ارایه روشی نوین جهت انتخاب بهینه شاخص های مرتبط با پوشش زمین به منظور شناسایی جزایر حرارتی شهری، با بکارگیری داده های سنجش از دور

نوع مقاله: مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری تخصصی، گروه سنجش از دور و سیستم اطلاعات مکانی، دانشکده منابع طبیعی و محیط زیست،واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران

2 استادیار گروه سنجش از دور و سیستم اطلاعات مکانی، دانشکده منابع طبیعی و محیط زیست، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران

3 استادیار دانشکده عمران، گروه حمل و نقل، دانشکده عمران، آب و محیط زیست، دانشگاه شهید بهشتی، تهران، ایران

4 استاد گروه علوم و صنایع چوب و کاغذ، دانشکده منابع طبیعی و محیط زیست، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران

10.22131/sepehr.2020.40471

چکیده

در تحقیقات اخیر، دانشمندان توجه ویژهای به مسئله گرمایش جهانی داشته‌‌اند، زیرا دمای سطح زمین در طول قرن گذشته به طور قابل توجهی افزایش یافته است. جزایر حرارتی شهری[1]به پدیده‌‌ای ناشی از آثار شهرنشینی اشاره دارد که درجه حرارت در محیط شهری از مناطق اطراف آن بالاتر می‌‌رود. بررسی این دما توسط سنسورها دارای مشکلاتی همچون هزینه و گسسته بودن نقاط اندازهگیری را دارد. بنابراین تحقیق حاضر تلاش می‌‌کند، با تکنیک سنجش از دور مدلی کمی و پیوسته را برای پوشش این مشکلات در شهر تهران ارائه دهد. لذا با استفاده از تصاویر لندست 8 [2]، و داده‌‌های سنجنده مودیس، فاکتورهایی تولید و بررسی می‌‌شوند که در تولید جزایر حرارتی شهری مؤثر هستند. به منظور تولید این فاکتورها ابتدا با انجام تصحیحات لازم برروی تصاویر مورد نیاز، تعداد چهارده شاخص انتخاب و در سه سناریو مختلف محاسباتی شامل روش رگرسیون خطی، رگرسیون بردار پشتیبان و با استفاده از الگوریتم ژنتیک بکارگرفته شد. به منظور مدلسازی رویکردهای بیان شده، مجموعاً 2400 نقطه دارای دما به عنوان داده میدانی از منطقه مورد مطالعه (شهر تهران) جمع‌‌آوری شده است. برای ارزیابی کارایی سناریوهای مورد استفاده، 30% داده‌‌ها (جمعاً 720 نقطه) به صورت اتفاقی انتخاب شده و بعنوان داده‌‌های آموزشی در نظر گرفته و مابقی 70% داده‌‌ها (جمعاً 1680 نقطه) به عنوان داده‌‌های تست مورد ارزیابی قرار گرفت.براساس نتایج بدست آمده، ترکیب مدل رگرسیون بردار پشتیبان و الگوریتم ژنتیک بهترین تطابق را (میانگین خطای مربعی 9324/0، نرمال شده میانگین خطای مربعی2695/0 و ضریب همبستگی 9315/0) با داده‌‌های زمینی مورد استفاده دارند.



[1]- Urban Heat Islands


[2]- Landsat 8

کلیدواژه‌ها


عنوان مقاله [English]

A novel method for optimal selection of land cover indices and urban heat islands determination using remote sensing data

نویسندگان [English]

  • Nikrouz Mostofi 1
  • Hossein Aghamohammadi Zanjiirabad 2
  • Alireza Vafaeinezhad 3
  • Mahdi Ramezani 2
  • Amir Houman Hemmasi 4
1 PhD Candidate, Department of remote sensing and geographic information system, Faculty of natural resources and environment, science and research Branch, Islamic Azad University, Tehran, Iran
2 Assistant professor of department of remote sensing and geographic information system, Faculty of Natural resources and environment, science and research Branch, Islamic Azad University, Tehran, Iran
3 Assistant professor, Department of transportation, Faculty of Civil, Water and environmental engineering, University of Shahid Beheshti, Tehran, Iran
4 Professor, department of wood and paper science, Faculty of natural resources and environment, science and research branch, Islamic Azad University, Tehran, Iran
چکیده [English]

Introduction
Surface temperature is considered to be a substantial factor in urban climatology. Italso affects internal air temperature of buildings, energy exchange, and consequently the comfort of city life. An Urban heat island (UHI) is an urban area with a significantly higher air temperature than its surrounding rural areas due to urbanization. Annual average air temperature of an urban area with a populationof almost one million can be one to three degreeshigher than its surrounding rural areas. This phenomenon can affect societies by increasing costs of air conditioning, air pollution, heat-related illnesses, greenhouse gas emissions and decreasing water quality. Today, more than fifty percent of the world’s population live in cities, and thus, urbanization has become a key factor in global warming. Tehran, the capital of Iran and one of the world’smegacities, is selected as the case study area of the present research. A megacity is usually defined as a residential area with a total population of more than ten million. We encountered significant surface heat island (SHI) effect in this area due to rapid urbanization progress and the fact that twenty percent of population in Iran are currently living in Tehran.SHI has been usually monitored and measured by in situ observations acquired from thermometer networks. Recently, observing and monitoring SHIs using thermal remote sensing technology and satellite datahave become possible. Satellite thermal imageries, especially those witha higher resolution, have the advantage of providing a repeatable dense grid of temperature data over an urban area, and even distinctive temperature data for individual buildings.Previous studies of land surface temperatures (LST) and thermal remote sensing of urban and rural areas have been primarily conducted using AVHRR or MODIS imageries.
Materials and Methods
Recently, most researchers use high resolution satellite imagery to monitor thermal anomalies in urban areas. The present study takes advantage of themost recentsatellite in the Landsat series (Landsat 8) to monitor SHI, and retrieve brightness temperatures and land use/cover types.Landsat 8 carries two kind of sensors: The Operational Land Imager (OLI) sensor has all former Landsat bands in addition of three new bands: a deep blue band for aerosol/coastal investigations (band 1), a shortwave infrared band for cirrus detection (band 9), and a Quality Assessment (AQ) band. The Thermal Infrared Sensor (TIRS) provides two high spatial resolution thirty-meter thermal bands (band 10 and 11). These sensors use corrected signal-to-noise ratio (SNR) radiometric performance quantized over a 12-bit dynamic range. Improved SNR performance results in a better determination of land cover type. Furthermore, Landsat 8 imageries incorporate two valuable thermal imagery bands with 10.9 µm and 12.0 µm wavelength. These two thermal bands improve estimation of SHI by incorporating split-window algorithms, and increase the probability of detectingSHI and urban climatemodification. Therefore, it is necessary to design and use new procedures to simultaneously (a) handle the two new high resolution thermal bands of Landsat 8 imageries and (b) incorporate satellite in situ measurement into precise estimation of SHI.Lately, quantitative algorithms written for urban thermal environment and their dependent factors have been studied. These include the relationship between UHI and land cover types, along with its corresponding regression model. The relation between various vegetation indices and the surface temperature was also modelled in similar works. The present paper employ a quantitative approach to detect the relationship between SHI and common land cover indices. It also seeks to select properland coverindices from indices like Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), Normalized Difference Water Index (NDWI), Normalized Difference Bareness Index (NDBaI), Normalized Difference Build-up Index (NDBI), Modified Normalized Difference Water Index (MNDWI), Bare soil Index (BI), Urban Index (UI), Index based Built up Index (IBI) and Enhanced Built up and Bareness Index (EBBI). Tasseled cap transformation (TCT) which is a method used for Landsat 8 imageries, compacts spectral data into a few bands related to thecharacteristics of physical scene with minimal information loss. The three TCT components, Brightness, Greenness and Wetness, are computed and incorporated to predict SHI effect.The main objectives of this research include developing a non-linear and kernel base analysis model for urban thermal environment area using support vector regression (SVR) method, and also comparing the proposed method with linear regression model (LRM) using a linear combination of incorporated land cover indices (features). The primary aim of this paper is to establish a framework for an optimal SHI using proper land cover indices form Landsat 8 imageries. In this regard, three scenarios were developed:  a) incorporating LRM with full feature set without any feature selection; b) incorporating SVR with full feature set without any feature selection; and c) incorporating genetically selected suitable features in SVR method (GA-SVR). Findings of the present study can improve the performance of SHI estimation methods in urban areas using Landsat 8 imageries with (a) an optimal land cover indices/feature space and (b) customized genetically selected SVR parameters.
 
Result and Discussion
The present study selects Tehran city as its case study area. It employs a quantitative approach to explore the relationship between land surface temperature and the most common land cover indices. It also seeks to select proper (urban and vegetation) indices by incorporating supervised feature selection procedures and Landsat 8 imageries. In this regards, a genetic algorithm is applied to choose the best indices by employing kernel, support vector regression and linear regression methods. The proposed method revealed that there is a high degree of consistency between affected information and SHI dataset (RMSE=0.9324, NRMSE=0.2695 and R2=0.9315).

کلیدواژه‌ها [English]

  • Urban heat island
  • Support vector regression
  • Linear regression model
  • genetic algorithm
  • Landsat8 Imagery
1.   Abbassi, Y., Ahmadikia, H., & Baniasadi, E. (2020). Prediction of pollution dispersion under urban heat island circulation for different atmospheric stratification. Building and Environment, 168, 106374. https://doi.org/10.1016/j.buildenv.2019.106374
2.      Actionbioscience. (2015). Urban Heat Islands: Hotter Cities. http://www.actionbioscience.org/environment/voogt.html
3.   As-syakur, A. R., Adnyana, I. W. S., Arthana, I. W., Nuarsa, I. W., As-syakur, A. R., Adnyana, I. W. S., Arthana, I. W., & Nuarsa, I. W. (2012). Enhanced Built-Up and Bareness Index (EBBI) for Mapping Built-Up and Bare Land in an Urban Area. Remote Sensing, 4(10), 2957–2970. https://doi.org/10.3390/rs4102957
4.   Dihkan, M., Karsli, F., Guneroglu, A., & Guneroglu, N. (2015). Evaluation of surface urban heat island (SUHI) effect on coastal zone: The case of Istanbul Megacity. Ocean & Coastal Management, 118, 309–316. https://doi.org/10.1016/j.ocecoaman.2015.03.008
5.   Drucker, H., Burges, C. J. C., Kaufman, L., Smola, A., & Vapnik, V. (1997). Support Vector Regression Machines. Advances in Neural Information Processing Systems 9, 155–161.
6.   Durbha, S. S., King, R. L., & Younan, N. H. (2007). Support vector machines regression for retrieval of leaf area index from multiangle imaging spectroradiometer. Remote Sensing of Environment, 107(1), 348–361. https://doi.org/10.1016/j.rse.2006.09.031
7.   Fabrizi, R., Bonafoni, S., Biondi, R., Fabrizi, R., Bonafoni, S., & Biondi, R. (2010). Satellite and Ground-Based Sensors for the Urban Heat Island Analysis in the City of Rome. Remote Sensing, 2(5), 1400–1415. https://doi.org/10.3390/rs2051400
8.   Gao, B. (1996). NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58(3), 257–266. https://doi.org/10.1016/S0034-4257(96)00067-3
9.   Goldberg, D. E., & Holland, J. H. (1988). Genetic Algorithms and Machine Learning. Machine Learning, 3(2–3), 95–99. https://doi.org/10.1023/A:1022602019183
10. Henao, J. J., Rendón, A. M., & Salazar, J. F. (2020). Trade-off between urban heat island mitigation and air quality in urban valleys. Urban Climate, 31, 100542. https://doi.org/10.1016/j.uclim.2019.100542
11. Huang, X., & Wang, Y. (2019). Investigating the effects of 3D urban morphology on the surface urban heat island effect in urban functional zones by using high-resolution remote sensing data: A case study of Wuhan, Central China. ISPRS Journal of Photogrammetry and Remote Sensing, 152, 119–131. https://doi.org/10.1016/j.isprsjprs.2019.04.010
12. Imhoff, M. L., Zhang, P., Wolfe, R. E., & Bounoua, L. (2010). Remote sensing of the urban heat island effect across biomes in the continental USA. Remote Sensing of Environment, 114(3), 504–513. https://doi.org/10.1016/j.rse.2009.10.008
13. 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, 571–575. https://doi.org/10.1016/j.proenv.2010.10.062
14. Jimenez-Munoz, J. C., Sobrino, J. A., Skokovic, D., Mattar, C., & Cristobal, J. (2014). Land Surface Temperature Retrieval Methods From Landsat-8 Thermal Infrared Sensor Data. IEEE Geoscience and Remote Sensing Letters, 11(10), 1840–1843. https://doi.org/10.1109/LGRS.2014.2312032
15. Kriegler, F. J., Malila, W. A., Nalepka, R. F., & Richardson, W. (1969). Preprocessing Transformations and Their Effects on Multispectral Recognition. 97. http://adsabs.harvard.edu/abs/1969rse..conf...97K
16. Kutner, M., Nachtsheim, C., results,  search, & Li, W. (2004). Applied Linear Statistical Models (5th edition). McGraw-Hill/Irwin.
17. Liu, K., Su, H., Zhang, L., Yang, H., Zhang, R., Li, X., Liu, K., Su, H., Zhang, L., Yang, H., Zhang, R., & Li, X. (2015). Analysis of the Urban Heat Island Effect in Shijiazhuang, China Using Satellite and Airborne Data. Remote Sensing, 7(4), 4804–4833. https://doi.org/10.3390/rs70404804
18. Mathew, A., Khandelwal, S., Kaul, N., & Chauhan, S. (2018). Analyzing the diurnal variations of land surface temperatures for surface urban heat island studies: Is time of observation of remote sensing data important? Sustainable Cities and Society, 40, 194–213. https://doi.org/10.1016/j.scs.2018.03.032
19. Mathew, A., Sreekumar, S., Khandelwal, S., & Kumar, R. (2019). Prediction of land surface temperatures for surface urban heat island assessment over Chandigarh city using support vector regression model (Vol. 186). https://doi.org/10.1016/j.solener.2019.04.001
20. Mijani, N., Alavipanah, S. K., Hamzeh, S., Firozjaei, M. K., & Arsanjani, J. J. (2019). Modeling thermal comfort in different condition of mind using satellite images: An Ordered Weighted Averaging approach and a case study. Ecological Indicators, 104, 1–12. https://doi.org/10.1016/j.ecolind.2019.04.069
21. MOD11A2. (2015). NASA Land Data Products and Services. https://lpdaac.usgs.gov/products/modis_products_table/mod11a2
22. Moser, G., & Serpico, S. B. (2009). Automatic Parameter Optimization for Support Vector Regression for Land and Sea Surface Temperature Estimation From Remote Sensing Data. IEEE Transactions on Geoscience and Remote Sensing, 47(3), 909–921. https://doi.org/10.1109/TGRS.2008.2005993
23. Odindi, J. O., Bangamwabo, V., & Mutanga, O. (2015). Assessing theValue ofUrbanGreen Spaces inMitigatingMulti-SeasonalUrban Heat usingMODISLand SurfaceTemperature (LST) andLandsat 8 data. International Journal of Environmental Research, 9(1), 9–18. https://doi.org/10.22059/ijer.2015.868
24. Sanchez, L., & Reames, T. G. (2019). Cooling Detroit: A socio-spatial analysis of equity in green roofs as an urban heat island mitigation strategy. Urban Forestry & Urban Greening, 44, 126331. https://doi.org/10.1016/j.ufug.2019.04.014
25. Streutker, D. R. (2002). A remote sensing study of the urban heat island of Houston, Texas. International Journal of Remote Sensing, 23(13), 2595–2608. https://doi.org/10.1080/01431160110115023
26. Voogt, J. A., & Oke, T. R. (2003). Thermal remote sensing of urban climates. Remote Sensing of Environment, 86(3), 370–384. https://doi.org/10.1016/S0034-4257(03)00079-8
27. Wang, R., Cai, M., Ren, C., Bechtel, B., Xu, Y., & Ng, E. (2019). Detecting multi-temporal land cover change and land surface temperature in Pearl River Delta by adopting local climate zone. Urban Climate, 28, 100455. https://doi.org/10.1016/j.uclim.2019.100455
28. Xian, G., & Crane, M. (2006). An analysis of urban thermal characteristics and associated land cover in Tampa Bay and Las Vegas using Landsat satellite data. Remote Sensing of Environment, 104(2), 147–156. https://doi.org/10.1016/j.rse.2005.09.023
29. Xiong, Y., Huang, S., Chen, F., Ye, H., Wang, C., & Zhu, C. (2012). The Impacts of Rapid Urbanization on the Thermal Environment: A Remote Sensing Study of Guangzhou, South China. Remote Sensing, 4(7), 2033–2056. https://doi.org/10.3390/rs4072033
30. Zhu, Z., Zhou, Y., C Seto, K., Stokes, E., Deng, C., S.T.A., P., & Taubenböck, H. (2019). Understanding an Urbanizing Planet: Strategic Directions for Remote Sensing (Vol. 228). https://doi.org/10.1016/j.rse.2019.04.020
31. Zohary and Sharifi, 2011, Thermal Anomalies Detection in Birjand Using Landsat Data, Geomatics 90 (National Conference & Exhibition).