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  • بررسی و ارزیابی روند تغییرات سطوح جنگل های حراء (مانگرو) با استفاده از تکنیک های سنجش از دور نمونه موردی: بندرعباس

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آدرس مقاله


کد مقاله : NEI-1609-1022 (R1) بازدید : 243 صفحه: 17 - 32

نوع مقاله: پژوهشی

بررسی و ارزیابی روند تغییرات سطوح جنگل های حراء (مانگرو) با استفاده از تکنیک های سنجش از دور نمونه موردی: بندرعباس

محورهای موضوعی : جنگلداری

محمد علی زنگنه اسدی 1 , ابراهیم تقوی مقدم 2 * , الهه اکبری 3

1 - عضو هیئت علمی دانشگاه حکیم سبزواری
2 - دانشجوی دکترا
3 - عضو هیئت علمی دانشگاه حکیم سبزواری و دانشجوی دکترا سنجش از دور دانشگاه تهران

تاریخ دریافت : 1395/07/03 تاریخ پذیرش : 1395/12/08 تاریخ انتشار : 1395/12/01

کلید واژه: الگوریتم, ماشین بردار پشتیبان, بندر‌عباس, جنگل‌های حراء, حداکثر احتمال,

چکیده مقاله :

آگاهی از تغییر و تحولات اولین و مهم‌ترین اقدام برنامه‌ریزان و متولیان محیط طبیعی و انسانی می باشد. استفاده از تصاویر ماهواره ای و تکنیک های پردازش آن ها، ابزار بسیار دقیقی برای پیمایش و ارزیابی تغییرات سطوح جنگلی محسوب میشود. این تحقیق با هدف بررسی و ارزیابی روند تغییرات سطوح جنگل های حراء بندرعباس با استفاده از تکنیک های سنجش از دور می باشد. جهت نیل به این هدف از اطلاعات و نقشه های توپوگرافی، تصاویر ماهواره‌ای سال-های 1989- 2005--2015 منطقه و الگوریتم‌های حداکثر احتمال(MLC)، حداقل فاصله(MD) و ماشین بردار پشتیبان (SVM) استفاده شده است. نتایج تحقیق حاضرنشان می-دهد روش حداکثر احتمال با دقت کلی 32/98% و ضریب کاپا 978/0 نسبت به روش ماشین بردار پشتیبان و حداقل فاصله روش دقیق‌تری در تهیه نقشه تغییرات پوشش زمین و روندیابی تغییرات جنگلی می باشد. نتایج نشان می دهد مجموع سطوح جنگل‌های حراء در منطقه مزبور در سال 1989 ، 09/76 کیلومتر مربع بوده است که این رقم در سال ۲۰۰۵، به ۹۶۱/۷۴ کیلومتر مربع کاهش یافته سپس با سیر صعودی به 08/125 کیلومتر مربع در سال 2015 افزایش یافته‌است. این تغییرات چشمگیر بر اثر تحولات بیولوژیکی و هیدرودینامیک در اکوسیستم طبیعی سواحل منطقه و به خصوص تنگه هرمز می باشد به نحوی که باعث گسترش جنگلهای حراء‌ در منطقه شده است. با این وجود اتخاذ هرگونه طرح های بهره برداری از این جنگل ها، احداث تاسیساتی ساحلی و یا اقدامات حفاظتی در منطقه باید براساس روند کاهش و یا افزایش بیولوژیکی جنگل های حراء منطقه باشد.

چکیده انگلیسی:

Knowledge of changes is first, most important action planners, and authority’s natural and human environment. Satellite images and satellite image processing techniques and methods very precise tool for navigation and assessment of changes in forest areas is the purpose of of this study is assess the changes in forest areas mangrove in Bandar using the technique of remote sensing. To achieve this purpose of we used the information and topographic maps, satellite images and the algorithm of maximum likelihood and minimum distance 1989, 2005 and 2015 years of area. The results show that the maximum likelihood method with 98/32% overall accuracy and kappa coefficient 0/978 accurate method than using support vector machine and the minimum distance for mapping land cover changes and monitoring changes in forest. According to calculations forest surface area’ of 76/09 sq km in 1989 has increased to 125/08 square kilometers in 2015. Which indicates the shores of the Strait of Hormuz is the hydrodynamic change. Thus adopting every environmental protection measures in the area is necessary, any facilities and infrastructure projects must comply with environmental considerations and ecological.

منابع و مأخذ:


References

 

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  25. Parente Maria,L., Hisle Uchôa Monteiro,L., Marques e Souza,G and Drude de Lacerda.L.2006. Changes in mangrove extension along the Northeastern Brazilian coast (1978-2003). ISME/GLOMIS Electronic Journal. Volume 5, No. pp9
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  26. Petropoulos, G, Kontoes, C. and Keramitsoglou, I, 2011, Burnt area delineation from a uni-temporal perspective based on Landsat TM imagery classification using Support Vector Machines, International Journal of Applied Earth Observation and Geoinformation, Volume 13, Issue 1, Pages 70–80
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  27. Pillay, T.V.R.2004 .Aquaculture and the environment .Blackwell publishing .secound edition. P31-38
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  28. Plieninger T, 2012. Monitoring directions and rates of change in trees outside forests through multi temporal analysis of map sequences Applied Geography, Volume 32, Issue 2, , Pages 566–576
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  29. Rabiei, H.r., C, Ziaeian, Ali, Ali Mohammad, 2005, discovery and recovery of the city land use changes and land cover remote sensing geographical information systems .lecturer in Human Sciences Journal of, Issue 4: 19-32(In Persian)
    30.
  30. Rasouli, A., 2008 Fundamentals of Remote Sensing Applications, p ,Tabriz University, 806 p. (In Persian)
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  31. Richards, J.A., 1999, Remote Sensing Digital Image Analysis, Springer-Verlag, Berlin, p. 240
    32.
  32. Rodringuez, W and Feller, I.C, .2004.Mangrove landscape characterization and change in Twin Cays, belize using aerial photography and IKONOS satellite data, Atoll research Bulletin.no.513.National Museum of National History. U.S.A pp1-22
    33.
  33. Senga H.O. Kihupi N. I Evaristo L.2014, Land Cover Changes along the Coastal Marine Ecosystems of Zanzibar Journal of Asian Scientific Research, no 4: pp 83-98
    34.
  34. Singh.H.S. 2003. Vulnerability and adaptability of Tidal forests in response to climate change in India.Indian forester journal in forestty research and eduction, vol.  129 n6, pp. 749-756 
    35.
  35. Soffianian A. A Study on Land Use Change in Isfahan Using Change vector analysis techniques in the years 1366 to 1377. Water and Soil Sciences Journal (JWSS). 2009; 13 (49) :153-164(In Persian)
    36.
  36. Tiempo, 2007.Climate and mangrove ecosystem.www.cru.uea.ac.uk /cru/tiempo/issue10/ mangrove.htm
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  37. Tso Brandt and Paul Mather, 2009. Classification methodds for Remotely Sensed Data. Chapter 2-3. 2nd ed., Pub., Technology & Engineering - 376 pages
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  38. vaipink, Vladimir,1995. The nature of statistical l earning Theory (new York:spring verlag),314 pages
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  39. Yamani M. Rahimi Herabadi  S.Godarzi mehr S. 2012 The periodic changes the coastline of East Strait of Hormuz preceding studies using remote sensing techniques Journal of Environmental Erosion E.E.R. No. 1390. (In Persian)
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_||_

References

 

  1. Ahmed E.A., and K.A Abdel-Hamid. 2007. .Zonation Pattern of Avicennia marina and Rizophora mucronata along the Red Sea Coast, Egypt. World Applied Sciences Journal no 2, p283-288
    2.
  2. Alongi, D M. 2008. Mangrove forests: Resilience, protection from tsunamis, and responses of global climate change, Estuarine, Coastal and Shelf Science Volume 76, Issue 1, Pages 1–13
    3.
  3. Alongi.D.M. 2002. Present state and future of the world's mangrove forests. Environmental Conservation, Volume 29: 331-349.
    4.
  4. Amiri S. Sajadi, J. Sadough Vanini ,H. 2011. Application Of Vegetation Indices Derived From IRS Data For Detecting The Avicennia Forest Area Near The South Parsoil Apparatus Environmental Sciences Vol.8, No.1, Autumn 2011(In Persian)
    5.
  5. Arkhi S, M.  Adibnejad, 2012 "Evaluating  the  capability  of  support  vector  machine  in  land  use mapping", Journal of Range and desert research of iran,Vol.3,pp.420-440,2012(In Persian)
    6.
  6. Arkhi, S. and Niazi, Y. 2009. Assessment of remote sensing methods for detecting land use change a case study in Dareh shahr- Elam province. Journal of Research of grassland and desert, 17(1), 74-95. (In Persian)
    7.
  7. Bhagawat R 2013, Application of Remote Sensing and GIS, Land Use/Land Cover Change In Kathmandu Metropolitan City, Nepal ,Journal Of Theoretical And Applied Information Technology Biological Forum  An International Journal ,Vol 3 P 2-7
    8.
  8. Daneh kar Afshin, 1998  " environment sensitive zones in margin iran  Journal of Environmental 7, no. 24 p: 28-38(In Persian)
    9.
  9. De Boer W.F., 2002. The rise and fall of the mangrove forests in Maputo Bay, Mozambique. Wetlands Ecology and Management.  Volume 10, Issue 4, pp 313-322
    10.
  10. ENVI User’s Guide, 2008, ENVI On-line Software User’s Manual, ITT Visual Information Solutions.
    11.
  11. FAO.2003. State of the Worlds Forest (SOFO). The situation and developments in the forest sector.2003.availabe online
    12.
  12. Feizizadeh, B. and T,Blaschke, 2012. Thermal remote sensing for examining the relationship between urban land surface temperature and land use land cover in Tabriz city, Iran. Paper presented at the IEEE international geoscience and remote sensing symposium, Igarss, 22–27 July, Munich, Germany.
    13.
  13. Goh, K. E. Chang, and K. T. Cheng, "SVM Binary classifier ensembles for image classification," ACM Int. Conf Information and knowledge And management (lCIKM), pp. 395-402, Nov. 2001
    14.
  14. Hina M, Sh A, Zamir ,B Umair and J. H Kazmi, 2015, Application of Comparative Remote Sensing Techniques for Monitoring Mangroves in Indus Delta, Sindh, Pakistan, Pakistan Journal of Botany, 47(2):797-805
    15.
  15. Huang, C., L S Davis, and J Townshend, 2002, assessment of support vector machines for land cover classification Int. J. International Journal of Remote Sensing., 23, pp. 725-749.
    16.
  16. Hurcom, S.J. & A.R. Harrison, 2003. The NDVI & spectral decomposition for semi-arid vegetation abundance estimation, International Journal of Remote Sensing, pp 3109-3125.
    17.
  17. Karimi, Y., S Orasher, O Patel, and S. H Kim (2006). Application of Support Vector Machine technology for weed and nitrogen stress detection in corn, Computers and Electronics in Agriculture journal, V.51, pp.99–109.
    18.
  18. Lillesand Thomas , Ralph Kiefer 2001, Remote Sensing and Image Interpretation, 4th ed, John Wiley and Sons, inc. USA, 2001, ISBN: 0471255157,205p.
    19.
  19. Luciana P.-Bolland, Edward A. Ellis b, Henry L. Gholz. 2007. Land use dynamics and landscape history in La Monta˜na, Campeche, Mexico. Landscape and Urban Planning journal no 82 pp 198–207.
    20.
  20. Mahini, AS. A,Azadeh ,J, feghhi,. B, riazi, 2012. Classification of forest areas in Golestan province maximum likelihood method using satellite images etm + 2001, Journal of Science, Technology and the Environment, (3): 97-106 (In Persian)
    21.
  21. Mahini, r., A. Najafi, M., Mohammad Zadeh, 2010. Chalus River Watershed land cover change detection using remote sensing and GIS, the first National Conference on Geomatics the new 14 Esfand 1389, Tehran University(In Persian)
    22.
  22. Mirzaee born, and Mahdavi and, as in 2014, forest cover change detection using remote sensing, spatial orientation (Case Study: city malekshahi), First National Conference on Environmental, Isfahan. Iran, (In Persian)
    23.
  23. Niazi Y, MR, Ekhtesasi. H, Malkinejad H, Hosseini.Z, Morshedi C. 2010. Comparing the maximum likelihood method and artificial neural network in land use map (Case Study: Ilam dam basin) Journal of Geography and Development, n20 p: 119-132(In Persian)
    24.
  24. Odum, E.P., Barrett, G.W., 2004. Fundamentals of Ecology, fifth ed. Brooks-Cole, Belmont, CA, 598 pp.
    25.
  25. Parente Maria,L., Hisle Uchôa Monteiro,L., Marques e Souza,G and Drude de Lacerda.L.2006. Changes in mangrove extension along the Northeastern Brazilian coast (1978-2003). ISME/GLOMIS Electronic Journal. Volume 5, No. pp9
    26.
  26. Petropoulos, G, Kontoes, C. and Keramitsoglou, I, 2011, Burnt area delineation from a uni-temporal perspective based on Landsat TM imagery classification using Support Vector Machines, International Journal of Applied Earth Observation and Geoinformation, Volume 13, Issue 1, Pages 70–80
    27.
  27. Pillay, T.V.R.2004 .Aquaculture and the environment .Blackwell publishing .secound edition. P31-38
    28.
  28. Plieninger T, 2012. Monitoring directions and rates of change in trees outside forests through multi temporal analysis of map sequences Applied Geography, Volume 32, Issue 2, , Pages 566–576
    29.
  29. Rabiei, H.r., C, Ziaeian, Ali, Ali Mohammad, 2005, discovery and recovery of the city land use changes and land cover remote sensing geographical information systems .lecturer in Human Sciences Journal of, Issue 4: 19-32(In Persian)
    30.
  30. Rasouli, A., 2008 Fundamentals of Remote Sensing Applications, p ,Tabriz University, 806 p. (In Persian)
    31.
  31. Richards, J.A., 1999, Remote Sensing Digital Image Analysis, Springer-Verlag, Berlin, p. 240
    32.
  32. Rodringuez, W and Feller, I.C, .2004.Mangrove landscape characterization and change in Twin Cays, belize using aerial photography and IKONOS satellite data, Atoll research Bulletin.no.513.National Museum of National History. U.S.A pp1-22
    33.
  33. Senga H.O. Kihupi N. I Evaristo L.2014, Land Cover Changes along the Coastal Marine Ecosystems of Zanzibar Journal of Asian Scientific Research, no 4: pp 83-98
    34.
  34. Singh.H.S. 2003. Vulnerability and adaptability of Tidal forests in response to climate change in India.Indian forester journal in forestty research and eduction, vol.  129 n6, pp. 749-756 
    35.
  35. Soffianian A. A Study on Land Use Change in Isfahan Using Change vector analysis techniques in the years 1366 to 1377. Water and Soil Sciences Journal (JWSS). 2009; 13 (49) :153-164(In Persian)
    36.
  36. Tiempo, 2007.Climate and mangrove ecosystem.www.cru.uea.ac.uk /cru/tiempo/issue10/ mangrove.htm
    37.
  37. Tso Brandt and Paul Mather, 2009. Classification methodds for Remotely Sensed Data. Chapter 2-3. 2nd ed., Pub., Technology & Engineering - 376 pages
    38.
  38. vaipink, Vladimir,1995. The nature of statistical l earning Theory (new York:spring verlag),314 pages
    39.
  39. Yamani M. Rahimi Herabadi  S.Godarzi mehr S. 2012 The periodic changes the coastline of East Strait of Hormuz preceding studies using remote sensing techniques Journal of Environmental Erosion E.E.R. No. 1390. (In Persian)
    40.

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