تحلیل هیدرودینامیک دریا و مورفولوژی ساحلی در ارتباط با تغییرات گستره جنگل های مانگرو (مطالعه موردی: غرب تنگه هرمز)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجو دکتری دانشکده جغرافیا و برنامه ریزی، دانشگاه تبریز.

2 استاد دانشکده جغرافیا و برنامه ریزی، دانشگاه تبریز.

3 استاد دانشکده جغرافیا ، دانشگاه تهران.

10.22034/gmpj.2021.137689

چکیده

هدف این پژوهش بررسی تغییرات جنگل‌های مانگرو و ارتباط این تغییرات با هیدرودینامیک دریا و مورفولوژی ساحلی در فاصله بین جزیره قشم و از رود مهران تا بندر پل طی بازه زمانی 47 ساله می‌باشد. با استفاده از تصاویر ماهواره‌ای و انجام پیش-پردازش‌ها و طبقه‌بندی آن‌ها به روش‌های SVM، MLC و ANN و ارزیابی دقت نقشه‌ها روش SVM با کسب بالاترین درصد دقت با ضریب کاپا 97/0 و صحت کلی 98، برای تهیه نقشه طبقه‌بندی تمام تصاویر انتخاب شد. نقشه‌ها برای سال‌های 1972، 1987، 2002 و 2019 با صحت کلی برابر با 40/92، 40/92، 62/96 و 98 و همچنین ضریب کاپا نیز به ترتیب 89/0، 90/0، 95/0 و 97/0 برآورد شدند. نتایج نشان می دهد که از سال‌های 1972 تا 1987 این جنگل ها روند کاهشی داشته اما پس از این دوره گسترش آنها آغاز شده است. این مناطق شامل، جنگل‌های مانگرو مردو، خور موریز دراز، خور هفت برم، جنگل‌های مانگرو در جنوب بندر لافت، همچنین جنگل‌های مانگرو بین اسکله طبل، ملکی و گورزین می‌شوند. با مقایسه نتایج حاصل از روند افزایشی و کاهشی جنگل‌های مانگرو با منحنی‌های منطبق با متوسط میزان جزر و مد و ویژگی‌های مورفولوژیک منطقه این نتیجه به دست می آید که محدوده مورد مطالعه از بابت ویژگی‌های هیدرودینامیک دریا، مانند متوسط دامنه جزرومد و گستردگی پهنه جزرو مدی، ارتفاع امواج و مورفولوژی ساحلی مانند شیب و داده‌های رسوبی و آب ورودی رودخانه مهران، پتاسیل بالاتری برای توسعه هر چه بیشتر جنگل‌های مانگرو دارد.

کلیدواژه‌ها


عنوان مقاله [English]

Marine Hydrodynamics Analysis and Coastal Morphology Related to Changes in Mangrove Forests (Case study: West of the Strait of Hormuz)

نویسندگان [English]

  • fatemeh parhizkar 1
  • masomeh rajabi 2
  • Mojtaba Yamani 3
  • davoud Mokhtari 2
1 Ph.D. candidate,, Faculty of Planning and Environmental Sciences, University of Tabriz, Tabriz, Iran
2 Faculty of Planning and Environmental Sciences, University of Tabriz, Tabriz, Iran
3 Department of Physical Geography, Faculty of Geography, University of Tehran, Tehran, Iran
چکیده [English]

Introduction
For thousands of years, mangrove forests have played a significant role in the economy and sustainable livelihoods of human societies. Therefore, identifying and measuring changes in the boundaries of mangroves over time can play an important role in planning and conducting effective protection measures and reducing the vulnerability of mangroves to natural and human hazards. The aim of this study was to investigate changes in mangrove forests and the relationship between these changes and marine hydrodynamics and coastal morphology in parts of the north and east of the Strait of Hormuz over a period of 47 years.
Methodology
In this study, Landsat satellite images, MSS, TM, ETM +, OLI sensors from 1972 to 2019 were used to monitoring mangrove forests changes in the west of the Hormuz strait. In the next step, the necessary preprocesses (radiometric and atmospheric corrections) were applied to the images in ENVI 5.3 software. And the classification of images was done by SVM, MLC and ANN methods, and considering that in order to finalize the land use map, all classification accuracy indicators should be adjusted with one or more valid statistical indicators. The kappa index and general accuracy are among the statistical methods used. Post-processing operations also included the integration of classes that were applied to make the land use map more eloquent and eliminate single pixels on different classes. In the next step, the Change Detection method was used to detect changes and tell the results of the classifications. The next step is to convert the classified image to polygon and transfer it to the Arc GIS environment to manage the classes. Of course, the class that is most important to us here is the Mangrove Forest class, which was examined in the period 1972-2019. After the changes in the mangrove forests were identified, with the help of 1: 25000 topographic maps, contours of 2 meters of the range was prepared and the slope map was prepared using DEM images of the area. Also, using the half-hour tide data, the minimum, maximum and average tide rates of Jask, Shahid Rajaee, Hormoz and Sirik stations were calculated and finally these data and maps were prepared to examine the development potential of mangrove forests, Was examined.
Result
Land use maps were developed using Landsat images using three pixel-based classification algorithms (MLC, SVM, ANN) and the accuracy of the results was assessed using random points. The results showed that the highest overall accuracy and kappa coefficient were 99.44 and 0.99 for region A, and 98.41, 0.97, for region B, for SVM, respectively. Our study showed that SVM could be the most appropriate classification method for this study area. Therefore, SVM land use maps were prepared for the study area for 1972, 1987, 2002 and 2019. After preparing the land use change map, it was stated that mangrove forests in region A accounted for 55.84% and in region B for 36.18%, tidal areas in region A accounted for 27.63% and in Area B is 36.58 percent, Water Areas A is 3.04 percent, Area B is 1.78 percent, dry land is 15.37 percent and region B is 99.99. 7% have changed over the past 47 years. To explore the potential for the expansion of mangrove forests, we examined the slope of the region and its relationship with the average tide in the region. Comparing the results of the increasingand decreasing trend of mangrove forests with curves corresponding to the average tidal level and morphological features of the region, we conclude that the study area is about the hydrodynamic characteristics of the sea such as the average tidal area and extent. The catchment area, the height of the waves and the coastal morphology such as slope and sediments and the water entering the areas from the Hasanlangi River and the Gaz and Hivi rivers have a very high potential for further development of mangrove forests.
Discussion and conclusion
The results show that in the northern part of the Strait of Hormuz, the area of mangrove forests has increased in all the years, but in the eastern part of the study, we have always faced a decreasing and increasing trend and We don't see this part significant development during these 47 years in mangroves.. However, according to the study of the geomorphic features of the region such as slope, topography and the presence of sabkha and Firth and sediments from the rivers of Hassan Langi, Gaz and Hivi, as well as the average tide of the region and the vast area it covers, The study has the potential to develop mangrove forests. The results of this study can provide significant information about the progress or regression of mangroves in different coastal areas, can significantly help to implement protection measures and rehabilitate Iranian mangroves.

کلیدواژه‌ها [English]

  • " Coastal Geomorphology"
  • " Sea Dynamics"
  • " Mangrove Forest
  • "
  • "East of the Strait of Hormuz"
آرخی, صالح؛ ادیب­نژاد, مصطفی. 1390, ارزیابی کارایی الگوریتم های ماشین بردار پشتیبان جهت طبقه بندی کاربری اراضی با استفاده از داده های ماهواره ای ETM+ لندست (مطالعه موردی: حوزه سد ایلام), فصلنامه تحقیقات مرتع و بیان ایران, شماره 3, صص 420-440.
اندریانی, صغری, 1393, کاربرد تکنیک­های سنجش ازدور و سیستم اطلاعات جغرافیایی در بررسی تغییرات کاربری اراضی و تأثیر آن بر دبی رودخانه (مطالعه موردی: صوفی چای), پایان­نامه کارشناسی ارشد, دانشکده جغرافیا و برنامه­ریزی, دانشگاه تبریز.
دانه­کار, افشین, 1374, جنگل­های مانگرو جهان, فصلنامه محیط زیست, (2) 7: 16-26.
دانه­کار, افشین, 1377, مناطق حساس دریایی ایران, فصلنامه محیط زیست, شماره 24, صفحات 38-28.
عرفانی, ملیحه؛ دانه­کار, افشین؛ نوری, غلامرضا؛ اردکانی, طاهره, 1389, بررسی عوامل مؤثر بر تغییرات جهانی وسعت جنگل مانگرو, مجموعه مقالات چهارمین کنگره بین­المللی جغرافی­دانان جهان اسلام, ایران-زاهدان.
فاطمی, باقر؛ رضایی, یوسف, 1391, مبانی سنجش از دور, تهران, انتشارات آزاده.
مجنونیان, هنریک؛ میراب­زاده, پرستو, 1381, مناطق حفاظت­شده ساحلی-دریایی (ارزش­ها و کارکردها), انتشارات سازمان محیط زیست, ص 406.
Ahmed,E.A. and Abdel-Hamid,K.A., 2007. “Zonation Pattern of Avicennia marina and Rhizophora mucronata along the Red Sea Coast”, Egypt.World Applied Sciences Journal, 2 (4): pp.283-288.
Akyürek, D., Koç, O., Akbaba, E. M. and Sunar, F.,  2018. Land use/ Land cover Change Detection Using MultiTemporal  Satellite  Dataset:  A  case  StudyIn  Istanbul  New  Airport.  The International  Archives  of  the Photogrammetry, Remote Sensing and Spatial Information  Sciences, Volume XLII-3/W4, Geo Information for Disaster Management (Gi4DM), 18–21 March, Istanbul, Turkey.
Alongi, D. M., 2008. Mangrove forests: Resilience, protection from tsunamis, and responses to global climate change. Estuarine, Coastal and Shelf Science, 76: p.113.
Anderson, G.P.,  Felde, G.W.,  Hoke, M.L.,  Ratkowski, A.J.,  Cooley, T.W.,  Chetwynd, J.H., Jr.,  Gardner, J.A.,Adler-Golden,  S.M.,  Matthew,  M.W.,  Berk,  A.,  2002.  MODTRAN4-based  atmospheric  correction algorithm:  FLAASH  (fast  line-of-sight  atmospheric  analysis  of  spectral  hypercubes).  In  Algorithms  and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery VIII (Proceedings of SPIE);  Shen, S.S., Lewis, P.E., Eds.; Society of Photo Optics: Orlando, FL, USA, pp. 65–71.
Boschetti, L., Stephane, Flasse, p.  and Pietro, A. Brivio., 2004. Analysis of the conflict between omission and commission in low spatial resolution dichotomic thematic products: The Pareto Boundary. Remote Sensing of Environment, 91: pp.280–292.
Carney,  J., Gillespie,  T.  W.  and  Richard Rosomoff R., 2014. Assessing forest change in a priority West African mangrove ecosystem: 1986–2010. Geoforum, 53: pp.126–135.
Congalton,  R.  G.,  Green,  K., 1999.  Assessing  the  accuracy  of  remotely  sensed  data: principles and practices, Boca Raton: Lewis Publications.
Chavez,  p., 1996.  "Image-based  atmospheric  corrections  -  Revisited  and  improved", Photogram. Eng. Remote Sensing, 62: pp.1025–1036.
De Lacerda, L. D., 2002. Mangrove ecosystems: function and management.
Duke. N. C.Meynecke.J-O.Dittmann. S.Ellison. A. M.Anger. K.Berger. U.Cannicci.S.Diele. K.Ewel. K. C.Field. C. D.Koedam. N.Lee. S. Y.Marchand .C.Nordhaus. I.Dahdouh-Guebas .F., 2007. A World Without Mangroves?, Science, 317(5834): pp 41 - 42 .
Danehkar,  A., 2001.  Mangroves  forests  zonation  in  Gaz  and  Harra  international  wetlands.  The  Environment  Scientific Quarterly Journal, 34: 43-49.
Dolan, R.,  Fenster, M.S. and Holme, S.J., 1991. Temporal analysis of shoreline recession and accretion. Journal of Coastal Research, 7: pp.723-744.
Ellison, J.C. and Zouh, I., 2012. Vulnerability to Climate Change of Mangroves: Assessment from Cameroon, Central Africa. Biology, 1: pp.617-638.
Gandini, M. L., Usunoff, E. J., 2004, SCS curve number estimation using remote sensing NDVI in a GIS environmental, Environmental Hydrology, (12): pp.168-179.
Gilman,  E.,  Ellison, J.,  Coleman,  R.,  2007.  Assessment  of  mangrove  response  to  projected relative sea-level  rise and recent historical reconstruction of shoreline position. Environ. Monit. Assess. 124: pp.112–134.
Jensen, J. R., 2007. Remote Sensing of the Environment: An Earth Resource Perspective. Pearson Hall, 592 p. Kathiresan,  K.  and  Rajendran,  N.,  2005.  Coastal  mangrove  forests  mitigated  tsunami.  Estuarine.  Coastal  and Shelf Science, 65: pp.601-606.
Kolios,  S.,  Stylios,  C.  D.,  2013.  Identification  of  land  cover  land  use  changes  in  the greater  area  of  the  Preveza  peninsula  in  Greece  using  Landsat  satellite  data,  Applied Geography, 40: pp.150-160.
Letchumy,  B.  M.,  and  MdSaid,  M.,  2009.  Land  Use  Land  Cover  Change  Detection  Using  Remote  Sensing Application for Land Sustainability. International Conference on Fundamental and Applied Sciences, (ICFAS 2012).
Maguire,  T.L.,  Saenger,  P.,  Baverstock,  P.  and  Henry,  R.,  2000.  Microsatellite  analysis  of genetic structure in the mangrove species Avicennia marina  (Forsk.)  Vierh. (Avicenniaceae).  Molecular Ecology, 9(11): pp.1853-1862.
Mantero, P., Moser, G., Serpico, S. B., 2005. Partially supervised classification of remote sensing  images  through  SVM-based  probability  density  estimation,  IEEE  Trans.  on Geoscience and Remote Sensing, 43: pp.559-570.
McLeod, E and V. Salm, R., 2006. Managing Mangroves for Resilience to Climate Change, IUCN Resilience Science Group Working Paper Series - No 2.
Mehrabian, A., Naqinezhad, A., Mahiny, A.S., Mostafavi, H., Liaghati, H. and Kouchekzadeh, M. 2008. Vegetation Mapping of the Mond Protected Area of Bushehr Province (South ‐west Iran). Journal of integrative plant biology, 51: pp.251-260.
Nitze, A., Schulthess, B., Asche, H., 2012. Comparison of machine Learning algorithms random forest, artificial neural network and support vector Machine to maximum Likelihood for supervised  crop  type  classification,  Proceedings  of  the  4th  Gambia,  Rio  de  Janeiro Brazil, pp 35-40.
Noori, R., Abdoli, M. A., Ameri, A., Jalili-Ghazizade, M., 2008. "Prediction of municipal solid waste generation with combination of support vector machine and principal component analysis: A case study of Mashhad",  Environmental Progress and Sustainable Energy, 28 (2): pp.249-258.
OtengAmoako, A. A., Louppe, D., Brink, M., Lemmens, R. H. M.J., Oyen, L.  P. A. and Cobbinah, J.R., 2008. International  Tropical  Timber  Organization  (ITTO),  (PROTA)  and  Dutch  Government.   DGIS,  Prota,  7(1): Timbers/Bois d’œuvre 1. [CD-Rom]. PROTA, Wageningen, Netherlands.
Paling, E.I., van Keulen, M., Tunbridge, D.J., 2007. Seagrass transplanting in Cockburn Sound, Western  Australia:  a  comparison  of  manual  transplantation  methodology  using  Posidonia  sinuosa Cambridge et Kuo. Restor. Ecol. 15: pp. 430–439.
Rakotomavo,  A.  and  Ois  Fromard  F.,  2010.  Dynamics  of  mangrove  forests  in  the  Mangoky  River  delta, Madagascar, under the influence of natural and human factors. Forest Ecology and Management, 259: pp.1161–1169.
Rao, S., Sharma, A., 2013. Cost parameter analysis and comparison of linear Kernel and Hollinger  Kernel  mapping  of  SVM  on  image  retrieval  and  effects  of  addition  of  positive images, International Journal of Computer Applications, 73 (2): pp.5 – 12.
Rodringuez,W and Feller,I.C., 2004. Mangrove landscape characterization and change in Twin Cays,belize using aerial photography and IKONOS satellite data, Atoll reserch Bulletin.no.513.National Museum of National History .U.S.A.
Roy, P. S., Sharma, K. P., Jain, A., 1996. Stratification of density in dry deciduous forest usingsatellite remote sensing digital data-An approach based on spectral indices,  J. Biosci, 21: pp.723–734.
Saenger,  P.,  2002.  Introduction:  The  Mangrove  Environment.  In  Mangrove  Ecology,  Silviculture  and Conservation, pp. 1-10. Springer, Dordrecht.
Shalkoff, R.  J., 1997. "Artificial Neural Networks", McGraw-Hill Companies Pub,  New yourk.
Srivastava, D. K., Bhambhu, L., 2009. Data classification using support vector machine", Theoretical and Applied Information Technology, 49: pp.1–7. ]on line[: www.jatit.org.
Thu, P. M. and Populus, J., 2007. Status and changes of mangrove forest in Mekong Delta:Case study in Tra Vinh, Vietnam،Estuarine, Coastal and Shelf Science, 71: 98-109.
Thom, B.G., 1967. Mangrove ecology and deltaic geomorphology: Tabasco, Mexico. Journal of Ecology, 55: pp.301343.
Thom, B.G., 1984. Coastal landforms and geomorphic processes. The Mangrove Ecosystem: Research Methods. UNESCO, Paris, pp.3–15.
Vapnik, V. N., 1999. The  nature of  statistical Learning  theory, Second Edition,  New York: Springer-Verlag.
Wells ,S., C. Ravilous, E. Corcoran., 2006. In the front line: Shoreline protection and other ecosystem services from mangroves and coral reefs, United Nations Environment Programme World Conservation Monitoring Centre, Cambridge, UK: 33.