scholarly journals LAND USE ANALYSIS ON LAND SURFACE TEMPERATURE IN URBAN AREAS USING A GEOGRAPHICALLY WEIGHTED REGRESSION AND LANDSAT 8 IMAGERY, A CASE STUDY: TEHRAN, IRAN

2017 ◽  
Vol XLII-4/W4 ◽  
pp. 117-122 ◽  
Author(s):  
A. Karimi ◽  
P. Pahlavani ◽  
B. Bigdeli
Keyword(s):  
Land Use ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Spatial Data ◽  
Geographically Weighted Regression ◽  
Land Surface ◽  
Urban Areas ◽  
Weighted Regression ◽  
Landsat 8 ◽  
Spatial Factors

Due to urbanization and changes in the urban thermal environment and because the land surface temperature (LST) in urban areas are a few degrees higher than in surrounding non-urbanized areas, identifying spatial factors affecting on LST in urban areas is very important. In this regard, due to the unique properties of spatial data, in this study, a geographically weighted regression (GWR) was used to identify effective spatial factors. The GWR is a suitable method for spatial regression issues, because it is compatible with two unique properties of spatial data, i.e. the spatial autocorrelation and spatial non-stationarity. In this study, the Landsat 8 satellite data on 18 August 2014 and Tehran land use data in 2006 was used for determining the land surface temperature and its effective factors. As a result, R<sup>2</sup> value of 0.765983 was obtained by taking the Gaussian kernel. The results showed that the industrial,military, transportation, and roads areas have the highest surface temperature.

scholarly journals Analysis of Land Use Land Cover Changes with Land Surface Temperature Using Spatial-Temporal Data for Nagpur City, India

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Farhan Khan ◽  
Bhumika Das ◽  
R. K. Mishra ◽  
Brijesh Patel
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Spatial Data ◽  
Land Surface ◽  
Water Bodies ◽  
Landsat 8 ◽  
Land Use Land Cover ◽  
Barren Land

Abstract Remote sensing and Geographic Information System (GIS) are the most efficient tools for spatial data processing. This Spatial technique helps in generating data on natural resources such as land, forests, water, and their management with planning. The study focuses on assessing land change and surface temperature for Nagpur city, Maharashtra, for two decades. Land surface temperature and land use land cover (LULC) are determined using Landsat 8 and Landsat 7 imageries for the years 2000 and 2020. The supervised classification technique is used with a maximum likelihood algorithm for performing land classification. Four significant classes are determined for classification, i.e., barren land, built-up, vegetation and water bodies. Thermal bands are used for the calculation of land surface temperature. The land use land cover map reveals that the built-up and water bodies are increasing with a decrease in vegetation and barren land. Likewise, the land surface temperature map showed increased temperature for all classes from 2000 to 2020. The overall accuracy of classification is 98 %, and the kappa coefficients are 0.98 and 0.9 for the years 2000 and 2020, respectively. Due to urban sprawl and changes in land use patterns, the increase in land surface temperature is documented, which is a global issue that needs to be addressed.

scholarly journals Seasonal Variation of the Spatially Non-Stationary Association Between Land Surface Temperature and Urban Landscape

2019 ◽  
Vol 11 (9) ◽  
pp. 1016 ◽  
Author(s):  
Huimin Liu ◽  
Qingming Zhan ◽  
Sihang Gao ◽  
Chen Yang
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Seasonal Variations ◽  
Geographically Weighted Regression ◽  
Land Surface ◽  
Urban Landscape ◽  
Urban Morphology ◽  
Weighted Regression ◽  
Landsat 8 ◽  
Environment Interaction

There has been a growing concern for the urbanization induced local warming, and the underlying mechanism between urban thermal environment and the driving landscape factors. However, relatively little research has simultaneously considered issues of spatial non-stationarity and seasonal variability, which are both intrinsic properties of the environmental system. In this study, the newly proposed multi-scale geographically weighted regression (MGWR) is employed to investigate the seasonal variations of the spatial non-stationary associations between land surface temperature (LST) and urban landscape indicators under different operating scales. Specifically, by taking Wuhan as a case study, Landsat-8 images were used to achieve the LSTs in summer, winter and the transitional season, respectively. Landscape composition indicators including fractional vegetation cover (FVC), albedo and water percentage (WP) and urban morphology indicators covering building density (BD), building height (BH) and building volume density (BVD) were employed as potential landscape drivers of LST. For reference, the conventional geographically weighted regression (GWR) and ordinary least squares (OLS) regression were also employed. Results revealed that MGWR outperformed GWR and OLS in terms of goodness-of-fit for all seasons. For the specific associations with LST, all six indicators exhibited evident seasonal variations, especially from the transition season to winter. FVC, albedo and BD were observed to possess great spatial non-stationarity for all seasons, while WP, BH and BD tended to influence LST globally. Overall, FVC exhibited certain positive effect in winter. The negative effect of WP was the greatest among all indicators, although it became the weakest in winter. Albedo tended to influence LST more complicatedly than simple cooling. BD, with a consistent heating effect, was testified to have a greater influence on LST than BH for all seasons. The BH-LST association tended to transfer into positive in winter, while the BVD-LST association remained negative for all seasons. The results could support the establishment of season- and site-specific mitigation strategies. Generally, this study facilitates our understanding of human-environment interaction and narrows the gap between climate research and city management.

scholarly journals Land Use/Land Cover Change With Impact On Land Surface Temperature: A Case Study Of Mkda Planning Area, West Bengal, India

2020 ◽  
Vol 13 (4) ◽  
pp. 43-53
Author(s):  
Samrin Fatema ◽  
Dr. Abhisek Chakrabarty
Keyword(s):  
Land Use ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Areas ◽  
Landsat 8 ◽  
Hard Material ◽  
Local Climate ◽  
Landsat 7 ◽  
Multi Temporal

The type of surface influences the temperature of a surface. If it is made of concrete or another hard material, the temperature will be higher. Hence it is essential to study the land surface temperature (LST) of urban areas. The LST is an important parameter in the estimation of radiation budgets and heat balance and is a controlling factor of dynamic climate changes. In this work, we made an effort to identify the LST of the Midnapore Kharagpur Development Authority planning region. Multi-temporal images acquired by Landsat 7 ETM+, Landsat 5 TM and Landsat 8 using OLI sensors on 3 May 2001, 7 May 2011 and 29 May 2019, respectively, were corrected for radiometric and geometric errors and processed to extract LULC classes and LST. Thermal remote sensing can be used to monitor the temperature and local climate of urban areas. This study has shown that the temperature varies across the surface according to land use. It was found that the urbanized area increased from 6.79% (40.39 sq. km) to 11.6% (69.2 sq. km) between 2001 and 2011 and from 11.6% (69.2 sq. km) to 17.22 % (102.79 sq. km) between 2011 and 2019. The LST study has shown that there has been a tremendous change in the spatial pattern of the temperature between 2001 and 2019. Whereas in 2001 the highest temperature did not exceed 34°C, by 2019 it had increased by nearly 8°C, reaching 41.29°C. So, the findings of this study are significant.

scholarly journals Impact of Multitemporal Land Use and Land Cover Change on Land Surface Temperature Due to Urbanization in Hefei City, China

2021 ◽  
Vol 10 (12) ◽  
pp. 809
Author(s):  
Jing Sun ◽  
Suwit Ongsomwang
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Areas ◽  
Decomposition Analysis ◽  
Lulc Change ◽  
Main Components ◽  
The Impact

Land surface temperature (LST) is an essential parameter in the climate system whose dynamics indicate climate change. This study aimed to assess the impact of multitemporal land use and land cover (LULC) change on LST due to urbanization in Hefei City, Anhui Province, China. The research methodology consisted of four main components: Landsat data collection and preparation; multitemporal LULC classification; time-series LST dataset reconstruction; and impact of multitemporal LULC change on LST. The results revealed that urban and built-up land continuously increased from 2.05% in 2001 to 13.25% in 2020. Regarding the impact of LULC change on LST, the spatial analysis demonstrated that the LST difference between urban and non-urban areas had been 1.52 K, 3.38 K, 2.88 K and 3.57 K in 2001, 2006, 2014 and 2020, respectively. Meanwhile, according to decomposition analysis, regarding the influence of LULC change on LST, the urban and built-up land had an intra-annual amplitude of 20.42 K higher than other types. Thus, it can be reconfirmed that land use and land cover changes due to urbanization in Hefei City impact the land surface temperature.

scholarly journals The Effects of Land Use Zoning and Densification on Changes in Land Surface Temperature in Seoul

2019 ◽  
Vol 11 (24) ◽  
pp. 7056 ◽  
Author(s):  
Jae-Ik Kim ◽  
Myung-Jin Jun ◽  
Chang-Hwan Yeo ◽  
Ki-Hyun Kwon ◽  
Jun Yong Hyun
Keyword(s):  
Land Use ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Descriptive Analysis ◽  
Landsat 8 ◽  
Land Use Type ◽  
Coverage Ratio

This study investigated how changes in land surface temperature (LST) during 2004 and 2014 were attributable to zoning-based land use type in Seoul in association with the building coverage ratio (BCR), floor area ratio (FAR), and a normalized difference vegetation index (NDVI). We retrieved LSTs and NDVI data from satellite images, Landsat TM 5 for 2004 and Landsat 8 TIRS for 2014 and combined them with parcel-based land use information, which contained data on BCR, FAR, and zoning-based land use type. The descriptive analysis results showed a rise in LST for the low- and medium-density residential land, whereas significant LST decreases were found in high-density residential, semi-residential, and commercial areas over the time period. Statistical results further supported these findings, yielding statistically significant negative coefficient values for all interaction variables between higher-density land use types and a year-based dummy variable. The findings appear to be related to residential densification involving the provision of more high-rise apartment complexes and government efforts to secure more parks and green spaces through urban redevelopment and renewal projects.

scholarly journals THE EFFECT OF GREEN SPACE ON THE LAND SURFACE TEMPERATURE IN HUE CITY

2018 ◽  
Vol 55 (4C) ◽  
pp. 129
Author(s):  
Nguyen Bac Giang
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Areas ◽  
Green Space ◽  
Satellite Image ◽  
Urban Green Space ◽  
Landsat 8 ◽  
Urban Green ◽  
Study Results

This paper presents the analysis of the effect of urban green space types on land surface temperature in Hue city. Data are collected with temperature monitoring results from each green space type and the interpretation of surface temperature based on Landsat 8 satellite image data to determine temperatures at different times of the year. Results showed that there was a significant correlation between types of urban green space and the surface temperature. Types of green space with a large area and vegetation indexes have a greater effect on temperature than areas with a smaller green space do. Green space types including forest green space, dedicated green space and agriculture green space have the most effect on the surface temperature. The forest area has the greatest influence on the temperature with a temperature difference of more than 1.6 degrees Celsius at 9:00 in the daytime. Besides, the results extracted from satellite images also show that the area of urban green space going to be reduced makes a contribution to increase the surface temperature of urban areas. The study results have established foundation for planning the green spaces in climate change challenges in Hue City.

scholarly journals Impact of urban environment on land surface temperature in local climate zones

2021 ◽  
Vol 333 ◽  
pp. 02008
Author(s):  
Anna Gosteva ◽  
Sofia Ilina ◽  
Aleksandra Matuzko
Keyword(s):  
Surface Temperature ◽  
Urban Environment ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Areas ◽  
Landsat 8 ◽  
Local Climate ◽  
Spatial Coverage ◽  
Artificial Environment ◽  
Local Climate Zones

The replacement of the natural landscape by artificial environment has led to changes in the ecosystem and physical properties of the surface, such as heat storage capacity, and thermal conductivity properties. These changes increase the difficulty of heat transfer between urban areas and the environment. Land surface temperature (LST) images from various satellites are widely used to represent urban thermal environments, which are more convenient and intuitive way. LST maps provide full spatial coverage, which distinguishes them from air temperature data obtained from meteorological stations. The study of LST according to the Landsat 8 data of Krasnoyarsk city over the past 10 years allowed the authors to talk about the observation of constant seasonal urban heat islands (UHI). For a more detailed consideration of the urban environment, this study further considers urban landscapes, thus the idea of local climate zone (LCZ) is introduced to study these diverse impacts in addition to the traditional map of LST. And analysis of the interaction of UHI and LCZ.

scholarly journals Remote sensing inversion and spatial variation of land surface temperature over mining areas of Jixi, Heilongjiang, China

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e10257
Author(s):  
Jia-shuo Cao ◽  
Zheng-yu Deng ◽  
Wen Li ◽  
Yuan-dong Hu
Keyword(s):  
Land Use ◽  
Surface Temperature ◽  
Coal Mining ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Coverage ◽  
Landsat 8 ◽  
Mining Activities ◽  
Patch Area ◽  
Mining Areas

Background Jixi is a typical mining city in China that has undergone dramatic changes in its land-use pattern of mining areas over the development of its coal resources. The impacts of coal mining activities have greatly affected the regional land surface temperature and ecological system. Methods The Landsat 8 Operational Land Imager (OLI) data from 2015 and 2019 were used from the Jiguan, Didao, and Chengzihe District of Jixi in Heilongjiang, China as the study area. The calculations to determine the land-use classification, vegetation coverage, and land surface temperature (LST) were performed using ArcGIS10.5 and ENVI 5.3 software packages. A correlation analysis revealed the impact of land-use type, vegetation coverage, and coal mining activities on LSTs. Results The results show significant spatial differentiation in the LSTs of Jixi City. The LSTs for various land-use types were ranked from high to low as follows: mining land > construction land > grassland > cultivated land > forest land > water area. The LST was lower in areas with high vegetation coverage than in other areas. For every 0.1 increase in vegetation coverage, the LST is expected to drop by approximately 0.75 °C. An analysis of mining land patches indicates that the patch area of mining lands has a significant positive correlation with both the average and maximum patch temperatures. The average patch temperature shows a logarithmic increase with the growth of the patch area, and within 200,000 m2, the average patch temperature increases significantly. The maximum patch temperature shows a linear increase with the patch area growth, and for every 100,000 m2 increase in the patch area of mining lands, the maximum patch temperature increases by approximately 0.81 °C. The higher the average patch temperature of mining land, the higher the temperature in its buffer zone, and the greater its influence scope. This study provides a useful reference for exploring the warming effects caused by coal mining activities and the definition of its influence scope.

scholarly journals ASSESSING THE VARIATION OF THE DRY SEASON’S SURFACE TEMPERATURE IN BINH DUONG PROVINCE FROM 2002 TO 2016 BY APPLYING THERMAL REMOTE SENSING

2018 ◽  
Vol 55 (4C) ◽  
pp. 136
Author(s):  
Nguyen Huynh Anh Tuyet
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Surface Temperature ◽  
Land Surface ◽  
Urban Areas ◽  
Dry Season ◽  
Landsat 8 ◽  
Large Area ◽  
Spectral Radiation ◽  
Thermal Remote Sensing

Thermal remote sensing with its own concepts and potentials has presented a variety of applications in the atmosphere and land surface temperature (LST) variation detection. The objective of this study is to access the LST variation in the dry season of Binh Duong province for understanding the effect of land-use change on the microclimate conditions. The spectral radiation value was determined from gray-scale of thermal infrared images of Landsat 7 ETM+ and Landsat 8 OLI/TIRs, followed by the LST calculation. Results showed that the LST in dry season decreased approximately 1.5 °C over the past 15 years from 30.8 °C in the year 2002 to 29.3 °C in the year 2016, due to a large area of newly planted land of industrial trees changed into mature ones in 2016. The area, in which temperature increased corresponding to 16.6 % of the natural square, has developed rapidly with new industrial parks, urban areas, and vacant land areas. Therefore, the Government should have solutions to promote its positive side and mitigate its negative side by a suitable land-use structure in order to both develop the economic continuously and help to mitigate the climate change effects.

scholarly journals An assessment of the Relationship between Vegetation (Forest Density Cover) Built-Up Area and City Land Surface Temperature, Generated by LANDSAT-8 OLI/ Thermal band - A Case study of Dehradun, India

2021 ◽  
Vol 16 (3) ◽  
Author(s):  
Rajeev Shankhwar ◽  
Rajlakshmi Datta ◽  
Navendra Uniyal
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Areas ◽  
Forest Cover ◽  
Past Research ◽  
Present Condition ◽  
Landsat 8 ◽  
Forest Density ◽  
Open Land

Dehradun city is the capital of Uttarakhand state of India. Evidence from the past research and literature [e.g. CDP 2007, Singh et al 2013, Gupta et al 2014] shows that in the late 80s, Dehradun city was much greener compared to the present condition. In the current study, we tried to identify the correlation between land surface temperature (LST) with Forest cover density classes (FCDC) and built-up area with open land. The current study reveals that there is a relationship between FCDC and LST in the study area. The range of LST recorded is between 32.07 to 43.99 °C. Among all the classes, minimum LST record in VDF class is 32.07°C and maximum LST record in built-up area 43.99°C. The present study shows the importance of vegetation cover in urban areas to reduce LST, air temperature and maintain the urban microclimate as well as to help reduce air pollution.

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