Volume 10, Issue 4 (12-2023)
Journal of Spatial Analysis Environmental Hazards 2023, 10(4): 1-18 |
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yari A, feyzolahpour M, kanani N. Analyzing the trend of deforestation using Support Vector Machine (SVM) and Maximum Likelihood Classification (MLC) models and its effect on land surface temperature (LST) and spectral indices (study area: Talesh forest area). Journal of Spatial Analysis Environmental Hazards 2023; 10 (4) :1-18
URL: http://jsaeh.khu.ac.ir/article-1-3397-en.html
URL: http://jsaeh.khu.ac.ir/article-1-3397-en.html
1- Associate Professor, Department of Geography and Rural Planning, University of Mohaghegh Ardabili , Ardabil, Iran. , arastoo.yari@gmail.com
2- Assistant Professor, Department of Geography, University of Zanjan, Zanjan, Iran.
3- PhD student in the field of geography and rural planning, University of Mohaghegh Ardabili , Ardabil, Iran.
2- Assistant Professor, Department of Geography, University of Zanjan, Zanjan, Iran.
3- PhD student in the field of geography and rural planning, University of Mohaghegh Ardabili , Ardabil, Iran.
Abstract: (1650 Views)
Earth surface temperature provides important information on the role of land use and land cover on energy balance processes. Therefore, the purpose of this research is to evaluate the LST patterns due to changes in land use (LULC). The studied area is located in Talesh region with an area of 300.6 square kilometers. For this purpose, Landsat images were downloaded in dry and wet seasons from 1365 to 1401. Four user classes were identified by maximum likelihood classification (MLC) and support vector machine (SVM) in 36-year intervals. The Kappa coefficient values for the SVM model were equal to 0.7802 and for the MLC model it was equal to 0.5328. NDVI, NDSI, and NDWI spectral indices were calculated for vegetation, barren soil, and water and were compared with LST in the above years. Changes in land use during the years 1365 to 1401 were an important factor in changes in the temperature of the earth's surface, which averaged from 13.7 degrees Celsius to 39.5 degrees Celsius in the wet season and -0.37 to 41.07 degrees Celsius in the dry season has been variable. Water areas and vegetation have the lowest and barren soil have the highest LST values. The highest negative correlation of -0.74 belongs to the NDVI index in 1365 and the highest positive correlation of 0.79 belongs to the NDSI index in 1365. The area of the forest area has decreased by 20.3% and agricultural land has increased by 217% in 36 years. Barren lands have changed the most and decreased from 2.68 square kilometers to 12 square kilometers. In general, LST has increased due to the increase of human activities such as the expansion of agricultural land and deforestation in the studied period.
Keywords: land use, land surface temperature, spectral index, support vector machine, deforestation, Talesh
Type of Study: Research |
Subject:
Special
Received: 2023/11/4 | Accepted: 2023/12/7 | Published: 2023/12/22
Received: 2023/11/4 | Accepted: 2023/12/7 | Published: 2023/12/22
References
1. اسلمی، فرنوش؛ اردوان قربانی، بهروز سبحانی و محسن پناهنده.1394. مقایسه روش های شبکه عصبی مصنوعی، ماشین بردار پشتیبان و شی گرا در استخراج کاربری و پوشش اراضی از تصاویر لندست 8. سنجش از دور و سامانه اطلاعات جغرافیایی در منابع طبیعی، 6: 14-1.
2. جهانبخشی، فرشید و محمدرضا اختصاصی.1397. ارزیابی عملکرد سه روش طبقه بندی تصویر (جنگل تصادفی، ماشین بردار پشتیبان و بیشترین شباهت) در تهیه نقشه کاربری اراضی. نشریه علوم آب و خاک، 22: 247- 235.
3. شنانی، مائده و حیدر زارعی. 1395. مقایسه الگوریتم های طبقه بندی شبکه عصبی مصنوعی، ماشین بردار پشتیبان و حداکثر احتمال در استخراج نقشه کاربری اراضی حوزه آبخیز ابوالعباس. نشریه علوم و مهندسی آبخیزداری، 10: 84- 73.
4. روستایی، شهرام؛ داوود مختاری، خلیل ولیزاده و لیلا خدایی. 1398. مقایسه روش پیکسل پایه(بیشترین شباهت) و شی گرا (ماشین بردار پشتیبان) در طبقه بندی کاربری اراضی (منطقه اهر و ورزقان). پژوهش های ژئومورفولوژی کمی، 8: 129- 118.
5. عبدلی، مهسا و مریم حقیقی. 1399. مقایسه روش های طبقه بندی ماشین بردار پشتیبان و شبکه عصبی مصنوعی در تهیه نقشه کاربری اراضی. پژوهش و فناوری محیط زیست، 5: 60- 47.
6. نجفی، احمد؛ سارا عزیزی و محمد حسین مختاری. 1396. کاربرد ماشین بردار پشتیبان در طبقه بندی کاربری اراضی حوزه چشمه کیله چالکرود. پژوهشنامه مدیریت حوزه آبخیز، 8: 92- 101.
7. Allen, D.E.; B.P, Singh, and R.C, Dalal. 2011. Soil Health indicators under Climate Change: A Review of current Knowledge. In: Singh, B.P. (Ed.), Soil Health and Climate Change. Springer, Heidelberg, 23: 25–35.
8. Berberoglu, S.; F, Evrendilek. C, Ozkan, and C, Donmez. 2007. Modeling Forest productivity using Envisat MERIS data. Sensors, 7: 2115–2127.
9. Bonn, F.J.; and N.T, O’Neill. 1993. Thermal infrared remote sensing of soils: evolution, trends and perspectives. Remote Sens. Rev, 7: 281–302.
10. Chen, X.-L.; H.-M, Zhao. P.-X, Li, and Z.-Y, Yin. 2006. Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes. Remote Sens. Environ, 104: 133–146.
11. Cohen, J. 1960. A coefficient of agreement for nominal scales. Educ. Psychol. Meas, 20: 37–46.
12. Dousset, B.; and F, Gourmelon. 2003. Satellitemulti-sensor data analysis of urban surface temperatures and land cover. ISPRS J. Photogrammetry Remote Sens, 58: 43–54.
13. Fall, S.; D, Niyogi. A, Gluhovsky. R.A, Pielke. E, Kalnay, and G, Rochon. 2010. Impacts of land use land cover on temperature trends over the continental United States: assessment using the North American Regional Reanalysis. Int. J. Climatol, 30: 1980–1993.
14. Feizizadeh, B.; T, Blaschke. H, Nazmfar. E, Akbari, and H.R, Kohbanani. 2013.Monitoring land surface temperature relationship to land use/land cover from satellite imagery in Maraqeh County, Iran. J. Environ. Plann. Manage. 56: 1290–1315.
15. Landis, J.R, and G.G, Koch. 1977. The measurement of observer agreement for categorical data. Biometrics 33: 159–174.
16. Laurance, W.F.; J, Sayer, and K.G, Cassman. 2014. Agricultural Expansion and its impacts on tropical nature. Trends Ecol. Evol, 29: 107–116.
17. Li, Z.; B, Tang, and H, Wu. 2013. Satellite- Derived Land Surface Temperature: Current Status and Perspectives. Remote Sens. Environ, 131: 14–37.
18. Mao, D.; Z, Wang. L, Luo, and C, Ren. 2012. Integrating AVHRR and MODIS data to monitor 5 NDVI changes and their relationships with climatic parameters in Northeast China. Int. J. Appl. Earth Observation Geoinformation, 18: 528–536.
19. McFeeters, S.K. 1996. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. Int. J. Remote Sens, 17:1425–1432.
20. Mondal, I.; Thakur, S.; Ghosh, P.B.; and De, T.K. 2021. Assessing the Impacts of Global Sea level rise (SLR) on the Mangrove Forests of Indian Sundarbans using Geospatial Technology, Geographic Information Science for Land Resource Management, 11, Wiley, 209–228.
21. Muster, S.; M, Langer. A, Abnizova. K.L, Young, and J, Boike. 2015. Spatio-temporal sensitivity of MODIS land surface temperature anomalies indicates high potential for large-scale land cover change detection in Arctic permafrost landscapes. Remote Sens. Environ, 168:1–12.
22. Pal, S.; and S.k Ziaul. 2017. Detection of land use and land cover change and land surface temperature in English Bazar urban centre. Egypt. J. Remote Sens. Space Sci, 20:125–145.
23. Robbins, G.B.; J.J, Bushell, and K.L, Butler. 1987. Decline in plant and animal production from ageing pastures of green panic (Panicum maximum var. trichoglume). J. Agric. Sci, 108:407–417.
24. Rouse, J.W.; R.H, Haas. J.A, Schell, and D.W, Deering. 1974. Monitoring vegetation systems in the Great Plains with ERTS. Third Earth Resources Technology Sattelite-1 Symposium, Greenbelt: NASA, 351: 309–317.
25. Rogers, A.S, and M.S, Kearney. 2004. Reducing signature variability in unmixing coastal marsh Thematic Mapper scenes using spectral indices. Int. J. Remote Sens, 25: 2317–2335.
26. Sayão, V.M.; J.A.M, Demattê. L.G, Bedin. M.R, Nanni, and R, Rizzo. 2018. Satellite land surface temperature and reflectance related with soil attributes. Geoderma, 325: 125–140.
27. Sobrino, J.A, and N, Raissouni. 2000. Toward remote sensingmethods for land cover dynamic monitoring: Application to Morocco. International. Journal. Remote Sensing, 21:353–366.
28. Sun, Q.,Wu, and Z J, Tan. 2012. The relationship between land surface temperature and land use/land cover in Guangzhou, China. Environmental Earth Sciences, 65:1687–1694.
29. Thakur, S.; Mondal, I.; Ghosh, P.B.; Das, P., and De, T.K. 2019. A review of the application of multispectral remote sensing in the study of mangrove ecosystems with special emphasis on image processing techniques, J Spat Info Res, 23: 123-139.
30. Tran, D.X.; F, Pla. P, Latorre-Carmona. S.W, Myint. M, Caetano, and H.V, Kieu. 2017. Characterizing the relationship between land use land cover change and land surface temperature. ISPRS Journal Photogramm. Remote Sens, 124:119–132.
31. USGS. 2016. Landsat5 (L5) data user’s handbook. Document number LSDS: 1574 version Available at https://landsat.usgs.gov/documents/.(Accessed on March 1, 2019).
32. Weng, Q.; D, Lu, and J, Schubring. 2004. Estimation of land surface temperature- vegetation abundance relationship for urban heat island studies. Remote Sensing Environment, 89:467–548.
33. Yuan, X.L.; W, Wang. J, Cui. F, Meng. A, Kurban, and P, Maeyer. 2017. Vegetation changes and land surface feedbacks drive shifts in local temperatures over Central Asia. Sci. Rep, 7: 3287-3321.
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