نوع مقاله : پژوهشی

نویسنده

استادیار گروه جغرافیا، دانشگاه زنجان

10.22034/hyd.2024.18681

چکیده

تغییرات پوشش و کاربری زمین در اثر فعالیت های انسانی تاثیرات نامطلوبی بر محیط زیست بر جای گذاشته است. مناطق شرقی استان اردبیل نمونه بارز این پدیده به شمار می آید. هدف از این تحقیق تجزیه و تحلیل تغییرات مکانی و زمانی در پوشش و کاربری زمین و اثرات آن بر دمای سطح زمین در دریاچه نئور می باشد. برای برآورد کاربری و پوشش زمین از مدل های جنگل تصادفی (RTC)، مدل حداکثر احتمال (MLC) و ماشین بردار پشتیبانی(SVM) استفاده شده و کارایی هر کدام توسط ضریب کاپا برآورد گردیده و مشاهده شد که مدل SVM از بیشترین میزان ضریب کاپا ( 87/0) برخوردار است.  برای استخراج شاخص LST نیز از باندهای 6 لندست 5 و 10 لندست 8 بهره گرفته شده و مشاهده شد که بخش غربی دریاچه با افزایش دمای سطح زمین مواجه گردیده است. در طول دوره زمانی 2002، 2013 و 2022 تغییرات قابل توجهی در پهنه آبی دریاچه نئور و پوشش های گیاهی مجاور آن مشاهده شد. زمین های بایر بیشترین وسعت را در تمام دوره های مورد مطالعه داشته است. پوشش گیاهی بر اساس مدل SVM حدود 04/1 کیلومتر مربع افزایش یافته است. مساحت سطح دریاچه بر اساس مدل MLC در سال 2002 معادل 19/3 کیلومتر مربع برآورد گردید. مساحت پهنه آبی در مدل MLC در بازه زمانی 2002 تا 2022 حدود 56/1 کیلومتر مربع کاهش یافته و این میزان کاهش برای مدل های RTC و SVM به ترتیب معادل 67/0 و 69/0 کیلومتر مربع می باشد. 
 

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

The effectiveness of Random Tree Algorithm (RTC), Maximum Likelihood (MLC), and Support Vector Machine (SVM) models in detecting changes in the water area of Lake Neor and the effects of these changes on the surface temperature using the LST model in the 2002-2022 period

نویسنده [English]

  • mehdi feyzolahpour

. Assistant Professor, Department of Geography, Zanjan University

چکیده [English]

Changes in land cover and land use due to human activities have left adverse effects on the environment. The eastern regions of Ardabil province are a clear example of this phenomenon. The purpose of this research is to analyze spatial and temporal changes in land cover and land use and its effects on the temperature of the surface of the earth in Lake Neor. To estimate land use and land cover, random forest models (RTC), maximum likelihood model (MLC) and support vector machine (SVM) were used and the efficiency of each was estimated by the Kappa coefficient and it was observed that the SVM model has the highest Kappa coefficient (0.87) Bands 6, 5 and 10 of Landsat 8 were also used to extract the LST index, and it was observed that the western part of the lake faced an increase in the temperature of the earth's surface. During the time period of 2002, 2013 and 2022, significant changes were observed in the water area of Neor Lake and its nearby vegetation. Barren lands had the largest extent in all studied periods. Vegetation has increased by 1.04 square kilometers based on SVM model. The surface area of the lake was estimated as 3.19 square kilometers based on the MLC model in 2002. The area of the water zone in the MLC model has decreased by 1.56 square kilometers between 2002 and 2022, and this decrease is 0.67 and 0.69 square kilometers for the RTC and SVM models, respectively.

کلیدواژه‌ها [English]

  • Random Tree
  • Maximum likelihood
  • Support vector Machine
  • LST
  • Neor Lake
  • Northwest of Iran
Abdoli, M., Haghighi, M. (2020). Comparison of support vector machine and artificial neural network classification methods to produce landuse maps (Case study: Bojagh National Park), Journal of environmental research and technology, 8(5): 26-42.
Abedi, M., Norouzi, G & Bahroudi, A., (2012). Support Vector machine for multi classification of mineral prospectivity areas, computers and Geosciences. 46, 272-283.
Alemu, M.M., (2019). Analysis of Spatio-temporal Land surface temperature and normalized difference vegetation index changes in the Andassa Watershed, Blue Nile Basin, Ethiopia. J. Resour. Ecol. 10 (1), 77–85.
Aslami, F., Ghorbani, A., Sobhani, B., Panahandeh, M. (2015). Comparing artificial neural network, support vector machine and object-based methods in preparation land use/cover mapsusing landSat-8 images, Journal of RS and GIS for Natural Resources, 3(20), 1-14.
Belay, T & Mengistu, D.A., (2019). Land use and land cover dynamics and drivers in the Muga watershed, Upper Blue Nile basin, Ethiopia. Remote Sens. Appl.s: Soc. Environ. 15, 100249
Bokaie, M., Zarkesh, M.K., Arasteh, P.D & Hosseini, A., (2016). Assessment of urban heat island based on the relationship between land surface temperature and land use/land cover in Tehran. Sustain. Cities Soc. 23, 94–104.
Boser, B., Guyon, I & Vapnik, V., (1992). A training algorithm fotoptimal margin classifier, in: Proceedings of the Fifth Annual ACM Conference on Computational Learning Theory, Pittsburgh, 8, 144–152.
Breiman, L. (2001). Random forests Machine learning. 45, 5- 32.
Burges, C., (1998). A tutorial on support vector machines for pattern recognition, Data Mining Know. Discov. 2, 121–167.
Campvalls, G., Mooij, J & Scholkopf, B., (2010). Remote sensing feature selection by Kernel dependence measures. IEEE Geoscience and remote sensing Letters. 7, 587- 591.
Cortes, C & Vapnik, V., (1995). Support-vector network, Mach. Learn. 20, 273–297.
Cristianini, N & Shawe-Taylor, J, (2000). An Introduction to SupportVector Machines and Other Kernelbased Learning Methods, Cambridge University Press, 2000,
Damtea, W., Kim, D., & Im, S (2020). Spatiotemporal analysis of land cover changes in the chemoga basin, Ethiopia, using Landsat and google earth images. Sustainability 12 (9), 3607
Denil, M., Matheson, D & Freitas, N. (2014). Narrowing the Gap: random forest in theory and in practice, Proceeding of the 31st international conference on machine learning Beijing China, JMLR: W and P, Vol 32. 9 pages.
Dinka, M.O & Chaka, D.D., (2019). Analysis of land use/land cover change in Adei watershed, Central Highlands of Ethiopia. J. Water Land Dev. 41 (IV–VI), 146–153.
Foody, G., Boyd, D & Sanchez-Hernandez, C., (2007). Mapping a specific class with an ensemble of classifiers, Int. J. Remote Sens. 28, 1733–1746.
Foody, G & Mathur, A., (2006). The use of small training sets containing mixed pixels for accurate hard image classification: training onmixed spectral responses for classification by a SVM, RemoteSens. Environ. 103, 179–189.
Geo, Y., De Jong, K., Liu, F., Wang, X & Li, C., (2012). A comparison of Artificial neural networks and support vector machines on landcover classification, Springer verlag Berlin Heidelberg, ISICA, CCIS. 316, 531- 539.
Granian, H., Tabatabaei, S., Asadi, H & Carranza, E., (2016). Application of Discriminant Analysis support vector machine Gold Potential areas for further Drilling in the Sari Gunay Gold Deposit, NW Iran, natural Resource Research. 25(2), 145-159.
Hassen, E.E., & Assen, M (2017). Land use/cover dynamics and its drivers in Gelda catchment, Lake Tana
Hegazy, I.R., & Kaloop, M.R (2015). Monitoring urban growth and land use change detection with GIS and remote sensing techniques in Daqahlia governorate Egypt. Int. J. Sustain. Built Environ. 4 (1), 117–124.
watershed, Ethiopia. Environ. Syst. Res. 6 (1), 1–13.
Hord, R. M., (1982). Digital imageprocessing of Remotly sensed Data, Academic press, Newyork, 256.
Huange, C., Davis, L.S & Townshend, J., (2002). An assessment of support vector machines for land cover classification. International Journal of Remote sensing. 23, 725- 749.
Jahanbakhshi, F., Ekhtesasi, M R. (2019). Performance Evaluation of Three Image Classification Methods (Random Forest, Support Vector Machine and the Maximum Likelihood) in Land Use Mapping. Journal of water and soil science, 22 (4) :235-247
Jiange, X., Lin, M & Zhao, C., (2011). Woodland cover change assessment using decision tree, support vector machines and artificial neaural networks classification algorithms, Fourth international conference on intelligent computation Technology and Automation. 312- 315.
Kafy, A.A. (2021). Impact of Vegetation Cover Loss on Surface Temperature and Carbon Emission in a Fastest-Growing City, Cumilla, Bangladesh, 207. Building and Environment.
Kikon, N., Singh, P., Singh, S.K & Vyas, A., (2016). Assessment of urban heat islands (UHI) of Noida City, India using multi-temporal satellite data. Sustain. Cities Soc. 22, 19–28.
Li, K., Feng, M., Biswas, A., Su, H., Niu, Y & Cao, J., (2020). Driving factors and future prediction of land use and cover change based on satellite remote sensing data by the LCM model: a case study from Gansu province, China. Sensors 20 (10), 2757.
Louppe, G. (2014). Understanding random forests from theory to practice, university of Liege. Faculty of applied science. Department of Electrical engineering and computer science. 223 pages.
Mountrakis, G., Im, J & Ogole, C., (2011). Support vector machine in remote sensing a review. ISPRS Journal of photogrammetry and remote sensing. 13, 247- 259.
Oommen, T., (2008). An objective analysis of support vector machine-based classification for remote sensing. Mathematical Geosciences. 40, 409-424.
Pal, S & Ziaul, S.K., (2017). Detection of land use and land cover change and land surface temperature in English Bazar urban centre. Egypt. J. Remote Sens. Space Sci. 20 (1), 125–145.
Patel, S.K., Verma, P & Singh, G.S., (2019). Agricultural growth and land use land cover change in peri-urban India. Environ. Monit. Assess. 191 (9), 1–17.
Rajani, A & Varadarajan, S., (2021). Estimation and validation of land surface temperature by using remote sensing & GIS for Chittoor District, Andhra Pradesh. Turk. J. Comput. Math. Educ. 12 (5), 607–617.
Rajendran, P & Mani, K., (2015). Estimation of spatial variability of land surface temperature using Landsat 8 imagery. Int. J. Eng. Sci. 11 (4), 19–23.
Shanani Hoveyzeh, M., Zarei, H. (2016). Comparison of Three Classification Algorithms (ANN, SVM and Maximum Likelihood) for Preparing Land Use Map (Case Study: Abolabbas Basin). Iranian Journal of watershed management science, 10 (33) :73-84
Thakur, S., Mondal, I., Bar, S., Nandi, S., Das, P., Ghosh, P.B & De, T.K. (2020). Shoreline changes and its impact on the mangrove ecosystems of some Islands of Indian Sundarbans, North- East coast of India, J Clean Prod, 284, 124764. Elsevier.
Vapnik, V. N., 1995. The nature of statistical learning Theory, Springer Verlag, Newyork.