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 Monitoring and Prediction of Land Use Land Cover Changes and its Impact on Land Surface Temperature in the Central Part of Hisar District, Haryana Under Semi-Arid Zone of India

2019 ◽  
Vol 12 (3) ◽  
pp. 117-140
Author(s):  
Sunil Kumar ◽  
Swagata Ghosh ◽  
Ramesh Singh Hooda ◽  
Sultan Singh
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Urban Growth ◽  
Land Surface ◽  
Agricultural Land ◽  
Land Use Land Cover ◽  
The City ◽  
Semi Arid

Abstract Land use Land cover have significance in relation to Land, the most vital and fundamental resource pertaining to the urban development. Unprecedented urban growth has a noteworthy impact on natural landscape by converting natural land-cover in Haryana. Hisar, an area recognized for rapid urban growth is less explored in terms of research. The present research has shown a significant change in land use in terms of expansion of built-up area from 3.7 % (1991) to 5.0 % (2001) and 6.2 % (2011) by encroaching into agricultural land. Despite the clear difference between average land surface temperature for built up and non-built up area, grazing land and sandy waste, bare land in the rural surrounding possess higher temperature compared to the city core which contradicts the reported impact of urbanization earlier. Such contrary pertains to sparse vegetation cover leading to reduced evaporative cooling during dry pre-monsoon summer in the rural surrounding. On the other side, green parks and plantation in the city contribute to lower mean temperature because of high rates of evapotranspiration and produce ‘oasis effect’ in the present study area located in semi-arid climatic zone. Regression analysis between temperature and Normalized Difference Vegetation Index, Normalized Difference Built-up Index exhibited a strong negative and positive correlation respectively (Pearson’s r: between -0.79 to -0.87 and between 0.79 to 0.84 respectively). Future land use prediction project an increase (1.3 %) in built-up area from 2011 to 2021. This study recommends urban plantation and prohibition to overgrazing to check the heat effect.

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 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 THE LAND SURFACE TEMPERATURE IMPACT TO LAND COVER TYPES

2016 ◽  
Vol XLI-B3 ◽  
pp. 871-876 ◽  
Author(s):  
I. Ibrahim ◽  
A. Abu Samah ◽  
R. Fauzi ◽  
N. M. Noor
Keyword(s):  
Land Cover ◽  
Surface Temperature ◽  
Temperature Variation ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Water Bodies ◽  
Land Cover Types ◽  
Tukey Test ◽  
Bare Land ◽  
Post Hoc

Land cover type is an important signature that is usually used to understand the interaction between the ground surfaces with the local temperature. Various land cover types such as high density built up areas, vegetation, bare land and water bodies are areas where heat signature are measured using remote sensing image. The aim of this study is to analyse the impact of land surface temperature on land cover types. The objectives are 1) to analyse the mean temperature for each land cover types and 2) to analyse the relationship of temperature variation within land cover types: built up area, green area, forest, water bodies and bare land. The method used in this research was supervised classification for land cover map and mono window algorithm for land surface temperature (LST) extraction. The statistical analysis of post hoc Tukey test was used on an image captured on five available images. A pixel-based change detection was applied to the temperature and land cover images. The result of post hoc Tukey test for the images showed that these land cover types: built up-green, built up-forest, built up-water bodies have caused significant difference in the temperature variation. However, built up-bare land did not show significant impact at p<0.05. These findings show that green areas appears to have a lower temperature difference, which is between 2° to 3° Celsius compared to urban areas. The findings also show that the average temperature and the built up percentage has a moderate correlation with R<sup>2</sup> = 0.53. The environmental implications of these interactions can provide some insights for future land use planning in the region.

scholarly journals Assessment and simulation of land use and land cover change impacts on the land surface temperature of Chaoyang District in Beijing, China

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9115 ◽  
Author(s):  
Muhammad Amir Siddique ◽  
Liu Dongyun ◽  
Pengli Li ◽  
Umair Rasool ◽  
Tauheed Ullah Khan ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Surface Temperature ◽  
Land Cover Change ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Rapid Urbanization ◽  
K Value ◽  
Vegetation Water ◽  
Landsat Satellite

Rapid urbanization is changing the existing patterns of land use land cover (LULC) globally, which is consequently increasing the land surface temperature (LST) in many regions. The present study is focused on estimating current and simulating future LULC and LST trends in the urban environment of Chaoyang District, Beijing. Past patterns of LULC and LST were identified through the maximum likelihood classification (MLC) method and multispectral Landsat satellite images during the 1990–2018 data period. The cellular automata (CA) and stochastic transition matrix of the Markov model were applied to simulate future (2025) LULC and LST changes, respectively, using their past patterns. The CA model was validated for the simulated and estimated LULC for 1990–2018, with an overall Kappa (K) value of 0.83, using validation modules in IDRISI software. Our results indicated that the cumulative changes in built-up to vegetation area were 74.61 km2 (16.08%) and 113.13 km2 (24.38%) from 1990 to 2018. The correlation coefficient of land use and land cover change (LULCC), including vegetation, water bodies and built-up area, had values of r =  − 0.155 (p > 0.005), −0.809 (p = 0.000), and 0.519 (p > 0.005), respectively. The results of future analysis revealed that there will be an estimated 164.92 km2 (−12%) decrease in vegetation area, while an expansion of approximately 283.04 km2 (6% change) will occur in built-up areas from 1990 to 2025. This decrease in vegetation cover and expansion of settlements would likely cause a rise of approximately ∼10.74 °C and ∼12.66 °C in future temperature, which would cause a rise in temperature (2025). The analyses could open an avenue regarding how to manage urban land cover patterns to enhance the resilience of cities to climate warming. This study provides scientific insights for environmental development and sustainability through efficient and effective urban planning and management in Beijing and will also help strengthen other research related to the UHI phenomenon in other parts of the world.

scholarly journals STUDY INFLUENCE OF LAND COVER CHANGE IN WETLAND AND VEGETATION ON LAND SURFACE TEMPERATURE

2021 ◽  
Vol 12 (2) ◽  
pp. 66-74
Author(s):  
Ricky Anak Kemarau ◽  
Oliver Valentine Eboy
Keyword(s):  
Land Cover ◽  
Surface Temperature ◽  
Land Cover Change ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Geographical Information ◽  
Gis Technology ◽  
Heat Effects ◽  
Urban Heat ◽  
The Impact

Wetlands are a vital component of land cover in reducing impacts caused by urban heat effects and climate change. Remote sensing technology provides historical data that can study the impact of development on the environment and local climate. The studies of wetland in reducing Land Surface Temperature (LST) in a tropical climate are still lacking. The objective of the study is to examine the influence of land cover change wetland and vegetation on land surface temperature between the years 1988 and 2019. First of all, step, pre-processing, namely geometric correction, atmosphere correction, and radiometric correction, were performed before retrieval of the LST dataset from thermal band Landsat 5 and 8. Then, Iso Cluster, unsupervised was chosen to produce the land cover map for 1988 and 2019. Geographical Information System (GIS) technology was utilized to determine changes to land cover and LST change between the years 1988 and 2019. With GIS technology, a study of the impact of wetland deforestation on local temperatures at a local scale was carried out. Next to that, correlations between LST and the wetland were analyzed. The results indicated the different land cover between the years 1988 and 2019. The areas of land cover for wetland and vegetation decrease and while area of urban increased. The land cover changed the influences of LST significantly in the study area. The LST increased with the decreasing in areas wetland areas for every 5-kilometer square (km²) wetland lost an increase in 1-degree Celsius of LS was estimated. The size of wetland influence on LST was significant. Wetland and vegetation function in reducing the urban heat island effect was vital in providing a comfortable environment to the Kuching population and indirectly reduce the demand for power energy.

scholarly journals ANALYZING THE EFFECTS OF LAND COVER CHANGE ON SURFACE TEMPERATURE IN MOUNT MAKILING FOREST RESERVE (MMFR) AND ITS NEIGHBORING MUNICIPALITIES USING LANDSAT DATA

2019 ◽  
Vol XLII-4/W19 ◽  
pp. 165-172
Author(s):  
V. K. M. Del Mundo ◽  
C. L. Tiburan Jr.
Keyword(s):  
Land Cover ◽  
Surface Temperature ◽  
Land Cover Change ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Centers ◽  
Forest Reserve ◽  
Land Cover Types ◽  
Rapid Economic Growth ◽  
Supervised Image Classification

Abstract. Land Surface Temperature (LST) is said to be affected by frequent changes in the land cover. Over the years, the immediate environs of Mount Makiling Forest Reserve (MMFR) have experienced such kind of change due to rapid economic growth of the area that also led to the expansion of urban centers. The study utilized Landsat imageries to determine the possible effects of land cover change on surface temperature using the integration of remote sensing and GIS technologies. Initially, the multispectral bands were radiometrically corrected using Dark Object Subtraction (DOS) while the thermal bands were corrected using Land Surface Emissivity (LSE). After these corrections were applied, the images were classified using supervised image classification technique where seven land cover types have been identified. The classified images were then validated using 200 reference data and this revealed an overall accuracy of 87.5% and 86.0% for the May 2003 and July 2015 images, respectively. Results showed that changes in land cover resulted to a significant change in Land Surface Temperature (LST). The LST in 2003 (16.49°C – 40.44°C) was found higher than that of 2015 which was observed between 13.35°C and 33.83°C only. The reason behind this is the increase in green spaces from 2003 to 2015. Among the major land cover types, forest lands exhibited the lowest mean surface temperature for both years having 27.27°C in 2003 and 21.35°C in 2015 while built-up areas had the highest surface temperature having 32.60°C in 2003 and 26.00°C in 2015.

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