پایش تغییرات ساختاری در مقیاس زمانی- فضایی بیوم زاگرس ایران با استفاده از مبانی بوم شناسی سیمای سرزمین

نوع مقاله : مقاله پژوهشی

نویسندگان

1 کارشناسی ارشد، گروه برنامه‌‌ریزی و طراحی محیط، پژوهشکده علوم محیطی، دانشگاه شهید بهشتی، تهران، ایران

2 دانشیار، گروه برنامه‌‌ریزی و طراحی محیط، پژوهشکده علوم محیطی، دانشگاه شهید بهشتی، تهران، ایران

چکیده

اطلاع از روند تغییر الگوهای ترکیب محتوایی و چیدمان فضایی ساختار اکوسیستم در مقیاس فضایی سیمای‌‌سرزمین بیوم‌‌ها، جهت مدیریت بهینه آن‌‌ها در گذر زمان حایز اهمیت است. در پژوهش حاضر، مطابق با مدل مفهومی برگرفته از اصول بوم‌‌شناسی سیمای‌‌سرزمین، شامل: ساختار، عملکرد و تغییر در مقیاس مکانی، تغییرات چیدمان فضایی عناصر سیستم سیمای‌‌سرزمین بیوم زاگرس ایران با مطالعه 26 حوضه‌‌آبخیز منتهی به استان کهگیلویه ‌‌و ‌‌بویر‌‌احمد انجام شد. تصاویر سنجنده‌‌هایTM و OLI /TIRS ماهواره‌‌ای لندست 5 و 8 برای سال‌های 1987 و 2017 پیش‌‌پردازش شد و باندهای مناسب با استفاده از آزمون تجزیه مولفه‌های اصلی انتخاب شد و طبقه‌‌بندی کاربری ‌‌اراضی با استفاده از دو روش حداکثر احتمال (MLE) و شبکه عصبی مصنوعی پرسپترون (MLP) با 7 لایه ورودی، 6 لایه میانی و 5 لایه خروجی انجام گرفت. اعتبار‌‌سنجی و انتخاب کاربری مناسب با مقایسه ضرایب کاپای‌‌کلی، صحت‌‌کلی، خطای‌‌کاهنده و خطای‌‌فزاینده بررسی شد. کاربری اراضی طبقه‌بندی ‌شده روش MLP، با توجه به قدرت تفکیک عوارض و صحت بیشتر، انتخاب گردید و بارز‌‌سازی زمانی- مکانی تغییرات انجام شد. نتایج نشان می‌‌دهد بیشترین میزان کاهش اراضی بین سال‌‌های 2017- 1987 در کاربری اراضی جنگلی و بیشترین میزان افزایش در کاربری مرتع دیده می‌شود. مساحت کاربری‌‌های منابع آب و طبقه انسان‌‌ساخت، دو برابر شده است. گر‌‌چه توسعه‌‌های انسان‌‌ساخت نقش کمتری در این تغییرات دارند، لیکن آثار اقدامات انسانی، به‌خصوص تغییر اراضی جنگلی به کشاورزی و همچنین ساخت سدهای بزرگ، بسیار وسیع بوده‌‌ است. از این‌رو مدیریت مبتنی بر مبانی بوم‌‌شناسی سیمای‌‌سرزمین در ارتباط فی‌مابین ساختارشناسی، عملکردشناسی و تغییرپذیری می‌‌تواند در جهت پایداری دارایی‌‌های طبیعی و تنوع‌‌زیستی بیوم‌‌ زاگرس ایران مورد ‌‌استفاده قرار گیرد.
 

کلیدواژه‌ها


عنوان مقاله [English]

The Structural Detection of Spatial-Temporal Changes in Zagros Biome of Iran Using Landscape Ecology Principles

نویسندگان [English]

  • Mostafa Keshtkar 1
  • Shahindokht Barghjelveh 2
  • Naghmeh Mobarghei Dinan 2
1 MSc, Department of Planning and Designing the Environment, Environmental Sciences Research Institute, Shahid Beheshti University, Tehran, Iran
2 Assoc. Profe. Department of Planning and Designing the Environment, Environmental Sciences Research Institute, Shahid Beheshti University, Tehran, Iran
چکیده [English]

The Structural Detection of Spatial-Temporal Changes in Zagros Biome of Iran Using Landscape Ecology Principles
It is important to know the process of changing the patterns of content composition and spatial configuration of the landscape structure of biomes, in order to optimally manage them over time. In this study, in accordance with the conceptual model of the principles of landscape ecology, including: structure, function and change, changes in the spatial configuration of the elements of Zagros Biome of Iran was studied by studying 26 catchment basins leading to Kohgiluyeh and Boyerahmad Province. The TM and OLI /TIRS images of Landsat 5 and 8 satellites were pre-processed for 1987-2017, appropriate bands were selected using the principal component analysis test and land-use classification was performed using two methods of maximum probability (MLE) and Perceptron Artificial Neural Network (MLP) with 7 input, 6 intermediate and 5 output layers. Classified land-use of MLP method was selected according to the resolution of features and greater accuracy and temporal-spatial identification of changes was performed. The results show that between 2017 and 1987 the highest rate of land reduction is seen in forest land-use and the highest rate of land increase is seen in pasture land-use. Landscape sustainability management based on landscape ecology principles in relations between structure, function and change can be used.
 

کلیدواژه‌ها [English]

  • Zagros Forests
  • Artificial Neural Network
  • Ecosystem Structure
  • Landscape Ecology
  • Remote Sensing
Aguirre-Gutiérrez, J.; Seijmonsbergen, A. C. & Duivenvoorden, J. F. 2012. Optimizing land cover classification accuracy for change detection, a combined pixel-based and object-based approach in a mountainous area in Mexico. Applied Geography, 34:29-37.
Akbari, E.; Ebrahimi, M. & AmirAhmadi, A. 2014. Land Use Mapping of Sabzevar using Maximum Likelihood and Artificial Multilayer Perceptron Neural Network. Environment planning, 23(6): 127-148. (In Persian).
Alavi Panah, S.K. 2001. Study of natural phenomena using principal component analysis method. Natural Resources.54 (3): 221-234. (In Persian).
Alavi Panah, S.K. 2003. Application of remote sensing in earth sciences (soil sciences). First Edition. Institute of Publishing and Printing of Tehran University, 393p. (In Persian).
Areekhi, S. & Isfahani, M. 2015. Video tutorial of Idrisi Selva software. Golestan University: 336pp. (In Persian).
Attarod, P.; Sanai Nejad, H.; Moein Sadeghi, S M.; Taheri Sarteshnizi, F.; Saroyi, S.; Abbasian, P.; Masihpoor, M. & Kordrostami, F. 2016. Meteorological parameters and evapotranspiration affecting the Zagros forests decline in Lorestan province. Forest and Range Protection Research, 13(2): 97-112.(In Persian)
Balmford, A.; Beresford, J.; Green, J.; Naidoo, R.; Walpole, M. & Manica, A. 2009. A global perspective on trends in nature-based tourism. PLoS biology, 7(6),: 100_144.
Barghjelveh, S. & Mobarghaee Dinan, N. 2013. Developing Sustainability Indicators of Greenways Network Based on Landscape Ecology Principles. Environmental Science and Technology, 15(1), 167-184. (In Persian).
Canty, M. J. & Nielsen, A. A. 2008. Automatic radiometric normalization of multitemporal satellite imagery with the iteratively re-weighted MAD transformation. Remote Sensing of Environment, 112(3): 1025-1036.
Daily, G. (Ed.). 1997. Nature's services: societal dependence on natural ecosystems. Island Press.
De Vreese, R.; Leys, M.; Fontaine, C. M. & Dendoncker, N. 2016. Social mapping of perceived ecosystem services supply–The role of social landscape metrics and social hotspots for integrated ecosystem services assessment, landscape planning and management. Ecological indicators, 66: 517-533.
DeFries, R. S.; Asner, G. P. & Houghton, R. 2004. Trade‐offs in Land‐Use Decisions: Towards a Framework for Assessing Multiple Ecosystem Responses to Land‐Use Change. American Geophysical Union:1-9.
Foley, J. A.; DeFries, R.; Asner, G. P.; Barford, C.; Bonan, G.; Carpenter, S. R. ... & Helkowski, J. H. 2005. Global consequences of land use. Science, 309(5734): 570-574.
Foody, G. M. 2002. Status of land cover classification accuracy assessment. Remote sensing of environment, 80(1): 185-201.
Forman, R. T. 1990. 14. Ecologically Sustainable Landscapes: The Role of Spatial Configuration. Changing landscapes: an ecological perspective, 261.
Forman, R. T. 2003. Road ecology: science and solutions. Island Press.
Frohn, R. C. 1997. Remote sensing for landscape ecology: new metric indicators for monitoring, modeling, and assessment of ecosystems. CRC Press.
Giti, A. 2011. Desert, desertification and desertification. Iranian agricultural science, 672 pp. (In Persian).
Gould, R. K. & Lincoln, N. K. 2017. Expanding the suite of Cultural Ecosystem Services to include ingenuity, perspective, and life teaching. Ecosystem Services, 25: 117-127.
Helmy, A. K. & El- Taweel, G. S. 2010. Neural network change detection model for satellite images using textural and spectral characteristics. American Journal of Engineering and Applied Sciences, 3(4).
Henareh Khalyani, A.; Falkowski, M. J. & Mayer, A. L. 2012. Classification of Landsat images based on spectral and topographic variables for land-cover change detection in Zagros forests. International journal of remote sensing, 33(21): 6956-6974.
Jiang, L.; Huang, X.; Wang, F.; Liu, Y. & An, P. 2018. Method for evaluating ecological vulnerability under climate change based on remote sensing: A case study. Ecological Indicators, 85: 479-486.
Kazemi, M.; Mahdavi, Y.; Nohegar. A. & Rezaei, P. 2011. Estimation of cover and land use changes using Remote Sensing and GIS technique (Case study: Tang Bostanak watershed, Shiraz). Application of Remote Sensing and GIS in Natural Resources Sciences.1: 101-111. (In Persian).
Kelishadi, H.; Mosaddeghi, M. R.; Hajabbasi, M. A. & Ayoubi, S. 2014. Near-saturated soil hydraulic properties as influenced by land use management systems in Koohrang region of central Zagros, Iran. Geoderma, 213: 426-434.
Kondratyev, K. Y.; Kozoderov, V. V. & Smokty, O. I. 2013. Remote sensing of the Earth from space: atmospheric correction. Springer Science & Business Media.
Leh, M.; Bajwa, S. & Chaubey, I. 2013. Impact of land use change on erosion risk: an integrated remote sensing, geographic information system and modeling methodology. Land Degradation & Development, 24(5): 409-421.
Leitão, R. C.; Van Haandel, A. C.; Zeeman, G. & Lettinga, G. 2006. The effects of operational and environmental variations on anaerobic wastewater treatment systems: a review. Bioresource Technology, 97(9): 1105-1118.
Lu, D.; Mausel, P.; Brondizio, E. & Moran, E. 2004. Change detection techniques. International journal of remote sensing, 25(12): 2365-2401.
Makhdoom, M. 2009. The foundation of land Use Planning. Tehran University. 283 pp. (In Persian).
Mertens, B. & Lambin, E. F. 2000. Land‐cover‐change trajectories in southern Cameroon. Annals of the association of American Geographers, 90(3): 467-494.
Moghadam, B. K.; Jabarifar, M.; Bagheri, M. & Shahbazi, E. 2015. Effects of land use change on soil splash erosion in the semi-arid region of Iran. Geoderma, 241: 210-220.
Naveh, Z. & Lieberman, A. S. 2013. Landscape ecology: theory and application. Springer Science & Business Media.
Nilsson, C.; Aradottir, A. L.; Hagen, D.; Halldórsson, G.; Høegh, K.; Mitchell, R. J.; Raulund-Rasmussen, K.; Svavarsdóttir, K.; Tolvanen, A. & Wilson, S. D. 2016. Evaluating the process of ecological restoration. Ecology and Society 21(1):41-58.
Niyazi, Y.; Malekinezhad, H.; Ekhtesasi, M.; Morshedi, J. & Hosseini, S. 2010. Comparibson Between two Classification Methods of Maximum likelihood and Artificial Neural Network for Providing Land use Maps Case Study: Ilam Dam Area. Geography And Development, 8(20), 119-132. (In Persian).
Novelli, A.; Tarantino, E.; Caradonna, G.; Apollonio, C.; Balacco, G. & Piccinni, F. 2016. Improving the ANN classification accuracy of landsat data through spectral indices and linear transformations (PCA and TCT) aimed at LU/LC monitoring of a river basin. In International Conference on Computational Science and Its Applications Springer, Cham. 420-432.
Rogan, J.; Miller, J.; Stow, D.; Franklin; J., Levien, L. & Fischer, C. 2003. Land-cover change monitoring with classification trees using Landsat TM and ancillary data. Photogrammetric Engineering & Remote Sensing, 69(7):793-804.
Salehi, A.; Wilhelmsson, E. & Söderberg, U. 2008. Land cover changes in a forested watershed, southern Zagros, Iran. Land Degradation & Development, 19(5):542-553.
Sanai Nejad, H.; Astaraii, A. & Ghaemi, M. 2011. Using ETM+ band ratios and principal component analysis for monitoring of vegetation cover in Neyshabour area. Agricultural Ecology, 2(1): 103-110.(In Persian).
Singh, P. & Khanduri, K. 2011. Land use and land cover change detection through remote sensing & GIS technology: case study of Pathankot and Dhar Kalan Tehsils, Punjab. International Journal of Geomatics and Geosciences, 1(4): 839.
Stürck, J.; Schulp, C. J. & Verburg, P. H. 2015. Spatio-temporal dynamics of regulating ecosystem services in Europe–The role of past and future land use change. Applied Geography, 63: 121-135.
Taleshian Jeloudar, F.; Ghajar Sepanlou, M. & Emadi, M. 2018. Impact of land use change on soil erodibility. Global Journal of Environmental Science and Management, 4(1): 59-70.
Torahi, A. A. & Rai, S. C. 2011. 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, 3(01):324-346
Torahi, A. A. & Rai, S. C. 2013. Modeling for prediction of land cover changes based on bio-physical and human factors in Zagros Mountains, Iran. Journal of the Indian Society of Remote Sensing, 41(4): 845-854.
Václavík, T. & Rogan, J. 2009. Identifying trends in land use/land cover changes in the context of post-socialist transformation in central Europe: a case study of the greater Olomouc region, Czech Republic. GIScience & Remote Sensing, 46(1): 54-76.
Wolberg, G. 1990. Digital image warping Los Alamitos, CA: IEEE computer society press. (Vol. 1066 (; 90720-1264
Yang, X. & Lo, C. P. 2002. Using a time series of satellite imagery to detect land use and land cover changes in the Atlanta, Georgia metropolitan area. International Journal of Remote Sensing, 23(9):1775-1798.
Yousefi, M.; Shahrab, A.; Shoemaker, & Davar, L. 2012. Protected Area Coverage for Terrestrial Biomes in Iran. Natural Environment (Natural Resources), 69(2):581-598. (In Persian).