بررسی خودهمبستگی فضایی دمای سطح زمین و ارتباط آن با کاربری اراضی شهر اهواز (مقاله علمی وزارت علوم)
درجه علمی: نشریه علمی (وزارت علوم)
آرشیو
چکیده
جزایر حرارتی شهری اثرات مخربی بر محیط زیست دارد و مصرف انرژی را افزایش می دهد. این پژوهش با هدف بررسی خودهمبستگی فضایی دمای سطح زمین و ارتباط آن با کاربری اراضی شهر اهواز انجام شد. دمای روزانه و شبانه سطح زمین به ترتیب با به کارگیری روش پنجره مجزا از تصویر لندست 8 و محصول مادیس MOD11A1 تعیین شد. نقشه کاربری اراضی در پنج کلاس شامل: اراضی ساخته شده، پوشش گیاهی، اراضی لخت، آب و رودخانه کارون تهیه شد. آماره موران جهانی در روز و شب به ترتیب برابر با 94/0 و 9/0 حاصل شد که بر این اساس، دمای سطح زمین روزانه و شبانه در شهر اهواز دارای الگوی خوشه ای است. براساس نتایج آماره گتیس- ارد جی بیشترین مساحت لکه های داغ روزانه و شبانه در شرق اهواز به ترتیب با 53/5050 و 69/8055 هکتار به دست آمد. طبق نتایج حاصل از آزمون کروسکال والیس، تفاوت معناداری بین میانگین دمای کاربری ها در سطح احتمال پنج درصد وجود داشت و کاربری ساخته شده از نوع صنعتی و اراضی لخت بیشترین دما و پهنه آبی و پوشش گیاهی کمترین دما را به خود اختصاص داده بودند. بین شاخص NDVI و LST همبستگی منفی (47/0-) وجود داشت. با وجود این، میانگین شاخص NDVI برابر با 17/0 بوده که نشان دهنده پوشش گیاهی ضعیف در شهر اهواز است؛ بنابراین ضرورت دارد که اقداماتی برای بهبود کیفیت پوشش گیاهی، گسترش فضای سبز شهری و حفظ منابع آبی برای تعدیل درجه دمای سطح زمین در شهر اهواز صورت پذیرد.Spatial Autocorrelation of Land Surface Temperature and its Relationship With Land Use in Ahvaz City
Urban heat islands could have destructive effects on the environment and increase energy consumption. The present study aimed to investigate the spatial autocorrelation of land surface temperature and its relationship with land use in Ahvaz city. Daily and Night Land surface temperature maps were extracted from the split window method for the Landsat 8 satellite image and MOD11A1 product, respectively. Land use maps of the study were extracted into five classes: Built-up area, vegetation, bare land, water, and Karun River. Based on the results, Global Moran's index on day and night was equal to 0.94 and 0.9, respectively, according to which Land Surface temperature is distributed in a cluster form. Results of Getis-ord GI statistics showed that the largest area of daily and nightly hot spots in the east of Ahvaz was obtained with 5050.53 and 8055.69 ha, respectively. The results of the Kruskal Wallis test showed that there was a statistically significant difference between the average temperature of land uses (p-value<0.05), so industrial areas and bare land had the highest temperature and the water bodies and vegetation had the lowest temperature. The results also showed that LST has a negative relationship with NDVI (r = -0.47); however, the average NDVI index in the study area was equal to 0.17 which indicated the poor quality of vegetation in Ahvaz. Therefore, it is necessary to take measures to improve the quality of vegetation and expand the urban green space and preserve water resources to adjust the land surface temperature in Ahvaz.Keywords: Landsat 8, Modis, Split Window, Global Moran, Getis-Ord GI. IntroductionLand surface temperature (LST) is one of the essential components of climate in any region. It controls physical, chemical, and environmental processes on the earth, which are influenced by factors such as surface reflection and roughness of different types of land use/land cover, topography, geographical location, biophysical characteristics, and climate parameter changes over time. Population growth and urbanization, industrial development, and human activities such as unprincipled changes in land use/land cover have created urban heat islands, which have devastating effects on the environment and the health of the community, as well as energy consumption, especially in cities with hot and dry climates. Using traditional methods to estimate land surface temperature is impossible and time-consuming for all ground conditions. On the other hand, meteorological stations only measure temperature information for specific points. In contrast, due to land cover and other temperature conditions, temperatures in different regions are different from the recorded temperatures for a particular point, and this data cannot be generalized to the whole region. Currently, remote sensing technology with continuity and data collection in broad ranges can be very efficient in estimating the land surface temperature. This study aimed to investigate the spatial autocorrelation of surface temperature and to identify the thermal islands of Ahvaz using remote sensing and GIS. Materials and MethodsDaily land surface temperature maps were extracted from the Landsat 8 satellite image in June 2020 using a split window method. The method of comparison with the temperature of the synoptic station was used to evaluate the accuracy of the LST map. To investigate the changes in land surface temperature at night, the land surface temperature product of the MODIS sensor (MOD11A1) was used. In the next step, the spatial autocorrelation of Land surface temperature was investigated using the global Moran index, and hot and cold spots were identified by Getis-Ord GI statistics. In the next step, land use maps of the study were extracted into five classes (Built-up area, vegetation, bare land, water, and Karun River) using the support vector machine classification method. To evaluate the efficiency of the classification method, the overall accuracy and kappa coefficient were used. The relationship between land surface temperature (LST) and land use type, and normalized vegetation difference index (NDVI) was investigated using the Kruskal-Wallis test and regression analysis, respectively. Research FindingsThe results showed that the difference between the air temperature recorded by Ahwaz Synoptic Station and the air temperature obtained from Landsat images is equal to 0.5 ° C, which indicates the high accuracy of the LST map. According to the results, Region 8 had the highest, and Region 6 had the lowest average surface temperature, equal to 53.72 and 51.53 ° C, respectively. The average LST was 51.96 ° C. The results of calculating the global Moran index showed that the LST in Ahvaz has a cluster pattern. The global Moran index for day and night was equal to 0.94 and 0.9, respectively, which was significant at the level of one percent probability. The results of calculating the Getis-Ord GI statistic for identifying the areas with high and low clusters showed that daily cold spots in the west and east of Ahvaz were equal to 4541.22 and 2644.74 ha, respectively. The largest area of daily and night cold spots related to region 3 is equal to 1404.27 and 344.68 ha, respectively, and the lowest area of daily cold spots related to region 7 is equal to 728.83 ha. In general, parks, areas around the Karun River, cultivated lands, tree and forest cover, and water areas formed the cold spots of Ahvaz city. The area of daily hot spots in the east and west of Ahvaz were obtained at 5050.53 and 2022.48 ha, respectively, and the area of night hot spots in the east and west of Ahvaz was obtained at 8055.69 and 4509.66 ha, respectively. The highest area of hot spots day and night is related to region eight equal to 2734.92 and 3160.65 ha, respectively. The hot spots mainly covered industrial areas and surrounding urban areas, hospitals, passenger terminals, gas stations, locations near busy roads, and bare land. Based on the result, the kappa coefficient and overall accuracy of classification were 95.1% and 91%, respectively. According to the results of the Kruskal-Wallis test, there was a statistically significant difference between the mean temperature of land (p-value <0.05), so industrial areas and bare land had the highest average mean, i.e., the highest temperature and the water bodies and vegetation had the lowest mean rank, i.e., the lowest temperature. The result also showed that LST has a negative relationship with NDVI (r = -0.47), and with increasing the positive values of the NDVI index, the average surface temperature decreased. Discussion of Results and ConclusionThe results showed that the type of land use affects the land surface temperature. The high concentration of industrial centers, large areas of barren lands, and lack of vegetation are the main reasons for the formation of thermal islands in Ahvaz city. Based on the results, the presence of vegetation and water zone modulates the land surface temperature. The average NDVI index was obtained at 0.17, which indicated the thin vegetation of Ahvaz city. Measures such as using bright colors on the facades of buildings, roads, streets, and parking, expanding vegetation and green space, creating green roofs, changing the use of bare land, paying more attention to the agricultural sector, and preventing construction on agricultural lands, planning, and optimal traffic management in busy places, streets, and squares with high traffic and preservation of water resources in the region and creating waterfalls in high-temperature areas are necessary and recommended adjusting the land surface temperature in Ahvaz. In this research, the study year was selected as near to normal conditions and summer season. It is suggested that further studies should be done in wet and drought conditions and other seasons and compared the results with the results of this research. References- Abdul Athick, A.S., Mohammed, K., Shankar, D., & Raja Naqvi, R. (2019). Data on time series analysis of land surface temperature variation in response to vegetation indices in twelve wereda of Ethiopia using mono window, split window algorithm and spectral radiance model. <em>Data in Brief</em>, <em>27</em>, 1-12.- Bhargava, A., Lakmini, S., & Bhargava, S. (2017). Urban heat island effect: It’s relevance in urban planning. <em>Journal of Biodiversity and Endangered Species, 5</em>(1), 1-4.- Ca, V.T., Asaede, T., & Abu. E. (1998). Reductions in air conditioning energy caused by a nearby park. <em>Journal of Energy and Building</em>, <em>29</em>, 83-92.- Choudhury, D., Kalikinkar, D., & Arijit, D. (2019). Assessment of land use land cover changes and its impact on variations of land surface temperature in Asansol-Durgapur Development Region. <em>The Egyptian Journal of Remote Sensing and Space Sciences</em>, <em>12</em>(22), 203–218.- Das, N., Mondal, P., Sutradhar, S., & Ghosh, R. (2021). Assessment of variation of land use/land cover and its impact on land surface temperature of Asansol subdivision. <em>The Egyptian Journal of Remote Sensing and Space Sciences, 24</em>(1), 131-149.- Das, S., & Angadi, D. P. (2020). Land use-land cover (LULC) transformation and its relation with land surface temperature changes: A case study of Barrackpore Subdivision, West Bengal, India. <em>Remote Sensing Applications: Society and Environment, 19</em>, 1-28.- Feng, Y., Gao, C., Tong, X., Chen, S., Lei, Z., & Wang, J. (2019). Spatial Patterns of Land Surface Temperature and Their Influencing Factors: A Case Study in Suzhou, China. <em>Remote Sensing</em>, <em>11</em>(82), 2-20.- Gaffin, S. R., Rosenzweig, C., Eichenbaum-Pikser, J., Khanbilvardi, R., & Susca, T. (2010). A Temperature and Seasonal Energy Analysis of Green, White, and Black Roofs, Columbia University.<em> Center for Climate Systems Research, New York,</em> 1-19.- Govil, H., Guha, S., Diwan, P., Gill, N., & Dey, A. (2020). Analyzing linear relationships of lst with ndvi and mndisi using various resolution levels of landsat 8 oli and tirs data. <em>Journal of Data Management, Analytics and Innovation</em>, <em>1</em>, 171-184.- Guha, S., & Govil, H. (2020). An assessment on the relationship between land surface temperature and normalized difference vegetation index, Environment. <em>Journal of Development and Sustainability</em>, <em>23</em>, 1944-1963.- Guo, G., Wu, Z., Xiao, R., Chen, Y., Liu, X., & Zhang, X. (2015). Impacts of urban biophysical composition on land surface temperature in urban heat island clusters. <em>Journal of Landscape and Urban Planning</em>, <em>135</em>, 1-10.- He, J., Zhao, W., Li, A., Wen, F., & Yu, D. (2019). The impact of the terrain effect on land surface temperature variation based on Landsat-8 observations in mountainous areas. <em>International Journal of Remote Sensing</em>, <em>40</em>(5), 1808-1827.- Kabano, P., Lindley, S., & Harris, A. (2021). Evidence of urban heat island impacts on the vegetation growing season length in a tropical city. <em>Journal of Landscape and Urban Planning</em>, <em>206</em>, 1-9.- Kaufmann, R., Zhou, L., Myneni, R., Tucker, C., Slayback, D., Shabanov, N., & Pinzon, J. (2003). The effect of vegetation on surface temperature: A statistical analysis of NDVI and climate data. <em>Journal of Geophysical Research Letters</em>, <em>30</em>(22), 1-4.- Kumari, M., Sarma, K., & Sharma, R. (2019). Using Moran's I and GIS to study the spatial pattern of land surface temperature in relation to land use/cover around a thermal power plant in Singrauli district, Madhya Pradesh, India. <em>Remote Sensing Applications: Society and Environment</em>, <em>15</em>, 1-6.- Liu, L., & Zhang, Y. (2011). Urban Heat Island Analysis Using the Landsat TM Data and ASTER Data: A Case Study in Hong Kong. <em>Remote Sensing</em>, <em>3</em>, 1535-1552.- McKee, T. B., Doesken, N. J., & Kleist, J. (1993). The relationship of drought frequency and duration to time scales. <em>Eighth Conference on Applied Climatology</em>. Anaheim, California, 17-22 January, 1-6.- Mujabar, S., & Rao, V. (2018). Estimation and analysis of land surface temperature of Jubail Industrial City, Saudi Arabia, by using remote sensing and GIS technologies. <em>Arabian Journal of Geosciences</em>, <em>11</em>, 1-13.- QIN, Z., & Karnieli, A. (2001). A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region. <em>International Journal of Remote Sensing</em>, <em>22</em>(18), 3719–3746.- Rongali, G., Ashok K. K., Gosain, A. K., & Khosa, R. (2018). Split-Window algorithm for retrieval of land surface temperature using Landsat 8 thermal infrared data. <em>Journal of Geo Visualization and Spatial Analysis</em>, <em>2</em>(14), 1-19.- Sekertekin, A., & Zadbagher, E. (2021). Simulation of future land surface temperature distribution and evaluating surface urban heat island based on impervious surface area. <em>Journal of Ecological Indicators</em>, <em>122</em>, 1-11.- Shahfahad., Kumari, B., Tayyab, M., Ahmed, I. A., Imam Baig, M. R., Firoz Khan, M., & Rahman, A.(2020) Longitudinal study of land surface temperature (LST) using mono- and split-window algorithms and its relationship with NDVI and NDBI over selected metro cities of India. <em>Arabian Journal of Geosciences,</em> <em>13</em>(104), 1-19.- Sharma, R., Pradhan, L., Kumari, M., & Bhattacharya, P. (2021). Assessing urban heat islands and thermal comfort in Noida City using geospatial technology. <em>Journal of Urban Climate</em>, <em>35</em>, 1-15.- Sun, C., Wu, Z., Lv, Z., & Wei, J. (2010). Spatial-temporal analysis of land surface temperature and its interplay with land use change. <em>Information Engineering and Computer Science</em> <em>(ICIECS)</em>, <em>25</em>, 1-4.- Tafesse, B., & Suryabhagavan, K. V. (2018). Systematic modeling of impacts of land-use and land-cover changes on land surface temperature in Adama Zuria District, Ethiopia. <em>Journal of Modeling Earth Systems and Environment,</em> <em>9</em>, 1-13.- Wang, R., & Murayama, Y. (2020). Geo-simulation of land use/cover scenarios and impacts on land surface temperature in Sapporo, Japan. <em>Journal of Sustainable Cities and Society, 36</em>, 1-11.- Weng, Q., Karimi firozjaei, M., Kiavarz, M., Alavipanah S. K., & Hamzeh, S. (2019). Normalizing land surface temperature for environmental parameters in mountainous and urban areas of a cold semi-arid climate. <em>Journal of Science of the Total Environment</em>, <em>650</em>, 515-529.- Xu, H., Yunhao, C., Shangming, D., & Wenxia, Q. (2011). Dynamical monitoring and evaluation methods to urban heat island effects based on RS & GIS. <em>Procedia Environmental Sciences</em>, <em>10</em>, 1228-1237.- Zhang, C., Luo, L., Xub, W., & Ledwitha, V. (2018). Use of local Moran's I and GIS to identify pollution hotspots of Pb in urban soils of Galway, Ireland. <em>Journal of Science of the Environment</em>, <em>398</em>, 212-221.
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