scholarly journals ANALYSING THE EFFECTS OF DIFFERENT LAND COVER TYPES ON LAND SURFACE TEMPERATURE USING SATELLITE DATA

2015 ◽  
Vol XL-1-W5 ◽  
pp. 665-667 ◽  
Author(s):  
A. Şekertekin ◽  
Ş. H. Kutoglu ◽  
S. Kaya ◽  
A. M. Marangoz
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Single Channel ◽  
Meteorological Data ◽  
Thermal Power ◽  
City Center ◽  
The City

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.

scholarly journals Impact of land cover change on land surface temperature over Greater Beirut Area – Lebanon

2021 ◽  
Vol 2 (1) ◽  
pp. 14-27
Author(s):  
Ali Khyami
Keyword(s):  
Remote Sensing ◽  
Twentieth Century ◽  
Land Cover ◽  
Surface Temperature ◽  
Land Cover Change ◽  
Land Surface Temperature ◽  
Urban Growth ◽  
Land Surface ◽  
Bare Land ◽  
The City

Remote sensing (RS) technology has been used together with geographic information systems (GIS) to determine the LC types, retrieve LST, and analyze their relationships. The term Greater Beirut Area (GBA) is used to refer to the city of Beirut and its suburbs which witnessed rapid urban growth, after the end of the civil war, in the last decade of the twentieth century, due to the increase in the number of its inhabitants, and the prosperity and development of sectors such as; industrial, trade, tourism, and construction. These factors led to a wide change in the land cover (LC) types and increased land surface temperature LST. The results showed an increase in built-up areas by 29.1%, and agricultural lands by 6%, while bare land, forests, and seawater decreased by 28.5%, 4.9%, and 1.9%, respectively. These changes caused large differences in the LST between built-up areas and other LC types. The highest LST recorded was in built-up areas (33.03°C in 1985, and 34.01°C in 2020), followed by bare lands (32.61 °C in 1985 and 33.49°C in 2020), cropland (31.23°C in 1985 and 32.17°C in 2020), forest (30.08°C in 1985 and 30.47°C in 2020), and water (24.97°C in 1985 and 28.15°C in 2020). Consequently, converting different LC types into built-up areas led to increases in LST and changed microclimate.

scholarly journals Remote Sensing Image-Based Analysis of the Impact of Land Use/Land Cover Changes on Land Surface Temperature

2019 ◽  
Vol 8 (4) ◽  
pp. 1834-1839
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Spectral Radiance ◽  
Land Use Land Cover ◽  
The Impact ◽  
The City

This study evaluated the land use/land cover (LULC) changes in Tuguegarao City and analyzed its impact on Land Surface Temperature (LST). It was carried out using Remote Sensing and Geographic Information System (GIS) techniques. Three Landsat TM and ETM+ images data were acquired for the years 1990, 2005 and 2016 from USGS Earth Explorer portal. ArcGIS software was used to determine the area statistics of the different land cover and to make the final LULC map. LST for the study area was taken from the thermal infrared band of the satellite images by converting the image digital number into degrees Kelvin using the LMin and LMax spectral radiance scaling factors. The largest areal change appeared in the built-up area with an increase of 1120.32 ha. However, this study detected higher LST in the crop land, grassland and barren land areas of the city rather than the built-up parts of the city which does not follow many of previous studies. The results of the study can be presented to the Local Government Unit so that they can draft appropriate laws for the betterment of the city specially that rapid urbanization and uncontrolled population growth may have extreme impact on the environment.

scholarly journals Thermal Remote Sensing: A tool to Determine Temporal Land Surface Temperature in Hawassa City, Ethiopia

2020 ◽  
Author(s):  
Mikias Biazen Molla
Keyword(s):  
Remote Sensing ◽  
Standard Deviation ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Expansion ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Thermal Remote Sensing ◽  
The City

Abstract This investigation was conducted for the estimation of the temporal land surface temperature value using thermal remote sensing of Landsat-8 (OLI) Data in Hawassa City Administration, Ethiopia. Satellite datasets of Landsat-7 (ETM+) for 22nd March 2002 and Landsat-8 (OLI) of 22nd March 2019 were taken for this study. Different algorisms were used to estimate the Normalized Difference Vegetation Index threshold from the Red and Near-Infrared band and the ground earth's surface emissivity esteem is legitimately recovered from the thermal infrared by coordinating with the outcome got from MODIS information. The land use land cover map of the city was prepared with better accuracy using the on-screen classification technique. The spatial distribution of surface temperature of the city range from 6.62°C to 22.54°C with a mean of 14.58°C and a standard deviation of 11.25 in the year of march 22nd 2002. The LST result derived from Landsat 8 for March 22nd, 2019, ranges from 11.97°C to 35.5°C with a mean of 23.735 °C and a standard deviation of 16.64. In both years the higher LST values correspond to built-up/settlement and bare/open lands of the city; whereas, lower LST values were observed in vegetation (trees/woodlot, shrubs, and grass forested) area. Urban expansion (built-up area roads, and another impervious surface), decline in vegetation levels due to deforestation and increasing population density. Increasing an evergreen tree and green space coverage, design and develop city parks and rehabilitate the existing degraded natural environments are among the recommended strategy to reduce the rate of LST.

scholarly journals Spatial Assessment of Land Surface Temperature and Emissivity in the Tropical Littoral City of Port Harcourt, Nigeria

2019 ◽  
pp. 88-103 ◽  
Author(s):  
P. Nwaerema ◽  
Ojeh N. Vincent ◽  
C. Amadou ◽  
Atuma, I. Morrison
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Dry Season ◽  
Wet Season ◽  
City Center ◽  
Multiple Wavelength ◽  
Port Harcourt ◽  
Land Surfaces ◽  
The City

The study examined Land Surface Temperature (LST) and Land Surface Emissivity (LSE) in a tropical coastal city of Port Harcourt and its environs. Satellite remote sensing of multiple-wavelength origin was employed to derive data from the Landsat Enhance Thematic Mapper (ETM+). Statistical mean and range were used to show pattern of LST and LSE. The study established the relationship and characteristics of land use land cover, built-up area and influence of population on land surfaces. With population of over 3,095,342 persons occupying surface area of approximately 458,28 Km2, rapid vegetal and water body lost have put the city area under pressure of 4.7°C heat bias at the interval of 15 years. From rural fringes to the city center, LST varies with 9.3°C in wet season and 4.8°C in the dry season. During the dry season, LSE is severe in the southern part of the city contributed by water bodies, more vegetal cover and urban pavement materials. Emissivity in the wet season varied with 0.0136 and 0.0006 during the dry season but differs with 0.0165 between the two seasons. One critical finding is that LSE decreases from the rural fringes to the city center and LST increases from the rural fringes to the city center. It is recommended that urban greening at the city center should be practiced and the rural fringes should be explored by decongesting activities at the city center to the outskirts in order to ameliorate the effects of urban heat bias without further delay.

scholarly journals Spatial Patterns of Land Surface Temperature and Their Influencing Factors: A Case Study in Suzhou, China

2019 ◽  
Vol 11 (2) ◽  
pp. 182 ◽  
Author(s):  
Yongjiu Feng ◽  
Chen Gao ◽  
Xiaohua Tong ◽  
Shurui Chen ◽  
Zhenkun Lei ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Additive Models ◽  
Spatial Proximity ◽  
Dominant Factor ◽  
City Center ◽  
Spatial Factors ◽  
Suzhou City ◽  
The City

Land surface temperature (LST) is a fundamental Earth parameter, on both regional and global scales. We used seven Landsat images to derive LST at Suzhou City, in spring and summer 1996, 2004, and 2016, and examined the spatial factors that influence the LST patterns. Candidate spatial factors include (1) land coverage indices, such as the normalized difference built-up index (NDBI), the normalized difference vegetation index (NDVI), and the normalized difference water index (NDWI), (2) proximity factors such as the distances to the city center, town centers, and major roads, and (3) the LST location. Our results showed that the intensity of the surface urban heat island (SUHI) has continuously increased, over time, and the spatial distribution of SUHI was different between the two seasons. The SUHIs in Suzhou were mainly distributed in the city center, in 1996, but expanded to near suburban, in 2004 and 2016, with a substantial expansion at the highest level of SUHIs. Our buffer-zone-based gradient analysis showed that the LST decays logarithmically, or decreases linearly, with the distance to the Suzhou city center. As inferred by the generalized additive models (GAMs), strong relationships exist between the LST and the candidate factors, where the dominant factor was NDBI, followed by NDWI and NDVI. While the land coverage indices were the LST dominant factors, the spatial proximity and location also substantially influenced the LST and the SUHIs. This work improved our understanding of the SUHIs and their impacts in Suzhou, and should be helpful for policymakers to formulate counter-measures for mitigating SUHI effects.

scholarly journals Impact of Land Cover Changes on Land Surface Temperature and Human Thermal Comfort in Dhaka City of Bangladesh

2021 ◽  
Author(s):  
H. M. Imran ◽  
Anwar Hossain ◽  
A. K. M. Saiful Islam ◽  
Ataur Rahman ◽  
Md Abul Ehsan Bhuiyan ◽  
...  
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Land Cover ◽  
Surface Temperature ◽  
Thermal Comfort ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Dhaka City ◽  
Human Thermal Comfort ◽  
The City

AbstractUrbanization leads to the construction of various urban infrastructures in the city area for residency, transportation, industry, and other purposes, which causes major land use change. Consequently, it substantially affects Land Surface Temperature (LST) by unbalancing the surface energy budget. Higher LST in city areas decreases human thermal comfort for the city dwellers and affects the urban environment and ecosystem. Therefore, a comprehensive investigation is needed to evaluate the impact of land use change on the LST. Remote Sensing (RS) and Geographic Information System (GIS) techniques were used for the detailed investigation. RS data for the years 1993, 2007 and 2020 during summer (March–May) in Dhaka city were used to prepare land cover maps, analyze LST, generate hazard maps and relate the land cover change with LST by using GIS. The results show that the built-up area in Dhaka city increased by 67% from 1993 to 2020 by replacing lowland mainly, followed by vegetation, bare soil and water bodies. LSTs found in the study area were ranged from 23.26 to 39.94 °C, 23.69 to 43.35 °C and 24.44 to 44.58 °C for the years 1993, 2007 and 2020, respectively. The increases of spatially distributed maximum and mean LST were found 4.62 °C and 6.43 °C, respectively, for the study period of 27 years while the change in minimum LST was not substantial. LST increased by around 0.24 °C per year and human thermal discomfort shifted from moderate to strong heat stress for the total study period due to the increase of built-up and bare lands. This study also shows that normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) were negatively correlated with LST while normalized difference built-up Index (NDBI) and normalized difference built-up Index (NDBAI) were positively correlated with LST. The methodology developed in this study can be adapted to other cities around the globe.

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