Satellite-Based Land Surface Temperature Estimation of Bogor, Indonesia

2016 ◽  
Vol 2 (1) ◽  
pp. 221 ◽  
Author(s):  
Ema Kurnia ◽  
I Nengah Surati Jaya ◽  
Widi Atmaka
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Capital City ◽  
Spectral Radiance ◽  
Mean Deviation ◽  
Landsat 8 ◽  
Temperature Estimation ◽  
Greenhouse Gasses ◽  
Average Temperature

<p> The earth’s average temperature has been a big issue on the global warming. The warming of the earth is largely the results of emission of carbon dioxide and other greenhouse gasses (GHG) from human activities. As a hinterland of the Capital City, in the last two decades, Bogor is also getting warmer in comparison with the previous decades. This paper presents how the land surface temperature (LST) had been estimated using Split-Window (SW) algorithm and how its spatial distribution in Bogor was computed. The spectral radiance of Landsat-8 TIR bands 10 and 11, the emissivity values, and water vapor used as the input on SW Algorithm. The study revealed that the temperature within the built-up area, have warmer temperature than their surrounding ranging from 40<sup>0</sup>C to 45<sup>0</sup>C of 3,403.9 ha. The use of SW algorithm is quite reliable and accurate to estimate the LST derived from Landsat-8 having a mean deviation of only 2.7%, less than standard acceptable of 10%.</p>

scholarly journals Estimation and Validation of Land Surface Temperature by using Remote Sensing & GIS for Chittoor District, Andhra Pradesh

2021 ◽  
Vol 12 (5) ◽  
pp. 607-617
Author(s):  
A. Rajani, Dr. S.Varadarajan
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Andhra Pradesh ◽  
Temperature Data ◽  
Sensor Data ◽  
Spectral Radiance ◽  
Landsat 8 ◽  
Thermal Band

Land Surface Temperature (LST) quantification is needed in various applications like temporal analysis, identification of global warming, land use or land cover, water management, soil moisture estimation and natural disasters. The objective of this study is estimation as well as validation of temperature data at 14 Automatic Weather Stations (AWS) in Chittoor District of Andhra Pradesh with LST extracted by using remote sensing as well as Geographic Information System (GIS). Satellite data considered for estimation purpose is LANDSAT 8. Sensor data used for assessment of LST are OLI (Operational Land Imager) and TIR (Thermal Infrared). Thermal band  contains spectral bands of 10 and 11 were considered for evaluating LST independently by using algorithm called Mono Window Algorithm (MWA). Land Surface Emissivity (LSE) is the vital parameter for calculating LST. The LSE estimation requires NDVI (Normalized Difference Vegetation Index) which is computed by using Band 4 (visible Red band) and band 5 (Near-Infra Red band) spectral radiance bands. Thermal band images having wavelength 11.2 µm and 12.5 µm of 30th May, 2015 and 21st October, 2015 were processed for the analysis of LST. Later on validation of estimated LST through in-suite temperature data obtained from 14 AWS stations in Chittoor district was carried out. The end results showed that, the LST retrieved by using proposed method achieved 5 per cent greater correlation coefficient (r) compared to LST retrieved by using existing method which is based on band 10.

scholarly journals Retrieval of Land Surface Temperature of Lahore Through Landsat-8 TIRS Data

2019 ◽  
Vol 10 (1) ◽  
pp. 70-77
Author(s):  
Muhammad Nasar -u-Minallah
Keyword(s):  
Radiative Transfer ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Vegetation Cover ◽  
Land Surface ◽  
Global Climate ◽  
Spectral Radiance ◽  
Landsat 8 ◽  
Barren Land ◽  
Data Continuity

Land surface temperature (LST) is an important parameter in global climate change and urban thermalenvironmental studies. The significance of land surface temperature is being acknowledged gradually and interest isincreasing in developing methodologies for the retrieval of LST from Satellite Remote Sensing (SRS) data. ThermalInfrared Sensor (TIRS) of Landsat-8 is the newest TIR sensor for the Landsat Data Continuity Mission (LDCM),offering two adjacent thermal infrared bands (10, 11), having significant beneficiary for the land surface temperatureinversion. The spectral radiance can be estimated through TIR bands 10 and 11 of Landsat-8 OLI_TIRS satellite image.In the present study, the radiative transfer equation-based method has been employed in estimating LST of Lahore andthe analysis demonstrated that estimated LST has the highest accuracy from the radiative transfer method through band10. Land Surface Emissivity (LSE) was derived with the aid of the NDVI’s threshold technique. The present studyresults show that as the built-up area increases and vegetation cover decreases in urban surface, they are linked toincrease in urban land surface temperature and conversely larger vegetation cover associated with lower urbantemperature. The output exposed that LST was high in built-up and barren land, whereas it was low in the area wherethere were more vegetation cover and water.

scholarly journals Retrieval of Land Surface Temperature of Lahore Through Landsat-8 TIRS Data

2019 ◽  
Vol 10 (1) ◽  
pp. 70-77
Author(s):  
Muhammad Nasar -u-Minallah
Keyword(s):  
Radiative Transfer ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Vegetation Cover ◽  
Land Surface ◽  
Global Climate ◽  
Spectral Radiance ◽  
Landsat 8 ◽  
Barren Land ◽  
Data Continuity

Land surface temperature (LST) is an important parameter in global climate change and urban thermalenvironmental studies. The significance of land surface temperature is being acknowledged gradually and interest isincreasing in developing methodologies for the retrieval of LST from Satellite Remote Sensing (SRS) data. ThermalInfrared Sensor (TIRS) of Landsat-8 is the newest TIR sensor for the Landsat Data Continuity Mission (LDCM),offering two adjacent thermal infrared bands (10, 11), having significant beneficiary for the land surface temperatureinversion. The spectral radiance can be estimated through TIR bands 10 and 11 of Landsat-8 OLI_TIRS satellite image.In the present study, the radiative transfer equation-based method has been employed in estimating LST of Lahore andthe analysis demonstrated that estimated LST has the highest accuracy from the radiative transfer method through band10. Land Surface Emissivity (LSE) was derived with the aid of the NDVI’s threshold technique. The present studyresults show that as the built-up area increases and vegetation cover decreases in urban surface, they are linked toincrease in urban land surface temperature and conversely larger vegetation cover associated with lower urbantemperature. The output exposed that LST was high in built-up and barren land, whereas it was low in the area wherethere were more vegetation cover and water.

scholarly journals LAND SURFACE TEMPERATURE (LST) ESTIMATION AT KUSHTIA DISTRICT, BANGLADESH

2021 ◽  
Vol 12 (2) ◽  
pp. 213-227
Author(s):  
Md. Jahir Uddin ◽  
Faisal Jahangir Swapnil
Keyword(s):  
Surface Temperature ◽  
Air Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Global Environmental Change ◽  
Thermal Infrared ◽  
Thematic Mapper ◽  
Spectral Radiance ◽  
Landsat 8 ◽  
North East

Land Surface Temperature (LST) is a key phenomenon in worldwide climate change. The knowledge of surface temperature is important to a range of issues and themes in earth sciences, central to urban climatology, global environmental change, and human-environment interactions. In this study, LST for Kushtia District, Khulna division, Bangladesh, is derived using Arc-GIS software version from the images of Landsat 8 Optical Land Imager (OLI) of 30 m resolution and Thermal Infrared Sensor (TIR) data of 100 m resolution, Landsat-7 Enhanced Thematic Mapper plus (ETM+) with opto-mechanical sensor and Spatial Resolution of 30 m (60 m – thermal, 15-m panchromatic) and Landsat-5 Thematic MAPPER (TM) satellites. A total time span of 20 years, starting from 1998 to 2018 is selected. At every 5 years interval starting from 1998, air temperature, LST, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) have been calculated. Using the equation from Landsat user’s handbook, the digital number of thermal infrared band is converted into spectral radiance. Plank’s Inverse Function is used to obtain the effective at-sensor brightness temperature from the spectral radiance. The surface emissivity based on NDVI classes is used to retrieve the final LST. The study reveals that LST is increasing with the passage of time. Maximum values of LST are found along the North-East and North-West regions of Kushtia district. NDVI is found to have positive correlation with LST. Also, it has been found that NDWI has little influence on LST. The reasons behind the rise and fall of LST in different years are explained from changes in total vegetation coverage and total abundance of water body coverage viewpoint. The spatial distribution figures of air temperature, LST, NDVI and NDWI could be used as a guideline for urban planning, strategies for quality improvement of urban environment and a smart solution to the reduction of LST.

scholarly journals Modeling the distribution of land surface temperature for Bystrytsia river basin using Landsat 8 data

2019 ◽  
Vol 27 (3) ◽  
pp. 453-465
Author(s):  
I. P. Kovalchuk ◽  
O. S. Mkrtchian ◽  
A. I. Kovalchuk
Keyword(s):  
Surface Temperature ◽  
River Basin ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Near Infrared ◽  
Vertical Gradient ◽  
Spectral Radiance ◽  
Landsat 8 ◽  
Practical Applications ◽  
Infrared Spectral

Development of accurate and practicable methods of land surface temperatures (LST) mapping has benefits for a range of scientific and practical applications. The paperconsiders mapping of LST for the Bystrytsia river basin located in Western Ukraine using Landsat 8 imagerywith two thermal infrared bands, which capture emissivity values closely related to land surface temperature (LST).Three multispectral images referring to different seasons (autumn, winter and summer) were used in the study. The method of LST estimation consists of several successive steps. After preprocessing (clipping, masking, and re-projecting), the images were converted from digital numbers to top of atmosphere spectral radiance,and then – to brightness temperature.However, the brightness temperature differs from LST due to emissivity of land surface being different from that of ideal blackbody.The emissivity can vary significantly with vegetation, surface moisture and surface roughness, and can be approximately estimated from land surface reflectivity at red and near-infrared spectral ranges. Estimated values of LST were compared with measurements of Ivano-Frankivsk state weather station, showing rather good compliance for all the three scenes.Obtained estimates of LST show some regularities of its spatial distribution, which also vary significantly from season to season.All the three scenes show conspicuous vertical gradient in LST; summer and autumn scenes are also characterized by significant local variability in LST due to different land cover types (e.g., urban development, forests, different agricultural lands), whereas in winter, differences in LST for mountainous slopes of different aspects appear to be more pronounced. Graphs of LST change with elevation have a parabolic form: sharper decrease of LST is typical for lower elevations, while the vertical LST gradient decreases above 700–1000 m a.s.l.

scholarly journals The Influence of Deforestation on Land Surface Temperature—A Case Study of Perak and Kedah, Malaysia

Forests ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 670 ◽  
Author(s):  
Wan Shafrina Wan Mohd Jaafar ◽  
Khairul Nizam Abdul Maulud ◽  
Aisyah Marliza Muhmad Kamarulzaman ◽  
Asif Raihan ◽  
Syarina Md Sah ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Forest Cover ◽  
Environmental Changes ◽  
Rapid Change ◽  
Spectral Radiance ◽  
Landsat 8 ◽  
Forest Cover Change

Over the past few decades, there has been a rapid change in forest and land cover, especially in tropical forests due to massive deforestation. The major factor responsible for the changes is to fulfill the growing demand of increasing population through agricultural intensification, rural settlements, and urbanization. Monitoring forest cover and vegetation are essential for detecting regional and global environmental changes. The present study evaluates the influence of deforestation on land surface temperature (LST) in the states of Kedah and Perak, Malaysia, between 1988 and 2017. The trend in forest cover change over the time span of 29 years, was analyzed using Landsat 5 and Landsat 8 satellite images to map the sequence of forest cover change. With the measurement of deforestation and its relationship with LST as an end goal, the Normalized Difference Vegetation Index (NDVI) was used to determine forest health, and the spectral radiance model was used to extract the LST. The findings of the study show that nearly 16% (189,423 ha) of forest cover in Perak and more than 9% (33,391 ha) of forest cover in Kedah have disappeared within these 29 years as a result of anthropogenic activities. The correlation between the LST and NDVI is related to the distribution of forests, where LST is inversely related to NDVI. A strong correlation between LST and NDVI was observed in this study, where the average mean of LST in Kedah (25 °C) is higher than in Perak (22.6 °C). This is also reflected by the decreased NDVI value from 0.6 to 0.5 in 2017 at both states. This demonstrated that a decrease in the vegetation area leads to an increase in the surface temperature. The resultant forest change map would be helpful for forest management in terms of identifying highly vulnerable areas. Moreover, it could help the local government to formulate a land management plan.

scholarly journals The Optimum Method for Urban Land Surface Temperature Estimation

2020 ◽  
Author(s):  
Ekkaluk Salakkham ◽  
Pantip Piyatadsananon
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Single Channel ◽  
Landsat 8 ◽  
Optimal Method ◽  
Temperature Estimation ◽  
Retrieval Method ◽  
Window Method ◽  
Split Window Algorithm

Land Surface Temperature (LST) estimation has been studied for several purposes, while the optimal method of estimating the LST has not been criticized yet. This research explores the optimum method in Land Surface Temperature (LST) estimation using LANDSAT-8 imagery data. Four different LST retrieval approaches, the Radiative Transfer Equation-based method (RTE), the Improved Mono-Window method (IMW), the Generalized Single-Channel method (GSC), and the Split-Window algorithm (SW), were calculated to present the LSTs over Buriram Town Municipality, Thailand. The calculated LSTs from these four methods were compared with the ground-based temperature data, taken on the same date and time of the employed LANDSAT-8 images. For this reason, the optimum method of the LST calculation was justified by considering the lowest normalized root means square error (NRMSE) values. As a result, the SW algorithm presents an optimum method in LST estimation. Regarding the SW, this algorithm requires not only the atmospheric profiles during satellite acquisition but also the retrieval of several coefficients. Besides, the LST retrieval method based on the SW algorithm is sensitive to water vapor content and coefficients. Although the SW algorithm is an optimum method explored in this study, it is emphasized that the adjustable values of coefficient response to the atmospheric state may be recommended. With these conditions, the SW algorithm can generate the land-surface temperature over the mixed land-use and land cover on the LANDSAT-8 images.

scholarly journals The Challenge of the Urban Compact Form: Three-Dimensional Index Construction and Urban Land Surface Temperature Impacts

2021 ◽  
Vol 13 (6) ◽  
pp. 1067
Author(s):  
Han Yan ◽  
Kai Wang ◽  
Tao Lin ◽  
Guoqin Zhang ◽  
Caige Sun ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Form ◽  
Three Dimensional ◽  
Compact Form ◽  
Landsat 8 ◽  
Driving Mechanism ◽  
Building Density ◽  
Building Height

Cities are growing higher and denser, and understanding and constructing the compact city form is of great importance to optimize sustainable urbanization. The two-dimensional (2D) urban compact form has been widely studied by previous researchers, while the driving mechanism of three-dimensional (3D) compact morphology, which reflects the reality of the urban environment has seldom been developed. In this study, land surface temperature (LST) was retrieved by using the mono-window algorithm method based on Landsat 8 images of Xiamen in South China, which were acquired respectively on 14 April, 15 August, 2 October, and 21 December in 2017, and 11 March in 2018. We then aimed to explore the driving mechanism of the 3D compact form on the urban heat environment (UHE) based on our developed 3D Compactness Index (VCI) and remote sensing, as well as Geo-Detector techniques. The results show that the 3D compact form can positively effect UHE better than individual urban form construction elements, as can the combination of the 2D compact form with building height. Individually, building density had a greater effect on UHE than that of building height. At the same time, an integration of building density and height showed an enhanced inter-effect on UHE. Moreover, we explore the temporal and spatial UHE heterogeneity with regards to 3D compact form across different seasons. We also investigate the UHE impacts discrepancy caused by different 3D compactness categories. This shows that increasing the 3D compactness of an urban community from 0.016 to 0.323 would increase the heat accumulation, which was, in terms of satellite derived LST, by 1.35 °C, suggesting that higher compact forms strengthen UHE. This study highlights the challenge of the urban 3D compact form in respect of its UHE impact. The related evaluation in this study would help shed light on urban form optimization.

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