پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 161 |
| تعداد شماره‌ها | 6,532 |
| تعداد مقالات | 70,504 |
| تعداد مشاهده مقاله | 124,123,486 |
| تعداد دریافت فایل اصل مقاله | 97,231,605 |
بررسی تغییرات مکانی-زمانی کاربری اراضی، گسترش شهری و تأثیر آن بر افزایش دمای سطح زمین با استفاده از تصاویر چندزمانة لندست (مطالعة موردی: شهر گرگان) | ||
| پژوهش‌های جغرافیای برنامه‌ریزی شهری | ||
| مقاله 7، دوره 6، شماره 3، مهر 1397، صفحه 545-568 اصل مقاله (2.68 M) | ||
| نوع مقاله: پژوهشی - کاربردی | ||
| شناسه دیجیتال (DOI): 10.22059/jurbangeo.2018.262673.945 | ||
| نویسندگان | ||
| سامان نادی‌زاده شورابه1؛ سعید حمزه* 2؛ مجید کیاورز2؛ سید کریم افشاری‌پور3 | ||
| 1دانشجوی کارشناسی ارشد سنجش‌ازدور و GIS، دانشکدۀ جغرافیا، دانشگاه تهران | ||
| 2استادیار گروه سنجش‌از دور و GIS، دانشکدۀ جغرافیا، دانشگاه تهران | ||
| 3دانشجوی کارشناسی ارشد سنجش‌از دور و GIS، دانشکدۀ جغرافیا، دانشگاه تهران | ||
| چکیده | ||
| هدف از پژوهش حاضر بررسی تغییرات مکانی-زمانی کاربری اراضی، گسترش شهری و تأثیر آن بر افزایش دمای سطح زمین است. براین‌اساس برای مدیریت بهینة آثار منفی ناشی از افزایش دمای سطح زمین، تعیین نوع تغییرات کاربری که بیشترین تغییرات دمای سطح زمین را در بازة زمانی ایجاد می‌کنند مفید است. بدین منظور از داده‌های ماهواره‌ای لندست که به دورة زمانی 23 ساله مربوط است استفاده شد تا نقشه‌های کاربری اراضی و تغییرات آن، همچنین دمای سطح زمین استخراج شود. پس از انتخاب باندهای بهینه، به‌کمک روش فاکتور شاخص مطلوبیت، تصاویر ماهواره‌ای با روش ماشین بردار پشتیبان طبقه‌بندی شدند. همچنین برای بازیابی دمای سطح زمین نیز از الگوریتم Mono-Window استفاده شد. طبقه‌بندی تصاویر و استخراج اطلاعات آماری هر کلاس نشان‌دهندة ارتباطی قوی میان نوع کاربری و دمای سطح زمین است؛ به‌طوری‌که بررسی رابطة NDVI و حرارت سطح با استفاده از تحلیل هم‌بستگی و رگرسیونی، نقش مؤثر پوشش گیاهی در تعدیل دمای سطح را نشان می‌دهد. باید توجه داشت که کاربری آیش دمای بیشتری از مناطق ساخته‌شده دارد؛ زیرا این مناطق ترکیبی غیریکنواخت از ساختمان، چمن و گیاه هستند. نتایج بررسی ارتباط تغییرات کاربری اراضی و تحولات دمای سطح زمین نشان می‌دهد با تبدیل کاربری فضای سبز به زمین آیش، بیشترین تغییرات دمای سطح زمین رخ می‌دهد. از دیگر نتایج این پژوهش کاهش طبقات دمایی خیلی سرد و سرد و افزایش طبقات دمایی نرمال و گرم است که مهم‌ترین دلیل آن افزایش مساحت کاربری‌های ساخته‌شده و آیش است. | ||
| کلیدواژه‌ها | ||
| پایش مکانی-زمانی؛ دمای سطح؛ کاربری اراضی؛ گرگان؛ گسترش شهری | ||
| عنوان مقاله [English] | ||
| Effects of Spatial and Temporal Land Use Changes and Urban Development on the Increase of Land Surface Temperature Using Landsat Multi-Temporal Images (Case study: Gorgan City) | ||
| نویسندگان [English] | ||
| Saman Nadizadeh Shorabeh1؛ Saeid Hamzeh2؛ Majid Kiavarz2؛ Seyed Karim Afsharipoor3 | ||
| 1MA Student in Remote Sensing and GIS, Faculty of Geography, Tehran University, Iran | ||
| 2Assistant Professor of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Iran | ||
| 3MA Student in Remote Sensing and GIS, Faculty of Geography, Tehran University, Iran | ||
| چکیده [English] | ||
| Introduction More than 50% of the world population lives in urban areas. It is predicted the value will be increased to 69.6% by 2050. In recent decades, urban population is rapidly increasing due to the natural growth of cities, the migration from villages to cities, climate change, reduction of water resources, loss of agricultural lands, animal husbandry and other factors. These factors have led to physical expansion of the cities and the subsequent destruction and reduction of green spaces and forests, and increase of streets, buildings and asphalt roads. These changes in land use and land cover in urban areas cause many environmental problems and warming of the temperature of the city and its surroundings. Gorgan city as one of the northern cities of Iran is noticeable in urban physical expansion and land use change mainly due to conversion of agricultural landuse and green space into built-up areas. These reasons have created a special climatic condition in terms of air temperature, humidity and precipitation. The purpose of this study is to investigate the increase in temperature as a result of changes in the various landuse and the impact of each landuse on the increase in surface temperature and identifying effective landuse to better management. Methodology In the present study, we have used Landsat satellite images in 1992, 2001, 2009 and 2015 in the sensors of TM5, ETM+, and OLI/TIRS. In order to complete the input parameters for mapping the surface temperature using satellite images, we have used MODIS water vapor product. To provide control points, we have used field views, Google Earth images, and topographic maps prepared by “Iran National Cartographic Center”. After providing Landsat time series, we applied preprocessing steps including atmospheric and geometrice correction. Then, the images were classified by Support Vector Machine method. They were classified into four classes including built up, fallow, agriculture and green space. After classifying the control points, the accuracy of the images was calculated. In the next step, we have used the Mono Window algorithm to obtain surface temperature for each image. At the end, we investigated the changes between different images and their relationship with the Earth's surface changes. Results and discussion The results of landuse changes in Gorgan indicated that during the first period (1992-2001), the extent of fallow and green space increased 48.55% and 31.95%, respectively. The agricultural and green spaces decreased 68.68% and 5.9%, respectively. This is the most important cause of this decline in agricultural landuse during this period in the fallow landuse. In the second time period (2001-2009), the area of green, agricultural and built up landuse increased by 17.1%, 86.59% and 14.51%. The fallow landuse because of cultivation decreased about 18.68%. Also, in the third time period (2009-2015), the extent of the built up and fallow landuse was increased by 12.24% and 7.84%, respectively. The area of the green spaces and agriculture landuse is decreased by 0.72% and 29.49%, respectively. The use of green space due to its particular geographic location, including special topographic conditions, has not changed during the study period. The highest temperature related to the fallow landuse, because of this increase in temperature for the fallow is the thermal capacity and low heat transfer capacity of the dry soil. Also, the highest temperature is related to green space landuse. This is resulted from evapotranspiration for reducing the temperature for the green space landuse. The variation in temperature classes is different. The very cold temperate class has a faster rate of reduction, so that the area of 5875.51 hectares in 1992 changed into an area of 1260.1 hectares in 2015. Also the normal and hot temperatures class in these years had the growing trend. The area of the warm class was zero in 1992. It increased by 319.73, 1226.91, and 1686.13 hectare in the years 2001, 2009 and 2015, respectively. Conclusion The results of the image classification in the research indicate a positive effect of the NDVI index and the LST map to increase the accuracy of image classification. Landuse changes indicated that the most changes were observed between the agriculture and fallow landuse. If this trend continues, other landuse will undergo fundamental changes. The trend of temperature changes in the earth surface is an increasing and the highest temperature is related to the fallow and built up landuse. Also, the highest increase in temperature is related to the changes in the green space to fallow landuse. Investigating the relationship between the characteristics of vegetation density and the earth surface temperature indicates that different classes of land use/cover, the presence of vegetation could decrease the surface temperature during study period. It was found that surface temperature in dense urban areas were higher than those in other areas. Hence, it can be noted that the role of vegetation in reducing the surface temperature of the city was important. With studying the temperature classes in the study area, it showed that cold temperatures classes have decreasing trend and warm temperature classes have increasing trend because of the changes occurred in landuse. | ||
| کلیدواژه‌ها [English] | ||
| spatial-temporal monitoring, Land Use, urban extension, surface temperature, Gorgan | ||
| مراجع | ||
|
داز، بی‌بی سارا، غفاری گیلانده، عطا و حسن خاوریان، ۱۳۹۵، بررسی تغییرات پوشش اراضی شهر گرگان با استفاده از تصاویر ماهوارۀ لندست، اولین کنفرانس بین‌المللی آب، محیط‌زیست و توسعة پایدار، دانشگاه محقق اردبیلی، اردبیل. علوی‌پناه، سید کاظم، 1385، کاربرد سنجش‌ازدور در علوم زمین، انتشارات دانشگاه تهران، تهران. علوی‌پناه، سید کاظم، هاشمی دره بادامی، سیروس و علی کاظم‌زاده، 1394، «تحلیل مکانی-زمانی جزیرة حرارتی شهر مشهد با توجه به گسترش شهر و تغییرات کاربری-پوشش زمین»، پژوهش‌های جغرافیا و برنامه‌ریزی شهری، سال سوم، شمارة 1، صص 1-17. متکان، علی‌اکبر و همکاران، 1393، «تحلیل نقش کاربری اراضی در شکل‌گیری جزایر حرارتی با استفاده از داده‌های چندزمانة سنجندة Aster (مطالعة موردی: شهر بندرعباس)»، کاربرد سنجش‌ازدور و سامانة اطلاعات جغرافیایی در منابع طبیعی، سال پنجم، شمارة 4، صص 1-14. ملاشاهی، سمانه و همکاران، ۱۳۹۴، آشکارسازی تغییرات کاربری اراضی و پوشش زمین (مطالعة موردی: شهرستان گرگان)، اولین همایش ملی مباحث نوین در مهندسی عمران، دانشگاه آزاد اسلامی واحد بندر گز. میرسنجری، میرمهرداد و سحر عابدیان، 1396، «بررسی تغییرات جمعیتی و اثرگذاری آن بر تغییرات کاربری اراضی (مطالعة موردی: شهر گرگان)»، پژوهش‌های محیط‌زیست، سال هشتم، شمارة 16، صص 3-14. نادی‌زاده شورابه، سامان، حمزه، سعید و سید کریم افشاری، 1396، پایش مکانی-زمانی تغییرات جزیرة حرارتی شهری و ارتباط آن با تغییرات کاربری اراضی و پوشش با ادغام داده‌های اپتیک و حرارتی سنجش‌ازدور، همایش ملی ژئوماتیک، شماره 24، صص 12-1. Amiri, R. et al., 2009, Spatial–Temporaldynamics of Land Surface Temperature in Relation to Fractional Vegetation Coverand Land Use/Cover in the Tabriz Urban Area, Iran, Remote Sensing of Environment, Vol. 113, No. 113: PP. 2606–2617. Bokaie, M. et al., 2016, Assessment of Urban Heat Island Based on the Relationship Between Land Surface Temperature and Land Use/Land Cover in Tehran, Sustainable Cities and Society, Vol. 23, No. 23: PP. 94-104. Boori, M. S. et al., 2015, A Compa rison of Land Surface Temperature, Derived from AMSR-2, Landsat and ASTER Satellite Data, Journal of Geography and Geology, Vol. 7, No. 3, PP. 61-69. Chander, G., and Groeneveld, D. P., 2009, Intra Annual NDVI Validation of the Landsat 5 TM Radiometric Calibration, International Journal of Remote Sensing, Vol. 30, No. 6, PP. 1621-1628. Chander, G., Markham, B. L., and Helder, D. L. 2009, Summary of Current Radiometric Calibration Coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI Sensors, Remote Sensing of Environment, Vol. 113, No. 5, PP. 893-903. Chen, Q. et al., 2006, Reversible Blockade of Electron Transport During Ischemia Protects Mitochondria and Decreases Myocardial Injury Following Reperfusion, Journal of Pharmacology and Experimental Therapeutics, Vol. 319, No. 3, PP. 1405-1412. Chudnovsky, A., Ben-Dor, E., and Saaroni, H., 2004, Diurnal Thermal Behavior of Selected Urban Objects Using Remote Sensing Measurements, Energy and Buildings, Vol. 36, No. 11, PP. 1063-1074. Ding, H., and Shi, W., 2013, Land-Use/Land-Cover Change and Its Influence on Surface Temperature: A Case Study in Beijing City, International Journal of Remote Sensing, Vol. 34, No. 15, PP. 5503-5517. Effat, H. A., and Hassan, O. A. K., 2014, Change Detection of Urban Heat Islands and Some Related Parameters Using Multi-Temporal Landsat Images; A Case Study for Cairo City, Egypt, Urban Climate, Vol. 10, No. 1, PP. 171-188. Firozjaei, M. et al., S. 2018, Monitoring and Forecasting Heat Island Intensity Through Multi-Temporal Image Analysis and Cellular Automata Markov Chain Modelling: A Case of Babol City, Iran, Ecological Indicators, Vol. 55, No. 91, 155-170. (In Persian) Gago, E. J. et al., 2013, The City and Urban Heat Islands: A Review of Strategies to Mitigate Adverse Effects, Renewable and Sustainable Energy Reviews, Vol. 25, No. 25: PP. 749-758. Giannini, M. B. et al., 2015, Land Surface Temperature from Landsat 5 TM Images: Comparison of Different Methods Using Airborne Thermal Data, Journal of Engineering Science and Technology Review, Vol. 8, No. 3, PP 83-90. Gu, B., and Sheng, V. S., 2017, A Robust Regularization Path Algorithm for $Nu $-Support Vector Classification, IEEE Transactions on Neural Networks and Learning Systems, Vol. 28, No. 5, PP. 1241-1248. Hu, Y., and Jia, G., 2010, Influence of Land Use Change on Urban Heat Island Derived from Multi Sensor Data, International Journal of Climatology, Vol. 30, No. 9, PP. 1382-1395. Huang, C., Davis, L. S., and Townshend, J. R. G., 2002, An Assessment of Support Vector Machines for Land Cover Classification, International Journal of Remote Sensing, Vol. 23, No. 4, PP. 725-749. Jiang, J., and Tian, G., 2010, Analysis of the Impact of Land Use/Land Cover Change on Land Surface Temperature with Remote Sensing, Procedia Environmental Sciences, Vol. 3, No. 2. PP. 571-575. Kato, S., and Yamaguchi, Y., 2005, Analysis of Urban Heat-Island Effect Using ASTER and ETM+ Data: Separation of Anthropogenic Heat Discharge and Natural Heat Radiation from Sensible Heat Flux, Remote Sensing of Environment, Vol. 99, No. 1, PP. 44-54. Kavzoglu, T., and Colkesen, I., 2009, A Kernel Functions Analysis for Support Vector Machines for Land Cover Classification, International Journal of Applied Earth Observation and Geoinformation, Vol. 11, No. 5, PP. 352-359. Landsat Project Science Office, 2002, Landsat 7 Science Data User’s Handbook. Goddard Space Flight Center, NASA, Washington, DC, Cited From: Http://Ltpwww.Gsfc.Nasa.Gov /IAS/Handbook/Handbook_Toc.Html. Latif, M. S., 2014, Land Surface Temperature Retrival of Landsat-8 Data Using Split Window Algorithm-A Case Study of Ranchi District, Int J Eng Dev Res (IJEDR), Vol. 4, No. 2, PP. 3840-3849. Li, J., and Zhao, H. M., 2003, Detecting Urban Land-Use And Land-Cover Changes In Mississauga Using Landsat TM Images, Journal of Environmental Informatics, Vol. 2, No. 1, PP. 38-47. Li, X. et al., 2016, Remote Sensing of the Surface Urban Heat Island and Land Architecture in Phoenix, Arizona: Combined Effects of Land Composition and Configuration and Cadastral–Demographic–Economic Factors, Remote Sensing of Environment, Vol. ???, No. 174, PP. 233-243. Lu, D., and Weng, Q., 2005, Urban Classification Using Full Spectral Information of Landsat ETM+ Imagery in Marion County, Indiana, Photogrammetric Engineering and Remote Sensing, Vol. 71, No. 11, PP. 1275-1284. Mantero, P., Moser, G., and Serpico, S. B., 2005, Partially Supervised Classification Of Remote Sensing Images Through SVM-Based Probability Density Estimation, IEEE Transactions on Geoscience and Remote Sensing, Vol. 43, No. 3, PP. 559-570. Mountrakis, G., Im, J., and Ogole, C., 2011, Support Vector Machines in Remote Sensing: A Review, ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 66, No. 3, PP. 247-259. Nemmour, H., and Chibani, Y., 2006, Multiple Support Vector Machines for Land Cover Change Detection: An Application for Mapping Urban Extensions, ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 61, No. 2, PP. 125-133. Otukei, J. R., and Blaschke, T., 2010, Land Cover Change Assessment Using Decision Trees, Support Vector Machines and Maximum Likelihood Classification Algorithms, International Journal of Applied Earth Observation and Geoinformation, Vol. ???, No. 12, PP. S27-S31. Pal, S., and Ziaul, S., 2017, Detection of Land Use and Land Cover Change and Land Surface Temperature in English Bazar Urban Centre, The Egyptian Journal of Remote Sensing and Space Science, Vol. 20, No. 1, PP. 125-145. Qin, Z., Karnieli, A., and Berliner, P., 2001, A Mono-Window Algorithm for Retrieving Land Surface Temperature from Landsat TM Data and Its Application to the Israel-Egypt Border Region, International Journal of Remote Sensing, Vol. 22, No. 18, PP. 3719-3746. Rehman, Z. U. et al., 2015, Analysis of Land Surface Temperature and NDVI Using Geo-Spatial Technique: A Case Study of Keti Bunder, Sindh, Pakistan, Journal of Basic and Applied Sciences, Vol. 23, No. 11, PP. 514-527. Rose, A. L., and Devadas., M. D., 2009, Analysis of Land Surface Temperature and Land Use/Land Cover Types Using Remote Sensing Imagary a Case Inchennal City, India, The Seventh International Conference on Urban Climate, Yokohama, Japan. Rumpf, T. et al., 2010, Early Detection and Classification of Plant Diseases With Support Vector Machines Based on Hyperspectral Reflectance, Computers and Electronics in Agriculture, Vol. 74, No. 1, PP. 91-99. Seto, K. C. et al., 2002, Monitoring Land-Use Change in the Pearl River Delta Using Landsat TM, International Journal of Remote Sensing, Vol. 23, No. 10, PP.1985-2004. Sobrino, J. A., Jiménez Muñoz, J. C., and Paolini, L., 2004, Land Surface Temperature Retrieval From LANDSAT TM 5, Remote Sensing of Environment, Vol. 90, No. 4, PP. 434-440. Srivastava, P. K., Majumdar, T. J., and Bhattacharya, A. K., 2009, Surface Temperature Estimation in Singhbhum Shear Zone of India Using Landsat-7 ETM+ Thermal Infrared Data, Advances in Space Research, Vol. 43, No. 10, PP. 1563-1574. United Nations, 2010, World Urbanization Prospects: The 2009 Revision Population Database, Http://Esa.Un.Org/Unpd/Wup/Index.Htm. Valor, E., and Caselles, V., 1996, Mapping Land Surface Emissivity from NDVI: Application to European, African, and South American Areas, Remote Sensing of Environment, Vol. 57, No. 3, PP. 167-184. Vlassova, L. et al., 2014, Assessment of Methods for Land Surface Temperature Retrieval from Landsat-5 TM Images Applicable to Multiscale Tree-Grass Ecosystem Modeling, Remote Sensing, Vol. 6, No. 5, PP. 4345-4368. Wang, F. et al., 2015, An Improved Mono-Window Algorithm for Land Surface Temperature Retrieval from Landsat 8 Thermal Infrared Sensor Data, Remote Sensing, Vol. 7, No. 4, PP. 4268-4289. Weng, Q., Liu, H., and Lu, D., 2007, Assessing the Effects of Land Use and Land Cover Patterns on Thermal Conditions Using Landscape Metrics in City of Indianapolis, United States, Urban Ecosystems, Vol. 10, No. 2, PP. 203-219. Wukelic, G. E. et al., 1989, Radiometric Calibration of Landsat Thematic Mapper Thermal Band, Remote Sensing of Environment, Vol. 33, No. 28, PP. 339-347. Xian, G., and 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, Vol. 104, No. 2, PP. 147-156. Yuan, F. et al., 2005, Land Cover Classification and Change Analysis of the Twin Cities (Minnesota) Metropolitan Area by Multitemporal Landsat Remote Sensing, Remote Sensing of Environment, Vol. 98, No. 2 and 3, PP. 317-328. Zareie, S., Khosravi, H., and Nasiri, A., 2016, Derivation of Land Surface Temperature From Landsat Thematic Mapper (TM) Sensor Data and Analyzing Relation Between Land Use Changes and Surface Temperature, Manuscript Under Review for Journal Solid Earth, Vol. 7, No. 6, PP. 1-8. (In Persian) Zheng, B. et al., 2015, A Support Vector Machine to Identify Irrigated Crop Types Using Time-Series Landsat NDVI Data, International Journal of Applied Earth Observation and Geoinformation, Vol. 34, No. 34, PP. 103-112. Alavipanah, S. K., 2008, Thermal Remote Sensing and Its Application in the Earth Sciences, Tehran University Press, Tehran. (In Persian) Alavipanah, S. K., Hashemi Darrehbadami, S., and 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), Vol. 3, No. 1, PP. 1-17. (In Persian) Amiri, R. et al., 2009, Spatial Temporaldynamics of Land Surface Temperature in Relation to Fractional Vegetation Coverand Land Use/ Cover in the Tabriz Urban Area, Iran, Remote Sensing of Environment, Vol. 113, No. 113, PP. 2606–2617. (In Persian) Daz, B. S., Guilan, A. GH., and Khavarian, H., 2016, Land Cover Changes in Gorgan City Using Landsat Satellite Images, First International Conference on Water, Environment and Sustainable Development, University of Mohaghegh Ardabili, Ardebil. Gu, B., and Sheng, V. S., 2017, A Robust Regularization Path Algorithm for $Nu $-Support Vector Classification, IEEE Transactions on Neural Networks and Learning Systems, Vol. 28, No. 5, PP. 1241-1248. Jiménez Muñoz, J. C., and Sobrino, J. A., 2003, A Generalized Single Channel Method for Retrieving Land Surface Temperature from Remote Sensing Data, Journal of Geophysical Research: Atmospheres, Vol. 108. No. 22(D22), PP. 1-9. Landsat Project Science Office, 2002, Landsat 7 Science Data User’s Handbook. Goddard Space Flight Center, NASA, Washington, DC, Cited from: Http://Ltpwww.Gsfc.Nasa.Gov /IAS/Handbook/Handbook_Toc.Html. Li, J., and Zhao, H. M., 2003, Detecting Urban Land-Use and Land Cover Changes in Mississauga Using Landsat TM Images, Journal of Environmental Informatics, Vol. 2, No. 1, PP. 38-47. Li, X., et al., 2016, Remote Sensing of the Surface Urban Heat Island and Land Architecture in Phoenix, Arizona: Combined Effects of Land Composition and Configuration and Cadastral–Demographic–Economic Factors, Remote Sensing of Environment, Vol. 202, No. 174, PP. 233-243. Lu, D., and Weng, Q., 2005, Urban Classification Using Full Spectral Information of Landsat ETM+ Imagery in Marion County, Indiana, Photogrammetric Engineering and Remote Sensing, Vol. 71, No. 11, PP. 1275-1284. Matkan, A. et al., 2014, Assessment Relations of Land Use in Heat Islands Using Time Series ASTER Sensor Data (Case Study: Bandar Abbas City), Journal of RS and GIS for Natural Resources, Vol. 5, No. 4, PP. 1-14. Mirsanjari, M., and Abedian, S., 2018, Investigation of Demographic Change and Its Impact on Land Use Changes (Case Study: Gorgan City), Environmental Researches, Vol. 8, No. 16, PP. 3-14. Molashahi, S. et al., 2015, Detection of Land Use Change and Land Cover (Case Study: Gorgan), The First National Conference on Modern Topics in Civil Engineering, Islamic Azad University of Bandar Gaz, Bandar Gaz. (In Persian) Nadizadeh Shorabeh, S., Hamzeh, S., and Afshari, S. Y., 2017, Spatial Temporal Monitoring of Urban Heat Island Changes and Its Relation to Land Use and Land Cover Changes by Integrating Optical and Thermal Data from Remote Sensing, Geomatics National Conference. (In Persian) Sobrino, J. A. et al., 2008, Land Surface Emissivity Retrieval from Different VNIR and TIR Sensors, IEEE Transactions on Geoscience and Remote Sensing, Vol. 46. No. 2, PP. 316-327. | ||
|
آمار تعداد مشاهده مقاله: 1,096 تعداد دریافت فایل اصل مقاله: 1,409 |
||