بررسی اثرات کاربری‌های مختلف شهرستان اصفهان بر ایجاد جزایر حرارتی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشیار گروه ژئومورفولوژی، دانشکده ادبیات و علوم انسانی، دانشگاه محقق اردبیلی، اردبیل، ایران

2 دانشجوی کارشناسی ارشد،گروه جغرافیای طبیعی، دانشکده ادبیات و علوم انسانی، دانشگاه محقق اردبیلی، اردبیل، ایران

چکیده

در این پژوهش، ارتباط کاربری اراضی با دمای سطح زمین شهرستان اصفهان و خودهمبستگی فضایی با بهره­گیری از شاخص موران بررسی شده است. بدین منظور، ابتدا، از داده­های تصاویر ماهواره­ای مربوط به ماهواره لندست هشت و لندست پنج در یک بازه زمانی 18 ساله دریافت و پیش‌پردازش‌های لازم اعمال شد. سپس، به منظور بررسی تغییرات کاربری اراضی، نقشه­­طبقه‌بندی با استفاده از روش شئ‌گرا نزدیکترین همسایگی و الگوریتم حداکثر احتمال صورت گرفت و دمای سطح زمین با استفاده از روش‌های الگوریتم پنجرهء مجزا برای لندست هشت و تک­کاناله برای لندست پنجاستخراج شد و از داده­های ایستگاه هواشناسی موجود در منطقه برای ارزیابی دمای به دست آمده استفاده گردید. تحلیل­ آماری بین مقادیر دمای بازیابی شده به روش تک­کاناله و پنجره مجزا نشان داد که مقادیر ریشه میانگین مربعات خطا برای سال 2000 و 2018 به ترتیب برابر 64/1 و 93/0 می­باشد که حاکی از، عملکرد بهتر روش پنجره مجزا نسبت به تک­کاناله است. همچنین، از دیگر نتایج تحقیق، رابطه بین کاربری اراضی و دما می­باشد؛ بدین صورت که، دمای مناطق کویری در سال 2000، دمای 76/42 درجه و در سال 2018، 06/46 درجه‌ی سانتی­گراد بوده است که به دلیل نبود پوشش گیاهی، این مناطق دمای بیشتری را به خود اختصاص داده‌اند. مناطق دارای آب نیز در سال 2000، دمای 16/21 و در سال 2018، دمای 09/32 درجه‌ی سانتی­گراد را به خود اختصاص داده است که دلیل آن را می­توان خشک­سالی­های اخیر، خشک شدن رودخانه زاینده­رود و افزایش مناطق شهری و سطوح نفوذ ناپذیر دانست. در نهایت، با استفاده از شاخص تحلیل لکه‌های داغ، خوشه‌های گرم و سرد جزایر حرارتی شهرستان اصفهان استخراج شد. تحلیل خود همبستگی فضایی با شاخص‌های موران جهانی نشان داد دمای سطح زمین به شکل خوشه‌ای توزیع شده است. تحلیل لکه‌های داغ تأییدی آشکار بر متمرکز و خوشه‌ای شدن جزایر حرارتی شهرستان اصفهان در فضا با افزایش دورة زمانی بوده است.
 

کلیدواژه‌ها


عنوان مقاله [English]

Analysis of land use changes and their effects on the creation of thermal islands in Isfahan City

نویسندگان [English]

  • Sayyad Asghari Sarasekanrood 1
  • Bahareh Asadi 2
1 Associate Professor, Department of Geomorphology, University of Mohaghegh Ardabili, Ardabil, Iran
2 Masters Student Remote Sensing, GIS, University of Mohaghegh Ardabili, Ardabil, Iran
چکیده [English]

Introduction: Climate change in cities has been in close focus in the past few decades. Heat stress in urban areas has also had adverse effects on human health and is expected to worsen in the future due to the global warming. In recent years, the mapping of urban biophysical and thermal conditions as well as their relation to land use and land cover (LULC) and air pollution has attracted increasing interest. In most cases, UHIs are a result of the use of fossil fuels which affect air-pollution in urban areas. It is a well-known fact that an increase in the population, particularly in developing countries, intensifies the pressure on natural resources. Rapid population growth, in conjunction with urbanization, expansion, and encroachment into limited agricultural and green areas lead to the destruction of vegetation coverage. It is obvious that such destruction, in combination with population growth, causes environmental impacts such as intensified land surface temperature (LST), UHIs and air-pollution. LST is considered as one of the important parameters in urban climate, which directly controls the UHI effects. LST is believed to be closely associated with LULC, resulting in heat islands. In general, thermal remote sensing is regarded as an efficient technology which provides a synoptic and uniform means of studying UHI effects on a regional scale. In the absence of a dense network of land-based meteorological stations, the spatiotemporal distribution of LSTs from thermal remote sensing imagery can be used as data to support UHI management and, potentially, countermeasures. Thermal satellite-measured LST has been utilized in various studies on heat balance, climate modelling, and global change. This is because LST is determined by the effective radiating temperature of the Earth’s surface to assess UHIs. There some methods devised in this regard, such as SplitWindow Algorithm (SWA) and Single Channel Method (SChM). In this line, the present research seeks to investigate the relationship between LST and LULC in Isfahan City, using Landsat thermal remote sensing satellite images. For this purpose, Landsat 8 (OLI) and Landsat 5(TM) satellite images were utilized, and the obtained images were received and preprocessed from 2000 to 2018.
Methodology: The study area is Isfahan City located at the  longitudes of 51° 50′ E and 51° 78′ E and the latitudes of 32° 50′ N and 32° 80′ N. The data used in this study were Landsat 8-sensor (TIRS -OLI) and Landsat 5-sensor (TM) satellite images. Land use classification was also done by the Ecognition and Envi 5.3 software programs. Then, Hot Spot analysis was done to determine the hot and cold clusters in Isfahan thermal islands. Finally, the ArcGIS 10.5 software was used to plot the corresponding maps.
Results and Discussion: Land surface temperature was determined by the split window algorithm for images obtained via TIRS and the single-channel TM sensor of regional weather stations. The statistical analysis of the temperature data was done via Roots of Mean Square Errors (RMSE), and the values found for 2000 and 2018 were 1.64 and 0.93 respectively. The split window method proved to have a better performance than the single-channel method. The other achievement of this study is to identify the relationship of land surface temperature and land use. According to the results, the temperature of the desert areas was 42.76° C and 46.06° C in 2000 and 2018 respectively. It can be claimed that the high temperature of these area is due to the lack of vegetation. The temperature of the lands with water consumption was also 21.6°C and 32.09°C in 2000 and 2018 respectively. This phenomenon can be attributed to the development of urban areas and the recent droughts which have occurred in the Zayandeh Rood River. Consequently, Hot Spot analysis was done to determine the hot and cold clusters in Isfahan thermal islands. The analysis of the spatial correlation with global Moron indexes suggested that the surface temperature of the earth is distributed in a cluster form. Hot Spot analysis, indeed, provided evidence for the spatial concentration and clustering of the thermal islands in Isfahan over the time.
Conclusion: The results of this study showed that the object-oriented classification method provides better results and has a better kappa coefficient and higher overall accuracy. There is also a close relationship between land use and surface temperature. The results indicated a negative correlation between LST on one hand and vegetation canopy and moisture, as already known from many other studies. LST was found to be very sensitive to vegetation and humidity. The study also rejected the hypothesis of irrelevance of spatial temperature of Isfahan (H0) and proved that the surface temperature data of Isfahan have a spatial structure and are distributed in clusters.

کلیدواژه‌ها [English]

  • OLI-TM Landsat images
  • Object-oriented classification
  • Land use
  • Land surface temperature
  • Spatial autocorrelation
Aghili Nasab, Z., Mohamad zade, M., Mahini, A., Zerai, H. (2013). Urban heat islands analysis using remote sensing and its relationship with the development of eco-friendly. Journal of Environment and Development, 8(4):79-88.
 Akbari, A., Abrahimi, M., Amirahmadi, A. (2012). Sabzevar land-use mapping using maximum likelihood methods and artificial neural network Multilayer Perceptron. Journal of Environmental Preparation, (23):127-148.
Akbarpr, A., Sharifi, M., Khalilabad, H. (2005) .The comparison of fuzzy and maximum likelihood methods in preparing of land use layer using ETM+ data (case study: kameh watershed). Journal of Range and Desert Research, 13(1):27-37.
Asghari. Saraskanrood, S., Faal. Naziri, M., Ghale, ehsan. (2019). The Relationship of Different Land Uses with Land Surface Temperature based on Spatial Correlation (Moran) Analysis Using Landsat 8 Satellite Images (OLI) (Case Study: Ardebil City). Journal of Geography and Environmental Planning, 30(1):92-110.
Asghari. Saraskanrood S., Emami. H. (2017). The Relationship of Different Land Uses with Land Surface Temperature Using Images (OLI) (ETM+)(Case Study: Ardebil City). Journal of Research and Geographical Sciences, 19(53): 195-2014.
Arakhi, S. (2015).  Detection of land cover / land use changes with object-oriented processing of satellite imagesUsing selvi Idrisi softwareCase study: Abdanan region. Geographical Information Scientific-Research Quarterly, 24(95), 51-62.
Azimi, D. (2008). Zoning the surface temperature of Ahvaz city using thermal images of ETM sensor. Geographical Quarterly of the Land, Scientific – Research, 5(17), 97-110.  
Fizizadeh, B., Didehban, KH., Gholamnya, KH. (2015). Estimation of Surface Temperature Using Landsat 8 Satellite Images and Spilet Window Algorithm Case Study: Mahabad Watershed. Journal of Geographical Data, 98(25), 171-181.
Fizizadeh, B., Jafari, F., Nazmfar. F. (1999). Application of Remote Sensing Data to Detect Urban Land Use Changes Case Study of Tabriz Green Area. Journal of Fine Arts, 34: 24-17.
Fizizadeh, B., Shahabi, H., Seifi, H. (2016). Identification of Sustainable Areas of Salt Storms in Lake Urmia Using Fuzzy-Objective Processing of Satellite Images.Journal of Environmental Hazard Management, 3(3): 269-284.
Fizizadeh, H., Bakhteyar, H. (2009). Comparison of pixel-based, object-oriented and effective classification parameters cover / land Use West Azerbaijan Province. Journal of Geographical Research, 71: 73-84.
Ghalhari, F., Asadi, M., Rodbari, A. (2015). Spatial Analysis of Humidity Propagation over Iran. Natural Geographical Research, 4(47): 637-350.
Hasanlu M, Mashrote H.2014. Investigation of Land Use Relationship between Land Coverage and Thermal Islands in Kerman City using Landsat 8 Images. Journal of The first conference on GIS.1-12.
Karamali, M., Rodbari, A. (2013). Evaluation of Precipitation Patterns of Khorasan Razavi Province Using Modern Spatial Statistics, Systematic Extension Journal, and Rainfall Ponds. Journal of Systematics and Surfaces of Rainwater Ponds, 3(2): 72-61. 
Matkan, A., Nohegar, A., Mirbagheri, B., Torkchin, N. (2013). Assessment relations of land use in heat islands using time series ASTER sensor data (Case study: Bandar Abbas city). Remote sensing and GIS in natural resources, 4(5): 1-14.
Shakiba, A., Firoze. Abadi, P., Ashorlu, D., Namdari, S. (2009). Analysis of the relationship between land cover and thermal islands of Tehran using ETM + data. Journal of Remote Sensing and GIS, 1(1):39-56.
Valizadeh, K., Gholamneya, Kh., Mosavi, M. (2014). Estimation land surface temperature and extract heat islands using split window algorithm and multivariate regression analysis (Case Study of Zanjan). Journal of Urban Research and Planning, 30(8): 35-50.
Li, J., Song, C., Cao, L., Zhu, F., Meng, X., Wu, J. (2011). Impacts of landscape structure on surface urban heat islands: A case of Shanghai, China. www.Elsevier.com / locate/rse, 3249-3263.
Bharath, Setturu. Rajan, K.S., Ramachandra, TV. (2013). Land Surface Temperature Responses to Land Use Land Cover Dynamics.  SciTechnol journal, 1-10.
Trigo, I., Freitas, S., Bioucas, D., Barroso, J., Monteiro, C., Viterbo, I.P. (2009). Algorithm Theoretical Basis Document for Land Surface Temperature (LST), Land SAF Project Manager (IM).
Liu, J. G., Mason, P. J. (2009). Essential Image Processing and GIS for Remote Sensing. Association of American geographers, 2660-2678.
Griffith, D. (). Spatial Autocorrelation: A Primer. Resource Publication in Geography. Association of American geographers, 64: 566-592.
Xiaolei, Y., Xulin, G., Zhaocong, W. (2014). Land Surface Temperature Retrieval from Landsat 8 TIRS—Comparison between Radiative Transfer Equation-Based Method, Split Window Algorithm and Single Channel Methd, remote sensing. 9829-9852.