other than the one you are looking for combining in a pixel. It allows you to develop spectral signatures that are scene-to-scene transferable [2] (Erdas 2013)
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