ارزیابی روش‌های پیکسل‌مبنا و شئ‏ گرا، جهت تعیین تغییرات کاربری اراضی حوضه آبریز دریاچه وان و مقایسه آن با حوضه دریاچه ارومیه

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانش آموخته کارشناسی ارشد مهندسی منابع آب/ دانشگاه تربیت مدرس تهران

2 استاد/ گروه مهندسی منابع آب، دانشگاه تربیت مدرس.

3 استادیار/ موسسه تحقیقات آب وزارت نیرو، تهران، ایران.

4 دانشیار/ گروه مهندسی محیط زیست، دانشگاهVan .

چکیده

دلیل رفتار غیر یکسان دریاچه ارومیه و وان ترکیه طی ده‌های اخیر یکی از سؤالات چالشی بوده است. بدین منظور بخشی از پاسخ این سوال را می‌توان با بررسی تغییر کاربری اراضی- به عنوان اصلی‌ترین مؤلفه از عوامل انسانی، در این حوضه‌ها جستجو نمود. در این راستا، این مهم هدف تحقیق حاضر قرارداده شد. بدین منظور، ابتدا کارکرد روش‌های پیکسل‌مبنا و شئ‏گرا در جهت تعیین کاربری اراضی حوضه دریاچه وان ترکیه طی سال‌های 1987 لغایت 2007 ارزیابی و سپس نتایج حاصل با پژوهش مشابه در حوضه آبریز دریاچه ارومیه مقایسه گردیدند.در این راستا، چهار روش بر مبنای دو رویکرد پیکسل پایه (ماهالانوبیس، حداکثر شباهت و ماشین بردار پشتیبان) و شئ‏گرا (ماشین بردار پشتیبان- فازی) برای طبقه‌بندی اراضی مورد مقایسه و ارزیابی قرار گرفتند که نتایج حاکی از کارایی روش شئ‏گرا (با ضرب کاپا 81/0 و دقت کلی 86/0) نسبت به سایر روش‌ها بود. همچنین نتایج مقایسه کاربری اراضی در این دو حوضه طی دوره 20 ساله فوق نشان داد؛ کاربری اراضی در حوضه وان چندان دستخوش تغییرات شدید نشده، به این نحو که افزایش سطح زیر کشت آبی در حوضه وان حدود 10 هزار و ارومیه 136هزار هکتار بوده است. نکته قابل توجه تغییرات کاربری باغی می‌باشد که در حوضه ارومیه حدود 273% (از 3513 به 13120 هکتار) و در حوضه وان بسیار ناچیز (کمتر از 20%) بوده است. این تغییرات می‌تواند عامل مهمی در افزایش مصرف و کاهش ورودی‌ها به دریاچه ارومیه نسبت به دریاچه وان بوده باشد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Evaluation of Pixel- Based and Object Oriented classification approaches for Determination of Land Use Changes in Van Lake Basin and it Comparison with Lake Urmia Basin

نویسندگان [English]

  • Morteza Rahimpour 1
  • Saeed Morid 2
  • Nematolla Karimi 3
  • Harun Aydın 4
1 Water Resources Engineering Graduate, Water Resources Engineering Department of Tarbiat Modares University, Tehran, Iran
2 Professor at Water Resources Engineering Department, Tarbiat Modares University, Tehran, Iran.
3 Assistant Professor, Water Research Institute of Ministry of Energy, Tehran, Iran.
4 Associate Professor, Department of Environmental Engineering, Yuzuncu Yıl University, Van, Turkey
چکیده [English]

The reason for dissimilar behaviors of Lake Urmia (Iran) and Lake Van (Turkey) during the recent decades is one of the main challenging questions. A part of the answer can be addressed by evaluation of their land use changes as the main indicator for the role of human impacts. This issue constructs the objective of present paper. For this aim, the pixel-based and object-oriented classification approaches were evaluated for obtaining the land use maps of Lake Van basin during 1987 to 2007. The applied pixel-based methods include: Mahalanobis Distance (MD), Maximum Likelihood (ML) and Support Vector Machine (SVM); and the object-oriented method includes SVM-fuzzy. Their comparison showed better performance of the object-oriented method such that Kappa Coefficient and Overall Accuracy were 0.81 and 0.86, respectively. Then, the results were compared with a similar research work for the Lake Urmia basin which was attempted. The results revealed that that the land use of Van basin has not significantly change, while the increase of cropped lands in Van basin was only 10000 ha, it was 136000 ha in Urmia basin. The most significant change in Urmia Lake relates to orchard area by increase of 273% (3513 ha to 13120 ha), whereas it was insignificant in Van (less than 20%). Definitely, these changes can increase consumption of water and reduce inflows to Lake Urmia.

کلیدواژه‌ها [English]

  • Classification
  • Pixel based
  • Object-Oriented
  • Van Lake
  • Urmia
Agarwal S Vailshery LS Jaganmohan M and Nagendra H (2013) Mapping urban tree species using very high resolution satellite imagery: comparing pixel-based and object-based approaches ISPRS International Journal of Geo-Information 2: 220-236
Chen M Su W Li L Zhang C Yue A and Li H (2009) Comparison of pixel-based and object-oriented knowledge-based classification methods using SPOT5 imagery WSEAS Transactions on Information Science and Applications 6: 477-489
Faroukhnia A (2014) Assessment of land use change and trend in climatological variables on Lake Urmia watershed hydrology (In Persian)
Tarbiat Moodares University
Furey TS Cristianini N Duffy N Bednarski DW Schummer M and Haussler D (2000) Support vector machine classification and validation of cancer tissue samples using microarray expression data Bioinformatics 16: 906-914
Geneletti D and Gorte B (2003) A method for object-oriented land cover classification combining Landsat TM data and aerial photographs International Journal of Remote Sensing 24: 1273-1286
Hosseini MH Hosseini HH Shayegan M Vatnefda A and Najafi A (2013) Study of land use change in Kajki dam of Hirmand basin of Afghanistan using the most similar categorization decision tree and support vector machines jounal of Remote Sensing and GIS 5 (In Persian(
Hussain M Chen D Cheng A Wei H and Stanley D (2013) Change detection from remotely sensed images: From pixel-based to object-based approaches ISPRS Journal of photogrammetry and remote sensing 80: 91-106
Jensen L and Gorte B (2001) Principle of remote sensing Chapter 12 Digital image classification ITC Enchede The Netherlands
Kazemi M Mahdavi J Noahiher A and Rezaei P (2011) Estimation of land cover and land use change detection using remote sensing and geographic information system Journal of Remote Sensing and GIS in Natural Resources Science 2(In Persian)
Liu D and Xia F (2010) Assessing object-based classification: advantages and limitations Remote Sensing Letters 1: 187-194
Mallinis G Koutsias N Tsakiri-Strati M and Karteris M (2008) Object-based classification using Quickbird imagery for delineating forest vegetation polygons in a Mediterranean test site ISPRS journal of photogrammetry and remote sensing 63: 237-250
Nazmfar RMRBF (2010) Land use /land cover classification based on Object-oriented technique and satellite imageCase study: West Azerbaijan Provinces Watershed Management Researches Journal (In Persian)
Omidipour R Moradi H and Arkhi S (2013) Comparison of Pixel Base and Object-oriented Classification Methods for Land Use Mapping Using Satellite Data Journal of Remote Sensing and GIS 5 (In Persian)
Paola JD and Schowengerdt RA (1995) A detailed comparison of backpropagation neural network and maximum-likelihood classifiers for urban land use classification IEEE Transactions on Geoscience and remote sensing 33: 981-996
Petropoulos GP Kalaitzidis C and Prasad Vadrevu K (2012) Support vector machines and object-based classification for obtaining land-use/cover cartography from Hyperion hyperspectral imagery Computers & Geosciences 41: 99-107
Platt RV and Schoennagel T (2009) An object-oriented approach to assessing changes in tree cover in the Colorado Front Range 1938–1999 Forest Ecology and Management 258: 1342-1349
Qian Y Zhou W Yan J Li W and Han L (2015) Comparing Machine Learning Classifiers for Object-Based Land Cover Classification Using Very High Resolution Imagery Remote Sensing 7: 153-168
Rahimpour M( 2015) Trends assessment of changes in water budget components and land use of Lake Urmia (Iran) and Lake Van (Turkey) basins using remote sensed data Tarbiat Moodares University (In Persian)
Ramezani N Parsley R and Isanlou A (2011) Study of land use change detection in the Esfarayen area of North Khorasan during the past four decades Journal of Remote Sensing and GIS 3 (persian(
Reusing M (2000) Change detection of natural high forests in Ethiopia using remote sensing and GIS techniques International archives of photogrammetry and remote sensing 33: 1253-1258
Roy P Ranganath B Diwakar P Vohra T Bhan S Singh I and Pandian V (1991) Tropical forest typo mapping and monitoring using remote sensing Remote Sensing 12: 2205-2225
Rozenstein O and Karnieli A (2011) Comparison of methods for land-use classification incorporating remote sensing and GIS inputs Applied Geography 31: 533-544
Sabzghabaei G Jafarzadeh K Dashti S Khanghah SY and Baleshti2 MB (2017) Land use change detection using remote sensing and GIS (Case study: Qhaemshahr city) Journal of Environmental Science and Technology 19
Shah NK and Gemperline PJ (1990) Combination of the Mahalanobis distance and residual variance pattern recognition techniques for classification of near-infrared reflectance spectra Analytical Chemistry 62: 465-470
Strahler AH (1980) The use of prior probabilities in maximum likelihood classification of remotely sensed data Remote Sensing of Environment 10: 135-163
Suykens JA and Vandewalle J (1999) Least squares support vector machine classifiers Neural processing letters 9: 293-300
Van Coillie FM Verbeke LP and De Wulf RR (2007) Feature selection by genetic algorithms in object-based classification of IKONOS imagery for forest mapping in Flanders Belgium Remote Sensing of Environment 110: 476-487
Wang L Sousa W and Gong P (2004) Integration of object-based and pixel-based classification for mapping mangroves with IKONOS imagery International Journal of Remote Sensing 25: 5655-5668
Yan G (2003) Pixel based and object oriented image analysis for coal fire research Enschede Holanda
Yu Q Gong P Clinton N Biging G Kelly M and Schirokauer D (2006) Object-based detailed vegetation classification with airborne high spatial resolution remote sensing imagery Photogrammetric Engineering & Remote Sensing 72: 799-811
Yuan F Sawaya KE Loeffelholz BC and Bauer ME (2005) Land cover classification and change analysis of the Twin Cities (Minnesota) Metropolitan Area by multitemporal Landsat remote sensing Remote Sensing of Environment 98: 317-328