DATA PROCESSING TECHNOLOGY OF REMOTE SENSING IN GLACIOLOGY
Journal Title: Вісник Київського національного університету імені Тараса Шевченка, серія «Географія» - Year 2016, Vol 65, Issue 2
Abstract
This article shows the analysis of remote sensing methods for needs of glaciology and trends of modern research. Remote sensing not only accelerates the process of investigation of natural resources, but also provides fundamentally new information about the nature of the Earth, its components, phenomena and processes, which cannot be obtained by conventional methods. For most of glacial regions of the world scientists continue collecting information about the current condition of glaciers and changes that occur to them. However, often it is difficult to organize direct observation of some glaciers because of their apartness. Therefore, the main method of such research is using remote sensing data. Here is an instance of remote sensing data processing technology on an example of the glacier Columbia (Alaska, USA) ice cover using modern GIS-package ArcGIS. For our research we used four Landsat satellite images during different years (1975, 1986, 2001, 2015) obtained through Earth Explorer Service of United States Geological Survey (USGS). Images have been synthesizing in various combinations of channels to improve displaying the object and its visual perception. We were able to estimate changes of the ice cover of the area for 40 years. We used a method of automated classification for determining glacier borders, which is based on snow index NDSI using. The result of work is a map of Columbia glacier ice cover changes for forty years, which has allowed exploring the dynamics of glacier retreat and demonstrates its border changes. Today remote sensing completely allows solving crucial problems of understanding the evolution of natural processes, such as glaciology research. Long-term monitoring of glaciers is currently one of the few ways to track global climate changing on the planet.
Authors and Affiliations
I. Pidlisetska, H. Silveistrov, O. Tomchenko
Keywords
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- EP ID EP240678
- DOI 10.17721/1728-2721.2016.65.11
- Views 72
- Downloads 0
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