Analyzing the relationship between Urban Patterns and Land Surface Temperature using Worldview-2 and LANDSTAT-ETM (original) (raw)
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International journal of Environmental Science and Technology, 2023
There is a relationship between spectral indices, urban morphology, and spatial patterns with land surface temperature (LST). In this paper, LST and spectral indices in Tehran have been calculated using Landsat satellite images from 2000 to 2019. LST was improved and the results were evaluated using synoptic stations in Tehran. Pearson correlation coefficient and mean root error are used to evaluate the accuracy of temperature difference. The spatial variables affecting LST are examined including Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Digital Elevation Model (DEM), Building Density (BD), and Road Density (RD). The rise in the temperature of the land surface temperature, which causes the formation of heat islands, is continuously increasing over time and can be seen in some parts of the city. The findings indicate that LST relative accuracy was 0.98 and the root-mean-square error is 2.65 °C. The distance pattern based on the Pearson correlation test showed an effective relationship between LST and the examined indicators. Spots prone to heat islands were identified. Each of the heat island areas of Tehran was studied according to population centers (dense areas using residential, commercial, and industrial lands). The built-up and barren areas index had the highest correlation with surface temperature in most areas. The results showed that regions 9, 13, 18, 21, and especially region 22, which has the most barren land, have the highest surface temperature compared to the surrounding areas. The findings of this study can be used in future urban planning and policy-making.
ESTIMATION OF LAND SURFACE TEMPERATURE TO STUDY URBAN HEAT ISLAND EFFECT USING LANDSAT ETM+ IMAGE
The urban air temperature is gradually rising in all cities in the world. One of the possible causes is the drastic reduction in the greenery area in cities. The distinguished climatic condition termed ‘Urban Heat Island’ (UHI) is developing in the rapidly urbanized cities. Vijayawada city of Andhrapradesh is experiencing rapid urbanization that has resulted in remarkable UHI. Understanding the distribution of Land Surface Temperature (LST) and its spatial variation will be helpful to decipher its mechanism and find out possible solution. This study tries to investigate and identify land use types which have the most influence to the increase of ambient temperature in Vijayawada city. For the present study Landsat ETM+ images of 2001 was obtained from USGS for the study area. Using bands 1-5 and 7 of the pre-processed images the land use / cover pattern was mapped by supervised classification with the maximum likelihood classification algorithm of ERDAS imagine 9.1 software. Five classes considered for the study are Built-up land, Barren Land, Water bodies, Agricultural fields and Vegetation. Normalized Difference Vegetation Index (NDVI) image was developed. The digital number of thermal infrared band is converted in to spectral radiance using the equation supplied by the Landsat user’s hand book. The effective at-sensor brightness temperature is obtained from the spectral radiance using Plank’s inverse function. The surface emissivity based on NDVI classes is used to retrieve the final LST. It was noted that maximum air temperature was observed in built up areas of the city and minimum temperatures are observed in areas where vegetation cover is more. Urban heat island phenomenon is evident from the LST images. NDVI is found to have negative correlation with LST. The study reveals that appropriate strategies are necessary for the sustainable management of the urban area.
2009
Cities often experience a distinguished climate termed the “Urban Climate”. Urban climates are characterized by differences in climatic variables (air temperature, humidity, wind speed and direction, and amount of precipitation) from those of less built-up areas. The major factors contributing to these differences are the land use and land cover transformations. These land use changes often includes replacements of natural surfaces with highly reflective parking lots, concrete masses, asphalt roads etc affecting the thermal environment in cities. Researches show that urban places are warmer than surrounding rural environments and are generally termed as "urban heat island". The aim of this study is to analyze the variations in the thermal environment that exists throughout the city due to different land cover conditions. This study analyzes the land surface temperature differences in the city of Chennai and compares with the land use and land cover types using TM and ETM+ ...
Calculation of surface urban heat index from LANDSAT-8 TIRS data and its relation with land cover
2021 XIX Workshop on Information Processing and Control (RPIC), 2021
Urban localities are mainly covered by concrete and asphalt paving material, which are impermeable surfaces with higher heat absorption capacity and a lower albedo, thus absorbing more radiation compared to the surrounding countryside. The urban surface heat island effect is described as a higher surface temperature in cities compared to a cooler temperature in surrounding areas. Canopy layer urban heat island (HI) are typically detected by in situ sensors at standard (screen-level) meteorological height. Ont he other hand, thermal remote sensors observe the surface urban heat island index (SUHI). The aim of this work is to calculate the spatial distribution of the SUHI index in Córdoba city and in its metropolitan area, and to analyse its relationship with different land covers using satellite information. Córdoba city, located in the central region of Argentina, is the second most populated city in the country. A LANDSAT-8 image of the study area was used to calculate urban heat island index, UHII, and SUHI. Urban and Non-urban region were defined and compared. It was observed that the same type of land use has significant different temperature mean value depending on whether it is located on an urban island or in a rural or open environment.
Pure and Applied Geophysics, 2014
The aim of this study was to identify typical and specific features of land surface temperature (LST) distribution in the city of Krakow and its surroundings with the use of Landsat/ ETM? data. The paper contains a detailed description of the study area and technical properties of the Landsat program and data, as well as a complete methodology of LST retrieval. Retrieved LST records have been standardized in order to ensure comparability between satellite images acquired during different seasons. The method also enables identification of characteristic thermal regions, i.e. areas always colder and always warmer than a zonal mean LST value for Krakow. The research includes spatial analysis of the standardized LST with regard to different land cover types. Basic zonal statistics such as mean standardized LST and percentage share of hot and cold regions within 10 land cover types were calculated. GIS was used for automated data processing and mapping. The results confirmed the most obvious dependence of the LST on different land cover types. Some more factors influencing the LST were recognized on the basis of detailed investigation of the LST pattern in the urban agglomeration of Krakow. The factors are: emission of anthropogenic heat, insolation of the surfaces depending first of all on land relief and shape of buildings, seasonal changes of vegetation and weather conditions at the time of satellite image acquisition.
Structuring, which is increasing due to the growth and the inevitable in urbanization, is one of the most important factor of urban climate change. City centers grow warmer than outer regions and due to the factors' intensity, urban heat islands form in the city. The aim of this paper is to introduce relationship between BAC (Basement Area Coefficient) and FAC (Floor Area Coefficient) datas on pixel base with the temperature values gained by remote sensing methods. The study involves juristical boundaries -Anatolian and European side-of Istanbul city.
Analysing the Effects of Different Land Cover Types on Land Surface Temperature Using Satellite Data
Monitoring Land Surface Temperature (LST) via remote sensing images is one of the most important contributions to climatology. LST is an important parameter governing the energy balance on the Earth and it also helps us to understand the behavior of urban heat islands. There are lots of algorithms to obtain LST by remote sensing techniques. The most commonly used algorithms are split-window algorithm, temperature/emissivity separation method, mono-window algorithm and single channel method. In this research, mono window algorithm was implemented to Landsat 5 TM image acquired on 28.08.2011. Besides, meteorological data such as humidity and temperature are used in the algorithm. Moreover, high resolution Geoeye-1 and Worldview-2 images acquired on 29.08.2011 and 12.07.2013 respectively were used to investigate the relationships between LST and land cover type. As a result of the analyses, area with vegetation cover has approximately 5 ºC lower temperatures than the city center and arid land., LST values change about 10 ºC in the city center because of different surface properties such as reinforced concrete construction, green zones and sandbank. The temperature around some places in thermal power plant region (ÇATES and ZETES) Çatalağzı, is about 5 ºC higher than city center. Sandbank and agricultural areas have highest temperature due to the land cover structure.
USING LANDSAT-8 DATA TO EXPLORE THE CORRELATION BETWEEN URBAN HEAT ISLAND AND URBAN LAND USES
On a local scale, climate change can potentially exacerbate the urban heat island (UHI) effect characterized by an abrupt thermal gradient between urbanized and nearby non-urbanized areas. While it is well-known that the presence of impervious surfaces and less vegetation influence urban microclimate, relatively little attention has been given to the spatial patterns of urban heat islands and how these patterns are affected by land use. In this study, we derive land surface temperature (LST) from Landsat 8 data over four time frames and analyze the relationship between urban thermal environments and urban land use. Landsat 8 Thermal Infrared Sensor (TIRS) and Operational Land Imager (OLI) band data are converted to top-of-atmosphere spectral radiance using radiance rescaling factors. At-satellite brightness temperature was retrieved and the land surface emissivity was calculated. In addition, Normalized Difference Vegetation Index and Normalized Difference Built-up Index were computed and their correlations with LST for each land use were examined. The results indicate that the highest maximum land surface temperature was observed in high density residential and commercial areas near city's downtown. Coastal areas and areas near water bodies are found to have lower land surface temperatures. The results from this study can inform planning and zoning practices aimed at reducing the urban heat island effect and creating a cooler and more comfortable thermal environment for city residents.
Urban heat evolution in a tropical area utilizing Landsat imagery
Keywords: Land Surface Temperature Land use Land cover Digital number GIS Cloud cover is the main limitation of using remote sensing to study Land Use and Land Cover (LULC) change, and Land Surface Temperature (LST) in tropical area like Malaysia. In order to study LULC change and its effect on LST, the Landsat images were utilized within Geographical Information System (GIS) with the aim of removing the effect of cloud cover and image's gaps on the Digital Number (DN) of the pixels. 5356 points according to pixels coordinate which represent the 960 m to 960 m area were created in GIS environment and matched with thermal bands of the study area in remote sensing environment. The DNs of these points were processed to extract LST and imported in GIS environment to derive the temperature maps. Temperature was found to be generally higher in 2010 than in 2000. The comparison of the highest temperature area in the temperature maps with ground stations data showed that the topographical characteristics of the area, and the wind speed, and direction influence the occurrence of Urban Heat Island (UHI) effect. This study concludes that integration of remote sensing data and GIS is a useful tool in urban LST detection in tropical area.