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Hindi Research Article
Land Use/Cover and Vulnerability Mapping Through Remote Sensing and GIS In Astrakhan, Russia
Mukesh SB1,2,3*, Komal C2 and Alexander K211American Sentinel University, Colorado, USA
22Samara National Research University, Samara, Russia
33Bonn University, Bonn, Germany
- *Corresponding Author:
- Mukesh SB
American Sentinel University, Colorado, USA
Tel: 0123456789
E-mail: msboori@gmail.com, komal.kc06@gmail.com
Received date: July 22, 2016; Accepted date: January 11, 2017; Published date: January 18, 2017
Citation: Mukesh SB, Komal C, Alexander K (2017) Land Use/Cover and Vulnerability Mapping Through Remote Sensing and GIS In Astrakhan, Russia. J Earth Sci Clim Change 8:380. doi: 10.4172/2157-7617.1000380
Copyright: © 2017 Mukesh SB, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
The main aim of this research work is to identify natural and environmental vulnerability distribution in Astrakhan, Russia. This paper identifies, assess, and classify natural and environmental vulnerability using landscape pattern from multidisciplinary approach, based on remote sensing and Geographical Information System (GIS) techniques. A model was developed by following thematic layers: land use/cover, vegetation, geology, geomorphology, and soil in ArcGIS 10.3 software. According to numerical results vulnerability classified into five levels: low, reasonable, moderate, high and extreme vulnerability by mean of cluster principal. Results are shows that in natural vulnerability maximum area covered by moderate (54.62%) and environmental vulnerability concentrated by moderate (54.00%) vulnerability. So, study area has at medial level vulnerability. In the study area encroachment, population growth, industrialization and governmental polices for environmental protection are main cause for vulnerability. This study is helpful for decision making for eco-environmental recovering and rebuilding as well as predicting the future development.
