Exploring Land Cover Changes in Arak Using GIS and Remote Sensing

Subject Areas : environmental management

Mozhgan Ahmadi Nadoushan ¹ (Master of Science in Environmental Science, Department of Natural Resources)
Alireza Soffianian ² (Associated Professor of Range and Watershed Management, Department of Natural Resources. Department of Natural Resources, Isfahan university of Technology, Isfahan.)
Sayed Jamaleddin Khajeddin ³ (Professor of Environmental Science, Department of Natural Resources. Department of Natural Resources, Isfahan university of Technology, Isfahan)

Received: 2009-07-11 Accepted : 2010-02-06 Published : 2014-12-22

Keywords: land cover, Artificial Neural Networks, Change detection, Post-classification comparison,

Abstract :

Introduction: Land cover changes are among the most important alterations of the Earth’s land surface which affect the environment and environmental processes. Material and Methods: In this study, aerial photos from 1956 and 1972, Landsat TM and IRS-P6 LISS-III images obtained in 1980 and 2006 as well as the post-classification method were used to detect land cover changes and to evaluate Arak and its periphery during 1956-2006. At first, geometric correction was done to prepare aerial photos and satellite images. For this purpose, topographic maps at scales of 1:50000 and 1:25000 and nearest neighbor method and resampling method were applied. Root mean square error for all aerial photos and satellite images was less than one pixel. Afterwards, all georefrenced photos were mosaicked and land cover maps with 4 classes (urban areas, vegetated areas, barren lands, and rocks) were generated using visual interpretation of aerial photos. Following satellite images geometric correction, topographic correction was applied to images using DEM and Lambert model. In the next step, thye artificial neural networks classification method was implemented after producing false color composite images and image fusion. Results: Land cover maps in four classes were generated with overall accuracy of over 90%. To detect the land cover changes during 4 periods between 1956 and 2006, land cover maps of 1956, 1972, 1990 and 2006 were compared, and change maps and Tables were made. The results showed significant urban expansion, vegetated and barren lands losses and stability in rocks and mountainous areas during 1956-2006.

References:

1. Foody, G. M. 2000. Mapping land cover from remotely sensed data with a softened feedforward neural network classification, Journal of Intelligent and Robotic Systems, Vol. 29, No. 4, pp. 433 – 449.


2. Yuan, F., Bauer, M. E., Heinert, N. J. and Holden, G. R. 2005. Multi-level Land Cover Mapping of the Twin Cities (Minnesota) Metropolitan Area with Multi-seasonal Landsat TM/ETM+ Data, Geocarto International, Vol. 20. No. 2, pp. 5-13.


3. Tachizuka, S., Hung, T., Ochi, S. and Yasuoka, Y. 2002. Monitoring of Long-term Urban Expansion by the use of Remote Sensing Images from Different Sensor, Asian conference on Remote sensing, Nepal.


4. ربیعی، ح ر، ضیائیان، پ و علی محمدی، ع. 1384. "کشف و بازیابی تغییرات کاربری و پوشش اراضی شهر اصفهان به کمک سنجش از دور و سیستم اطلاعات جغرافیایی"،  فصلنامه مدرس علوم انسانی،  دوره 9،  شماره 4، ص ص 32-19.
   
   
   1. Lu, D., Mausel, P., Brondizio, E. and Moran, E., 2004. Change detection techniques, International Journal of Remote Sensing, Vol. 25, No. 12, pp. 2365 – 2401.
   
   
   2. Ridd, M. K. and Liu, J. 1998. A comparison of four algorithms for change detection in an urban environment, Remote Sensing of Environment, Vol. 63, No. 2, pp. 95-100.
   
   
   3. Ward, D., Phinn, S. R. and Murray, A. T. 2000. Monitoring growth in rapidly urbanizing Areas using remotely sensed data, Professional Geographer, Vol. 52, No. 3, pp. 371-386.
   
   
   4. رضایی مقدم، م ح و ثقفی، م. 1385. "بررسی تحولات ژئومرفولوژیک پلایای کهک، استان خراسان جنوبی بر اساس روش های استفاده از تصاویر ماهواره ای و منطق فازی"، مجله جغرافیا و توسعه، شماره4، ص ص 60-43.
      
      
      1. Omo-Irabor, O. O. and Oduyemi, K. 2007. A hybrid image classification approach for the systematic analysis of land cover (LC) changes in the Niger delta region, 5th International Symposium on spatial data quality, The Netherlands.
      
      
      2. رضایی، ع، مهدوی، م، لوکس، ک، فیض نیا، س و مهدیان، م، ح. 1386. "مدلسازی منطقه ای دبی های اوج در زیرحوزه های آبخیز سد سفیدرود با استفاده از شبکه عصبی مصنوعی"، مجله علوم و فنون کشاورزی و منابع طبیعی، سال 11، شماره اول(الف)، ص ص 39-25.
      
      
   
   

11.Madhavan, B. B., Kubo, S., Kurisaki, N. and Sivakumar, T. 2001. Appraising the anatomy and spatial growth of the Bangkok Metropolitan area using a vegetation-impervious-soil model through remote sensing, International Journal of Remote Sensing, Vol. 22, No. 5, pp.789-806.

12.Shalaby, A. and Tateishi R. 2007. Remote sensing and GIS for mapping and monitoring land cover and land-use changes in the Northwestern coastal zone of Egypt, Applied Geography, Vol. 27, No. 1, pp. 28-41.

13.Fan, F., Wang, Y. and Wang, Z. 2008. Temporal and spatial change detecting (1998–2003)and predicting of land use and land cover in Core corridor of Pearl River Delta (China) by using TM and ETM⁺ images, Environmental Monitoring and Assessment, Vol. 137, No. 1-3, pp. 127-147.

1. اسلام بنیاد، ا و حاجی قادری، ط. 1386. "تهیه نقشه جنگل های طبیعی استان زنجان با استفاده از داده‌های سنجنده ETM⁺ ماهواره لندست 7"، مجله علوم و فنون کشاورزی، سال یازدهم، شماره چهل و دوم، ص ص 638-627.

15. Wentz, E. A., Stefanov, W. L., Gries, C., Hope, D. 2006. Land use and land cover mapping from diverse data sources for an arid urban environment, Computers, Environment and Urban Systems, Vol. 30, No. 3, pp. 320–346.

1. 16.  Deguchi, C. and Sugio, S. 1994. Estimations for percentage of impervious area by the use of satellite remote sensing imagery, Water science and technology, Vol. 29, pp. 135-144.        

17.Tole, L. 2008. Changes in the built vs. non-built environment in a rapidly urbanizing region: A case study of the Greater Toronto Area, Computers, Environment and Urban Systems, Vol. 32, No. 5, pp. 355-364.