The objective of the Infrared Laboratory-LIR has been the design and development of simple and low-cost IR systems for the resolution of specific problems in real time. The study of clouds in the IR region is a problem studied since the first on-board measurement systems. Nowadays imaging systems are multispectral instruments that provide accurate information on clouds. These systems are generally large in size, weight and cost. However, in many situations, the goal is simply to get auxiliary information about the clouds. Then, the low-cost approach of LIR is ideal for designing simple cloud characterization systems. In this work, a methodology is presented to determine the emissivity and temperature of the clouds based on the brightness temperature measured from space by a bi-spectral camera in the 10 and 12 microns bands. In addition, we provide quantitative information on the capabilities of the methodology based on real data provided by MODIS instrument.
In the last decades, composite materials, particularly thermosetting carbon fiber reinforced polymers, have become the main structural material for the aerospace industry. Recently, interest has grown in thermoplastic composites, since they are chemically more stable, faster to process, fatigue-resistant and recyclable. Nevertheless, when submitted to high temperatures these materials may degrade in ways not presently well known. Therefore, the study of the thermo-mechanical properties of thermoplastic composites when exposed to fire or high-temperature events is of primary interest. In particular, a good knowledge of its behavior could improve physical modeling to the point of reducing the number of prescribed fire tests by virtualizing some of them. The first step is to measure the thermal parameters of real samples in a practical way. We have established a methodology that extends the classical flash method to obtain the effective thermal parameters (diffusivity, specific heat, heat conductivity, and Biot number) of thermoplastic composite materials by a non-contact method based on IR imaging. Values obtained have been used to simulate thermal behavior with a FEM-based solver, from room temperature up to 900°C, with an agreement with experimental data better than 1% in temperature (K) for temperatures below ∼ 260°C and better than 3% up to ∼ 850°C.
Cloud parameters such as the Cloud Top Height (CTH), Cloud Top Temperature (CTT), emissivity, particle size and optical depth have always been matter of interest for the atmospheric community. Particularly the CTH provides information leading to better understand the cloud radiative effects. Although there are many meteorological satellites providing the CTH, there are other sensors, not devoted to this purpose, that give some information from which this crucial parameter can be estimated. In this contribution we will describe three different methodologies to retrieve the CTH. The first technique is based on stereo-vision algorithms and requires two different views of the same scene and does not need of extra atmospheric information. In the second one, brightness temperatures in two IR spectral bands are converted to real cloud temperature by means of the proposed algorithms. From the CTT, the CTH is estimated using temperature vertical profiles (measured or modeled). The third technique retrieves the CTH from the output parameters of post event simulations performed by a Numerical Weather Prediction (NWP) model that in this work will be the mesoscale model WRF (Weather Research Forecast). This article presents a preliminary work, in which the heights retrieved by the three methodologies applied to the geostationary satellite Meteosat 10 are compared with the heights given by MODIS sensor installed on the polar satellite AQUA. This promising results show that valuable information about CTH can be retrieved from Meteosat which provide high frequency and large scale data useful for weather and climate research.
The Extreme Universe Space Observatory (EUSO) is an astronomical telescope that will be hosted by the Japan Experiment Module (JEM) on the International Space Station. The telescope will determine ultrahigh-energy cosmic ray properties by measuring the UV fluorescence light generated in the interaction between the cosmic rays and the atmosphere. Therefore, cloud information is crucial for a proper interpretation of the data. To obtain the cloud top height, an infrared (IR) camera is being designed. The design is constrained by JEM-EUSO requirements, which have led to a bi-spectral camera option (10.8- and 12-μm bands). The bi-spectral design has allowed us to develop a split-window algorithm to correct the atmospheric effects and retrieve the cloud temperature from the brightness temperatures (BTs) in the bands aforementioned. The algorithm has been validated in synthetic scenarios at pixel level. The results show that the algorithm is able to retrieve the temperature with accuracy much better than the requirement of 3K. It has also been tested in two-dimensional scenarios by applying it to moderate resolution imaging spectroradiometer (MODIS) images of BTs in bands 31 similar to those of the IR camera. The retrieved temperatures are in a very good agreement with the temperatures given by MODIS.
The Extreme Universe Space Observatory (EUSO) is an astronomical telescope that will be hosted by the Japan
Experiment Module (JEM) on the International Space Station (ISS). The telescope will determine Ultra High Energy
Cosmic Rays properties by measuring the UV fluorescence light emitted by the particles generated in the interaction
between the cosmic rays and the atmosphere. Therefore, cloud information is crucial for a proper interpretation of the
data. To obtain the cloud top height an IR camera is being designed. The design is constrained by JEM-EUSO
requirements which are mainly the instrument weight, power and data rate. These requirements have led to a bi-spectral
camera option with 1 μm-wide bands centered at 10.8 and 12 μm. The bi-spectral design has allowed us to develop a
Split Window Algorithm to correct the atmospheric effects and retrieve the cloud temperature from the brightness
temperatures in the bands aforementioned. The algorithm has been checked in synthetic scenarios at pixel level. The
simulations consider clouds at different levels with diverse atmospheric conditions. The results show that the algorithm
is able to retrieve the temperature with accuracy much better than the required one by the JEM-EUSO mission of 3K. It
has also been tested in 2D real scenarios (MODIS images). The algorithm has been applied to MODIS brightness
temperatures in bands 31 and 32 which are similar to those of the IR camera. The temperatures retrieved by the
algorithm are in a very good agreement with the cloud top temperatures given by MODIS.
It is widely known that methane, together with carbon dioxide, is one of the most effective greenhouse gases
contributing to climate global change. According to EMEP/CORINAIR Emission Inventory Guidebook1, around 25% of
global CH4 emissions originate from animal husbandry, especially from enteric fermentation. However, uncertainties in
the CH4 emission factors provided by EMEP/CORINAIR are around 30%. For this reason, works addressed to calculate
emissions experimentally are so important to improve the estimations of emissions due to livestock and to calculate
emission factors not included in this inventory.
FTIR spectroscopy has been frequently used in different methodologies to measure emission rates in many
environmental problems. Some of these methods are based on dispersion modelling techniques, wind data,
micrometeorological measurements or the release of a tracer gas. In this work, a new method for calculating emission
rates from livestock buildings applying Open-Path FTIR spectroscopy is proposed.
This method is inspired by the accumulation chamber method used for CO2 flux measurements in volcanic areas or CH4
flux in wetlands and aquatic ecosystems. The process is the following: livestock is outside the building, which is
ventilated in order to reduce concentrations to ambient level. Once the livestock has been put inside, the building is
completely closed and the concentrations of gases emitted by livestock begin to increase. The Open-Path system
measures the concentration evolution of gases such as CO2, CH4, NH3 and H2O. The slope of the concentration evolution
function, dC/dt, at initial time is directly proportional to the flux of the corresponding gas.
This method has been applied in a cow shed in the surroundings of La Laguna, Tenerife Island (Spain). As expected,
evolutions of gas concentrations reveal that the livestock building behaves like an accumulation chamber. Preliminary
results show that the CH4 emission factor is lower than the proposed by the Emission Inventory.
Quantitative analysis of absorbance spectra to retrieve gas concentrations in open-path FTIR air monitoring is not always
a straightforward task. Most of commercial software use classical-least-squared algorithms to retrieve the unknown
concentrations. These codes usually work in real time and give appropriate results. However, sometimes these codes fail
when the background reference spectrum presents absorption lines of the gas to be monitorized. This effect is frequent in
some applications. Line-by-line approaches give satisfactory results because these codes solve the problem associated to
the reference spectrum generating a synthetic reference background. The main drawback is that these algorithms do not
work in real time, and need a skilled operator.
In this work, we propose the use of artificial neural networks to analyze absorbance spectra in real time to retrieve the
unknown concentrations in a simultaneous way. In addition, capabilities of the method to solve spectral overlapping will
be studied. In this sense, simultaneous analysis of four atmospheric gases (CO2, CO, H2O and N2O) will be included in
this first version. The effectiveness of the method will be evaluated from the experimental point of view. Experimental
open-path FTIR spectra (0.5 cm-1 of spectral resolution) will be analyzed with the proposed method, as well as with CLS
and LBL codes for comparison purposes. Moreover, in these experiments CO concentration has been measured by using
standard extractive equipment and can be compared with the values provided by our method. Finally, some indications
will be pointed to extend the method to other gases and spectral regions.
Fourier Transform Infrared (FTIR) spectroscopy is a well-established technique for monitoring air pollutants by extractive methods. Remote sensing by Open-Path FTIR technique incorporates the advantages of a non-intrusive technique. EPA and VDI have recommended some guidelines for the application of this promising technique. However, it is necessary to do more research to assess the quality of these systems on the basis of European standards.
The analysis of FTIR spectra are usually carried out by using methods based on classical least squares (CLS) procedures. In this work a line-by-line method (SFIT) is additionally used. SFIT is a non-linear least-squares fitting program that was designed to analyse solar absorption spectra. For this work, SFIT has been adapted and applied to Open-Path FTIR spectra. The objective of this work is to study the capability of both methods to analyse open-path measurements of carbon monoxide.
From a previous work it was inferred that the selection of the analysis spectral window is a relevant parameter of SFIT analysis. Therefore, the first step has been to analyse synthetic spectra of known concentration to select the best spectral region and other parameters of analysis. Afterwards, the SFIT software has been applied to Open-Path experimental spectra. Results of the SFIT method have been compared with the results of the two methods of EVAL analysis. EVAL is a commercial software (provided with the instrument) that is based on a CLS procedure and on the absorption peak intensity. The result has been validated by comparison to a standard extractive method.
Early remote sensing of forest fires from specifically dedicated low cost satellites has recently been proposed as one of the most promising techniques for the improvement in the efficiency of forest fire fighting on a global basis. Efficient forest fire remote sensing requires a high probability of detection for small fires combined to a low' false alarm rate. In this paper, a very simple algorithm based on the so-called fire index (FI) has been implemented in an acquisition system developed within the framework of the UE-DGXII Project FUEG02. This system is composed by tw;o infrared cameras operating in the mid and thermal infrared spectral regions and acquire simultaneous digital images of the scene that are calibrated in radiometric units. An image of the FI is then computed improving greatly the discrimination of false alarms. A new approach using only the mid infrared band is also suggested, and spectral intraband processing is studied as a basis for this approach.
An IR absorption system for real-time measurement of pollutants in exhaust gases from moving vehicles is proposed. The system consists of an IR source at one side of the road, and a wheel with specific IR filters and a detector at the other side. We have, firstly, simulated the expected exhaust gases concentrations at different inspection conditions. The well-known HITRAN database has been used in order to estimate intensities, signal-to-noise ratio and threshold concentration levels. These results have then been verified by open-path Fourier transform IR (FTIR) spectroradiometry of the exhaust gases from both gasoline and Diesel vehicles. This confirms the feasibility of such a device by using some conventional filters -those of CO, CO2 and NO2 for example - some ultra-narrow sold state Fabry-Perot filters - that of NO for example. As the proposed system monitors all kinds of oxides and hydrocarbons in exhaust gases the stoichiometry of the combustion or 'lambda coefficient' may be also deduced and it is fully applicable for 'ministry of transport' test of vehicles.
Radiometric characterization of the infrared emission of forest fires has been performed for different primary carriers (grass, shrub, and slash) and different ambient conditions in a combustion tunnel (temperature, relative humidity and wind speed). Fourier Transform-based FTIR spectroradiometer (2.2 - 18 micrometer range) as well as a thermal camera have been used in the study. Also a 'classical' characterization, based on the fuel weight before and after the burn, and the measurement of soil and fire temperatures, has been made. Temperature profiles at different heights in different points of the heating zone have been measured by using an automatically controlled thermocouple network. The thermocouples measure the temperatures each 5 seconds in the 0 - 1200 degrees Celsius range. Parameters defining the fire behavior, mainly determined by the fuel consumption ratio, rate of spread, angle and length of flames, and the fire lineal intensity, are obtained from this characterization. Comparison between these results and the radiometric measurements has been done. Information from the camera is used to obtain infrared emissivities of the fires.
This paper describes some of the work performed in the course of the design and development of a new IR sensor system for early detection of forest fires. The proposed device is a non- imaging sensor that would discriminate angular position by means of a simple IR array, working in the 3 - 5 microns wavelength region, placed at the focal plane of the optical system. In order to accomplish low cost requirements, a system with a sole IR lens has been designed. In this work, a study of the spot shape, size and optical IR power on the detector has been performed. From the analysis of the influence of lens-detector distance and incidence angle, we have derived an optimum pixel size and optical configuration. The use of TE- cooled PbSe detectors is proposed, as well as a simplified cell array.
In this work, a new spectral selection system for imaging of CO infrared emission in combustion environments is proposed. The CO and CO2 medium infrared emission bands are spectrally overlapped, so cross-talk effects would appear when trying to image CO using a IR camera equipped with a conventional band-pass filter. The system proposed belongs to a new family of infrared multilayer filters, called solid state Fabry-Perot (SSFP) filters. The transmittance of such filters can be spectrally matched to the CO fine structure emission band. Thus, it is possible to discriminate the CO emitted IR radiation from that which comes from CO2 in combustion environments. With a Fourier Transform Infrared spectroradiometer, we have studied and spectrally characterized the IR emission from hydrocarbon flames, varying the O2 inlet, i.e. the CO generated. Using these experimental data and the theoretical spectral transmission for both band-pass filter and the SSFP filter, we have demonstrated the uselessness of the former and the suitability of the latter, in order to discriminate CO and CO2. A silicon wafer, both sides covered by a reflecting multilayer, is proposed as a SSFP. The design and spectral parameters of such a filter are developed on the basis of the optical properties of the multilayers and substrate, and specific designs for CO imaging are presented.
Commercial Fourier transform based FTIR spectroradiometers can be used in an easy way as ground-based remote sensing systems. Information of the ozone column can be obtained from the spectral IR radiance in the 900-1200 cm-1 range. Tropospheric and stratospheric ozone are sounded by an appropriate selection of the experimental conditions. A theoretical study of the best choice for the wavenumbers of zenith angles for the direction of view is presented. The problem of the superimposed water emission is analyzed for the selected wavenumbers. Finally, some experimental results are presented, with a fast method to obtain the water and ozone columns from a spectral radiance spectrum.
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