scholarly journals Quantifying the Cooling Effect and Scale of Large Inner-City Lakes Based on Landscape Patterns: A Case Study of Hangzhou and Nanjing

2021 ◽  
Vol 13 (8) ◽  
pp. 1526
Author(s):  
Yaoyao Zheng ◽  
Yao Li ◽  
Hao Hou ◽  
Yuji Murayama ◽  
Ruci Wang ◽  
...  
Keyword(s):  
Inner City ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Areas ◽  
Landscape Patterns ◽  
Water Bodies ◽  
Cooling Effect ◽  
Landsat 8 ◽  
West Lake

The rapid urbanization worldwide has brought various environmental problems. The urban heat island (UHI) phenomenon is one of the most concerning issues because of its strong relation with daily lives. Water bodies are generally considered a vital resource to relieve the UHI. In this context, it is critical to develop a method for measuring the cooling effect and scale of water bodies in urban areas. In this study, West Lake and Xuanwu Lake, two famous natural inner-city lakes, are selected as the measuring targets. The scatter plot and multiple linear regression model were employed to detect the relationship between the distance to the lake and land surface temperature based on Landsat 8 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) and Sentinel-2 data. The results show that West Lake and Xuanwu Lake massively reduced the land surface temperature within a few hundred meters (471 m for West Lake and 336 m for Xuanwu Lake) and have potential cooling effects within thousands of meters (2900 m for West Lake and 3700 m for Xuanwu Lake). The results provide insights for urban planners to manage tradeoffs between the large lake design in urban areas and the cooling effect demands.

scholarly journals Estimating the Cooling Effect of Pocket Green Space in High Density Urban Areas in Shanghai, China

2021 ◽  
Vol 9 ◽  
Author(s):  
Caiyan Wu ◽  
Junxiang Li ◽  
Chunfang Wang ◽  
Conghe Song ◽  
Dagmar Haase ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface ◽  
Urban Areas ◽  
High Density ◽  
Landscape Patterns ◽  
Green Spaces ◽  
Cooling Effect ◽  
Cooling Efficiency ◽  
Landscape Features ◽  
Cooling Effects

Recently, pocket green spaces (PGS), i.e., small green spaces, have attracted growing attention for their various ecological and social services. As a crucial part of urban green spaces in high-density urban areas, PGS facilitates recreation and relaxation for neighborhoods and thus improves the livability of cities at the local scale. However, whether and how the PGS cools the urban heat island effect is still unclear. This research was performed in the highly developed areas of the city of Shanghai during hot summer daytime. We applied a set of cooling effect indicators to estimate the cooling extent, cooling intensity, and cooling efficiency of PGS. We further examined whether and how landscape features within and surrounding the PGS influence its cooling effects. The results showed that 90% of PGS are cooler than their surroundings. Among the landscape features, the land surface temperature of PGS logarithmically decreased with its area, and the maximum local cool island intensity and maximum cooling area logarithmically increased with the area of PGS. The vegetation types and their composition within the PGS also influenced their surface temperature and the cooling effect. The PGS dominated by tree-shrub-grass showed the highest cooling efficiency. The surrounding landscape patterns, especially the patch density and the landscape shape index, influence the cooling effect of PGS at both class and landscape levels. These findings add new knowledge on factors influencing the cooling effect of PGS, and provide the biophysical theoretical basis for developing nature-based cooling strategies for urban landscape designers and planners.

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 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.

scholarly journals Extents of Predictors for Land Surface Temperature Using Multiple Regression Model

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
R. M. Yuvaraj
Keyword(s):  
Surface Temperature ◽  
Regression Model ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Areas ◽  
Climate Models ◽  
Multiple Linear Regression Model ◽  
Multiple Regression Model ◽  
Landsat 8 ◽  
Study Region

Land surface temperature (LST) is a key factor in numerous areas such as climate change, land use/land cover in the urban areas, and heat balance and is also a significant participant in the creation of climate models. Landsat data has given numerous possibilities to understand the land processes by means of remote sensing. The present study has been performed to identify the LST of the study region using Landsat 8 OLI/TIRS satellite images for two time periods in order to compare the data. The study also attempted to identify and predict the role and importance of NDVI, NDBI, and the slope of the region on LST. The study concludes that the maximum and minimum temperatures of 40.44 C and 20.78 C were recorded during the November month whereas the maximum and minimum LST for month March has increased to 42.44 C and 24.57 C respectively. The result indicates that LST is inversely proportional to NDVI (−6.369) and slope (−0.077) whereas LST is directly proportional to NDBI (+14.74). Multiple linear regression model has been applied to calculate the extents of NDVI, NDBI, and slope on the LST. It concludes that the increase in vegetation and slope would result in slight decrease in temperature whereas the increase in built-up will result in a huge increase in temperature.

Quantitative Estimation of Land Surface Temperature and Its Relationship with Land Use/Cover around Sonipat District, Haryana, India

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Diksha Rana ◽  
Maya Kumari ◽  
Rina Kumari
Keyword(s):  
Land Use ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Areas ◽  
Environmental Influence ◽  
Quantitative Estimation ◽  
Inverse Correlation ◽  
Urban Ecosystem ◽  
Water Bodies

Urbanization is a human activity that changes the surface of the earth and degrades the surroundings of major cities all over the world. The problem is more acute in many developing cities with a high population and rapid economic growth. The present study focuses on the effect of land use/land cover (LULC) on the land surface temperature (LST) in Sonipat district, Haryana India. The LULC derived from multispectral satellite data of two periods, 2011 and 2021, indicated a significant increase in urban areas by (3%) and barren and fallow land by (7%), whereas crop land has decreased by (11%) and water bodies have remained the same, in comparison with 2011. The LST, derived from a thermal infrared sensor, showed an overall increase in LST by 5 °C from 2011 to 2021. The results also showed that there was a significant LST difference across the LULC units. Pearson’s correlation analysis results showed an inverse correlation between LST and NDVI across urban areas and other land use classes, whereas a positive correlation over water bodies were observed in the study area. Therefore, LST and its relationship with NDVI via LULC, is a key parameter to investigate the thermal glitches in an urban ecosystem. This can be adopted as a useful tool for analyzing the environmental influence on the ecological unit.

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 THE EFFECTS OF BUILT-UP AND GREEN AREAS ON THE LAND SURFACE TEMPERATURE OF THE KUALA LUMPUR CITY

2017 ◽  
Vol XLII-4/W5 ◽  
pp. 107-112 ◽  
Author(s):  
N. A. Isa ◽  
W. M. N. Wan Mohd ◽  
S. A. Salleh
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Areas ◽  
Vegetation Index ◽  
Urban Climate ◽  
Geographical Information ◽  
Landsat 8 ◽  
Kuala Lumpur ◽  
Green Areas

A common consequence of rapid and uncontrollable urbanization is Urban Heat Island (UHI). It occurs due to the negligence on climate behaviour which degrades the quality of urban climate condition. Recently, addressing urban climate in urban planning through mapping has received worldwide attention. Therefore, the need to identify the significant factors is a must. This study aims to analyse the relationships between Land Surface Temperature (LST) and two urban parameters namely built-up and green areas. Geographical Information System (GIS) and remote sensing techniques were used to prepare the necessary data layers required for this study. The built-up and the green areas were extracted from Landsat 8 satellite images either using the Normalized Difference Built-Up Index (NDBI), Normalized Difference Vegetation Index (NDVI) or Modified Normalize Difference Water Index (MNDWI) algorithms, while the mono-window algorithm was used to retrieve the Land Surface Temperature (LST). Correlation analysis and Multi-Linear Regression (MLR) model were applied to quantitatively analyse the effects of the urban parameters. From the study, it was found that the two urban parameters have significant effects on the LST of Kuala Lumpur City. The built-up areas have greater influence on the LST as compared to the green areas. The built-up areas tend to increase the LST while green areas especially the densely vegetated areas help to reduce the LST within an urban areas. Future studies should focus on improving existing urban climatic model by including other urban parameters.

scholarly journals SPATIOTEMPORAL FUSION OF HIGH RESOLUTION LAND SURFACE TEMPERATURE USING THERMAL SHARPENED IMAGES FROM REGRESSION-BASED URBAN INDICES

2020 ◽  
Vol V-3-2020 ◽  
pp. 247-254
Author(s):  
M. Kim ◽  
K. Cho ◽  
H. Kim ◽  
Y. Kim
Keyword(s):  
High Resolution ◽  
Surface Temperature ◽  
Brightness Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Areas ◽  
Absolute Difference ◽  
Landsat 8 ◽  
Thermal Images ◽  
Urban Regions

Abstract. Obtaining spatially continuous, high resolution thermal images is crucial in order to effectively analyze heat-related phenomena in urban areas and the inherent high spatial and temporal variations. Spatiotemporal Fusion (STF) methods can be applied to enhance spatial and temporal resolutions simultaneously, but most STF approaches for the generation of Land Surface Temperature (LST) have not focused specifically on urban regions. This study therefore proposes a two-phase approach using Landsat 8 and MODIS images acquired on a study area in Beijing to first, investigate the sharpening of the fine resolution image input with urban-related spectral indices and second, to explore the potential of implementing the sharpened results into the Spatiotemporal Adaptive Data Fusion Algorithm for Temperature Mapping (SADFAT) to generate high spatiotemporal resolution LST images in urban areas. For this test, five urban indices were selected based on their correlation with brightness temperature. In the thermal sharpening phase, the Fractional Urban Cover (FUC) index was able to delineate spatial details in urban regions whilst maintaining its correlation with the original brightness temperature image. In the STF phase however, FUC sharpened results returned relatively high levels of correlation coefficient values up to 0.689, but suffered from the highest Root Mean Squared Error (RMSE) and Average Absolute Difference (AAD) values of 4.260 K and 2.928 K, respectively. In contrast, Normalized Difference Building Index (NDBI) sharpened results recorded the lowest RMSE and AAD values of 3.126 K and 2.325 K, but also the lowest CC values. However, STF results were effective in delineating fine spatial details, ultimately demonstrating the potential of using sharpened urban or built-up indices as a means to generate sharpened thermal images for urban areas, as well as for input images in the SADFAT algorithm. The results from this study can be used to further improve STF approaches for daily and spatially continuous mapping of LST in urban areas.

scholarly journals Remote Sensing Based Land Surface Temperature Analysis in Diverse Environment of Lalgudi Block

2019 ◽  
pp. 142-149
Author(s):  
J. Ramachandran ◽  
R. Lalitha ◽  
K. Sivasubramanian
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Forest Area ◽  
Water Bodies ◽  
Spatial And Temporal Variation ◽  
Landsat 8 ◽  
River Sand ◽  
Barren Land ◽  
Diverse Environment

Introduction: Land Surface Temperature (LST) is a significant climatic variable and defined as how hot the "surface" of the Earth would feel to the physical touch in a particular location. A spatial analysis of the land surface temperature with respect to different land use/cover changes is vital to evaluate the hydrological processes. Methods: The objective of this paper is to assess the spatial variation of land surface temperature derived from thermal bands of the Landsat 8 Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS) by using split window algorithm. Place and Data: The study was conducted in Lalgudi block of Trichy District, Tamil Nadu, India. The block has diverse environment like forest area, barren land, river sand bed, water bodies, dry vegetation, cultivated areas (paddy, sugarcane, banana etc.) and settlements. Landsat 8 satellite images for four selected scenes (December 2014 & January 2015 and December 2017 & January 2018) were used to estimate the LST. Results: The spatial and temporal variation of Normalized Difference Vegetation Index (NDVI) and LST were estimated. The average NDVI values of cropped fields varied from 0.3 to 0.5 in all the scenes. The maximum value of LST ranging from 35 to 40°C was recorded in river sand bed. Subsequently, semi-urban settlements in the central part of Lalgudi block exhibited higher temperature ranging from 28 – 30°C. The LST of paddy crop and sugarcane was in the range of 23 to 25°C. The water bodies exhibited LST around 20°C. The coconut plantations, forest area and Prosopis juliflora showed LST value ranging from 24 – 29°C. This kind of block level monitoring studies helps in adopting suitable policies to overcome or minimize the problems triggered by increase in land surface temperature.

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