Marc-André Gagnon, Pierre Tremblay, Simon Savary, Vincent Farley, Philippe Lagueux and Martin Chamberland. Telops, 100-2600 Saint-Jean Baptiste Ave., ...
AIRBORNE THERMAL HYPERSPECTRAL IMAGING OF URBAN AND RURAL AREAS Marc-André Gagnon, Pierre Tremblay, Simon Savary, Vincent Farley, Philippe Lagueux and Martin Chamberland Telops, 100-2600 Saint-Jean Baptiste Ave., Québec, Qc, G2E 6J5, Canada ABSTRACT Airborne thermal infrared (TIR) imaging is often used to study the problematic of urban heat islands (UHI). However, surface temperature measurement using remote sensing techniques often fail to take into account the spectral emissivity of the investigated materials. This leads to mismatches between the true surface temperature and the measured brightness temperature obtained from conventional broadband TIR sensors. Airborne TIR hyperspectral imaging (HSI) was carried out on urban and rural areas at high spectral resolution. The thermodynamic temperature maps obtained upon temperature-emissivity separation (TES) display sharper thermal contrast associated with UHI than their corresponding brightness temperature maps. A chemical map of quartz could also be obtained from the emissivity datacubes. The results illustrate the potential of TIR HSI to address the UHI problematic. Index Terms— Infrared, Airborne, Hyperspectral, Imaging, Urban Heat Island 1. INTRODUCTION Characterization of urban heat islands (UHI) is a problematic in which temperature measurement plays a decisive role. A UHI is an area which is characterized by an average temperature significantly higher than its surroundings as a result of a higher density of man-made objects like asphalt pavement and concrete structures. The later absorbs solar radiation and reemits the energy as thermal infrared radiation. Consequently, the average temperature measured in a UHI is higher than what would be measured in less populated areas, as in rural environments, on the same day [1]. A traditional way of characterizing temperature gradients on vast areas is thermal infrared (TIR) remote sensing (8 to 12 m) [2]. Both broadband and multispectral TIR imaging, provide radiometric temperature values which can be assumed as representative of the thermodynamic surface temperature. However, the brightness temperature values obtained from these measurements include a mixture of both the self-emission, inherent to TIR, and the reflective components. In order to estimate the temperature, i.e. the thermodynamic temperature, from remote sensing
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measurements, the model must account for many parameters including the atmospheres’ absorption and the surface emissivity [3]. Unfortunately, it can be difficult to estimate all these parameters from a single measurement, especially the spectral emissivity ( ). In the case where the emissivity has a spectral dependency, e.g. concrete and polymers, one cannot use this approach without introducing additional variability in the temperature estimation. In addition, the limited number of spectral bands typically available in TIR multispectral sensors makes atmospheric absorption and/or sky reflection compensations difficult. Airborne TIR hyperspectral imaging (HSI) provides valuable information for the characterization of urban and rural environments. The high spatial resolution allows to clearly differentiate objects on the ground at the same time as providing spectral information. The high spectral resolution, in the order of hundreds of bands, allows efficient atmospheric correction since the sharp absorption bands associated with water vapor can easily be distinguished from any other signal. The reflective component can also be efficiently estimated from high spectral resolution data since it mostly consists of sharp water vapor and ozone emission bands which are spectral features associated with the sky. Therefore, a temperatureemissivity separation (TES) algorithm can efficiently be applied to the scenes in order to estimate both temperature and spectral emissivity of the objects at ground level. Airborne thermal infrared hyperspectral mapping was carried out over urban and rural areas in order to demonstrate the approach in different contexts and environments. A great diversity of elements and man-made objects can be found in the scenes like vegetation, soils, basements, metallic surfaces, rivers, concrete and asphalt pavement. The TES algorithm was applied to airborne datacubes recorded in both urban and rural areas. Every time the procedure is used, two maps are generated: the estimated temperature map and its associated spectral emissivity map. Both results can be compared with the temperature map, derived from the hyperspectral data, which would be obtained using a conventional broadband TIR camera. In order to illustrate the chemical information contained in the spectral emissivity, a chemical map of quartz is also presented. The results illustrate the benefits of airborne TIR hyperspectral imaging for characterizing temperature gradients associated with UHI.
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the radiance from the surface which is also function of the surface thermodynamic temperature ( ), is the atmospheric transmittance, and the radiance associated with TIR self-emission of all atmospheric components.
2. EXPERIMENTAL 2.1. The Telops Airborne Hyper-Cam Platform All measurements were carried out using the Telops HyperCam airborne platform. The Hyper-Cam-LW (longwave) is a lightweight and compact hyperspectral imaging instrument which uses Fourier Transfer Infrared (FTIR) technology. The Telops Hyper-Cam features a focal plane array (FPA) detector which contains 320×256 pixels over a basic 6.4°×5.1° field of view (FOV). The FOV can be extended to 25.6°×20.4° using a de-magnifying 0.25× telescope. The spectral resolution is user-selectable up to 0.25 cm-1 over the 7.7 to 11.7 µm spectral ranges. The Telops Hyper-Cam airborne platform is equipped with a global positioning system (GPS) and inertial motion unit (IMU) for georeferencing and tracking of the aircraft movements in flight. An image-motion compensation (IMC) mirror uses the GPS/IMU data to compensate efficiently for the aircraft movements during data acquisition since acquiring a full datacube typically lasts about one second. The data includes all the relevant information for orthorectification and stitching. Visible images are simultaneously recorded along with the infrared datacubes using a boresight CCD camera on the airborne platform.
[
(
)]
(
)
(Eq.1)
A smoothing criterion, similar to the one described in the work of Borel [4] was used to minimize both atmospheric and downwelling radiance contributions. 3. RESULTS AND DISCUSSION In order to illustrate the challenges associated with determining surface temperature using remote sensing, the temperature map of a typical scene displaying a great variety of surfaces is represented in Figure 1.
2.2. Flight Conditions The first flight was carried out in the South-East portion of Québec City (Canada) in April 2013 around 3 PM at an altitude of 2150 meters and a speed of 110 knots, leading to a ground pixel size of 9 m2/pixel. A spectral resolution of 4 cm-1 was used which gives a total of 125 spectral bands over the whole range covered by the FPA detector. The second flight was carried out the next day in a rural area located on the south shore of Quebec City at the same time of the day. The flight was carried out at an altitude of 715 meters leading to a ground pixel size of 1 m2/pixel. A spectral resolution of 6cm-1 was used which gives a total of 85 spectral bands over the whole range covered by the FPA detector. In both cases, outside temperature and relative humidity at ground level were 11 °C and 30 % respectively. 2.2. Data Processing The broadband images associated with the hyperspectral data were obtained by summing, for each pixel, the radiance measured at each wavenumber over the whole FPA detector spectral range. In order to obtain the associated temperature maps, the spectral radiance measured for each pixel was converted to brightness temperature using the Planck curve relationship. The mean brightness temperature value was then calculated for each pixel. Temperature emissivity separation (TES) was carried out by solving Eq.1 where is the measured radiance, the downwelling radiance,
Figure 1 Visible image (left) and associated temperature map (right) on a brightness temperature scale. Typical areas representing a road ( ), non-metallic roof ( ) and a metallic roof ( ) are also labeled in the scene. The various temperature contrasts depicted in Figure 1 show that brightness temperature values obtained from airborne TIR imaging are not fully representative of the actual surface temperature. As an example, temperatures lower than water’s freezing point were recorded in some areas which is unlikely considering the ambient temperature reported at the time of the measurements. Therefore, one must account for the surface emissivity in order to estimate surface temperature using TIR remote sensing. This is not a straightforward task as the measured radiance contains both the self-emission and reflective components. In order to solve Eq. 1 properly, one must estimate the various contributions within the measured signal in order to retrieve the «real» surface temperature. The infrared spectra associated with the selected locations in Figure 1 are shown in Figure 2 in order to illustrate how these individual contributions can be estimated. The spectrum associated with the black roof (non-metallic) suggests that this surface has a relatively high emissivity since most of the sharp spectral features correspond to absorption bands from atmospheric water vapor. On the other side, the infrared spectrum associated with the metallic roof suggests a low emissivity surface reflecting the sky. These types of
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structures play a critical role when analyzing TIR HSI data since they both help to estimate and in Eq.1 respectively. The area corresponding to the road in Figure 1 is highly representative of a quartz-containing material as it can be concluded by analyzing the corresponding infrared spectrum displayed in Figure 2. Such spectral features are commonly encountered in airborne TIR HIS as many materials (concrete, asphalt…) and minerals (soils, sand …) contain quartz. The sky and atmospheric contribution in this infrared spectrum can readily be estimated due to the high spectral resolution of the airborne TIR HSI data.
Figure 2 TIR spectra of selected locations (top): road (blue curve, dash line), grass (green curve, thin line) and metallic roof (red curve, bold line). Reference absorption spectra (bottom) are displayed for comparison purposes. The TES algorithm was first applied to an urban scene represented by the visible image in Figure 3. As it can be readily observed in Figure 3, the scene contains a great variety of environments such as a park (vegetation), a residential area (houses), a highway road and an industrial area (plants, tanks, harbor…). Both the thermodynamic temperature map, obtained upon TES, and the corresponding brightness temperature map are presented on the same temperature scale in Figure 3. At first sight, the UHI resulting from the residential area is more obvious in the thermodynamic temperature map since sharper contrasts are obtained. As expected, the industrial area is also warmer than the harbor and the park. This contrast enhancement results from accounting for both atmospheric absorption and
reflections from a «cold» sky. In addition, no «unrealistic» ground temperature values can be found in the thermodynamic temperature map.
Figure 3 Visible image (left), brightness temperature map (center) and thermodynamic temperature map (right) of an urban area. In order to visualize the impact of the TES procedure for the temperature characterization using remote sensing, the temperature mismatch, the difference between the thermodynamic and the brightness temperature is plotted in Figure 4.
Figure 4 Temperature mismatch between brightness temperature and thermodynamic temperature obtained from TES algorithm in urban (left) and rural (right) areas.
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As expected, the distribution is highly asymmetric and multimodal, reflecting the presence of low emissivity surfaces in the scene. The median value of the distribution also gives a good idea about how much broadband TIR measurements underestimate the actual temperature by neglecting the atmospheric compensation and reflective component in the measurements. In the case of the investigated urban area, a -3.72 K offset is estimated.
whole investigated area. The relatively low density of installations in such areas somewhat explains why no significant UHI effect can be seen. Nevertheless, a temperature mismatch between the thermodynamic temperature and the brightness temperature was observed as reported in Figure 4. Once again, the distribution is asymmetric mostly due to the presence of low-emissivity metallic structures (barns, silo). The spectral emissivity reflects the chemical nature of the investigated surfaces. Therefore, the emissivity datacube resulting from the TES procedure contains the information required to carry out chemical mapping. In order to illustrate this fact, the chemical map of quartz was drawn from the emissivity datacube associated with the rural area and the results can be seen in Figure 6.
Figure 6 Chemical map of quartz (red) drawn from the spectral emissivity datacube obtained upon TES. The visible image is displayed for comparison purposes. 4. CONCLUSION Airborne TIR hyperspectral data recorded at a high spectral resolution allows efficient compensation of atmospheric and sky reflection contributions in the measured radiance. The thermodynamic temperatures estimated from the TES algorithm emphasizes the temperature contrasts resulting from the higher density of high-emissivity materials found in urban areas. Chemical maps of different components drawn from emissivity datacubes combined with the thermodynamic temperature data could lead to a better understanding of the UHI. 5. REFERENCES [1] A.J. Arnfield, “Two Decades of Urban Climate Research: A Review of Turbulence, Exchanges of Energy and Water, and the Urban Heat Island,” Int. J. Climatol., 23, 1-26 (2003). [2] C.P. Lo, D.A. Quattrochi and J.C. Luvall, “Application of highresolution thermal infrared remote sensing and GIS to assess the urban heat island effect,’’ Int. J. Remote Sensing, 18, 287-304 (1997).
Figure 5 Visible image (left), brightness temperature map (center) and thermodynamic temperature map (right) of a rural area.
[3] A.R. Gillespie, S. Rokugawa, S.J. Hook, T. Matsunaga and A.B. Kahle, “Temperature/Emissivity Seperation Algorithm Theoretical Basis Document, Version 2.4,” NASA, 64 p (1999).
The TES algorithm was then applied to the TIR HSI measurements carried out in rural environments and the results are shown in Figure 5. As expected, the estimated ground temperature is fairly homogeneous throughout the
[4] C.C. Borel, “ARTEMISS – an Algorithm to Retrieve Temperature and Emissivity from Hyper-Spectral Thermal Image Data,” 28th Annual GOMACTech Conference., Hyperspectral Imaging Session, (2003).
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