Land Cover Classification and Forest Change Analysis, Using Satellite Imagery-A Case Study in Dehdez Area of Zagros Mountain in Iran
Ali Asghar Torahi, Suresh Chand Rai
DOI: 10.4236/jgis.2011.31001   PDF    HTML     11,139 Downloads   24,403 Views   Citations


The importance of accurate and timely information describing the nature and extent of land resources and changes over time is increasing, especially in mountainous areas. We have developed a methodology to map and monitor land cover change using multitemporal Landsat Thematic Mapper (TM) and ASTER data in Zagros mountains of Iran for 1990, 1998, and 2006.Land-use/cover mapping is achieved through interpreta-tion of Landsat TM satellite images of 1990, 1998 and TERRA-ASTER image of 2006 using ENVI 4.3. Based on the Anderson land-use/cover classification system, the land-use and land-covers are classified as forest land, rangeland, water bodies, agricultural land and residential land. The unsupervised image classifi-cation method carried out prior to field visit, in order to determine strata for ground truth. Fieldwork carried out to collect data for training and validating land-use/cover interpretation from satellite image of 2006, and for qualitative description of the characteristics of each land-use/cover class. The land-use/cover maps of 1990, 1998 and 2006 were produced by using supervised image classification technique based on the Maxi-mum Likelihood Classifier (MLC) and 132 training samples. Error matrices as cross-tabulations of the mapped class vs. the reference class were used to assess classification accuracy. Overall accuracy, user’s and producer’s accuracies, and the Kappa statistic were then derived from the error matrices. A multi-date post-classification comparison change detection algorithm was used to determine changes in land cover in three intervals, 1990–1998, 1998–2006 and 1990–2006. To evaluate the change maps for the 1990 to 2006 interval, we randomly sampled the areas that classified as change and no-change and determined whether they were correctly classified. The maps showed that between 1990 and 2006 the amount of forest land de-creased from 67% to 38.5% of the total area, while rangelands, agriculture, settlement and surface water in-creased from 30.8% to 45%, 1.2% to 7.0%, 0.3% to 7.5% and 0.6% to 1.8%, respectively. The area was dominated by 35.9%, 28.9% and 29.3% dense forest, 42.2%, 46.4% and 43.2% open forest and 21.9%, 24.8% and 27.5% degraded forest in 1990, 1998 and 2006, respectively. During 16 years span period (1990-2006) about 10170.3 ha, 2963.4 ha, 351.7 ha and 3039.2 ha of forest lands were converted to range-land, agriculture, water body and settlement. The overall five-class classification accuracies averaged 78.6% for the three years. The overall accuracy of land cover change maps, generated from post-classification change detection methods and evaluated using several approaches, reached to 80.1%. The results quantify the land cover change patterns in the Zagrous highlands and demonstrate the potential of multitemporal Landsat and ASTER data to provide an accurate, economical means to map and analyze changes in land cover over time that can be used as inputs to land management and policy decisions.

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A. Torahi and S. Rai, "Land Cover Classification and Forest Change Analysis, Using Satellite Imagery-A Case Study in Dehdez Area of Zagros Mountain in Iran," Journal of Geographic Information System, Vol. 3 No. 1, 2011, pp. 1-11. doi: 10.4236/jgis.2011.31001.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] M. N. Siddiqui, S. Maajid, Z. Jamil and J. Afser, “Integrated Applications of Remote Sensing and GIS for Mapping and Monitoring Changes in Forest Cover,” Proceedings of the In-ternational Seminar on Natural Hazard Monitoring, Karachi, Pakistan, 2002, pp. 429- 435. doi:10.1016/j.foreco.2003.07.035
[2] FAO, “Global Forest Resources Assessment 2000” FAO Forestry Paper 140, Food and Agriculture Organization of the United Nations, Rome, 2001.
[3] C. de Wasseige and P. Defourny, “Remote Sensing of Selective Logging Impact for Tropical Forest Management,” Forest Ecology and Management, Vol. 188, No. 1-3, 2004, pp. 161-173. doi:10.1016/j.foreco.2003.07.035
[4] G. M. Foody, “Remote Sensing of Tropical Forest Environments: Towards the Moni-toring of Environmental Resources for Sustainable Develop-ment,” International Journal of Remote Sensing, Vol. 24, No. 20, 2003, pp. 4035-4046. doi:10.1080/0143116031000103853
[5] S. A. Sader, D. J. Hayes, J. A. Hepinstall, M. Coan and C. Soza, C, “Forest Change Monitoring of A Remote Biosphere Reserve,” Interna-tional Journal of Remote Sensing, Vol. 22, No. 10, 2001, pp. 1937-1950.
[6] S. S. Murai, “Development of Global Eco-Engineering Using Remote Sensing and Geographic In-formation Systems,” Proceeding of 8th Toyota Conference, 1995.
[7] M. Fattahi, “Investigation of Zagros Natural Re-sources and the Important Factors of Demolition,” Institute of Forests and Range Lands Researches Press, 1995.
[8] M. Fattahi, “Management of Zagros Forests,” Institute of Forests and Range Lands Researches Press, 2002.
[9] E. Etezadi, “The Complete Studies of Khuzestan Province, Section of Geological Study,” Planning and Management Organzation of Khuzestn Province Press, 1996.
[10] T. M. Lillesand, J. W. Chipman, D. E. Nagel, H. M. Reese, M. R. Bobo and R. A. Goldmann, “Upper Midwest Gap Analysis Program Image Processing Protocol,” U.S. Geological Survey, Environmental Management Technical Center,Onalaska, Wisconsin, 1988, p. 25.
[11] R. S. Lunetta and M. Balogh, “Apication of Multi-Temporal Landsat 5 TM Imagery for Wetland Identifica-tion,” Photogrammetric Engineering and Remote Sensing, Vol. 65, No. 11, 1999, pp. 1303-1310.
[12] D. R. Oettera, W. B. Cohenb, M. Berterretchea, T. K. Maierspergera and R. E. Ken-nedy, “Land Cover Mapping in An Agricultural Setting Using Multiseasonal Thematic Mapper Data,” Remote Sensing of Environment, Vol. 76, 2000, pp. 139-155. doi:10.1016/S0034-4257(00)00202-9
[13] P. T. Wolter, D. J. Mladenoff, G. E. Host and T. R. Crow, “Improved Forest Clas-sification in the Northern Lake States Using Multi-Temporal Landsat Imagery,” Photogrammetric Engineering and Remote Sensing, Vol. 61, No. 9, 1995, pp. 1129-1143.
[14] F. Yuan, M. E. Bauer, N. J. Heinert and G. Holden, “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, 2005, pp. 5-14. doi:10.1080/10106040508542340
[15] A. Anderson, “Land Use and Land Cover Classification System,” Geological Sur-vey Professional Paper 964, Washington, D. C., 1976.
[16] J. R. Jensen, “Introductory digital image processing,” New Jersay: Trentice Hall, 1996.
[17] J. A. Richards, “Remote Sensing Digital Image Analysis, an Introduction,” Second Edition, Springer-Velarg, 1993.
[18] R. G. Congalton and K. Green, “Assessing the Accuracy of Remotely Sensed Data: Principles and Practicesboca Rotan,” Lewis Publishers, Florida, 1999.
[19] J. R. Jensen, “Digital Change Detection. Introduc-tory Digital Image Processing: A Remote Sensing Perspective,” Prentice-Hall, New Jersey, 2004.
[20] X. Yang, “Satellite Monitoring of Urban Spatial Growth in the Atlanta Metropoli-tan Area,” Photogrammetric Engineering and Remote Sensing, Vol. 68, No. 7, 2002, pp. 725-734.
[21] D. Yuan, C. D. Elvidge and R. S. Lunetta, “Survey of Multispectral Methods for Land Cover Change Analysis. Remote Sensing Change Detection: Environmental Moni- Toring Methods and Applica-tions,” Michigan’ Ann Arbor Press, 1998.
[22] R. M. Fuller, G. M. Smith, and B. J. Devereux, “The Characterization and Measurement of Land Cover Change through Remote Sensing: Problems in Operational Applications,” International Journal of Applied Earth Observation and Geoinformation, Vol. 4, No. 3, 2003, pp. 243-253. doi:10.1016/S0303-2434(03)00004-7

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