نوع مقاله : مقاله پژوهشی
نویسندگان
1 استادیار، دانشکده منابع طبیعی، دانشگاه ارومیه، ایران
2 دانشجوی دکتری، علوم و مهندسی آبخیزداری، دانشکده منابع طبیعی، دانشگاه ارومیه، ایران
چکیده
امروزه سنجش از دور، به‌عنوان منبع تولید اطلاعات مکانی در جهت مطالعه پوشش زمین و کاربری‌های اراضی شناخته شده است. حوضه باراندوزچای از لحاظ وسعت و فراوانی بالای اراضی زراعی و باغی در بین حوضه‌های دریاچه ارومیه از اهمیت بالایی برخوردار است. به‌منظور مطالعه تغییرات کاربری اراضی حوضه مذکور تصاویر ماهواره‌ای Landsat و Sentinel سنجنده TM و 2A مربوط به سال‌های 2005، 2010 و 2016 مورد ارزیابی قرار گرفت. از الگوریتم بیشینه شدت احتمال، به‌منظور تهیه نقشه کاربری اراضی استفاده شد. نتایج حاصل از ارزیابی صحت و دقت و همچنین، بررسی ضرایب توافق کاپا نشان از دقت بالای نقشه‌های مستخرج از الگوریتم داشته است. به‌منظور آشکارسازی تغییرات از روش تفاضل تصویر استفاده شد. نتایج نشان می‌دهد که مساحت اراضی درختزار و درختان غیرمثمر طی سال‌های 2010 تا 2016 در حوضه باراندوزچای با افزایش روبرو بوده است. اراضی دیم و اراضی آیش نیز جز طبقاتی بوده‌اند که در طی سال‌های 2010 تا 2016 با رشد مواجه بوده‌اند. طبق نتایج به‌دست آمده، مشاهده شد که در بازه زمانی 2010 -2005 و 2016-2010 به‌ترتیب بیشترین تغییر مساحت صورت گرفته در این حوضه، مربوط به سطوح نفوذناپذیر و اراضی آیش و کمترین آن، مربوط به اراضی دیم و درختزار بوده است.
کلیدواژه‌ها
عنوان مقاله [English]
Land use change detection in Barandouzchay Watershed from Lake Urmia River Basin using remotely sensed Landsat5 and Sentinel imagery
نویسندگان [English]
- Mirhassan Miryaghoubzadeh 1
- Seyed-Amin Khosravi 2
1 Assistant Professor, Department of Watershed Management Engineering, Faculty of Natural Resources, Urmia University, Iran
2 Ph.D Student, Faculty of Natural Resources, Urmia University, Iran
چکیده [English]
Nowadays remote sensing is known as practical method for studying Land Use (LU)/Land Cover (LC) changes. Due to the vast area of agricultural lands, Barandouzchay Basin is one of the important watersheds among all of watersheds in Lake Urmia River Basin. In this study, in order to evaluate LU/LC change, Landsat-5 TM and Sentinel-2A satellite images were used from 2005 to 2016. The maximum likelihood classification method was used to prepare LU/LC maps. The results of overall accuracy and Kappa coefficient showed high accuracy of maximum likelihood classification method. In order to extract the change detection maps, image difference method was used. Results showed that orchard and nonproductive trees have been increased during 2010-2016 years in Barandouzchay Basin. In the years before 2010, trees were relocated by young trees in Barandouzchay Basin. Drylands and bare lands are classified in the 2005-2010 years which has been increased. The most land use change was related to urban and lowest change was related to rainfed area from 2005 to 2010 and the most land use change is related to bare lands and lowest rate is related to nonproductive tree area from 2010 to 2016.
کلیدواژه‌ها [English]
- Image difference
- Kappa coefficient
- Maximum likelihood
- Remote sensing
- Young trees
- Akbari, E. and A. Shekari Badi. 2014. Processing and extracting information from satellite data using ENVI software. Hakim Sabzevari University Press, 3(1): 72-85 (in Persian).
- Alavi Panah, S.K. 1996. Land-use mapping using TM Landsat satellite digital data and geographic information system in case study of Mook area in Fars Province. Journal of Agriculture and Natural Resources Science, 7(1): 65-76.
- Arzani, H., Kh. Mirakhorlou and S.Z. Hosseini. 2009. Land use mapping using Landsat7 ETM data, case study in Middle catchments of Taleghan. Iranian Journal of Range and Desert Research, 4(2):150-160 (in Persian).
- Chan, J.C., K. Chan and A.G. Yeh. 2001. Detecting the nature of change in an urban environment: a comparison of machine learning algorithms. Photogrammetric Engineering and Remote Sensing, 67(2): 213–225.
- Crowley, J.K. 1991. Visible and near-infrared (0.4-2.5 um) reflectance spectra of playa evaporite minerals. Journal of Geophysical Research, 96(10): 16231-16240.
- Darvishsefat, A.A., F. Ghaffari Dafchahi and A. Eslam Bonyad. 2014. Feasibility of satellite imagery for poplar plantation mapping, case study: Sowme`eh Sara. Iranian Journal of Forest and Poplar Research, 22(3): 392-401.
- Da Silva, V.P.R., M.T Silva and E.P. De Souza. 2016. Influence of land use change on sediment yield: a case study of the submiddle of the SAO Francisco River Basin. Soil and Water Engineering, 36 (6): 1005-1015.
- Goetz, A.F.H. and V. Srivastava. 1985. Mineralogical mapping in the Cuprite Mining District, Nevada. Proceedings of the Airborne Imaging Spectrometer Data Analysis Workshop. Jet Propulsion Laboratory, Pasadena, CA.
- Han, S., H. Liand and H. Gu. 2008. The study on image fusion for high spatial resolution remote sensing images. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 37(7): 1159-1164.
- Jafari, M. 2009. Evaluation and study of land use change trends in Rasht City using remote sensing and GIS. MSc Thesis, Islamic Azad University Science and Research Branch, 171 Pages (in Persian).
- Kelarestaghi, A., H. Ahmadiand and M. Jafari. 2006. Land use changes detection and spatial distribution using digital and satellite data, case study: Farim Drainage Basin Northern of Iran. Desert Journal, 11(2): 33-47.
- Liu, Y.B., F.D. Smedt, L. Hoffmann and L. Pfister. 2004. Assessing land use impacts on flood processes in complex terrain by using GIS and modeling approach. Environmental Modeling and Assessment, 9: 227-235.
- Lu, D.S., P. Mausel, E.S. Brondı´zio and E. Moran. 2004. Change detection techniques. International Journal of Remote Sensing, 25: 2365–2407.
- Mahmoodzadeh, H. 2007. Digital change detection using remotely sensed data for monitoring green space destruction in Tabriz. International Journal of Environmental Resource, 1(1): 35-41.
- Marcos, H.C., B. Aurelie and A.C. Jeffrey. 2003. Effects of large-scale changes in land cover on the discharge of the Rocantins River, Southeastern Amazonia. Journal of Hydrology, 283(1): 206-217.
- Pohl, C. and J. Van Genderen. 2016. Remote sensing image fusion, a practical guide. CRC Press, 288pp.
- Prates-Clark, C.D.C., S.S. Saatchi and D. Agosti. 2008. Predicting geographical distribution models of high-value timber trees in the Amazon Basin using remotely sensed data. Ecological Modelling, 211(3): 309-323.
- Rahmani, N., K. Shahedi, K. Soleimani and M.H. Miryaghoubzadeh. 2016. Evaluation of the land use change impact on hydrologic characteristics, case study: Kasilian Watershed. Journal of Watershed Management Research, 7(13): 23-32 (in Persian).
- Rashidi, F. 2004. Investigating the capability of digital data of ETM sensor in forest type separation. MSc Thesis, University of Mazandaran, 130 Pages (in Persian).
- Roberts, D.A., Y. Yamaguchi and R.J.P. Lyon. 1986. Comparison of various techniques for calibration of AIS data. Proceedings of 2nd AIS Workshop, Jet Propulsion Laboratory, Pasadena, CA.
- Santos J.C.N., E.M. Andrade, P.H.A. Medeiros and M. Joao. 2017. Land use impact on soil erosion at different scales in the Brazilian semi-arid. Revista Ciencia Agronomica, 48(2): 251-260.
- Shao, Y., R. Lunetta, A. Macpherson, J. Luo and G. Chen. 2013. Assessing sediment yield for selected watersheds in the Laurentian Great Lakes Basin under future agricultural scenarios. Environmental Management, 51: 59-69.
- Singh, A. 1989. Digital change detection techniques using remotely sensed data. International Journal of Remote Sensing, 10: 989–1003.
- Zubiri. M. and M. Alireza. 2001. Introduction to remote sensing technology and the use of natural resources. Tehran University Press, 317 pages (in Persian).
)