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بررسی و مدل‌سازی تأثیر ترکیب و آرایش چشم‌انداز شهر یزد بر دمای سطح زمین با استفاده از یادگیری ماشین و داده‌های لندست-8 و سنتینل-2

نوع مقاله : علمی - پژوهشی

نویسندگان

1 دانشجوی دکتری سنجش از دور، مرکز مطالعات سنجش از دور و GIS، دانشگاه شهید بهشتی، تهران

2 استادیار اقلیم‌شناسی ماهواره‌ای، دانشگاه یزد، یزد

3 استادیار علوم ریاضی، دانشگاه یزد، یزد

4 دانشیار سنجش ‌از‌ دور، دانشگاه یزد، یزد

5 گروه سنجش ‌از دور، دانشگاه تهران، تهران

6 استاد سنجش از دور، دانشگاه تهران، تهران

چکیده

اثر جزیره گرمایی شهری به‌دلیل تلاقی با چالش‌های محیط زیستی مهم قرن بیست و یکم یکی از مهم‌ترین بررسی‌ها در مورد پدیده‌های محیط زیستی است. در همین راستا، مطالعه دمای سطح زمین (LST)، چشم‌انداز واضحی از بررسی جزایر گرمایی در شهرها به‌دست می‌دهد که با توجه به اقلیم گرم و خشک شهر یزد، بررسی وضعیت و عوامل اثرگذار بر LST در این شهر را ضروری می‌نمایاند. این پژوهش با استفاده از تصویر فیوژن شده طیفی و مکانی لندست-8 برای ماه آگوست سال 2020 میلادی و با بهره‌گیری از الگوریتم‌های یادگیری ماشین سعی دارد تا تغییرات LST را با محاسبه پارامترهای مختلف مرتبط با چشم‌انداز سطح زمین شهری مدل کند. بر اساس نتایج این پژوهش، فیوژن طیفی-مکانی تصویر لندست-8 با سنتینل-2 به روش بارزسازی پن، موجب افزایش 10.7%ی دقت کلی و 16.5%ی ضریب کاپا در طبقه بندی این تصویر شد. این پژوهش همچنین نشان داد که اکثر پارامترهای مرتبط با همسایگی با پوشش اراضی در رده 1 تا 11 تأثیرگذاری بر LST شهر یزد قرار دارند. دراین‌بین، مجاورت با پوشش زمین‌های بایر در شعاع 100، 50 و 150 متر به‌ترتیب رتبه 1 تا 3 مهم‌ترین پارامترهای اثرگذار بر LST را از آن خود کردند. این پژوهش نشان داد که تغییر آرایش پوشش اراضی می‌تواند بر LST اثرگذار بوده و تغییر پوشش زمین‌های بایر به مناطق ساخته‌شده، تا °C 1.1، به پوشش گیاهی، تا °C 2.1 و تغییر 30% از زمین‌های بایر به پوشش گیاهی، تا °C 1.6 می‌تواند میانگین LST را در شهر یزد کاهش دهد. همچنین این پژوهش با بررسی دو رویکرد مختلف شبیه‌سازی ایجاد پوشش گیاهی در سطح شهر یزد نشان داد که رویکرد صرفه جویی در زمین می‌تواند میانگین LST را در شهر یزد تا 1.3 درجه و رویکرد تقسیم زمین تا °C 1.4 کاهش دهد.

کلیدواژه‌ها

عنوان مقاله [English]

Investigating and Modeling the Effect of the Composition and Arrangement of the Landscapes of Yazd City on the Land Surface Temperature Using Machine Learning and Landsat-8 and Sentinel-2 Data

نویسندگان [English]

  • Mohammad Mansourmoghaddam 1
  • Iman Rousta 2
  • Mohammad Sadegh Zamani 3
  • Mohammad Hossein Mokhtari 4
  • Mohammad Karimi Firozjaei 5
  • Seyed Kazem Alavipanah 6

1 Ph.D. Student of Remote Sensing, R.S. & GIS, Research Center, Shahid Beheshti University,

2 Assistant Prof. of Satellite Climatology, Yazd University

3 Assistant Prof. of Mathematics, Yazd University

4 Associate Prof. of Remote Sensing, Yazd University

5 Dep. of Remote Sensing, Tehran University

6 Prof. of Remote Sensing, Tehran University

چکیده [English]

The effect of urban thermal islands due to intersections with major environmental challenges of the 21st century is one of the most important studies on environmental phenomena, and in this regard, the study of the land surface temperature gives a clear perspective of the thermal islands in cities, which, according to the warm and dry climate of Yazd, examines the status and factors affecting the land surface temperature in this city seem to be necessary. This research, using the spectrally and spatially fused image of Landsat-8, for August 2020, and using machine learning algorithms, tries to model the changes in land surface temperature by calculating different parameters related to urban land perspective. Based on the results of this study, the spectral-spatial fusion of Landsat-8 with Sentinel-2 by Pan sharpening, increased 10.7% of the overall accuracy and 16.5% of the Kappa coefficient in the classification of this image. The study also showed that most neighboring parameters associated with land cover are ranked 1 to 11 of influencing the land surface temperature of Yazd city. In this area, the proximity to bare lands in the radius of 100, 50, and 150 meters ranked 1 to 3 of the most important parameters affecting the land surface temperature respectively. This study showed that the change in land cover arrangement could affect the land surface temperature and changing the bare lands to the built-up areas, up to 1.1°C, to vegetation, up to 2.1°C, and changing 30% of bare land to vegetation, up to 1.6°C can reduce the average land surface temperature in Yazd. Also, this study showed that two different models of vegetation simulation in Yazd city showed that the "land-sparing " model could reduce the average land surface temperature in Yazd by 1.3° and the "land-sharing" model by 1.4°C.

کلیدواژه‌ها [English]

  • Gradient boosting
  • Image fusion
  • Urban parameters
  • Land cover simulation
  • Remote sensing
مراجع
Alavipanah, S.K., Hashemi Darrehbadami, S. & Kazemzadeh, A., 2015, Spatial- Temporal Analysis of Urban Heat- Island of Mashhad City Due to Land Use/ Cover Change and Expansion, Geographical Urban Planning Research (GUPR), 3(1), PP. 1-17.
Alexander, C., 2021, Influence of the Proportion, Height and Proximity of Vegetation and Buildings on Urban Land Surface Temperature, International Journal of Applied Earth Observation and Geoinformation, 95, P. 102265.
Amiri, R., Weng, Q., Alimohammadi, A. & Alavipanah, S.K., 2009, Spatial–Temporal Dynamics of Land Surface Temperature in Relation to Fractional Vegetation Cover and Land Use/Cover in the Tabriz Urban Area, Iran, Remote Sensing of Environment, 113(12), PP. 2606-2617.
Avdan, U. & Jovanovska, G., 2016, Algorithm for Automated Mapping of Land Surface Temperature Using LANDSAT 8 Satellite Data, Journal of Sensors, 2016.
Bonafoni, S. & Keeratikasikorn, C., 2018, Land Surface Temperature and Urban Density: Multiyear Modeling and Relationship Analysis Using MODIS and Landsat Data, Remote Sensing, 10(9), P. 1471.
Colditz, R.R., Wehrmann, T., Bachmann, M., Steinnocher, K., Schmidt, M., Strunz, G. & Dech, S., 2006, Influence of Image Fusion Approaches on Classification Accuracy: A Case Study, International Journal of Remote Sensing, 27(15), PP. 3311-3335.
Collas, L., Green, R.E., Ross, A., Wastell, J.H. & Balmford, A., 2017, Urban Development, Land Sharing and Land Sparing: The Importance of Considering Restoration, Journal of Applied Ecology, 54(6), PP. 1865-1873.
CustomWeather, 2022, Climate & Weather Averages in Yazd, Iran, Retrieved from https://www.timeanddate.com/weather/iran/yazd/climate.
Dos Santos, A.R., de Oliveira, F.S., da Silva, A.G., Gleriani, J.M., Gonçalves, W., Moreira, G.L…, & da Silva, R.G., 2017, Spatial and Temporal Distribution of Urban Heat Islands, Science of the Total Environment, 605, PP. 946-956.
 
eesa. MultiSpectral Instrument (MSI) Overview, Retrieved from https://sentinel.esa.int/web/ sentinel/technical-guides/sentinel-2-msi/msi-instrument.
Grimmond, C., 2006, Progress in Measuring and Observing the Urban Atmosphere, Theoretical and Applied Climatology, 84(1-3), PP. 3-22.
Grimmond, S.U., 2007, Urbanization and Global Environmental Change: Local Effects of Urban Warming, Geographical Journal, 173(1), PP. 83-88.
Guha, S. & Govil, H., 2021, An Assessment on the Relationship between Land Surface Temperature and Normalized Difference Vegetation Index, Environment, Development and Sustainability, 23(2), PP. 1944-1963.
IBM, 2020, What is Machine Learning?, Retrieved from https://www.ibm.com/cloud/ learn/machine-learning.
Ishtiaque, A., Shrestha, M. & Chhetri, N., 2017, Rapid Urban Growth in the Kathmandu Valley, Nepal: Monitoring Land Use Land Cover Dynamics of a Himalayan City with Landsat Imageries, Environments, 4(4), P. 72.
Jensen, J.R., 1996, Introductory Digital Image Processing: A Remote Sensing Perspective, Prentice-Hall Inc.
Jiang, J. & Tian, G., 2010, Analysis of the Impact of Land Use/Land Cover Change on Land Surface Temperature with Remote Sensing, Procedia environmental sciences, 2, PP. 571-575.
Kafy, A.A., Dey, N.N., Al Rakib, A., Rahaman, Z.A., Nasher, N.R. and Bhatt, A., 2021. Modeling the relationship between land use/land cover and land surface temperature in Dhaka, Bangladesh using CA-ANN algorithm. Environmental Challenges, 4, p.100190. https://doi.org/10.1016/j.envc.2021. 100190
Karakuş, C.B., 2019, The Impact of Land Use/Land Cover (LULC) Changes on Land Surface Temperature in Sivas City Center and Its Surroundings and Assessment of Urban Heat Island, Asia-Pacific Journal of Atmospheric Sciences, 55(4), PP. 669-684.
Kikegawa, Y., Genchi, Y., Yoshikado, H. & Kondo, H. 2003, Development of a Numerical Simulation System toward Comprehensive Assessments of Urban Warming Counter-measures Including Their Impacts upon the Urban Buildings' Energy-Demands, Applied Energy, 76(4), PP. 449-466.
LANDSAT 8 Data Users Handbook, 2015, Department of the Interior US Geological Survey.
Li, X., Zhou, Y., Asrar, G.R., Imhoff, M. & Li, X., 2017, The Surface Urban Heat Island Response to Urban Expansion: A Panel Analysis for the Conterminous United States, Science of the Total Environment, 605, PP. 426-435.
Mansourmoghaddam, M., Rousta, I., Zamani, M., Mokhtari, M.H., Karimi Firozjaei, M. & Alavipanah, S.K., 2021, Study and Prediction of Land Surface Temperature Changes of Yazd City: Assessing the Proximity and Changes of Land Cover, Journal of RS and GIS for Natural Resources, 12(4), PP. 1-27.
Mansourmoghaddam, M., Ghafarian Malamiri, H.R., Arabi Aliabad, F., Fallah Tafti, M., Haghani, M. & Shojaei, S., 2022a, The Separation of the Unpaved Roads and Prioritization of Paving These Roads Using UAV Images, Air, Soil and Water Research, 15, P. 11786221221086285.
Mansourmoghaddam, M., Rousta, I., Ghaffarian, H. & Mokhtari, M.H., 2022b, Evaluating the Capability of Spatial and Spectral Fusion in Land-Cover Mapping Enhancement, Earth Observation and Geomatics Engineering, 6(1), PP. 161-174.
Maurer, T., 2013, How to Pan-Sharpen Images Using the Gram-Schmidt Pan-Sharpen Method–A Recipe, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 1, P. W1.
Meineke, E.K., Dunn, R.R. & Frank, S.D., 2014, Early Pest Development and Loss of Biological Control Are Associated with Urban Warming, Biology Letters, 10(11), P. 20140586.
Module, F., 2009, Atmospheric Correction Module: QUAC and FLAASH User’s Guide, Version, 4, P. 44.
Mustafa, E.K., Liu, G., El-Hamid, A., Hazem, T. & Kaloop, M.R., 2021, Simulation of Land Use Dynamics and Impact on Land Surface Temperature Using Satellite Data, GeoJournal, 86(3), PP. 1089-1107.
Mwakapuja, F., Liwa, E. & Kashaigili, J., 2013, Usage of Indices for Extraction of Built-Up Areas and Vegetation Features from Landsat TM Image: A Case of Dar es Salaam and Kisarawe Peri-Urban Areas, Tanzania, International Journal of Agriculture and Forestry, 3(7): PP. 273-283.
Oke, T.R., 1982, The Energetic Basis of the Urban Heat Island, Quarterly Journal of the Royal Meteorological Society, 108(455), PP. 1-24.
Osborne, P.E. & Alvares-Sanches, T., 2019, Quantifying How Landscape Composition and Configuration Affect Urban Land Surface Temperatures Using Machine Learning and Neutral Landscapes, Computers, Environment and Urban Systems, 76, PP. 80-90.
Peng, J., Ma, J., Liu, Q., Liu, Y., Li, Y. & Yue, Y., 2018, Spatial-Temporal Change of Land Surface Temperature Across 285 Cities in China: An Urban-Rural Contrast Perspective, Science of the Total Environment, 635, PP. 487-497.
Peng, X., Wu, W., Zheng, Y., Sun, J., Hu, T. & Wang, P., 2020, Correlation Analysis of Land Surface Temperature and Topographic Elements in Hangzhou, China, Scientific Reports, 10(1), PP. 1-16.
Piringer, M., Grimmond, C.S.B., Joffre, S.M., Mestayer, P., Middleton, D., Rotach, M., …, & Guilloteau, E., 2002, Investigating the Surface Energy Balance in Urban Areas–Recent Advances and Future Needs, Water, Air and Soil Pollution: Focus, 2(5-6), PP. 1-16.
Plocoste, T., Jacoby-Koaly, S., Molinié, J. & Petit, R., 2014, Evidence of the Effect of an Urban Heat Island on Air Quality Near a Landfill, Urban climate, 10, PP. 745-757.
Quattrochi, D.A. & Luvall, J.C., 1999, Thermal Infrared Remote Sensing for Analysis of Landscape Ecological Processes: Methods and Applications, Landscape Ecology, 14(6), PP. 577-598.
Richards, J.A. & Richards, J., 1999, Remote Sensing Digital Image Analysis (Vol. 3), Springer.
Rizwan, A.M., Dennis, L.Y. & Chunho, L., 2008, A Review on the Generation, Determination and Mitigation of Urban Heat Island, Journal of Environmental Sciences, 20(1), PP. 120-128.
Rokni, K., Ahmad, A., Solaimani, K. & Hazini, S., 2015, A New Approach for Surface Water Change Detection: Integration of Pixel Level Image Fusion and Image Classification Techniques, International Journal of Applied Earth Observation and Geoinformation, 34, PP. 226-234.
Story, M. & Congalton, R.G., 1986, Accuracy Assessment: A User’s Perspective, Photo-grammetric Engineering and Remote Sensing, 52(3), PP. 397-399.
Stott, I., Soga, M., Inger, R. & Gaston, K.J., 2015, Land Sparing Is Crucial for Urban Ecosystem Services, Frontiers in Ecology and the Environment, 13(7), PP. 387-393.
Taha, H., 1997, Urban Climates and Heat Islands: Albedo, Evapotranspiration, and Anthropogenic Heat, Energy and Buildings, 25(2), PP. 99-103.
Thompson, W.D. & Walter, S.D., 1988, A Reappraisal of the Kappa Coefficient, Journal of Clinical Epidemiology, 41(10), PP. 949-958.
Tran, D.X., Pla, F., Latorre-Carmona, P., Myint, S.W., Caetano, M. & Kieu, H.V., 2017, Characterizing the Relationship between Land Use Land Cover Change and Land Surface Temperature, ISPRS Journal of Photogrammetry and Remote Sensing, 124, PP. 119-132.
Ullah, S., Ahmad, K., Sajjad, R.U., Abbasi, A.M., Nazeer, A. & Tahir, A.A., 2019, Analysis and Simulation of Land Cover Changes and Their Impacts on Land Surface Tempera-ture in a Lower Himalayan Region, Journal of Environmental Management, 245, PP. 348-357.
USGS, What Are the Band Designations for the Landsat Satellites?, 2021, Retrieved from https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites?qt-news_science_products=0#qt-news_science_products. 
USGS, 2014, OLI and TIRS Calibration Notices, In: Landsat 8 Reprocessing to Begin February, 3, 2014.
Weatherbase, 2022, Yazd, Iran, Monthly - Weather Averages Summary. Retrieved from http://www.weatherbase.com/weather/weather.php3?s=592536& cityname=Yazd-Yazd.
Willmott, C.J. & Matsuura, K., 2006, On the Use of Dimensioned Measures of Error to Evaluate the Performance of Spatial Interpolators, International Journal of Geographical Information Science, 20(1), PP. 89-102.
Xu, H., 2006, Modification of Normalised Difference Water Index (NDWI) to Enhance Open Water Features in Remotely Sensed Imagery, International Journal of Remote Sensing, 27(14), PP. 3025-3033.
Xu, H., 2010, Analysis of Impervious Surface and Its Impact on Urban Heat Environment Using the Normalized Difference Impervious Surface Index (NDISI), Photogrammetric Engineering & Remote Sensing, 76(5), PP. 557-565.
Yazd Municipality website, 2019, History of Yazd, Retrieved from https://www.yazd.ir/fa/ web/yazd/w/%D8%AA%D8%A7%D8%B1%DB%8C%D8%AE%DA%86%D9%87-%DB%8C%D8%B2%D8%AF. Accessed on 2022-03-03.
Ziaul, S. & Pal, S., 2016, Image Based Surface Temperature Extraction and Trend Detection in an Urban Area of West Bengal, India, Journal of Environmental Geography, 9(3-4), PP. 13-25.
نشریه سنجش از دور و GIS ایران
دوره 15، شماره 3 - شماره پیاپی 59
مهر 1402
صفحه 1-26
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هم رسانی
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