آشکارسازی تغییرات کاربری اراضی و پهنه‌های جنگلی با استفاده از سنجش از دور (مطالعه موردی: شهرستان آستارا)

نوع مقاله : پژوهشی

نویسندگان

1 استاد گروه جغرافیای طبیعی، دانشکده علوم اجتماعی، دانشگاه محقق اردبیلی، اردبیل، ایران

2 کارشناسی ارشد سنجش ازدور و سیستم اطلاعات جغرافیایی، دانشکده علوم اجتماعی، دانشگاه محقق اردبیلی، اردبیل، ایران

چکیده

با توجه به ثبت جنگل­های هیرکانی در یونسکو، امروزه آگاهی از تغییرات و روند تخریب اراضی جنگلی جهت برنامه­ریزی و مدیریت اراضی ملی امری ضروری است. هدف از این پژوهش، پایش تغییرات کاربری اراضی و پوشش جنگلی در شهرستان آستارا با استفاده از تصاویر ماهواره­ای سری لندست سنجنده­های TM, OLI 1 & 2 مربوط به سال­های 1995 و 2022 است. در این پژوهش ابتدا تصاویر در روزهای با پوشش ابر کمتر از 10 % در سه بازه زمانی، در دو دوره انتخاب، سپس بر اساس روابط بین باندی شاخص پوشش گیاهی EVI تعریف شد. در ادامه با ترکیب شاخص­ها نسبت به استخراج نقشه کاربری اراضی با استفاده از الگوریتم ماشین بردار پشتیان اقدام شد. نتایج ارزیابی صحت نشان داد که دقت کلی و ضریب کاپا نقشه کاربری اراضی با استفاده از الگوریتم ماشین بردار پشتیبان برای سال­های 1995 و 2022 به ترتیب برابر با 89، 92 % و 0/86 و 0/75 است. نتایج بررسی تغییرات در شهرستان آستارا در بازه زمانی 1995 تا 2022 نشان داد که کاربری مسکونی به میزان 7 % یعنی 2954 هکتار افزایش یافته در مقابل کاربری اراضی مرتعی و کاربری اراضی کشاورزی به ترتیب 1 و 2 % به میزان 258 و 997 هکتار کاهش یافته­اند؛ اما یکی از کاربری­های مهم کرانه خزری، پوشش جنگلی در سال 1995 دارای مساحت 34283 هکتار بوده است که 80 % سطح پوشش منطقه مورد مطالعه را در بر گرفته که در سال 2022 سطح این کاربری به 32522 هکتار برابر با 76 % می­رسد که به میزان 4 درصد برابر با 1761 هکتار کاهش یافته است. بر مبنای یافته­های این پژوهش می­توان اظهار نمود که سامانه گوگل ارث انجین با آرشیوی از تصاویر ماهواره­ای مختلف می­تواند به عنوان یک ابزار قوی و مناسب جهت پایش و مدیریت اراضی جنگلی باشد.

کلیدواژه‌ها

عنوان مقاله [English]

Remote Sensing Used to Detect Changes in Land Use and Forest Cover (A Case Study of Astara City)

نویسندگان [English]

  • sayyad Asghari Saraskanrood 1
  • Hosein Sharifi Tolaroud 2
1 Professor, Department of Physical geography, Faculty of Social Sciences, University of Mohaghegh Ardabili, Iran.
2 Msc in Remote Sensing & Gis, Faculty of Social Sciences, Mohaghegh Ardabili University, Ardabil, Iran.
چکیده [English]

Given the significance of the Hyrcanian Forests, inscribed by the UNESCO as a world heritage site, it is essential to monitor the changes in and the devastation of the forest cover in this ecoregion for the planning and management of national lands. It is the objective of the present study to monitor changes in both land use and forest cover in Astara region using Landsat TM, OLI 1 & 2 sensors for the years 1995 and 2022. For this purpose, images captured on days with cloud covers of less than 10% were selected over three time intervals and the relevant enhanced Vegetation Index (EVI) values were determined based on Landsat inter-band relations. In the next stage, the index values were combined to derive the land use map using the support vector machine (SVM) algorithm. The results of accuracy evaluation showed that the overall accuracy and Kappa coefficients of the land use map for the year 1995 were equal to 89 and 92% and those for 2022 were 0.86 and 0.75%, respectively, indicating acceptable results. The results of land use changes in Astara city during the period from 1995 to 2022 showed that residential land use had increased by 7% equal to 2954 ha while rangeland and agricultural uses had decreased by 1 to 2% equal to 258 and 997 ha, respectively. However, an important land use along the Caspian coast line – that is, forest cover - stretched over an area of 34283 ha equal to 80% of the study area, which declined in 2022 to 32522 ha equal to 76%, showing a decrease of 4% equal to 1761 ha. It is clear that, owing to its rich archive of satellite images, the GEE system can be used as a strong and useful tool for monitoring and managing forest lands.

کلیدواژه‌ها [English]

  • Forest cover
  • Deforestation
  • Google Earth Engine
  • Landsat

مراجع

  1. Ahmadi, M., & M. Narangifard. 2015. Quality assessment and detection of forest area changes using satellite images (Case study: Rustam, Fars). Journal of RS and GIS for Natural Resources, 6(3): 87-100. (in Persian)
  2. Asghaari Sereskanrod, S., & Aradashiri, A-A. . Prediction of land use change using CA-Markov: A Case Study of Yasuj City. Town & Country Planing, 12(2): ): 407-430. (in Persian)
  3. Amin Amlashi, M., & Kh. K. Mirakhorlou. 2019. Evaluation of area and canopy density of forests in the Guilan Province using satellite data. Iranian Journal of Forest and Poplar Research, 27(1): 100-111.(in Persian)
  4. Ebrahimian, M. 2018. Investigating the Factors Affecting the Destruction of Forests and Promotion Strategies to deal with it from the point of view of the experts of the General Department of Natural Resources of Mazandaran Province, Master's Thesis, Tarbiat Modares University, Tehran.  (in Persian)
  5. Fatemti Talab, S. R., Madanipour Kermanshahi, M., and S. A. Hashemi. 2015. Estimating changes in forest cover in the Rudsar county by using neural network and maximum likelihood methods. Journal of RS and GIS for Natural Resources, 6(2): 33-44. (in Persian)
  6. Fadli, H. Kosugo, A.   Ichii, K. and R. Ramli. 2108.  Satellite -Based Monitoring of Forest Cover Change in Indonesia Using Google Earth Engine from 2000 to 2016. Journal of Physics: Conference Series, Volume 1317, The 3rd International Conference on Mathematics, Sciences, Education, and Technology 4–5 October 2018, Padang, Indonesia.
  7. Foroutan, S., and N. islamzadeh. 2022. The Study of Mazandaran Province Forest and Rangeland Vegetation Changes Trend by Satellite Images. PEC 2022; 9 (19): 197-215. (in Persian)
  8. Ghanbari, F., & Sh. Shataiee. 2010. Investigation on Forest Extend Change Using Aerial Photos and Aster Imagery (Case atudy: border forset in south and southwest of gorgan city). Journal of Wood and Forest Science and Technology, 17(4): 1-18. (in Persian)
  9. Gillespie, TW. Ostermann-Kelm, S. Dong, C. Willis, KS. Okin, GS. AND GM. MacDonald. 2018. Monitoring changes of NDVI in protected areas of southern California. Ecological Indicators. 88: 485-494.
  10. Hashemi, S. A., Fatemi Talab, S. R., Kavousi Kalashmi, H., M. Madanipour Kermanshahi. 2016. Change detection in the forest cover of Siyahmezgi watershed of Guilan using LandSat images. Journal of RS and GIS for Natural Resources, 7(3): 78-88. (in Persian)
  11. Huete, A. R. Liu, H. Q., Batchily, K. V. and W. J. D. A. Van Leeuwen. 1997. A comparison of Vegetation Indices Over a Global set of TM Images for EOS-MODIS. Remote sensing of Environment. 59(3): 440-451.
  12. Haddadi, A., Sahibi, M.R., Mokhtarzadeh, M., and Fatahi. 2009. Presenting a combined method of supervised and unsupervised methods in the classification of remote sensing images. Iranian Journal of Remote Sensing & GIS, 1(3): 33-50. (in Persian)
  13. Isazadeh, V. 1401. Extraction of Changes in Forest Areas Using the Landsat Forest product in the Google Earth Engine system (study area: Mazandaran province (2000-2018). 5th International Conference on Applied Research in Science and Engineering - Amsterdam. (in Persian)
  14. Javan, F., Hasani Moghaddam, H., and H. Torabi. 2020. Evaluation Of Deforestation Process Using Artificial Neural Networks Algorithm (Case Study: Namin County Hazelnut Forests). Environment and Interdisciplinary Development, 5(69): 63-74. (in Persian)
  15. Javan, F., and H. Hasani Moghaddam. 2016. Deforestation Detection of Hyrcania forest using satellite imagery and Support Vector Machine (Case study: Rezvanshahr County). Forest Strategic Quarterly Journal, second year, number 5: 1-11. (in Persian)
  16. Khalile, L. Rhinane, H. Kaoukaya, A. and H. Lahlaoi. 2018. Forest Cover Monitoring and Change Detection in Nfifikh Forest (Morocco). Journal of Geographic Information System. . 10: 219-233.
  17. Li, B. Tang, H. and D. Chen. 2009. Drought Monitoring Using the Modified Temperature/ Vegetation Dryness Index, 2nd International Congress on Image and Signal Processing. 17-19 Octber. 2009, China.
  18. Li, Y. Wu, Zh. Xu, X. Fan, H. Tong, x. and J. Liu. 2021. Forest Disturbances and the Attribution Derived from Yearly Landsat Time Series Over 1990–2020 in the Hengduan Mountains Region of Southwest China. Forest. Ecosystem. 8, 73.
  19. Mahdavi, A. 2017. Assessment of Forest Cover Change Trends and Determination of the main Physiographic Factors on Forest Degradation in Ilam Province (Case Study: Sirvan County). Iranian Journal of Forest and Range Protection Research, 15(1): 1-16. (in Persian)
  20. Mahmoudzadeh, H., and M. Azizmoradi. 2020. Deforestation modeling using artificial neural network and GIS (Case study: forests of Khorramabad environs). Journal of RS AND GIS For Natural Resours (Journal OF Applied RS and GIS Techniques in Natural Resource Science), 10(4 (37): 74-90. (in Persian)
  21. B. Wei, Y. Jin, c. Yuyichi, O. and Q. Guoyn. 2007. Sensitivity of the Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) to Topographic effects: A case Study in high-Density Cypress Forest. Sensors.
  22. Noghreh Alizadeh Deravi, B., Ghodskhah Daryaee, M., and HeYdari safari kouchi. 2020. Prioritization of forest degradation factors in West Gilan during 24-years, using remote sensing techniques. Physical Geography Quarterly, 13(49): 23-34. (in Persian)
  23. Osei, J.D. Andam-Akorful, S.A. and E.M Osei Jnr. 2019. Long Term Monitoring of Ghana’s Forest Reserves Using Google Earth Engine. Preprints 2019.
  24. Omrani, b. 2016. Forest; Revival,Protection and Sustainable Development, the First National Conference on Protection and Protection of Arsbaran Forests, Tabriz. (in Persian).
  25. Pirbavaghar, M. 2015. Deforestation Modelling Using Logistic Regression And GIS. Journal Of Forest Science. 61(5): 193-199.
  26. Rezvani, M., and F. Hashemzadeh. 2013. Investigating the Effective Factors on Forest Degradation and Impact of Removing Livestock from District 14 of The Northern Forests of Iran: an environmental and economic perspective (Fuman). Journal of Wood and Forest Science and Technology, 20(3): 125-138. (in Persian)
  27. Rostam Zadeh, H., Darabi, S., and H. Shahabi. 2017. Change detection of Oak forests using object-based classification of multitemporal Landsat imageries (Case study: forests of the northern province of Ilam). Journal of RS and GIS for Natural Resources, 8(2), 92-110. (in Persian)
  28. Sarli, R., G.R. Roshan, and G. Stefan. 2019. Evaluation and prediction of vegetation changes of Mazandaran, Iran from 2005 to 2017 using Markov chain method and Geographical Information Systems (GIS). Geographical Data, 28(111 ), 149-162. (in Persian)
  29. Soltani, N & V. Mohamadnezhad. 2021. Efficiency of Google earth Engine (GEE) Platform in land use change assessment and predicting it using CA-Markov model (Case study of Urmia plain). RS & GIS for Natural Resources, 12(3), 101-114. (in Persian)
  30. Sarai, b. and S. Asghari-Saraskanrod. 1400. Monitoring and Analysis of land Cover and Land use Changes from Forest to Agriculture Using Satellite Images and Remote Sensing (Case Study of Tankabon city), the Second International Conference and the Fifth National Conference on Protection of Natural Resources and Environment, Ardabil. (in Persian)
  31. A, Jiango. Y, Li. Q, Gao. J, Lu. L, Soufan. W, Almutairi. KF and M. Habib-Ur-Rahman. 2023. Modelling, Mapping and Monitoring of Forest Cover Changes, Using Support Vector Machine, Kernel Logistic Regression and Naive Bayes Tree Models with Optical Remote Sensing Data. Heliyon. Jan 26: 9(2)
  32. Shimizu, K. Ota, T. Mizoue, N. and Y. Shigejiro. 2016. Forest Monitoring Using Time Series Satellite Images and Its Applications to Tropical Forests. 98(2): 79-89.
مدیریت اراضی
دوره 11، شماره 2
دی 1402
صفحه 195-208

فایل ها

هم رسانی

ارجاع به این مقاله

آمار