scholarly journals Assessment of Land Surface Temperature Variation in Anaiyur Catchment Using Remote Sensing Algorithm

2020 ◽  
pp. 26-33
Author(s):  
K. Baladeepa ◽  
M. Balapreethi ◽  
M. Ashique ◽  
R. Akila ◽  
J. Ramachandran
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Landsat 8 ◽  
The Earth ◽  
Spatial Changes ◽  
Different Seasons ◽  
Higher Temperature ◽  
Surface Temperature Variation

Land Surface Temperature (LST) is one of the important indicators to understand the spatial changes and surface processes on the earth surface that leads to actual assessment of environmental quality from local to global scales. In this paper, the thermal infrared bands of the Landsat 8 data were used to retrieve Land Surface Temperature for Anaiyur catchment located at Ramanathapuram district. Two images of April 05, 2017 and August 22, 2019 were used in this study to assess the land surface temperature. The results showed that LST from April, 2017 has higher temperature than August, 2019 because of the different season. The period of images taken were based on two different seasons. Overall, Remote sensing algorithms were effective for monitoring and analysing spatially and temporal variation of Land Surface Temperature.

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 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 Pemetaan Wilayah Lahan Basah Berdasarkan Data Penginderaan Jauh Optik Dan Radar

2020 ◽  
Vol 4 (2) ◽  
pp. 48-61
Author(s):  
Rian Nurtyawan ◽  
Ervan Muktamar Hendarna
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Satellite Image ◽  
Soil Surface ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Radar Sensing

ABSTRAKPada umumnya lahan basah dikelola menjadi area pertanian ataupun perkebunan. Fungsi lahan basah memiliki fungsi ekologis seperti pengendali banjir, pencegah intrusi air laut, erosi, pencemaran, dan pengendali iklim global. Data pengindraan jauh yang digunakan pengelolaan lahan basah yaitu pengindraan jauh optik dan radar. Tujuan dari penelitian ini adalah mengeksplorasi korelasi potensial dari data optik dan radar untuk mengamati dinamika pada kawasan lahan basah tersebut dan melakukan pemetaan. Metode yang digunakan pada pengindraan jauh optik yaitu LST (Land Surface Temperature) berdasarkan Citra Satelit Landsat-8 dan metode yang digunakan pada pengindraan jauh radar yaitu estimasi kelembaban tanah berdasarkan Citra Satelit Sentinel-1A. Hasil pengamatan dinamika dan pemetaan pada wilayah Kabupaten Bandung Raya memiliki nilai kelembaban tanah tertinggi pada Bulan Mei dengan nilai kelembapan tanah tanah rata-rata sebesar 20,9 % pada polarisasi VH. Suhu permukaan tanah terendah terjadi pada bulan Mei dengan nilai suhu rata-rata sebesar 19.5 °C. Kolerasi antara nilai kelembapan tanah tanah dan suhu permukaan tanah pada wilayah Kabupaten Bandung Raya berdasarkan metode koefisien determinasi sebesar R2=0.705 didapatkan bahwa semakin tinggi nilai kelembapan tanah tanah maka nilai suhu permukaan tanah akan semakin rendah.Kata kunci: Kawasan lahan basah, Pengindraan Jauh Optik, Pengindraan Jauh Radar, Pengamatan Dinamika, Pemetaan. ABSTRACTIn general wetlands managed become an area of agriculture or plantations. The extent of wetland that has been used can be damaged if it is not managed properly and integrated.. The purpose of this research is to explore the potential correlations between several parameters of optical and radar data to observe the dynamics of wetlands area and mapping the wetlands area. The methodology that was used in optical remote sensing is LST (Land Surface Temperature) based on Landsat-8 Satellite Image and the method used in remote radar sensing is estimation of soil moisture based on Sentinel-1A Satellite Image. The result of the observation in the area and mapping the dynamics in Bandung Raya District had the highest soil moisture values in May with 27% of soil water level in VH polarization and 78.1% in VV polarization and the lowest value in each month is 11.8% and the highest soil surface temperature in August with a value 37.9 ° C and the minimum value 19 ° C..Keywords: Wetland Area, Optical Remote Sensing, Remote Radar Sensing, Dynamics Observation, Mapping.

scholarly journals Estimation of Land Surface Temperature Using LANDSAT 8 Satellite Data of Panchkula District, Haryana

2020 ◽  
pp. 47-66
Author(s):  
Ravi Kumar ◽  
Anup Kumar
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Satellite Data ◽  
Land Surface ◽  
Global Scale ◽  
Sensor Data ◽  
Landsat 8 ◽  
Heat Island ◽  
The Earth ◽  
Satellite Sensor Data

Land surface temperature (LST) represents hotness of the surface of the Earth at a particular location. Land surface temperature is useful for meteorological, climatological changes, heat island, agriculture, hydrological processes at local, regional and global scale. Presently many satellite sensor data are available for calculation of land surface temperature like Landsat 8 and MODIS. In the present study land surface temperature in Panchkula district of Haryana have been calculated using Landsat 8 satellite data of 5th May 2019 and 28th October 2019. Already available equations were used for computation of LST in the study area. LST in the study area varies from 18°C to 56°C. High LST is observed in cultivation land, urban area while low LST is observed in hilly forest area in the study area. In the study validation of LST could not be done because of not available of temperature data of studied dates, however, the result gives idea of land surface temperature on a particular day and location.

scholarly journals Downscale MODIS Land Surface Temperature Based on Three Different Models to Analyze Surface Urban Heat Island: A Case Study of Hangzhou

2020 ◽  
Vol 12 (13) ◽  
pp. 2134 ◽  
Author(s):  
Rui Wang ◽  
Weijun Gao ◽  
Wangchongyu Peng
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Urban Heat Island ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Landsat 8 ◽  
Heat Island ◽  
Time Dimension ◽  
Urban Heat ◽  
Surface Urban Heat Island

Remote sensing technology plays an increasingly important role in land surface temperature (LST) research. However, various remote sensing data have spatial–temporal scales contradictions. In order to address this problem in LST research, the current study downscaled LST based on three different models (multiple linear regression (MLR), thermal sharpen (TsHARP) and random forest (RF)) from 1 km to 100 m to analyze surface urban heat island (SUHI) in daytime (10:30 a.m.) and nighttime (10:30 p.m.) of four seasons, based on Moderate Resolution Imaging Spectroradiometer (MODIS)/LST products and Landsat 8 Operational Land Imager (OLI). This research used an area (25 × 25 km) of Hangzhou with high spatial heterogeneity as the study area. R2 and RMSE were introduced to evaluate the conversion accuracy. Finally, we compared with similarly retrieved LST to verify the feasibility of the method. The results indicated the following. (1) The RF model was the most suitable to downscale MODIS/LST. The MLR model and the TsHARP model were not applicable for downscaling studies in highly heterogeneous regions. (2) From the time dimension, the prediction precision in summer and winter was clearly higher than that in spring and autumn, and that at night was generally higher than during the day. (3) The SUHI range at night was smaller than that during the day, and was mainly concentrated in the urban center. The SUHI of the research region was strongest in autumn and weakest in winter. (4) The validation results of the error distribution histogram indicated that the MODIS/LST downscaling method based on the RF model is feasible in highly heterogeneous regions.

scholarly journals Variations of Land Surface Temperature and Its Relationship with Land Cover and Changes in IPB Campus, Dramaga Bogor 2013-2018

2019 ◽  
Vol 125 ◽  
pp. 01004
Author(s):  
Nafiriair Yufan Madakarah ◽  
Supriatna ◽  
Adi Wibowo ◽  
Masita Dwi Mandini Manessa ◽  
Yoanna Ristya
Keyword(s):  
Land Cover ◽  
Surface Temperature ◽  
Spatial Pattern ◽  
Temperature Variation ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Northern Region ◽  
Landsat 8 ◽  
Vegetation Density ◽  
Surface Temperature Variation

At the present time university can be called "small cities" due to their size of area, population and various kinds of activities. Bogor Agricultural University is a campus that can represent a city in smaller scope with a high variety of land cover. Further, the variation of land cover will affect the surface temperature variation. This study aims to determine spatial pattern of land surface temperature variation and relationship with land cover and also the changes. The data used in this study generated from Landsat 8 imagery and field surveys, then analyze using spatial and statistical analysis tools. The results show temperature has a spatial pattern associated with the land cover. Where the highest temperature tends to be located in the central region in the form of a built-up area and the lowest temperature tends to be located in northern region in the form of forest area. The highest increase in temperature tends to appear in the area that shows changes from vegetation to built-up area. Moreover, this study also found that this phenomenon only appears with temperature value were 7ºC greater than the increase in temperature on a similar land cover. Finally, this study proves that the higher vegetation density will create a lower temperature of land surface, while the higher building density creates a higher land surface temperature.

scholarly journals Analysis of Land Surface Temperature (LST) in Bur Ni Geureudong Geothermal Field, Aceh, Indonesia Using Landsat 8 OLI / TIRS Images

2021 ◽  
Vol 20 (4) ◽  
Author(s):  
Deviyani R. Putri ◽  
◽  
Nazli Ismail ◽  
Rinaldi Idroes ◽  
Syamsul Rizal ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
High Surface ◽  
Geothermal Field ◽  
Landsat 8 ◽  
Mountainous Area ◽  
Landsat 8 Oli ◽  
Geothermal Potential

Abstract Bur Ni Geureudong is one of geothermal areas that potentially to be developed for geothermal power plant in Aceh Province, Indonesia. Prior to the development, detail investigation based on geological, geophysical and geochemical methods are needed for estimating its potential. However, this site is located in a mountainous area with dense forests that are difficult to reach and research of geothermal exploration in site is still very poor considering its promising potential. So that the use of remote sensing method is very suitable to be done to investigate geothermal potential in these remote areas. For reconnaissance survey, Land Surface Temperature (LST) mapping using Landsat 8 OLI/TIRS (Operational Land Imager/Thermal Infrared Sensor) image data was conducted to investigate the geothermal potential in the area. Radiometric correction, Normalized Difference Vegetation Index (NDVI) mapping and emissivity calculations were performed to obtain the LST map. Results show temperatures in the area ranged 17⁰C to 40⁰C, the area with high surface temperatures are caused by geothermal activities. NDVI map also shows an agreement with the high surface temperature region and they are mostly indicated by occurrence of vegetation stress. Keywords: Bur Ni Geureudong geothermal field, Landsat 8 OLI/TIRS., land surface temperature, Thermal remote sensing

scholarly journals Spatio-Temporal Change of Land Surface Temperature at Tiris Geothermal Potential Area, East Java, Indonesia.

2019 ◽  
Vol 3 (1) ◽  
pp. 13-21
Author(s):  
Andre Prayogo ◽  
Sukir Maryanto ◽  
Ahmad Nadhir
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Hot Spots ◽  
Land Surface ◽  
Vegetation Index ◽  
Hot Springs ◽  
Landsat 8 ◽  
Geothermal Potential ◽  
Geological Information

AbstractOne of the areas that have geothermal potential in Indonesia is Tiris because there are found some manifestation in the form of hot springs. Several studies are needed to determine its geothermal potential before exploitation is carried out. Some previous studies have been carried out in the area, one of which uses Landsat 7 remote sensing data. There are other studies that state that knowledge of geology is needed to implement remote sensing in determining geothermal areas. This study uses 3-years data from Landsat 8 and geological information from the regional geological map of the study area. The result show changes in the value of Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) from year to year, where each year the NDVI value decrease which is interpreted as reduced vegetation in the study area. From the distribution of LST values in the study area, it was found that there were hot spots that had higher temperatures than the surrounding area. When geological information and LST distribution map overlaid with regional geological maps, it is known that the hot spots inside the research area are possible to be a geothermal reservoir.

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