scholarly journals Multi-Resolution Study of Thermal Unmixing Techniques over Madrid Urban Area: Case Study of TRISHNA Mission

2019 ◽  
Vol 11 (10) ◽  
pp. 1251 ◽  
Author(s):  
Carlos Granero-Belinchon ◽  
Aurelie Michel ◽  
Jean-Pierre Lagouarde ◽  
Jose A. Sobrino ◽  
Xavier Briottet
Keyword(s):  
Surface Temperature ◽  
Land Surface ◽  
Infrared Imaging ◽  
Thermal Infrared ◽  
Resource Assessment ◽  
Regression Kriging ◽  
Scale Invariant ◽  
Thermal Infrared Imaging ◽  
Spatio Temporal ◽  
Classification Index

This work is linked to the future Indian–French high spatio-temporal TRISHNA (Thermal infraRed Imaging Satellite for High-resolution natural resource Assessment) mission, which includes shortwave and thermal infrared bands, and is devoted amongst other things to the monitoring of urban heat island events. In this article, the performance of seven empirical thermal unmixing techniques applied on simulated TRISHNA satellite images of an urban scenario is studied across spatial resolutions. For this purpose, Top Of Atmosphere (TOA) images in the shortwave and Thermal InfraRed (TIR) ranges are constructed at different resolutions (20 m, 40 m, 60 m, 80 m, and 100 m) and according to TRISHNA specifications (spectral bands and sensor properties). These images are synthesized by correcting and undersampling DESIREX 2008 Airborne Hyperspectral Scanner (AHS) images of Madrid at 4 m resolution. This allows to compare the Land Surface Temperature (LST) retrieval of several unmixing techniques applied on different resolution images, as well as to characterize the evolution of the performance of each technique across resolutions. The seven unmixing techniques are: Disaggregation of radiometric surface Temperature (DisTrad), Thermal imagery sHARPening (TsHARP), Area-To-Point Regression Kriging (ATPRK), Adaptive Area-To-Point Regression Kriging (AATPRK), Urban Thermal Sharpener (HUTS), Multiple Linear Regressions (MLR), and two combinations of ground classification (index-based classification and K-means classification) with DisTrad. Studying these unmixing techniques across resolutions also allows to validate the scale invariance hypotheses on which the techniques hinge. Each thermal unmixing technique has been tested with several shortwave indices, in order to choose the best one. It is shown that (i) ATPRK outperforms the other compared techniques when characterizing the LST of Madrid, (ii) the unmixing performance of any technique is degraded when the coarse spatial resolution increases, (iii) the used shortwave index does not strongly influence the unmixing performance, and (iv) even if the scale-invariant hypotheses behind these techniques remain empirical, this does not affect the unmixing performances within this range of resolutions.

scholarly journals Study of ice accretion on wind turbine blade profiles using thermal infrared imaging

2020 ◽  
pp. 0309524X2093394
Author(s):  
Adeel Yousuf ◽  
Jia Yi Jin ◽  
Pavlo Sokolov ◽  
Muhammad S Virk
Keyword(s):  
Temperature Distribution ◽  
Surface Temperature ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Infrared Imaging ◽  
Thermal Infrared ◽  
Ice Accretion ◽  
Blade Profile ◽  
Thermal Infrared Imaging ◽  
Surface Temperature Distribution

Atmospheric icing has been recognized as hindrance in proper utilization of good wind resources in cold regions. There is a growing need to better understand the ice accretion physics along wind turbine blades to improve its performance and for optimal design of anti/de-icing system. This article describes a study of ice accretion along wind turbine blade profiles using thermal infrared imaging. Surface temperature distribution along four different blade profile surfaces is studied at different operating conditions. Analysis shows that surface temperature distribution along blade profile surface during ice accretion process is a dynamic process and change in atmospheric conditions and blade geometric characteristics significantly affects the surface temperature and resultant ice accretion. The effect of blade geometry on ice accretion is more prominent in case of wet ice conditions due to low freezing fraction and water run back along blade profile surface.

scholarly journals Temperature and Emissivity Inversion Accuracy of Spectral Parameter Changes and Noise of Hyperspectral Thermal Infrared Imaging Spectrometers

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2109 ◽  
Author(s):  
Honglan Shao ◽  
Chengyu Liu ◽  
Chunlai Li ◽  
Jianyu Wang ◽  
Feng Xie
Keyword(s):  
Land Surface ◽  
Infrared Imaging ◽  
Quadratic Function ◽  
Thermal Infrared ◽  
Wavelength Shift ◽  
Atmospheric Conditions ◽  
Center Wavelength ◽  
Spectral Parameters ◽  
Thermal Infrared Imaging ◽  
S Curve

The emergence of hyperspectral thermal infrared imaging spectrometers makes it possible to retrieve both the land surface temperature (LST) and the land surface emissivity (LSE) simultaneously. However, few articles focus on the problem of how the instrument’s spectral parameters and instrument noise level affect the LST and LSE inversion errors. In terms of instrument development, this article simulated three groups of hyperspectral thermal infrared data with three common spectral parameters and each group of data includes tens of millions of simulated radiances of 1525 emissivity curves with 17 center wavelength shift ratios, 6 full width at half maximum (FWHM) change ratios and 6 noise equivalent differential temperatures (NEDTs) under 15 atmospheric conditions with 6 object temperatures, inverted them by two temperature and emissivity separation methods (ISSTES and ARTEMISS), and analyzed quantitatively the effects of the spectral parameters change and noise of an instrument on the LST and LSE inversion errors. The results show that: (1) center wavelength shifts and noise affect the inversion errors strongly, while FWHM changes affect them weakly; (2) the LST and LSE inversion errors increase with the center wavelength shift ratio in a quadratic function and increase with FWHM change ratio slowly and linearly for both the inversion methods, however they increase with NEDT in an S-curve for ISSTES while they increase with NEDT slightly and linearly for ARTEMISS. During the design and development of a hyperspectral thermal infrared instrument, it is highly recommended to keep the potential center wavelength shift within 1 band and keep NEDT within 0.1K (corresponding LST error < 1K and LSE error < 0.015) for normal applications and within 0.03K (corresponding LST error < 0.5K and LSE error < 0.01) for better application effect and level.

scholarly journals Roof Color-Based Warm Roof Evaluation in Cold Regions Using a UAV Mounted Thermal Infrared Imaging Camera

Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6488
Author(s):  
Kirim Lee ◽  
Jinhwan Park ◽  
Sejung Jung ◽  
Wonhee Lee
Keyword(s):  
Surface Temperature ◽  
Infrared Imaging ◽  
Pearson Correlation ◽  
Thermal Infrared ◽  
Thermal Infrared Imaging ◽  
Cool Roof ◽  
Color Differences ◽  
Aerial Vehicle ◽  
Heating Load ◽  
Objective Basis

Existing studies on reducing urban heat island phenomenon and building temperature have been actively conducted; however, studies on investigating the warm roof phenomenon to in-crease the temperature of buildings are insufficient. A cool roof is required in a high-temperature region, while a warm roof is needed in a low-temperature or cold region. Therefore, a warm roof evaluation was conducted in this study using the roof color (black, blue, green, gray, and white), which is relatively easier to install and maintain compared to conventional insulation materials and double walls. A remote sensing method via an unmanned aerial vehicle (UAV)-mounted thermal infrared (TIR) camera was employed. For warm roof evaluation, the accuracy of the TIR camera was verified by comparing it with a laser thermometer, and the correlation between the surface temperature and the room temperature was also confirmed using Pearson correlation. The results showed significant surface temperature differences ranging from 8 °C to 28 °C between the black-colored roof and the other colored roofs and indoor temperature differences from 1 °C to 7 °C. Through this study, it was possible to know the most effective color for a warm roof according to the color differences. This study gave us an idea of which color would work best for a warm roof, as well as the temperature differences from other colors. We believe that the results of this study will be helpful in heating load research, providing an objective basis for determining whether a warm roof is applied.

Use of thermal infrared imaging for monitoring renewed dome growth at Mount St. Helens, 2004

2008 ◽  
pp. 347-359 ◽  
Author(s):  
David J. Schneider ◽  
James W. Vallance ◽  
Rick L. Wessels ◽  
Matthew Logan ◽  
Michael S. Ramsey
Keyword(s):  
Infrared Imaging ◽  
Thermal Infrared ◽  
Thermal Infrared Imaging ◽  
Dome Growth ◽  
Mount St Helens

scholarly journals Clarifying the role of radiative mechanisms in the spatio-temporal changes of land surface temperature across the Horn of Africa

2019 ◽  
Vol 221 ◽  
pp. 210-224 ◽  
Author(s):  
Temesgen Alemayehu Abera ◽  
Janne Heiskanen ◽  
Petri Pellikka ◽  
Miina Rautiainen ◽  
Eduardo Eiji Maeda
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Temporal Changes ◽  
Horn Of Africa ◽  
Spatio Temporal

scholarly journals Comparison of Land Surface Temperature During and Before the Emergence of Covid-19 using Modis Imagery in Wuhan City, China

2020 ◽  
Vol 34 (1) ◽  
Author(s):  
Hamim Zaky Hadibasyir ◽  
Seftiawan Samsu Rijal ◽  
Dewi Ratna Sari
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Anthropogenic Activities ◽  
Temporal Analysis ◽  
The Past ◽  
Modis Imagery ◽  
Rapid Transmission ◽  
The Mean ◽  
Spatio Temporal

Coronavirus disease (COVID-19) was firstly identified in Wuhan, China. By 23rd January 2020, China’s Government made a decision to execute lockdown policy in Wuhan due to the rapid transmission of COVID-19. It is essential to investigate the land surface temperature (LST) dynamics due to changes in level of anthropogenic activities. Therefore, this study aims (1) to investigate mean LST differences between during, i.e., December 2019 to early March 2020, and before the emergence of COVID-19 in Wuhan; (2) to conduct spatio-temporal analysis of mean LST with regards to lockdown policy; and (3) to examine mean LST differences for each land cover type. MODIS data consist of MOD11A2 and MCD12Q1 were employed. The results showed that during the emergence of COVID-19 with lockdown policy applied, the mean LST was lower than the mean LST of the past three years on the same dates. Whereas, during the emergence of COVID-19 without lockdown policy applied, the mean LST was relatively higher than the mean LST of the past three years. In addition, the mean LST of built-up areas experienced the most significant differences between during the emergence of COVID-19 with lockdown policy applied in comparison to the average of the past three years.

scholarly journals CONSTRUCTION AND ANALYSIS OF LONG-TERM SURFACE TEMPERATURE DATASET IN FUJIAN PROVINCE

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 1223-1227 ◽  
Author(s):  
W. E. Li ◽  
X. Q. Wang ◽  
H. Su
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Land Surface ◽  
Rapid Development ◽  
Temporal Distribution ◽  
Heat Fluxes ◽  
Urban Region ◽  
Fujian Province ◽  
Spatio Temporal

Land surface temperature (LST) is a key parameter of land surface physical processes on global and regional scales, linking the heat fluxes and interactions between the ground and atmosphere. Based on MODIS 8-day LST products (MOD11A2) from the split-window algorithms, we constructed and obtained the monthly and annual LST dataset of Fujian Province from 2000 to 2015. Then, we analyzed the monthly and yearly time series LST data and further investigated the LST distribution and its evolution features. The average LST of Fujian Province reached the highest in July, while the lowest in January. The monthly and annual LST time series present a significantly periodic features (annual and interannual) from 2000 to 2015. The spatial distribution showed that the LST in North and West was lower than South and East in Fujian Province. With the rapid development and urbanization of the coastal area in Fujian Province, the LST in coastal urban region was significantly higher than that in mountainous rural region. The LST distributions might affected by the climate, topography and land cover types. The spatio-temporal distribution characteristics of LST could provide good references for the agricultural layout and environment monitoring in Fujian Province.

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