کارایی روش‌های مختلف تهیۀ نقشۀ کاربری/پوشش اراضی در حوضۀ آبخیز معرف کسیلیان

کمری یکدانگی, فائزه; سارونه, فاطمه; خالدی درویشان, عبدالواحد; موسوی, وحید; آقابیگی امین, سهیلا

doi:10.22059/ije.2023.361471.1743

کارایی روش‌های مختلف تهیۀ نقشۀ کاربری/پوشش اراضی در حوضۀ آبخیز معرف کسیلیان

نوع مقاله : پژوهشی

نویسندگان

فائزه کمری یکدانگی ¹

فاطمه سارونه ¹

عبدالواحد خالدی درویشان ¹

وحید موسوی ¹

سهیلا آقابیگی امین ²

¹ گروه آبخیزداری، دانشکدۀ منابع طبیعی، دانشگاه تربیت مدرس، نور، ایران

² گروه مرتع و آبخیزداری، دانشکدۀ منابع طبیعی، دانشگاه رازی، کرمانشاه، ایران

10.22059/ije.2023.361471.1743

چکیده

در راستای مطالعۀ کاربری و پوشش زمین، فناوری سنجش از دور به‌ عنوان منشأ تولیدی اطلاعات مکانی و ابزارهای مناسبی که دارد، مورد استقبال بسیاری از پژوهشگران قرار گرفت که دقت و صحت این نقشه‌ها اعتبار و قابلیت کارایی آن‌ها را نشان می‌دهد. هدف از انجام تحقیق حاضر، ارزیابی و مقایسۀ صحت تهیۀ نقشۀ کاربری اراضی به دو روش سنجش از دور و یک روش تفسیر چشمی تصاویر Google Earth در حوضۀ آبخیز معرف کسیلیان است. در تحقیق حاضر پس از برداشت نمونه‌های تعلیمی با استفاده از نرم‌افزار Google Earth و پیاده‌سازی روی تصویر 9 Landsat مربوط به سال 2021، طبقه‌بندی تصاویر در نرم‌افزار ENVI انجام شده و نقشۀ کاربری اراضی بر اساس نمونه‌های آموزشی و روش‌های شبکۀ عصبی و ماشین ‌بردار پشتیبان تهیه شد. در روش تفسیر چشمی تمام کاربری‌ها در تصاویر Google Earth به ‌صورت دستی رقومی شد و نقشۀ کاربری اراضی به ‌دست آمد. سپس صحت‌سنجی نقشه برای هر سه روش صورت گرفت و نتایج نشان داد نقشۀ حاصل از تفسیر چشمی تصاویر Google Earth با صحت کلی و ضریب Kappa 100 درصد نسبت به روش‌های شبکۀ عصبی و ماشین ‌بردار پشتیبان با صحت کلی به‌ترتیب 6/87 و 2/88 درصد و ضریب Kappa به‌ترتیب 76 و 8/77 درصد، به واقعیت زمینی نزدیک‌تر بود. با این‌حال به دلیل زمان‌بر بودن روش تفسیر چشمی به‌ویژه برای آبخیزهای بزرگ و صحت قابل قبول روش‌های شبکۀ عصبی و ماشین ‌بردار پشتیبان، پیشنهاد می‌شود که به‌ویژه در آبخیزهای بزرگ برای تهیۀ نقشۀ کاربری اراضی از روش‌های نوین استفاده شود.

کلیدواژه‌ها

تفسیر چشمی

سنجندۀ Landsat

شبکۀ عصبی

صحت‌سنجی

ماشین بردار پشتیبان

Eskandari S, Moradi A, Oladi J. Land use and landscape analysis of gel sefid village in terms of environment using RS and GIS. Town and Country Planning. 2011;3(4):137-162. (In Persian)
Kakehmami A, Ghorbani A. Comparison of three visual, object-based, and supervised classification methods of land use/cover mapping in Mollah-Ahmad watershed, Ardabil. Natural Ecosystems of Iran. 2018;8(4):29-43. (In Persian)
Johnson RD, Kasischke E. Change vector analysis: A technique for the multispectral monitoring of land cover and condition. International Journal of Remote Sensing. 1998;19(3):411-426.
Pour Ahmad A, Seifoddini F, Parnoon Z. the role of migration in change of Islamshahr land use. Armanshahr Architecture & Urban Development. 2011;4(6):49-61. (In Persian)
Shao Y, Lunetta RS, Macpherson AJ, Luo J, Chen G. Assessing sediment yield for selected watersheds in the Laurentian Great Lakes Basin under future agricultural scenarios. Environmental management. 2013;51:59-69.
Miryaghoub Zadeh M, Khosravi S. Land use change detection in barandouz chay watershed from Lake Urmia River Basin using remotely sensed landsat5 and sentinel imagery. Watershed Engineering and Management. 2022;14(4):481-493. (In Persian)
Zakerinejad R, Vosooghy S, Entezari M. Comparison of accuracy of difference supervised classification methods for land use mapping (case study: Alamarvdasht Watershed). Journal of Environmental Erosion Research. 2022;12(2):138-153. (In Persian)
Soltani N, Mohammad Nejad V. Efficiency of Google Earth Engine (GEE) system in land use change assessment and predicting it using CA-Markov model (case study of Urmia plain). Journal of RS and GIS for Natural Resources. 2021;12(3):101-114. (In Persian)
Afify HA. Evaluation of change detection techniques for monitoring land-cover changes: A case study in new Burg El-Arab area. Alexandria Engineering Journal. 2011;50(2):187-195.
Jansen LJM, Gregorio ADi. Obtaining land-use information from a remotely sensed land cover map: results from a case study in Lebanon. International Journal of Applied Earth Observation and Geoinformation. 2004;5(2):141-157.
Singh SK, Mustak S, Srivastava PK, Szabó S, Islam T. Predicting spatial and decadal LULC changes through cellular automata Markov chain models using earth observation datasets and geo-information. Journal of Environmental Processes. 2015;2(1):107-115.
Mohammadpour P, Arjmandi R, Hasani A H, Ghoddousi J. Classification and assessment of the land use changes using landsat satellite imagery (case study: Rey Plain). Human & Environment. 2022;20(3):279-297. (In Persian)
Mokhtari M, Najafi A. Comparison of support vector machine and neural network classification methods in land use information extraction through landsat TM data. Journal of Water and Soil Science. 2015;19(72):35-45. (In Persian)
Arazi S. Zoning by eye interpretation in Google Earth (case study: Lipar Region, Chabahar). Environment and Interdisciplinary Development. 2020;5(68):1-10. (In Persian)
Ghorbani A, Kakehmami A, Mohamad Hasanpoor M, Aslami F, Ghafari S, Raufi Masole A. Comparison of different methods with common method of producing land use/cover maps of natural resources studies (case study: Ghoshchi Watershed, Urmia). Natural Ecosystems of Iran. 2018;9(1):19-32. (In Persian)
Mohammad Hasanpour M. Preparation of land use map of Gardne Ghoshchi area of Urmia using Google Earth Images and Geographic Information System. the third Environmental Planning and Management Conference, Tehran. 2013. (In Persian)
Pandian M, Rajagopal N, Sakthivel G, Amrutha DE. Land use and land cover change detection using remote sensing and GIS in parts of Coimbatore and Tiruppur districts, Tamil Nadu, India. International Journal of Remote Sensing & Geoscience. 2014;3(1):15-20.
Kiyani V, Alizade Shaabani A, Nazari Samani A. Assessing the classification accuracy of LISS-III Sensor Image of IRS-P6 Satellite using Google Earth's database to provide land coverage/ land use maps (case study: Taleghan Watershed). Scientific-Research Quarterly of Geographical Data (SEPEHR). 2014;23(90):59-51. (In Persian)
Wibowo A, Salleh KO, Frans FTRS, Semedi JM. Spatial temporal land use change detection using Google Earth data. In IOP Conference Series: Earth and Environmental Science. 2016;47(1):012031.
Geetha M, Karegowda A, Sudhira HS. Land use and land cover mapping of davangere using Google Earth Engine. International Journal of Recent Technology and Engineering (IJRTE). 2019;8(3):339-347.
Javaheri S, Jorahi AA, Javakoli Jabour SM. Ability to prepare methods land use maps using satellite images (case study: Kamyaran city). Application of Geography Information System and Remote Sensing in Planning. 2020;10(4):90-106. (In Persian)
Liu C, Li W, Zhu G, Zhou H, Yan H, Xue P. Land use/land cover changes and their driving factors in the Northeastern Tibetan Plateau based on Geographical Detectors and Google Earth Engine: A case study in Gannan Prefecture. Remote Sensing. 2020;12(19):3139.
Damtea W, Kim D, Im S. Spatiotemporal analysis of land cover changes in the chemoga basin, Ethiopia, using Landsat and Google Earth Images. Sustainability. 2020;12(9):1-14.
Moradi H, Rezaei V. Comparison of land use type classification algorithms in the land use mappreparation in Zenouzchai Watershed. Degradation and Rehabilitation of Natural Land. 2021;1(2):80-88. (In Persian)
Mikaeli Hajikandi K, Sobhani B, Varamesh S. Assessment of land-cover change in south part of Lake Urmia using satellite imagery. Journal of Applied Research in Geographical Sciences. 2023;23(68):1-15. (In Persian)
Saadati H, Gholami SA, Sharifi F, Ayub Zadeh SA. Investigation of the effects of land use change on simulating surface runoff using SWAT mathematical model. Iranian Journal of Natural Resources (Not Publish). 2009;4(3):1-15. (In Persian)
Derakhshan S. Studying the flood potential of Kasilian watershed using Geographic Information System. Journal of Applied Research in Geographical Sciences. 2009;10(10):51-63. (In Persian)
Janizadeh S, Vafakhah M. Flood hydrograph modeling using artificial neural network and adaptive neuro-fuzzy inference system based on rainfall components. Arabian Journal of Geosciences. 2021;14(5):1-14.
Sadeghi SHR, Safaiyan NA, Ghanbari SA. Investigating the role of land use on the type and intensity of soil erosion (case study: Kasilian watershed). Journal of Agricultural Engineering Research. 2006;7(26):85-98. (In Persian)
Zare M, Nazari Samani A, khalighi S, bazrafshan J, hasan joury M. Anticipating of spatial changes trend of land uses based on the cellular Automaton-Markov model in Kasilian watershed. Journal of Range and Watershed Management. 2017;70(2):373-383. (In Persian)
Song C, Woodcodk CE, Seto KC, Lenney MP, Macomber SA. Classification and change detection using Landsat TM data: when and how to correct atmospheric effect. Remote Sensing of Environment. 2001;75(2):230–244.
Chander G, Markham BL, Helder DL. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote sensing of environment. 2009;113(5):893-903.
San BT, Suzan ML. Evaluation of different atmospheric correction algorithms for EO-1 Hyperion imagery. International Archives of the Photogrammetry Remote Sensing and Spatial Information Science. 2010;38(8):392-397.
Rafei Sharifabad J, Nohegar A, Zehtabian G, Khosravi H, Gholami H. An assessment of the impacts of land use changes on groundwater quality in Yazd-Ardakan plain. Geography (Regional Planning). 2017;6(25):189-199. (In Persian)
Omidvar K, Narangifard M, Abbasi H. Detecting the changes of land uses and vegetation cover using remote sensing in Yasooj city. Geography and Territorial Spatial Arrangement. 2015;16(5):111-126. (In Persian)
Mountrakis G, Im J, Ogole C. Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing. 2011;66(3):247-259.
Oommen T, Misra D, Twarakavi NK, Prakash A, Sahoo B, Bandopadhyay S. An objective analysis of support vector machine based classification for remote sensing. Mathematical Geosciences. 2008;40(4):409-424.
Chen J, Zhu X, Vogelmann JE, Gao F, Jin S. A simple and effective method for filling gaps in Landsat ETM+ SLC-off images. Remote sensing of environment. 2011;115(4):1053-1064.
Asghari S, Jalilyan R, Jirozineghad N, Madadi A, Yadeghari M. Evaluation of water extraction indices using landsat satellite images (case study: Gamasiab River of Kermanshah). Journal of Applied Research in Geographical Sciences. 2020;20(58):53-70. (In Persian)
Rostamizad G, khanbabaei Z, Tahmoreth M. Assessing the accuracy of supervised classification algorithms for land use map extraction (study area: Taham Watershed). Journal of Environmental Erosion Research. 2022;12(4):141-157. (In Persian)
Das N, Mondal P, Sutradhar S, Ghosh R. Assessment of variation of landuse/land cover and its impact on land surface temperature of Asansol subdivision. The Egyptian Journal of Remote Sensing and Space Science. 2021;24(1):131–14.
Kafy AA, Naim MdNH, Subramanyam G, Faisal AA, Ahmed NU, Rakib A, et al. Cellular Automata approach in dynamic modelling of land cover changes using RapidEye images in Dhaka, Environmental Challenges. 2021;4: 100084.
Wang SW, Munkhnasan L, Lee WK. Land use and land cover change detection and prediction in Bhutan’s high altitude city of Thimphu, using cellular automata and Markov chain. Environmental Challenges. 2021;2: 100017.
Abbas Z, Jaber HS. Accuracy assessment of supervised classification methods for extraction land use maps using remote sensing and GIS techniques. IOP Conference Series: Materials Science and Engineering. 2020;745(1):159-166.
Mahzooni-Kachapi SS, Ebrahimi A, Tahmasebi P, Jouri MH. Investigation of classification tree analysis algorithm using Landsat 8 and Sentinel 2 satellite images and visual interpretation of Google Earth Images in separation and classification of plant ecological units. Iranian Journal of Range and Desert Research. 2022;29(4): 608-626. (In Persian)
Darvish Sefat AA, Ghaffari Dafchahi F, Bonyad AE. Feasibility of satellite imagery for Poplar plantation mapping (case study: Sowme`eh Sara). Iranian Journal of Forest and Poplar Research. 2014;22(3):392-401. (In Persian)