مقایسه روش‌های مختلف تهیه نقشه کاربری/ پوشش اراضی با روش‌های رایج مطالعات منابع طبیعی (مطالعه موردی، حوزه آبخیز گردنه قوشچی ارومیه)
محورهای موضوعی : کاربری اراضی
اردوان قربانی
1
,
آزاد کاکه ممی
2
,
محمود حسن پور
3
,
فرنوش اسلمی
4
,
سحر غفاری
5
,
آرش رئوفی ماسوله
6
1 - دانشیار گروه مرتع و آبخیزداری، دانشکده فناوری کشاورزی و منابع طبیعی، دانشگاه محقق اردبیلی، اردبیل، ایران
2 - دانش‌آموخته کارشناسی ارشد مرتعداری دانشگاه محقق اردبیلی
3 - دانش آموخته کارشناسی ارشد مرتعداری، گروه مرتع و آبخیزداری، دانشگاه محقق اردبیلی
4 - دانش آموخته کارشناسی ارشد سنجش از دور و GIS دانشگاه محقق اردبیلی
5 - دانشگاه محقق اردبیلی
6 - دانش آموخته کارشناسی منابع آب دانشگاه آزاد اراک
کلید واژه: کاربری/ پوشش اراضی, تفسیر چشمی, شی پایه, Google Earth,
چکیده مقاله :
شرکت‌های خصوصی با عنوان مهندسین مشاور در مطالعات بخش منابع طبیعی، نقش بسیار مهمی را ایفا می‌کنند. نقشه کاربری اراضی به‌عنوان یکی از اطلاعات‌پایه تولیدشده در مطالعات توسط مهندسی مشاور است و صحت این اطلاعات بر نتیجه نهایی، هزینه‌های صرف شده در بخش منابع طبیعی و برنامه‌ریزی‌های آتی بسیار مؤثر است. هدف از این مطالعه، ارزیابی قابلیت تفسیر چشمی تصاویر موجود در Google Earth (GE) در مقایسه با نقشه تولیدشده توسط مهندسین مشاور و تفسیر رقومی شیءگرای تصاویر مورداستفاده نظیر لندست به‌عنوان روشی نوین و کم‌هزینه در تهیه نقشه‌های کاربری/ پوشش اراضی در مطالعات منابع طبیعی کشور است. برای این منظور نقشه کاربری/ پوشش اراضی تهیه‌شده توسط مهندسین مشاور (1386) و نقشه‌ی کاربری/ پوشش اراضی حاصل از پردازش شیءگرای تصویر سنجنده TM (1386) در محیط نرم‌افزار eCognition با نقشه کاربری/ پوشش اراضی تهیه شده در نرم‌افزار ArcGIS با استفاده از تفسیر چشمی تصاویر GE (1388) ازنظر صحت باهم مقایسه شدند. صحت کلی و ضریب کاپای نقشه تولیدی از GE به‌ترتیب 99 درصد و 99/0 و صحت کلی و ضریب کاپای نقشه کاربری/ پوشش اراضی تهیه‌شده توسط مشاور و شیءگرا به‌ترتیب 59 درصد، 32/0، 89 درصد و 86/0 برآورد گردید که نشان‌دهنده برتری تصاویر موجود در GE است. درمجموع نقشه تولیدشده از تصویر GE صحت بسیار مناسب و بهتری نسبت به دو نقشه دیگر داشت و نقشه تولیدشده توسط مهندسین مشاور با صحت غیرقابل‌قبول و ضریب کاپای پایین، غیرقابل استناد است.
Private companies as consulting engineers play important role to the study of natural resources. Land use map is one of the information generated in the studies by the consulting engineer that the accuracy of this information is effective on the final results, expenditures spending on the natural resources and future projects. The purpose of this study was to evaluate the capability of the visual interpretation of images from Google Earth (GE) in comparison with the map, which was produced by consulting engineers and object-based interpretation of Landsat images as a new and low-cost method for land use /cover mapping in the natural resources studies of Iran. For this purpose, the land use/cover map provided by consulting engineers (2007) was compared with land use maps, which was produced using object-based method of TM Image (2007) with eCognition software and GE images (visual interpretation) with ArcGIS software (2009) in terms of accuracy assessment results. Overall accuracy and Kappa of the land use/cover maps using GE images were 0.99 % and 0.99, and Overall accuracy and Kappa of consultant engineers and object-based method based on Landsat image were calculated 59% and 0.32, and 89% and 86%, respectively. Result of the study demonstrated the high capability of the GE images in land use/cover mapping. Overall, the map generated from the GE image has higher accuracy in comparison with the other two maps, and the map produced by the consultant engineers with the low Kappa coefficient was unacceptable.
Reference:
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7. Ghorbani, A., D. Bruce, & F. Tiver, 2006. Stratification: a problem in rangeland monitoring. In proceeding of the first International Conference on Object-based Image Analysis (OBIA), July 4-5, Salzburg, Austria.
8. Ghorbani, A., A. Sattarian, & H. Elyasi Brojeni, 2007. Identification and analysis of ecological agriculture relationship with watershed management, in proceeding of the second Conference of Ecological Agriculture in Gorgan, 403-435. (In Persian).
9. Ghorbani, A. & M. Pakravan, 2013. Land use mapping using visual vs. digital image interpretation of TM and Google earth derived imagery in Shrivan-Darasi watershed (Northwest of Iran), European Journal of Experimental Biology, 3(1): 576-582.
10. Iranmanesh, F., A.H. Charkhabi, & N. Jalali, 2006. Measuring gully morphometric characteristics in the south east of Iran using digital processing of ETM+ sensor. Journal of Science and Technology of Agriculture and Natural Resources, Water and Soil Science, 10(1): 233-245. (In Persian).
11. Jaafari, Sh., & A.A. Nazarisamani, 2013. Comparison between land use/land cover mapping through Landsat and Google Earth imagery, American-Eurasian Journal of Agriculture & Environment Science, 13(6): 763-768.
12. Jansen L. J.M., & A. Di Gregorio, 2004. Obtaining land-use information from a remotely sensed land cover map: results from a case study in Lebanon, International Journal of Applied Earth Observation and Geoinformation, 5: 141–157.
13. KeshtAb Pazhohan Espota Consulting Engineering, 2011. Reports of detailed studies of Behestan Watershed Management (East Azerbaijan province), Department of Natural Resources and Watershed Management of East Azerbaijan, 54pp. (In Persian).
14. KeshtAb Pazhohan Espota Consulting Engineering, 2012. Report of detailed studies of Ghoro Chah Watershed Management (East Azerbaijan province), Department of Natural Resources and Watershed Management of East Azerbaijan, 47pp. (In Persian).
15. Lillesand, T. M., R.W. Kiefer, & J.W. Chipman, 2008. Remote Sensing and Image Interpretation, John Wiley & Sons, Inc., 6th Ed., 812pp.
16. Mohammad Hassanpour, M. 2013. Modeling site selection process for bunch planting on the area with rangeland capability using geographic information system in Qushchy Ghat (Uromieh) watershed, MSc. Thesis, The University of Mohaghegh Ardabili, 95pp. (In Persian).
17. Myint, S. W., P. Gober, A. Brazel, S. Grossman-Clarke, & Q. Weng, 2012. Per-pixel vs. object-based classification of urban land covers extraction using high spatial resolution imagery. Remote Sensing of Environment, 115: 1145–1161.
18. Nashtak Consulting Engineering, 2008. Reports of semi detailed studies of Zilbarchay Watershed Management (East Azerbaijan province), Department of Natural Resources and Watershed Management of East Azerbaijan, 185pp. (In Persian).
19. Pars Paiab Consulting Engineering, 2010. Reports of detailed studies of Amrabad Watershed Management (East Azerbaijan province), Department of Natural Resources and Watershed Management of East Azerbaijan, 63pp. (In Persian).
20. Rafieian, A., E.A. Darvishsefat, & V.M. Namiranian, 2006. Evaluation the capability of Landsat 7 satellite images to map forest area (case study of Babol forest), Iranian Natural Resources Journal, 59(4): 843-852. (In Persian).
21. Rasouli, A. A. 2008. Basics of applied remote sensing with emphasis on satellite image processing, 1st Ed. Tabriz University Press, Tabriz. 544pp. (In Persian).
23. Safianian, A. R., & L. Khodakarami, 2011. Land-use mapping using fuzzy logic (case study three sub-watershed Kabudarahang, Razan - Qahavand and Khvnjyn - Talkhab in Hamadan province), Journal of Land use planning, 4: 95-114. (In Persian).
24. Tapiador, F.J. & J.L. Casanova, 2003. Land use mapping methodology using remote sensing for the regional planning directives in Segovia, Spain Landscape and Urban Planning Journal, 62(2): 103-115.
25. TarhAbriz Consulting engineering, 2007a. Reports of detailed studies of Ghoshchi ghat Watershed Management (East Azerbaijan province), Department of Natural Resources and Watershed Management of East Azerbaijan, 105pp. (In Persian).
26. TarhAbriz Consulting engineering, 2007b. Reports of detailed studies of Kaftareh Watershed Management (Ardabil province), Department of Natural Resources and Watershed Management of Ardabil, 135pp. (In Persian).
27. Taylor, J. R., & S. Taylor Lovell, 2012. Mapping public and private spaces of urban agriculture in Chicago through the analysis of high-resolution aerial images in Google Earth, Landscape and Urban Planning, 108: 57– 70.
28. Walter, V., 2004. Object-based classification of remote sensing data for change detection, ISPRS Journal of Photogrammetry & Remote Sensing, 58: 225– 238.
29. Yang, X., G.M. Jiang, X. Luo, & Z. Zheng, 2012. Preliminary mapping of high-resolution rural population distribution based on imagery from Google Earth: A case study in the Lake Tai basin, eastern China, Applied Geography, 2: 221-227.
_||_Reference:
1. Baban, S. J., & M. Wan Yusof, 2001. Mapping land use/cover distribution on a mountainous tropical island using remote sensing and GIS, International Journal Remote Sensing, 22(10): 1909–1918.
2. Congalton, R.G., & K. Green, 1999. Assessing the accuracy of remotely sensed data: principles and practices, CRC Press, Boca Raton, Florida, 137p.
3. Dellepiane, S.G., & P.C. Smith, 1999. Quality assessment of image classification algorithms for land cover mapping: A review and a proposal for a cost based approach. International Journal Remote Sensing, 20: 1461-1486.
4. Dorais, A., & C. Cardille, 2011. Strategies for incorporating high-resolution Google Earth databases to guide and validate classifications: understanding deforestation in Borneo, Remote Sensing, 3: 1157-1176.
5. Feizizadeh, B., F. Jafari, & H. Nazmfar, 2008. Application of remote sensing satellite images in change detection of urban land uses, an object-based image analysis approach for green space area of Tabriz city, Journal of Fine arts, 34: 17-24. (In Persian).
6. Feizizadeh, B., & H. Helali, 2010. Comparison pixel-based, object-oriented methods and effective parameters in Classification Land cover/ land use of west province Azerbaijan, Journal of Physical Geography Research, 71: 73-84. (In Persian).
7. Ghorbani, A., D. Bruce, & F. Tiver, 2006. Stratification: a problem in rangeland monitoring. In proceeding of the first International Conference on Object-based Image Analysis (OBIA), July 4-5, Salzburg, Austria.
8. Ghorbani, A., A. Sattarian, & H. Elyasi Brojeni, 2007. Identification and analysis of ecological agriculture relationship with watershed management, in proceeding of the second Conference of Ecological Agriculture in Gorgan, 403-435. (In Persian).
9. Ghorbani, A. & M. Pakravan, 2013. Land use mapping using visual vs. digital image interpretation of TM and Google earth derived imagery in Shrivan-Darasi watershed (Northwest of Iran), European Journal of Experimental Biology, 3(1): 576-582.
10. Iranmanesh, F., A.H. Charkhabi, & N. Jalali, 2006. Measuring gully morphometric characteristics in the south east of Iran using digital processing of ETM+ sensor. Journal of Science and Technology of Agriculture and Natural Resources, Water and Soil Science, 10(1): 233-245. (In Persian).
11. Jaafari, Sh., & A.A. Nazarisamani, 2013. Comparison between land use/land cover mapping through Landsat and Google Earth imagery, American-Eurasian Journal of Agriculture & Environment Science, 13(6): 763-768.
12. Jansen L. J.M., & A. Di Gregorio, 2004. Obtaining land-use information from a remotely sensed land cover map: results from a case study in Lebanon, International Journal of Applied Earth Observation and Geoinformation, 5: 141–157.
13. KeshtAb Pazhohan Espota Consulting Engineering, 2011. Reports of detailed studies of Behestan Watershed Management (East Azerbaijan province), Department of Natural Resources and Watershed Management of East Azerbaijan, 54pp. (In Persian).
14. KeshtAb Pazhohan Espota Consulting Engineering, 2012. Report of detailed studies of Ghoro Chah Watershed Management (East Azerbaijan province), Department of Natural Resources and Watershed Management of East Azerbaijan, 47pp. (In Persian).
15. Lillesand, T. M., R.W. Kiefer, & J.W. Chipman, 2008. Remote Sensing and Image Interpretation, John Wiley & Sons, Inc., 6th Ed., 812pp.
16. Mohammad Hassanpour, M. 2013. Modeling site selection process for bunch planting on the area with rangeland capability using geographic information system in Qushchy Ghat (Uromieh) watershed, MSc. Thesis, The University of Mohaghegh Ardabili, 95pp. (In Persian).
17. Myint, S. W., P. Gober, A. Brazel, S. Grossman-Clarke, & Q. Weng, 2012. Per-pixel vs. object-based classification of urban land covers extraction using high spatial resolution imagery. Remote Sensing of Environment, 115: 1145–1161.
18. Nashtak Consulting Engineering, 2008. Reports of semi detailed studies of Zilbarchay Watershed Management (East Azerbaijan province), Department of Natural Resources and Watershed Management of East Azerbaijan, 185pp. (In Persian).
19. Pars Paiab Consulting Engineering, 2010. Reports of detailed studies of Amrabad Watershed Management (East Azerbaijan province), Department of Natural Resources and Watershed Management of East Azerbaijan, 63pp. (In Persian).
20. Rafieian, A., E.A. Darvishsefat, & V.M. Namiranian, 2006. Evaluation the capability of Landsat 7 satellite images to map forest area (case study of Babol forest), Iranian Natural Resources Journal, 59(4): 843-852. (In Persian).
21. Rasouli, A. A. 2008. Basics of applied remote sensing with emphasis on satellite image processing, 1st Ed. Tabriz University Press, Tabriz. 544pp. (In Persian).
23. Safianian, A. R., & L. Khodakarami, 2011. Land-use mapping using fuzzy logic (case study three sub-watershed Kabudarahang, Razan - Qahavand and Khvnjyn - Talkhab in Hamadan province), Journal of Land use planning, 4: 95-114. (In Persian).
24. Tapiador, F.J. & J.L. Casanova, 2003. Land use mapping methodology using remote sensing for the regional planning directives in Segovia, Spain Landscape and Urban Planning Journal, 62(2): 103-115.
25. TarhAbriz Consulting engineering, 2007a. Reports of detailed studies of Ghoshchi ghat Watershed Management (East Azerbaijan province), Department of Natural Resources and Watershed Management of East Azerbaijan, 105pp. (In Persian).
26. TarhAbriz Consulting engineering, 2007b. Reports of detailed studies of Kaftareh Watershed Management (Ardabil province), Department of Natural Resources and Watershed Management of Ardabil, 135pp. (In Persian).
27. Taylor, J. R., & S. Taylor Lovell, 2012. Mapping public and private spaces of urban agriculture in Chicago through the analysis of high-resolution aerial images in Google Earth, Landscape and Urban Planning, 108: 57– 70.
28. Walter, V., 2004. Object-based classification of remote sensing data for change detection, ISPRS Journal of Photogrammetry & Remote Sensing, 58: 225– 238.
29. Yang, X., G.M. Jiang, X. Luo, & Z. Zheng, 2012. Preliminary mapping of high-resolution rural population distribution based on imagery from Google Earth: A case study in the Lake Tai basin, eastern China, Applied Geography, 2: 221-227.
