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Advances in 3-D infrared remote sensing gas monitoring. Application to an urban atmospheric environment
Remote Sensing of Environment 175 (2016) 301–309
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Remote Sensing of Environment journal homepage: www.elsevier.com/locate/rse
Advances in 3-D infrared remote sensing gas monitoring. Application to an urban atmospheric environment Ph. de Donato ⁎, O. Barres, J. Sausse, N. Taquet Université de Lorraine, CNRS, CREGU, GeoRessources Laboratory, BP 70239, F-54506 Vandoeuvre-lès-Nancy, France
a r t i c l e
i n f o
Article history: Received 26 June 2015 Received in revised form 11 December 2015 Accepted 31 December 2015 Available online xxxx Keywords: Infrared Troposphere Gas cloud Stereoscopy 3-D reconstruction
a b s t r a c t Remote sensing technologies are some of the most powerful tools for atmospheric monitoring of natural or anthropogenic ecosystems. Extensive developments were observed in the two last decades concerning both satellite, airborne, on-board and ground systems. The present paper focuses on an advanced 3D reconstruction of a gas cloud detected in the atmosphere of an urban area using a scanning infrared (IR) gas system (SIGIS2, Bruker). Several measurements were carried out from 3 different positions in order to monitor an atmospheric volume around 108 m3. The images generated by the imaging remote sensing system correspond to the 2-D projections of the 3-D gas cloud. All the 2-D data are fully georeferenced (x, y, z and t). Each pixel of the 2-D images is associated to an IR spectrum, which was approximated to a linear combination of reference spectra and expressed as a coefficient of correlation (0 to 1). Data with a correlation coefficient higher than 0.75 are selected for 3-D modeling. The method for 3-D reconstruction of gas clouds is based on the combination and relocation of all the oriented and georeferenced measurement data. The 3-D gas cloud is determined from the 2-D images in the volume of interest processing a 3-D interpolation using the gOcad® DSI procedure. This integrated approach was applied to a local case study in an urban area. It leads to the identification and the spatial demarcation of a cloud of SO2 with a total volume of 65 × 106 m3. The existence of this pollutant may be related to the presence of ancient underground tanks of gasoline, leaking because of a defect of waterproofness. Another source of SO2 can be the emission of gases stemming from diesel machines used for important public works in this urban area. This study demonstrates that the combination of scanning imaging IR spectroscopy with the measurement setup and the 3-D gOcad® processing can be used as a generic approach for 3-D reconstruction of gas clouds applied to any kind of ground emissive sites. © 2016 Elsevier Inc. All rights reserved.
1. Introduction Atmospheric monitoring development is associated with the development of remote sensing technologies. These technologies are of first importance in the early detection and the mapping of many types of air pollutants (molecules and particles) and to improve the understanding of global atmospheric chemical equilibriums from short to long terms and their environmental impacts (Richter et al., 2005; Martin, 2008; Edwards et al., 2006; Frankenberg et al., 2008; Worden et al., 2008; Kim et al., 2009). Since the first spatially resolved measurement of atmospheric pollutants by satellite in 1972 (Landsat ERST-1, Griggs, 1975), new technologies based on spectroscopy were developed to measure the concentrations of many greenhouse and hazardous gases (CH4, CO2, H2O, SO2, NOx, NH3,…) at different spatial scales (global, regional and local scale) (Barrett & Curtis, 1999). Global and regional emissions are usually ⁎ Corresponding author. E-mail address: [email protected] (P. de Donato).
http://dx.doi.org/10.1016/j.rse.2015.12.045 0034-4257/© 2016 Elsevier Inc. All rights reserved.
monitored by low level remote sensing satellite at an altitude higher than 820 km and a spatial resolution larger than 12 km (Chédin et al., 2003; Barret et al., 2005; Clerbaux et al., 2007). Hyperspectral imaging was also implemented for remote sensing applications using satellite imaging data mapping (Thenkaball et al., 2004; Plaza et al., 2009). Regional and local emission monitoring is generally carried out by airborne or on-board remote sensing monitoring at an elevation generally lower than 2000 m. The spatial resolution depends on the type of the remote sensors (Worden et al., 1997; Philbrick, 2002; Kim et al., 2009). Because of their spatial complementarity, spaceborne and airborne measurements are often cross-compared (Clerbaux et al., 2008). More recently, terrestrial remote sensing techniques were developed for local scale monitoring, with a spatial area covering 10 × 10 × 10 km to 5 × 5 × 5 m (Jones et al., 2009). Among these ground remote sensing technologies, passive remote sensing by Fourier-transform infrared spectrometry (FTIR) appears as a very promising technique for such applications (Herget & Brashers, 1980; Beil et al., 1998). In this case, all the infrared radiations are collected as well as the cloud infrared emission. This has practical and
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experimental advantages such as i) easy adaptation to the topography of the site, ii) relatively independence to the weather conditions, iii) mobile and fast operation, iv) easy handling system and v) remote sensing distance from meter to ten kilometers. Several papers concerning chemical monitoring by passive remote sensing of natural (Burton et al., 2000; Goff et al., 2001; Allard et al., 2005) or anthropogenic clouds (Bradley et al., 2000; Harig & Matz, 2001; Harig, 2004; Harig et al., 2005; Schütze et al., 2013) were published in the last 15 years. Scientific and technical developments of passive remote sensing by FTIR spectrometry were strongly focused on 2-D/3-D reconstruction (Todd et al., 2001). The multiple positions of the remote infrared sensor were combined with a specific reconstruction procedure (Schowengerdt, 2007; Brito Junior et al., 2008) that transformed 2-D projections to 3D images. Rusch and Harig (2010) made significant advances in 3-D reconstruction of gas clouds by scanning imaging spectroscopy and tomography. The purpose of this paper is to propose a generic approach for spatial gas clouds monitoring which can be applied to any kind of emissive sites. The generic approach is illustrated from imaging a SO2 cloud in an urban area. Infrared data (2-D gas traces) were collected using a scanning imaging IR system (SIGIS2 Bruker) positioned at three different locations of a large urban area. Corresponding 2-D projections were then combined using a specific gOcad® procedure in order to obtain a full 3-D reconstruction of the gas cloud. 2. Material and methods 2.1. Infrared scanning system Infrared spectra are collected using a scanning imaging IR system (SIGIS2 Bruker, called SIGIS in the text) which is described in details by Harig et al. (2007). The SIGIS spectrometer is based on the combination of a modified Michelson interferometer with cube-corner mirrors (interferometer OPAG 33, Bruker Daltonics, Leipzig, Germany) connected to a cooled single MCT detector element and a scanning mirror. The system includes also i) a telescope to collect and focus the IR emission radiation, ii) a rotating head containing an azimuth-elevationscanning mirror activated by stepper motors for a full x, y, z monitoring, iii) a visible and an infrared video camera to make analysis both during the daytime and at night and iv) an internal GPS system in the upper part of the head for the georeferencing of the data. In order to improve the velocity and the adaptability of the analysis, the whole device of remote detection by IR imaging is adapted to a small van-like vehicle (Fig. 1). First, the video image is used to interactively define the spatial area of the IR analysis, constituted by a rectangular 2-D pixel grid. The size and the direction of the field of regard and the spatial resolution are variable and defined by the user. Then, the rotating head is kept at a fixed position and the scanning mirror is sequentially set to all the positions or pixels within the field of regard.
Table 1 Measurement specifications and parameters of the scanning imaging SIGIS system applied to SO2 detection. Interferometer type Spectral range SO2 detection window Spectral resolution Apodization function Zerofilling Phase correction Number of scans Maximum spectral rate (Resolution: 4 cm−1) Field of view Maximal horizontal field of regard Maximal vertical field of regard Average number of pixels in a measurement grid
Bruker OPAG 33 3900–600 cm−1 1450–1050 cm−1 4 cm−1 Triangular 1 Power spectrum 16 16 spectra/s 10 mrad 360° 60° 1500 (30 × 50)
The main measurement specifications and parameters are reported in Table 1. 2.2. IR data processing: from raw data to the coefficient of correlation The internal algorithm of data processing is described in detail by Harig and Matz (2001). Briefly, the method is based on the approximation of a measured spectrum to a linear combination of reference spectra. The recorded IR data are transformed in brightness temperature spectra using the internal black body radiometric calibration. After baseline approximation, the signals of one target compound, of atmospheric gases and of possible interferents are fitted to the spectrum. The contribution of the baseline and of the fitted signals of interferents and atmospheric species are subtracted to the measured spectrum that leads to a so-called corrected spectrum. The presence of one target gas (here SO2) is expressed in terms of a coefficient of correlation (R), calculated in a certain number of compound-specific spectral windows. This calculation is the result of the subtraction of the corrected spectrum to the reference spectrum of the targeted gas. This procedure is successively repeated for each pixel of the 2-D projection and can be repeated for all the gas compounds of the spectral library. In order to improve the SO2 detection and localization, only the pixels having a correlation coefficient higher than 0.75 are selected for the 3-D reconstruction procedure. This value (0.75) appears as a compromise between spectral quality and a sufficient number of data. The measured SO2 spectrum and the theoretical infrared profile (PNNL library) for a correlation coefficient of 0.75 are compared in Fig. 2. 2.3. Urban area and screening procedure Fig. 3 presents an overall sky view of the measured urban area (blue quadrangle zone of interest ZOI) spread on more than 3 km2. The
Fig. 1. Left: Mobile station integrating the infrared remote sensing system of gases (Bruker SIGIS). Right: Schematic SIGIS system (Bruker — Brochure SIGIS).
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Fig. 2. Comparison of typical infrared profiles of SO2 for a correlation coefficient of 0.75 (measured spectrum in black, reference spectrum PNNL library in red).
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different optical pathways have been chosen in order i) minimize obstructed zones, ii) to have the sky as a main background and iii) to take into consideration surface gas emissions. The three reference locations of SIGIS measurements (blue circles) and the corresponding fields of view (blue lines) are also indicated and define 36° angles for the field of scanner regard. The blue quadrangle displays the common area of measurement which corresponds to an intersection atmospheric volume about 135 × 106 m3 (760 m × 935 m × 190 m, dimensions fitting the current measurement area). For each SIGIS position, the operator defines a specific 2-D grid constituted of a certain number of pixels. For each grid, all the different pixels are continuously analyzed step by step from the top-left corner down to the lower right corner using the procedure described above. Table 2 summarizes the main characteristic parameters of each 2-D grid including time of measurement and weathering conditions. The operation is repeated three times for each position. In spite of small differences in measurement times (cf. Table 2), the procedure
Fig. 3. Administrative map and sky view of the urban area of Nancy, France. The zone of interest (ZOI) where measurements were performed is highlighted in the quadrangle blue zone. The three origin points (blue circles) correspond to the SIGIS camera locations. For each point, IGN Extended Lambert II coordinates are mentioned.
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Table 2 Main characteristics of the three 2-D grids used for the SO2 cloud analysis (April 17, 2013).
GPS position of SIGIS Total number of pixels Minimun–maximum size of the 2-D grid (m2) in the ZOI Minimum–maximum pixel side (m) in the ZOI Measurement time Acquisition time (s) Atmospheric pressure (hPa) Temperature (°C) Relative humidity (%) Wind direction (°) Wind velocity (km/h) Visibility (km) Precipitation (mm/h)
Conditions for SIGIS 1
Conditions for SIGIS 2
Conditions for SIGIS 3
XLII+ 290 522 m YLII+ 5 393 220 m 98 × 11 (1078) 551 075–729 200 22–26 1:35 pm 86 1020.2 22.4 41 140 13 50 0
XLII+ 290 599 m YLII+ 5 397 992 m 82 × 13 (1066) 202 162–386 829 14–19 2:21 pm 85 1019.0 23.2 42 130 15 55 0
XLII+ 295 590 m YLII+ 5 399 814 m 102 × 19 (1938) 1 064 620–1 363 840 23–27 3:20 pm 154 1018.3 23.3 41 150 17 55 0
can be qualified as a quite real-time procedure. The results remain valid for slowly changing environmental conditions.
2.4. 3-D reconstruction Each field of regard forms a rectangular-base pyramid (Fig. 4) at a geo-referenced position of the spectrometer in order to define a common intersection volume (Fig. 3). The aim of reconstruction of the 3-D structure of the SO2 cloud is to obtain information about the third dimension by evaluating at least three series of 2-D images of SO2 correlation coefficients recorded at the three different positions. The Paradigm gOcad® software is mainly used in the oil industry for seismic, geological and reservoir modeling (Mallet, 1992, 1997, 2002). The gOcad® software offers some facilities to work with various types of 3D data, with the possibility of specific and manual construction and/or adjustment of 3D models. The gOcad® objects, such as point set, polygonal lines, triangulated surfaces and regular grids (voxet) are used in this case to reproduce and model the SIGIS measurements. From the SIGIS origin, two horizontal straight lines (Fig. 4) separated by a 36° angle (specific fields of regards of this measurement) are drawn on the urban area map. The bisector of this angle is the SIGIS optical axis. Along these two lines, series of parallel 2-D sections, orthogonal to the optical axis, are built and relocated with an increasing width from the origin point to a chosen distance in the ZOI. The height of these images is a function of the SIGIS vertical field of regard and is adjusted respecting several markers in the urban area (high buildings in the vicinity of the ZOI are used as XYZ references). Each 2-D section is characterized by a constant height of 250 m for SIGIS 1(ground–top of the field view SIGIS 1), 280 m for SIGIS 2 and 440 m for SIGIS 3. For this study, we only focused on SO2. As described above, each 2-D grid pixel property is expressed in terms of a correlation coefficient of SO2, which can be sometimes also interpreted as a surface of probability of the presence of the gas. In the case of passive remote sensing by FTIR, all the infrared emissions in the direction of measurement are collected for each pixel of the 2-D grid. The detection of SO2 in a pixel of a grid means that the gas is present somewhere in the conical volume of the considered direction from the background to the detector of the spectrometer (Fig. 4). Taking into account this experimental consideration, the 2-D SO2 coefficient correlation grids can be relocated to n equiprobable locations at a precise distance from the SIGIS origin point along the optical axis. For each of the 3 SIGIS measurement positions, we arbitrary defined 10
sections area with a spacing of 125 m between them covering the whole ZOI (Fig. 5). Assuming this geometrical consideration, the ZOI volume is then characterized by 3 × 10 2-D images coming from the 3 SIGIS positions, each of these sections of multiple widths and proportional heights being used to fill the ZOI volume. All of the 30 2-D images are triangulated surfaces. Each triangle points are defined by X, Y, Z coordinates and a SO2 correlation coefficient that could be further interpolated in the entire volume. The surface mesh shows a resolution respecting the SIGIS grid resolution of 1500 pixels (30 × 50) (see Table 1).
3. Results and discussion 3.1. Interpretation of the 2-D SO2 coefficient correlation grids Fig. 5 illustrates the principle of ZOI filling with the 10 × 3 2-D correlation coefficient grids from the SIGIS 1 and SIGIS 3 positions. Four grids are presented as examples of the distribution of the correlation coefficient value. The color scale corresponds to the raw SO2 correlation coefficients ranging from 0 (no SO2 is detected, blue) to 1 (SO2 is detected and its infrared fits to a SO2 reference spectrum, orange). The spatial distribution of the correlation coefficients clearly indicates that the presence of SO2 (orange zones, correlation coefficients higher than 0.5 in Fig. 5) is limited to the bottom part of the 2-D grids.
Fig. 4. Schematic view of the SIGIS 1 image of the geometrical realization. The optical axis is defined from the SIGIS 1 origin. From the optical axis, the horizontal and vertical fields of view are defined respecting 2*β for the horizontal view and a height of 250 m for the vertical view (fit respecting building heights). The SIGIS image is defined by a superimposition of 1500 pixels, each of them corresponding to a specific and individual gas measurement and property recording.
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Fig. 5. Zoom on the ZOI 3D view. Blue lines illustrate the horizontal fields of view of the SIGIS camera. For a better visualization, only the grids of SIGIS 1 and SIGIS 3 are filled with the SO2 correlation coefficients. The other grids coming from SIGIS 1, SIGIS 2 and SIGIS 3 are simply underlined with black borders. The spatial correlation of high values of correlation coefficients (orange zones) must be observed to define the presence of SO2 in the zone.
It corresponds to an altitude level between 20 and 185 m for the 3 measurements. SO2 is not always detected at a given XYZ position depending on the SIGIS position. For example, the A zone of the SIGIS 1 grid in Fig. 5 shows some SO2 whereas it is absent in the SIGIS 3 grid. On the contrary, in zone B both SIGIS 1 and SIGIS 3 grids show the presence of SO2, at the same 3D location. It implies that SO2 is not specifically present in the A zone of the ZOI but is detected farther or closer from this grid, in another parallel grid along the optical axis of SIGIS 1 and 3. Point A: SO2 is detected only in one measurement direction.
Point B: SO2 is detected in the two measurement directions. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) By combining the 3 SIGIS measurements, two cases can be determined: • The SO2 correlation coefficient is measured with a value higher than 0.75, at a specific XYZ position, simultaneously by the 3 measurements: SO2 is actually present in this zone.
Fig. 6. Zoom on the ZOI 3D view. Blue lines illustrate the horizontal fields of view of the SIGIS camera. The ZOI is underlined with a red line. The final point set results from the application of two filters on the 1500 × 30 points: 1) black dots (SO2 correlation coefficients higher than 0.75) and 2) final red dots showing both higher correlation and the triple correlation between SIGIS grids. Red dots correspond to the presence of SO2 in the ZOI volume.
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Fig. 7. A). Definition of the voxet volume respecting the correlated SO2 points of the SIGIS measurements (SIGIS 1 section as an example). B). The voxet is defined by voxels (nu × nv × nw) and a property of occurrence is defined: a SO2 point contained in a voxel transfers its property equal to “10” to the voxel center and voxels that do not contain a SO2 points are characterized by a “0” value property. C). After a Discrete Smooth Interpolation (DSI) of the property, a region of connected voxels is created representing the cloud external envelop. D). From this region and property volume distribution an isosurface is created that represents the external envelop of the SO2 cloud.
• The 3 grids do not simultaneously show correlation coefficients higher than 0.75 at a specific XYZ position: SO2 is not present in this zone.
Respecting these hypothesis, the 3-D localization of a SO2 cloud can be deduced, pixel per pixel, by studying the regions where the 2-D correlation coefficient grids present a triple correlation in the ZOI quadrangle.
Table 3 Geometrical parameters of the interpolated SO2 cloud.
3.2. Data filtration preparing for 3D reconstruction
Total volume (initial data from SIGIS sections, Fig. 7A) Total volume (voxet region, stairsteps envelop, Fig. 7C) Total volume (isosurface, Fig. 7D) Maximum length (isosurface, Fig. 7D) Maximum width (isosurface, Fig. 7D) Maximum height (isosurface, Fig. 7D) X, Y coordinates of the center of the cloud (Fig. 3)
28 × 106 m3 6
3
98 × 10 m
65 × 106 m3 915 m 735 m 165 m X: 292 164 m Y: 5 396 873 m
The triple correlation hypothesis is represented by a new XYZ point set property fixed at a value of 0 (SO2 is not detected) or 1 (SO2 is simultaneously detected by the 3 SIGIS measurement positions). Then, among the 45 000 XYZ points (1500 pixels × 30 triangulated surfaces) of the ZOI volume, only 3949 points (black dots in Fig. 6) show correlation coefficients higher than 0.75 on at least one of the three grids. Among these 3949 points, 1905 points (red dots in Fig. 6) respect the triple correlation between SIGIS grids (Fig. 6). The resulting point set showing the presence of SO2 is mainly located in the basal part of the ZOI volume and at a maximum altitude of 185 m as previously mentioned. This point set is the XYZ database that it is then used to define the 3-D SO2 cloud envelop.
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3.3. 3-D SO2 cloud reconstruction by gOcad® interpolation gOcad® proposes interpolation tools to model and interpolate a volumic property from a point set within a regular volumic grid called a voxet. A voxet grid is made of (nu × nv × nw) voxels containing X, Y, Z and property information. Such a grid is therefore built respecting the main volume of triply correlated SO2 points in the ZOI (Fig. 7A). The grid is fitted to the volume of the SO2 points and the voxet cage is defined by a 760 m × 920 m × 240 m box. The number of voxels is fixed proportionally to 38 × 46 × 12 that defines a voxel size of 20 m × 20 m × 20 m. It allows defining a lower resolution compared to the SIGIS resolution and make sure of the final creation of a connected volume, the cloud of SO2.
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A voxel property is first created and initialized to a constant value of “0”. Then the same property is created for the SO2 points (Fig. 7B) with a value of “10”. The objective is just to paint the point set property in the voxel to discriminate voxels where SO2 is present (“10”) or nonobserved (“0”). The point set property is imported in the voxet and each X, Y, Z point that enters a specific u, v, w voxel transfers its property to it (Mallet, 2002). Then, a smoothing of the property is realized using the gOcad® DSI tool (Discrete Smooth Interpolation, Mallet, 1992, 1997, 2002; Caumon et al., 2009) defining a connected property and a view of the external envelop of the cloud. Voxels corresponding to SO2 are then extracted and stored in a voxet region (Fig. 7C). Finally, an isoproperty surface can be fit to this region to better represent the external envelop of the SO2 cloud (Fig. 7D).
Fig. 8. Visualization of the SO2 cloud above the investigated urban area.
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Table 3 summarizes the main geometrical parameters of the identified SO2 cloud calculated after the 3-D gOcad® procedure. The SO2 cloud is presented as the external envelope of the isosurface. However, the initial point set corresponding to the SO2 correlated points is more discretized respecting the SIGIS section proposed in Fig. 5. SO2 is therefore not homogeneously distributed within this external envelope. In Table 3, three types of volume are mentioned. The initial point set volume corresponding to the raw data coming from the SIGIS image is equal to 28 × 106 m3. After the DSI, this volume representing connected voxels, i.e. stair steps mesh, corresponds to an external envelope of a volume of 98 × 106 m3. This volume is reduced to 65 × 106 m3 with the final isosurface that reduced the stair steps effects. Visualization of the SO2 cloud is given in Fig. 8. The SO2 cloud morphology is well described within the ZOI volume. However border effects can be noticed especially at the south east side where the cloud is truncated by the external limit of the ZOI. This border effect is directly linked to the SIGIS fields of view. In the case of a visible cloud, the ZOI could be more efficiently adjusted. The specific localization of the SO2 cloud is related to the presence of ancient underground tanks of gasoline presenting a defect of waterproofness. Moreover, during the period of measurements, important renovation works were operated. Then, exhaust gases stemming from diesel machines of public works constituted a complementary source of SO2 that was clearly detected by the SIGIS system. Although it was not the initial purpose of this paper, this step is also of first importance for quantitative treatment of SO2 cloud. The transformation of gas concentrations from ppm·m to ppm needs to know exactly, for each measurement pixel, the thickness of the cloud. Indeed, a cloud is not necessary a homogeneous objet and can present regions only filled with air. The thickness of these gas fields must be determined between each entry point and each exit point of the cloud. The 3D gOcad® interpolation results (Table 2 and Fig. 7) show the data necessary to this type of calculation. 4. Conclusion This work puts into highlight the potentiality of remote sensing using terrestrial scanning infrared imaging spectroscopy for local scale atmospheric environmental monitoring. The combination of three different measurement positions delimits an urban area where SO2 emissions can be spatially localized. Raw infrared data constitute a rectangular 2-D pixel grid processed in terms of correlation coefficient (R) where only the values higher than 0.75 are used for 3D reconstruction. Each grid is distributed within its corresponding field of regard to form a regular succession of 2D planes filled with the correlation coefficients of SO2. The SO2 observed on these different measurement planes can be interpreted as follows: Single plane: SO2 is detected from only one position of measurement. Double plane correlation: SO2 is detected from two different positions of measurements Triple plane correlation: SO2 is detected from the three different positions of measurements. Only the pixels corresponding to a simultaneous triple detection by the three different measurement positions (triple intersection) provide information to spatially localize a SO2 cloud. Only these specific pixels were used to build the SO2 external envelope using gOcad® tools and to estimate its volume. In the current domain of the urban area investigated this external envelope represents a volume of 65 × 106 m3. Such SO2 emissions are mainly related to the presence of ancient underground tanks of gasoline presenting a defect of waterproofness and to exhaust gases stemming from diesel machines of public works consecutive to the important public works in this urban area. For this study, because only one SIGIS was used, weather conditions appear as the main limited factor for successively measurements. This aspect can be improved by using simultaneously 3 scanning imaging IR systems. Finally, the combination of
scanning imaging IR spectroscopy, the measurement setup and the 3D gOcad® volumic reconstruction can be used like a generic approach for 3-D reconstruction of gas clouds in the case of environmental atmospheric monitoring and in the case of every type of ground emissive sites. Automation of the full procedure can strongly simplify the system to be manipulated by a single operator for a generalization of this type of real-time terrestrial remote sensing monitoring. The next step of this study will led to quantitative analysis. Acknowledgments The authors wish to acknowledge TOTAL E&P (Contract TOTAL CREGU n° FR00006482) for its financial support. The authors associate the National French Agency (ANR, Program N° ANR-07-PCO2-007-08, SENTINELLE) and the European Fund of Regional Development (FEDER) for its help to provide the SIGIS remote sensing system used in this study. 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Contents lists available at ScienceDirect
Remote Sensing of Environment journal homepage: www.elsevier.com/locate/rse
Advances in 3-D infrared remote sensing gas monitoring. Application to an urban atmospheric environment Ph. de Donato ⁎, O. Barres, J. Sausse, N. Taquet Université de Lorraine, CNRS, CREGU, GeoRessources Laboratory, BP 70239, F-54506 Vandoeuvre-lès-Nancy, France
a r t i c l e
i n f o
Article history: Received 26 June 2015 Received in revised form 11 December 2015 Accepted 31 December 2015 Available online xxxx Keywords: Infrared Troposphere Gas cloud Stereoscopy 3-D reconstruction
a b s t r a c t Remote sensing technologies are some of the most powerful tools for atmospheric monitoring of natural or anthropogenic ecosystems. Extensive developments were observed in the two last decades concerning both satellite, airborne, on-board and ground systems. The present paper focuses on an advanced 3D reconstruction of a gas cloud detected in the atmosphere of an urban area using a scanning infrared (IR) gas system (SIGIS2, Bruker). Several measurements were carried out from 3 different positions in order to monitor an atmospheric volume around 108 m3. The images generated by the imaging remote sensing system correspond to the 2-D projections of the 3-D gas cloud. All the 2-D data are fully georeferenced (x, y, z and t). Each pixel of the 2-D images is associated to an IR spectrum, which was approximated to a linear combination of reference spectra and expressed as a coefficient of correlation (0 to 1). Data with a correlation coefficient higher than 0.75 are selected for 3-D modeling. The method for 3-D reconstruction of gas clouds is based on the combination and relocation of all the oriented and georeferenced measurement data. The 3-D gas cloud is determined from the 2-D images in the volume of interest processing a 3-D interpolation using the gOcad® DSI procedure. This integrated approach was applied to a local case study in an urban area. It leads to the identification and the spatial demarcation of a cloud of SO2 with a total volume of 65 × 106 m3. The existence of this pollutant may be related to the presence of ancient underground tanks of gasoline, leaking because of a defect of waterproofness. Another source of SO2 can be the emission of gases stemming from diesel machines used for important public works in this urban area. This study demonstrates that the combination of scanning imaging IR spectroscopy with the measurement setup and the 3-D gOcad® processing can be used as a generic approach for 3-D reconstruction of gas clouds applied to any kind of ground emissive sites. © 2016 Elsevier Inc. All rights reserved.
1. Introduction Atmospheric monitoring development is associated with the development of remote sensing technologies. These technologies are of first importance in the early detection and the mapping of many types of air pollutants (molecules and particles) and to improve the understanding of global atmospheric chemical equilibriums from short to long terms and their environmental impacts (Richter et al., 2005; Martin, 2008; Edwards et al., 2006; Frankenberg et al., 2008; Worden et al., 2008; Kim et al., 2009). Since the first spatially resolved measurement of atmospheric pollutants by satellite in 1972 (Landsat ERST-1, Griggs, 1975), new technologies based on spectroscopy were developed to measure the concentrations of many greenhouse and hazardous gases (CH4, CO2, H2O, SO2, NOx, NH3,…) at different spatial scales (global, regional and local scale) (Barrett & Curtis, 1999). Global and regional emissions are usually ⁎ Corresponding author. E-mail address: [email protected] (P. de Donato).
http://dx.doi.org/10.1016/j.rse.2015.12.045 0034-4257/© 2016 Elsevier Inc. All rights reserved.
monitored by low level remote sensing satellite at an altitude higher than 820 km and a spatial resolution larger than 12 km (Chédin et al., 2003; Barret et al., 2005; Clerbaux et al., 2007). Hyperspectral imaging was also implemented for remote sensing applications using satellite imaging data mapping (Thenkaball et al., 2004; Plaza et al., 2009). Regional and local emission monitoring is generally carried out by airborne or on-board remote sensing monitoring at an elevation generally lower than 2000 m. The spatial resolution depends on the type of the remote sensors (Worden et al., 1997; Philbrick, 2002; Kim et al., 2009). Because of their spatial complementarity, spaceborne and airborne measurements are often cross-compared (Clerbaux et al., 2008). More recently, terrestrial remote sensing techniques were developed for local scale monitoring, with a spatial area covering 10 × 10 × 10 km to 5 × 5 × 5 m (Jones et al., 2009). Among these ground remote sensing technologies, passive remote sensing by Fourier-transform infrared spectrometry (FTIR) appears as a very promising technique for such applications (Herget & Brashers, 1980; Beil et al., 1998). In this case, all the infrared radiations are collected as well as the cloud infrared emission. This has practical and
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experimental advantages such as i) easy adaptation to the topography of the site, ii) relatively independence to the weather conditions, iii) mobile and fast operation, iv) easy handling system and v) remote sensing distance from meter to ten kilometers. Several papers concerning chemical monitoring by passive remote sensing of natural (Burton et al., 2000; Goff et al., 2001; Allard et al., 2005) or anthropogenic clouds (Bradley et al., 2000; Harig & Matz, 2001; Harig, 2004; Harig et al., 2005; Schütze et al., 2013) were published in the last 15 years. Scientific and technical developments of passive remote sensing by FTIR spectrometry were strongly focused on 2-D/3-D reconstruction (Todd et al., 2001). The multiple positions of the remote infrared sensor were combined with a specific reconstruction procedure (Schowengerdt, 2007; Brito Junior et al., 2008) that transformed 2-D projections to 3D images. Rusch and Harig (2010) made significant advances in 3-D reconstruction of gas clouds by scanning imaging spectroscopy and tomography. The purpose of this paper is to propose a generic approach for spatial gas clouds monitoring which can be applied to any kind of emissive sites. The generic approach is illustrated from imaging a SO2 cloud in an urban area. Infrared data (2-D gas traces) were collected using a scanning imaging IR system (SIGIS2 Bruker) positioned at three different locations of a large urban area. Corresponding 2-D projections were then combined using a specific gOcad® procedure in order to obtain a full 3-D reconstruction of the gas cloud. 2. Material and methods 2.1. Infrared scanning system Infrared spectra are collected using a scanning imaging IR system (SIGIS2 Bruker, called SIGIS in the text) which is described in details by Harig et al. (2007). The SIGIS spectrometer is based on the combination of a modified Michelson interferometer with cube-corner mirrors (interferometer OPAG 33, Bruker Daltonics, Leipzig, Germany) connected to a cooled single MCT detector element and a scanning mirror. The system includes also i) a telescope to collect and focus the IR emission radiation, ii) a rotating head containing an azimuth-elevationscanning mirror activated by stepper motors for a full x, y, z monitoring, iii) a visible and an infrared video camera to make analysis both during the daytime and at night and iv) an internal GPS system in the upper part of the head for the georeferencing of the data. In order to improve the velocity and the adaptability of the analysis, the whole device of remote detection by IR imaging is adapted to a small van-like vehicle (Fig. 1). First, the video image is used to interactively define the spatial area of the IR analysis, constituted by a rectangular 2-D pixel grid. The size and the direction of the field of regard and the spatial resolution are variable and defined by the user. Then, the rotating head is kept at a fixed position and the scanning mirror is sequentially set to all the positions or pixels within the field of regard.
Table 1 Measurement specifications and parameters of the scanning imaging SIGIS system applied to SO2 detection. Interferometer type Spectral range SO2 detection window Spectral resolution Apodization function Zerofilling Phase correction Number of scans Maximum spectral rate (Resolution: 4 cm−1) Field of view Maximal horizontal field of regard Maximal vertical field of regard Average number of pixels in a measurement grid
Bruker OPAG 33 3900–600 cm−1 1450–1050 cm−1 4 cm−1 Triangular 1 Power spectrum 16 16 spectra/s 10 mrad 360° 60° 1500 (30 × 50)
The main measurement specifications and parameters are reported in Table 1. 2.2. IR data processing: from raw data to the coefficient of correlation The internal algorithm of data processing is described in detail by Harig and Matz (2001). Briefly, the method is based on the approximation of a measured spectrum to a linear combination of reference spectra. The recorded IR data are transformed in brightness temperature spectra using the internal black body radiometric calibration. After baseline approximation, the signals of one target compound, of atmospheric gases and of possible interferents are fitted to the spectrum. The contribution of the baseline and of the fitted signals of interferents and atmospheric species are subtracted to the measured spectrum that leads to a so-called corrected spectrum. The presence of one target gas (here SO2) is expressed in terms of a coefficient of correlation (R), calculated in a certain number of compound-specific spectral windows. This calculation is the result of the subtraction of the corrected spectrum to the reference spectrum of the targeted gas. This procedure is successively repeated for each pixel of the 2-D projection and can be repeated for all the gas compounds of the spectral library. In order to improve the SO2 detection and localization, only the pixels having a correlation coefficient higher than 0.75 are selected for the 3-D reconstruction procedure. This value (0.75) appears as a compromise between spectral quality and a sufficient number of data. The measured SO2 spectrum and the theoretical infrared profile (PNNL library) for a correlation coefficient of 0.75 are compared in Fig. 2. 2.3. Urban area and screening procedure Fig. 3 presents an overall sky view of the measured urban area (blue quadrangle zone of interest ZOI) spread on more than 3 km2. The
Fig. 1. Left: Mobile station integrating the infrared remote sensing system of gases (Bruker SIGIS). Right: Schematic SIGIS system (Bruker — Brochure SIGIS).
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Fig. 2. Comparison of typical infrared profiles of SO2 for a correlation coefficient of 0.75 (measured spectrum in black, reference spectrum PNNL library in red).
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different optical pathways have been chosen in order i) minimize obstructed zones, ii) to have the sky as a main background and iii) to take into consideration surface gas emissions. The three reference locations of SIGIS measurements (blue circles) and the corresponding fields of view (blue lines) are also indicated and define 36° angles for the field of scanner regard. The blue quadrangle displays the common area of measurement which corresponds to an intersection atmospheric volume about 135 × 106 m3 (760 m × 935 m × 190 m, dimensions fitting the current measurement area). For each SIGIS position, the operator defines a specific 2-D grid constituted of a certain number of pixels. For each grid, all the different pixels are continuously analyzed step by step from the top-left corner down to the lower right corner using the procedure described above. Table 2 summarizes the main characteristic parameters of each 2-D grid including time of measurement and weathering conditions. The operation is repeated three times for each position. In spite of small differences in measurement times (cf. Table 2), the procedure
Fig. 3. Administrative map and sky view of the urban area of Nancy, France. The zone of interest (ZOI) where measurements were performed is highlighted in the quadrangle blue zone. The three origin points (blue circles) correspond to the SIGIS camera locations. For each point, IGN Extended Lambert II coordinates are mentioned.
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Table 2 Main characteristics of the three 2-D grids used for the SO2 cloud analysis (April 17, 2013).
GPS position of SIGIS Total number of pixels Minimun–maximum size of the 2-D grid (m2) in the ZOI Minimum–maximum pixel side (m) in the ZOI Measurement time Acquisition time (s) Atmospheric pressure (hPa) Temperature (°C) Relative humidity (%) Wind direction (°) Wind velocity (km/h) Visibility (km) Precipitation (mm/h)
Conditions for SIGIS 1
Conditions for SIGIS 2
Conditions for SIGIS 3
XLII+ 290 522 m YLII+ 5 393 220 m 98 × 11 (1078) 551 075–729 200 22–26 1:35 pm 86 1020.2 22.4 41 140 13 50 0
XLII+ 290 599 m YLII+ 5 397 992 m 82 × 13 (1066) 202 162–386 829 14–19 2:21 pm 85 1019.0 23.2 42 130 15 55 0
XLII+ 295 590 m YLII+ 5 399 814 m 102 × 19 (1938) 1 064 620–1 363 840 23–27 3:20 pm 154 1018.3 23.3 41 150 17 55 0
can be qualified as a quite real-time procedure. The results remain valid for slowly changing environmental conditions.
2.4. 3-D reconstruction Each field of regard forms a rectangular-base pyramid (Fig. 4) at a geo-referenced position of the spectrometer in order to define a common intersection volume (Fig. 3). The aim of reconstruction of the 3-D structure of the SO2 cloud is to obtain information about the third dimension by evaluating at least three series of 2-D images of SO2 correlation coefficients recorded at the three different positions. The Paradigm gOcad® software is mainly used in the oil industry for seismic, geological and reservoir modeling (Mallet, 1992, 1997, 2002). The gOcad® software offers some facilities to work with various types of 3D data, with the possibility of specific and manual construction and/or adjustment of 3D models. The gOcad® objects, such as point set, polygonal lines, triangulated surfaces and regular grids (voxet) are used in this case to reproduce and model the SIGIS measurements. From the SIGIS origin, two horizontal straight lines (Fig. 4) separated by a 36° angle (specific fields of regards of this measurement) are drawn on the urban area map. The bisector of this angle is the SIGIS optical axis. Along these two lines, series of parallel 2-D sections, orthogonal to the optical axis, are built and relocated with an increasing width from the origin point to a chosen distance in the ZOI. The height of these images is a function of the SIGIS vertical field of regard and is adjusted respecting several markers in the urban area (high buildings in the vicinity of the ZOI are used as XYZ references). Each 2-D section is characterized by a constant height of 250 m for SIGIS 1(ground–top of the field view SIGIS 1), 280 m for SIGIS 2 and 440 m for SIGIS 3. For this study, we only focused on SO2. As described above, each 2-D grid pixel property is expressed in terms of a correlation coefficient of SO2, which can be sometimes also interpreted as a surface of probability of the presence of the gas. In the case of passive remote sensing by FTIR, all the infrared emissions in the direction of measurement are collected for each pixel of the 2-D grid. The detection of SO2 in a pixel of a grid means that the gas is present somewhere in the conical volume of the considered direction from the background to the detector of the spectrometer (Fig. 4). Taking into account this experimental consideration, the 2-D SO2 coefficient correlation grids can be relocated to n equiprobable locations at a precise distance from the SIGIS origin point along the optical axis. For each of the 3 SIGIS measurement positions, we arbitrary defined 10
sections area with a spacing of 125 m between them covering the whole ZOI (Fig. 5). Assuming this geometrical consideration, the ZOI volume is then characterized by 3 × 10 2-D images coming from the 3 SIGIS positions, each of these sections of multiple widths and proportional heights being used to fill the ZOI volume. All of the 30 2-D images are triangulated surfaces. Each triangle points are defined by X, Y, Z coordinates and a SO2 correlation coefficient that could be further interpolated in the entire volume. The surface mesh shows a resolution respecting the SIGIS grid resolution of 1500 pixels (30 × 50) (see Table 1).
3. Results and discussion 3.1. Interpretation of the 2-D SO2 coefficient correlation grids Fig. 5 illustrates the principle of ZOI filling with the 10 × 3 2-D correlation coefficient grids from the SIGIS 1 and SIGIS 3 positions. Four grids are presented as examples of the distribution of the correlation coefficient value. The color scale corresponds to the raw SO2 correlation coefficients ranging from 0 (no SO2 is detected, blue) to 1 (SO2 is detected and its infrared fits to a SO2 reference spectrum, orange). The spatial distribution of the correlation coefficients clearly indicates that the presence of SO2 (orange zones, correlation coefficients higher than 0.5 in Fig. 5) is limited to the bottom part of the 2-D grids.
Fig. 4. Schematic view of the SIGIS 1 image of the geometrical realization. The optical axis is defined from the SIGIS 1 origin. From the optical axis, the horizontal and vertical fields of view are defined respecting 2*β for the horizontal view and a height of 250 m for the vertical view (fit respecting building heights). The SIGIS image is defined by a superimposition of 1500 pixels, each of them corresponding to a specific and individual gas measurement and property recording.
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Fig. 5. Zoom on the ZOI 3D view. Blue lines illustrate the horizontal fields of view of the SIGIS camera. For a better visualization, only the grids of SIGIS 1 and SIGIS 3 are filled with the SO2 correlation coefficients. The other grids coming from SIGIS 1, SIGIS 2 and SIGIS 3 are simply underlined with black borders. The spatial correlation of high values of correlation coefficients (orange zones) must be observed to define the presence of SO2 in the zone.
It corresponds to an altitude level between 20 and 185 m for the 3 measurements. SO2 is not always detected at a given XYZ position depending on the SIGIS position. For example, the A zone of the SIGIS 1 grid in Fig. 5 shows some SO2 whereas it is absent in the SIGIS 3 grid. On the contrary, in zone B both SIGIS 1 and SIGIS 3 grids show the presence of SO2, at the same 3D location. It implies that SO2 is not specifically present in the A zone of the ZOI but is detected farther or closer from this grid, in another parallel grid along the optical axis of SIGIS 1 and 3. Point A: SO2 is detected only in one measurement direction.
Point B: SO2 is detected in the two measurement directions. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) By combining the 3 SIGIS measurements, two cases can be determined: • The SO2 correlation coefficient is measured with a value higher than 0.75, at a specific XYZ position, simultaneously by the 3 measurements: SO2 is actually present in this zone.
Fig. 6. Zoom on the ZOI 3D view. Blue lines illustrate the horizontal fields of view of the SIGIS camera. The ZOI is underlined with a red line. The final point set results from the application of two filters on the 1500 × 30 points: 1) black dots (SO2 correlation coefficients higher than 0.75) and 2) final red dots showing both higher correlation and the triple correlation between SIGIS grids. Red dots correspond to the presence of SO2 in the ZOI volume.
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Fig. 7. A). Definition of the voxet volume respecting the correlated SO2 points of the SIGIS measurements (SIGIS 1 section as an example). B). The voxet is defined by voxels (nu × nv × nw) and a property of occurrence is defined: a SO2 point contained in a voxel transfers its property equal to “10” to the voxel center and voxels that do not contain a SO2 points are characterized by a “0” value property. C). After a Discrete Smooth Interpolation (DSI) of the property, a region of connected voxels is created representing the cloud external envelop. D). From this region and property volume distribution an isosurface is created that represents the external envelop of the SO2 cloud.
• The 3 grids do not simultaneously show correlation coefficients higher than 0.75 at a specific XYZ position: SO2 is not present in this zone.
Respecting these hypothesis, the 3-D localization of a SO2 cloud can be deduced, pixel per pixel, by studying the regions where the 2-D correlation coefficient grids present a triple correlation in the ZOI quadrangle.
Table 3 Geometrical parameters of the interpolated SO2 cloud.
3.2. Data filtration preparing for 3D reconstruction
Total volume (initial data from SIGIS sections, Fig. 7A) Total volume (voxet region, stairsteps envelop, Fig. 7C) Total volume (isosurface, Fig. 7D) Maximum length (isosurface, Fig. 7D) Maximum width (isosurface, Fig. 7D) Maximum height (isosurface, Fig. 7D) X, Y coordinates of the center of the cloud (Fig. 3)
28 × 106 m3 6
3
98 × 10 m
65 × 106 m3 915 m 735 m 165 m X: 292 164 m Y: 5 396 873 m
The triple correlation hypothesis is represented by a new XYZ point set property fixed at a value of 0 (SO2 is not detected) or 1 (SO2 is simultaneously detected by the 3 SIGIS measurement positions). Then, among the 45 000 XYZ points (1500 pixels × 30 triangulated surfaces) of the ZOI volume, only 3949 points (black dots in Fig. 6) show correlation coefficients higher than 0.75 on at least one of the three grids. Among these 3949 points, 1905 points (red dots in Fig. 6) respect the triple correlation between SIGIS grids (Fig. 6). The resulting point set showing the presence of SO2 is mainly located in the basal part of the ZOI volume and at a maximum altitude of 185 m as previously mentioned. This point set is the XYZ database that it is then used to define the 3-D SO2 cloud envelop.
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3.3. 3-D SO2 cloud reconstruction by gOcad® interpolation gOcad® proposes interpolation tools to model and interpolate a volumic property from a point set within a regular volumic grid called a voxet. A voxet grid is made of (nu × nv × nw) voxels containing X, Y, Z and property information. Such a grid is therefore built respecting the main volume of triply correlated SO2 points in the ZOI (Fig. 7A). The grid is fitted to the volume of the SO2 points and the voxet cage is defined by a 760 m × 920 m × 240 m box. The number of voxels is fixed proportionally to 38 × 46 × 12 that defines a voxel size of 20 m × 20 m × 20 m. It allows defining a lower resolution compared to the SIGIS resolution and make sure of the final creation of a connected volume, the cloud of SO2.
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A voxel property is first created and initialized to a constant value of “0”. Then the same property is created for the SO2 points (Fig. 7B) with a value of “10”. The objective is just to paint the point set property in the voxel to discriminate voxels where SO2 is present (“10”) or nonobserved (“0”). The point set property is imported in the voxet and each X, Y, Z point that enters a specific u, v, w voxel transfers its property to it (Mallet, 2002). Then, a smoothing of the property is realized using the gOcad® DSI tool (Discrete Smooth Interpolation, Mallet, 1992, 1997, 2002; Caumon et al., 2009) defining a connected property and a view of the external envelop of the cloud. Voxels corresponding to SO2 are then extracted and stored in a voxet region (Fig. 7C). Finally, an isoproperty surface can be fit to this region to better represent the external envelop of the SO2 cloud (Fig. 7D).
Fig. 8. Visualization of the SO2 cloud above the investigated urban area.
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Table 3 summarizes the main geometrical parameters of the identified SO2 cloud calculated after the 3-D gOcad® procedure. The SO2 cloud is presented as the external envelope of the isosurface. However, the initial point set corresponding to the SO2 correlated points is more discretized respecting the SIGIS section proposed in Fig. 5. SO2 is therefore not homogeneously distributed within this external envelope. In Table 3, three types of volume are mentioned. The initial point set volume corresponding to the raw data coming from the SIGIS image is equal to 28 × 106 m3. After the DSI, this volume representing connected voxels, i.e. stair steps mesh, corresponds to an external envelope of a volume of 98 × 106 m3. This volume is reduced to 65 × 106 m3 with the final isosurface that reduced the stair steps effects. Visualization of the SO2 cloud is given in Fig. 8. The SO2 cloud morphology is well described within the ZOI volume. However border effects can be noticed especially at the south east side where the cloud is truncated by the external limit of the ZOI. This border effect is directly linked to the SIGIS fields of view. In the case of a visible cloud, the ZOI could be more efficiently adjusted. The specific localization of the SO2 cloud is related to the presence of ancient underground tanks of gasoline presenting a defect of waterproofness. Moreover, during the period of measurements, important renovation works were operated. Then, exhaust gases stemming from diesel machines of public works constituted a complementary source of SO2 that was clearly detected by the SIGIS system. Although it was not the initial purpose of this paper, this step is also of first importance for quantitative treatment of SO2 cloud. The transformation of gas concentrations from ppm·m to ppm needs to know exactly, for each measurement pixel, the thickness of the cloud. Indeed, a cloud is not necessary a homogeneous objet and can present regions only filled with air. The thickness of these gas fields must be determined between each entry point and each exit point of the cloud. The 3D gOcad® interpolation results (Table 2 and Fig. 7) show the data necessary to this type of calculation. 4. Conclusion This work puts into highlight the potentiality of remote sensing using terrestrial scanning infrared imaging spectroscopy for local scale atmospheric environmental monitoring. The combination of three different measurement positions delimits an urban area where SO2 emissions can be spatially localized. Raw infrared data constitute a rectangular 2-D pixel grid processed in terms of correlation coefficient (R) where only the values higher than 0.75 are used for 3D reconstruction. Each grid is distributed within its corresponding field of regard to form a regular succession of 2D planes filled with the correlation coefficients of SO2. The SO2 observed on these different measurement planes can be interpreted as follows: Single plane: SO2 is detected from only one position of measurement. Double plane correlation: SO2 is detected from two different positions of measurements Triple plane correlation: SO2 is detected from the three different positions of measurements. Only the pixels corresponding to a simultaneous triple detection by the three different measurement positions (triple intersection) provide information to spatially localize a SO2 cloud. Only these specific pixels were used to build the SO2 external envelope using gOcad® tools and to estimate its volume. In the current domain of the urban area investigated this external envelope represents a volume of 65 × 106 m3. Such SO2 emissions are mainly related to the presence of ancient underground tanks of gasoline presenting a defect of waterproofness and to exhaust gases stemming from diesel machines of public works consecutive to the important public works in this urban area. For this study, because only one SIGIS was used, weather conditions appear as the main limited factor for successively measurements. This aspect can be improved by using simultaneously 3 scanning imaging IR systems. Finally, the combination of
scanning imaging IR spectroscopy, the measurement setup and the 3D gOcad® volumic reconstruction can be used like a generic approach for 3-D reconstruction of gas clouds in the case of environmental atmospheric monitoring and in the case of every type of ground emissive sites. Automation of the full procedure can strongly simplify the system to be manipulated by a single operator for a generalization of this type of real-time terrestrial remote sensing monitoring. The next step of this study will led to quantitative analysis. Acknowledgments The authors wish to acknowledge TOTAL E&P (Contract TOTAL CREGU n° FR00006482) for its financial support. The authors associate the National French Agency (ANR, Program N° ANR-07-PCO2-007-08, SENTINELLE) and the European Fund of Regional Development (FEDER) for its help to provide the SIGIS remote sensing system used in this study. 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