Document Type : پژوهشی

Authors

1 Assistant Professor, Faculty of Natural Resources and Desertification, Yazd University (Corresponding author), E-mail:makavan_ghalibaf@hotmail.com.

2 Ph.D candidate of Desertification, Faculty of Natural Resources and Desertification, Yazd University.

3 Professor, Baku State University of Civil Engineering

4 Associate Professor of Economics and Finance of the Russian Academy of Finance

5 Assistant Professor, Faculty of Natural Resources and Desertification, Yazd University

Abstract

Introduction
The ability of a system to detect changes depends on its capacity to estimate variables on a scale. In any case, observing changes at successive times is the first step towards identifying the active processes and change forces. The assessment of land use change and land cover has been considered as one of the main techniques of assessing environmental changes and has played a major role in environmental planning. Vegetation change and land use due to human activities are among important issues in regional and developmental planning. Given the advantages and capabilities of satellite data, this technology can help identify and discover these changes.
Materials and methods
In this research, the investigation of the land cover change of the Lake Urmia basin was based on the use of the MODIS Annual Coverage (MCD12Q1) with the HDF format and spatial resolution of 500 m. These images were categorized as Type I with 17 classes of land cover. Then, the image was taken annually by the mask region border and a geometric correction which Converted to UTM system Through

 
entering the annual descriptive information tables into the Excel software, the change trend of land cover area was estimated between the years 2005 and 2016.
Results and discussion
The review of Tables 2 and 3 showed that there were significant variations in coverage over the period of 2005 to 2016. The area of the water zones had been declining since 2009. The grasslands had a relatively stable area between 2005 and 2015 and showed a decreasing trend over the last two years. Urban coverage during this period had not changed much, and the population growth seemed to be moderated by increasing urban densities. Between 2009 and 2014, water level changes were steeper than they were in previous years. In addition, since 2014, the slope had become even more intense. The area of the water zones in 2008 had a slight and noticeable decline compared to its following and preceding years.
Conclusion
According to the MODIS image information, the proportion of land area and water zones in 2016, compared to 2005, were respectively 1.39 and 0.69. Between 2005 and 2016, the greatest increase in the area of use was related to agricultural land and solid or dense floor coverings, respectively with an increase of 1648 and 837.  The greatest reduction in the use of the area was related to water zones and desert cover, respectively with decreasing 1383 and 1159 km2. The results of the research showed that satellite images ha high potential for rapid decoupling of agricultural land, the preparation of map of different types of crops in the region, and determining under cultivation with a relatively accurate accuracy in a regional scale.

Highlights

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References
-Alizadeh, H., (2014), Delineation rice cropping patterns using time-series vegetation Indices. Geomatics 93 (National Conference).
-Chandra, G., Zhiliang, Z., Bradley, R., (2005), AComparative Analysis of the Global Land Cover2000 and MODIS Land Cover Data Sets. www.elseier.com/locate/asr.
-Fernando, S.F., Gong, P., Ferrao, M., (2005), LandCover Assessment with MODIS Imagery in Southern African MIOMBO Ecosystem. International Journal of Remote sensing, PP. 429-441.
-Hung, T., Yoshifumi,Y., (2005), MODISApplications in Environmental ChangeResearches in the Southeast-Asian Region. International Journal of Geoinformatics, Vol.1, No. 1, PP. 25-33.
-Li, Z., (2014), Cross-comparison of Vegetation Indices Derived from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) sensors. Remote Sens, Vol. 6, PP. 310-329.
-Sima,S., Tajrishy, M., (2013), Using satellite data to extract volume-area-elevation relationship for Urmia Lake, Iran. Journal of Great Lakes Research, Vol.39, PP. 90-99.
-Sharifi, L., Kamel, S., Feizizadeh, B., (2015), Monitoring Bioenvironmental Impacts of Dam Construction on Land Use/Cover Changes in Sattarkhan Basin Using Multi-Temporal Satellite Imagery, Iranica Journal of Energy and Environment,Vol. 6 , No.1, PP. 39-46.