Environmental data display Large amounts of information can be portrayed in very compact f o r m and used f o r many applications, thanks to a combination of color-coded graphics and image processing techniques
Kevin J. Hussey Richard J. Blackwell Jet Propulsion Laboratory California Institute of Technology Pasadena, Calif. 91 125 Gregory J. McRae John H. Seinfeld Enuironmental Quality Laboratory California Institute of Technology Pasadena, Calif. 91 125 The development of effective means for presenting large volumes of physical and chemical information in a form that can be readily assimilated is a formidable challenge. Nevertheless, the analysis of environmental systems frequently requires the acquisition, processing, and display of sizable quantities of numerical data. A relatively new and promising technique for the visual presentation of large amounts of environmental data combines the analytical power of image processing methods with the simplicity of computer-generated color displays. In this combined approach two basic components are involved. One is the use of computer graphics in which the primary concern is effective visual presentation of information. The other is the use of image processing techniques, which involves the analysis and manipulation of pictorial information. While both computer graphics and image processing deal with digital representations of pictures, they have until recently been quite separate disciplines. The availability of low-cost computer hardware that is compatible with both computer graphics and image processing procedures has led to a situation in which it is now feasible 78A
Environ. Sci. Technol., Vol. 17, No. 2, 1983
to utilize together the strengths of each approach. While the proposed methodology is widely applicable, the illustration chosen for this study is the display of emissions and air quality data over the South Coast Air Basin (SCAB) of California. This example embodies most of the problems that are likely to be encountered in practice-a huge amount of information and complex variations in the data fields. For descriptions of other applications or details about particular algorithms, the reader is referred to References 1-3 for discussions of computer graphics and to References 4-9 for further background on image processing.
Data display techniques Understanding complex physical and chemical phenomena often requires the analysis of huge amounts of numeric data, generated either from a mathematical model or from field and laboratory measurements. In many cases, the sheer volume of information can make it very difficult to assimilate such information in a meaningful manner. A common technique for reducing the amount of required data is to report only those summary statistics that might characterize the data field as a whole. The major drawback of this reduction is that it usually does not give much insight into details of the phenomenon, such as spatial or temporal variations of the variables. One way to circumvent this limitation is to use graphical displays that present information in a visual form. Contour plots and wire frame perspectives are two of the most commonly used graphical display techniques. An example of each method is
presented in Table 1 together with a summary of its key attributes. These representations have some undesirable properties ( I O ) . For example, it is not easy to identify the extreme values in a contour plot. In fact, attention is usually focused on sharp gradients within the field, because that is where the contour lines are most closely spaced. Although perspective plots facilitate the identification of peak values under some conditions, features at the front of the graph can obscure details in the background. Despite their limitations, both display types take advantage of the human ability to absorb easily information that is presented in a pictorial manner. The emergence of inexpensive graphics terminals based on television technology has added another dimension to the array of possible display techniques-the use of color. With suitable choices of shade, degree of brightness, and level of saturation the amount of information that can be conveyed in a color image can be considerable. In fact, one of the primary motivations for employing color is that the human visual system can easily discern thousands of shades and intensities of color but only 20 to 30 shades of gray ( 7 ) . Computer-generated color graphics are being increasingly used in many disciplines, including, for example, cartography, animation, computeraided design, and process control ( I , 2). Colored contour plots have been used in such diverse applications as chemical kinetic calculations (1O ) , the dynamics of complex fluid flows (1 1), and remote sensing of pollutants (12). While the use of color as a means of presenting large amounts of informa-
0013-936X/83/09 16-0078A$0l .50/0 @ I983 American Chemical Society
tion compactly is rapidly expanding, there is also a need, particularly in many environmental applications, for this capability to be combined with a means for manipulating and interpreting the pictorial information. One means of accomplishing this objective is through the use of digital image processing techniques.
Basic concepts An understanding of what is meant by the terms “digital image” and “image processing” is essential for an appreciation of what has been undertaken in this work. Photographs, paintings, and maps are examples of images. In the form in which they usually occur, images are usually not directly amenable to computer analysis. Since computers work with numerical rather than pictorial data, an image must be converted into a numerical or digital form before processing. One way of accomplishing this task is illustrated in Figure 1. The first step is to subdivide the original image into small subareas calledpicture elements, pix&, or pels. A common subdivision scheme is to sample the image at each point along a series of horizontal grid or scan lines. In this way, the entire area of the image is represented by an array of picture elements. At each pixel location (x, y). a number must be generated that corresponds to the image value or brightness at that point. This is called the gray leuel. Each gray level can be defined in terms of a scale that has many different shades of gray between white and black. If the gray scale has only two discrete intensity levels, every pixel must be either black or white, whereas a gray scale with 64 levels can yield a fairly realistic reproduction ( 5 ) . The amount of detail retained from the original image is primarily a function of the rectilinear grid density and the accuracy with which the image can be characterized at each point (7,8). A “digital image” is therefore a numerical representation, f(x. y) of the image brightness a t a set of discrete spatial points. The value of f(x, y) is proportional to the gray level a t coordinates (x, y). Another way to define a digital image is as a matrix whose row and column indices identify a point in the image and in which the corresponding matrix element characterizes the image brightness a t that point. The associated term “image processing” refers to the subsequent computer manipulation of digital images. Other commonly used technical terms are defined in Table 2.
display techniques for spatially distributed data
,,..
Air quality field
..
,presentation
..
Numerical dis
..
. 0.17 0.200.22 .. 0.18 0.19 0.23 ..
importat present rIIIyIIIIa(IuII Its pEwk,av manner. One drawback, however, is thai ihe SI “lli”ld
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m:
FIGURE 1
Preparing a color-coded image Image
,Column of samples Color coding
Line
Picture
I
5-
Sampling
Ptxel
Line
White
-0 Gray
scale
Pseudo
color
4lSample spacing
Environ. Sci.Techno1.. VoI. 17, NO.2, 1983
1SA
field Typically a digital image IS defined
O.ay scale Pseudowlor coding
image enhancement
corresponding denotes the brighmessat hat point An indivlduai point lied a pixel M pel. Both of these terms are abbreviationsof "picture element " The inlensily or btigMneSS of a monwhrome image at a parllwiar point Is called me gray level. Ea level can be delined in terms of a scale the1 has ail shades of gray between white and black This is a very simple means for enhmclng the visual dynamic range 01 a gray s a l e image. The procedure i n d v e s assigning wlors lo each gray level. Application of diilal processing techniques lo improve the visual quality of an image or the emphasis of particular features in a scene.
D ! l a i filtering
Mathematicaloperations mat are applied to me Image in an anernpi to reduce the effects 01 noise. wmpensate for poor sampling. or 10 emphasize Characteristiclealures wnthln me image. techfmlogy. Images are formed on the displa es. Each Scan line is composedof individua
Then ways to .-mple p_I =-... _....;initial discretization, including the use of microdensitometers, flying spot scanners, image dissectors, television cameras, and charge-coupled semiconductor devices (5-9). Alternatively, the raw data needed for image ' ated d' processing can be 1
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.
.
...
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Environ. Sci. Techmi.. Voi. 17, No. 2. 1983
digita .ge repreirqua ..., ..eldcan be created by interpolating concentration measurements at monitoring sites to an array of grid points (13, 14). Images of the spatial distribution of emissions data needed as inputs to mathematical models of the formation .and transport .on can be of photochemical air p I
-.
develope, i simi anner The use L. m a g e uuoJ form,. -,*.I makes it very easy to incorporateoutputs from mathematical models, since the numerical solutions are typically calculated a t individual grid points. Once an image has been created in a form suitable for computer processing, a variety of mathematical opera-
-..
tions can be carried out to enhance the visual quality of the display. For example, a typical defect of photographic or electronic images is poor contrast resulting from a reduced, and perhaps nonlinear, image brightness range. The appearance of these images can often be considerably improved through the use of relatively simple scaling transformations that modify the brightness of each pixel. Such techniques are called intensity mapping operations and are usually expressed in the form.
where I [f(x,y)] is a nonlinear mapping of f(x,y) into a new value g(x,y). These functions are normally independent of the spatial position, and, as a result, the output pixel value depends only on the input at that point. These point operations can be applied to correct for the effects of nonlinearities in the original sampling or to expand the effective range of gray levels, thereby enhancing thevisual contrast of the image. There are many other applications of Equation l , including adding contour lines to images or identifying threshold values that might define boundaries (6-8). A closely related point-by-point operation is the use of pseudocolor enhancement. With this technique, the objective is to increase the effective viewing dynamic range of the original gray scale by appealing to the human's visual response to color. A pseudocolor display can be created from the original gray level image by a sequence of mappings of the form R(x,Y) = IR[f(xJ')l
(2)
G(X.Y) = IG[f(X.Y)l
(3)
B(X,Y) = lB[f(x,y)l
(4)
where IR, IC, and l e are a series of transformations that map the original gray levels into the threecolors used by the particular display device. For example, if a television monitor is used, the outputs from the three independent transformations (red, green, and blue), can be used to produce a color-additive composite imagc. One example of :I color scale gcnerated rrom transformations of the form of Equations 2-4 is shown in Figure I . Some of the factors that must be considered in the design of transformation func ions are presented elsewhere ( I , 7 , 8 , lo). Despite these complexities, use of pseudocolor enhancement can dramatically increase the amount of information that can be displayed in an image. In addition to the range of tech-
niques that can be applied to improve the visual quality of images, there are other procedures useful for emphasizing particular features, or removing spurious effects caused by poor sampling of the initial data field. For example, if a particularly coarse sampling is used, it may be desirable to smooth the transitions between adjacent pixels. In such an exercise, the first step is to expand the s.ze of the image by increasing the number of pixels surrounding each point. Once this has been done, the expanded image can be filtered to minimize the effects of discontinuities. One of the
simplest filtering procedures is known as neighborhood averaging. Given an image f(x,y), the approach is to generate a smoothed image g(x,y) whose value at each point (x,y) is obtained by averaging the values off contained in a predefined neighborhood of (x,y). The filtered image is obtained by using relations of the form (7)
where S is a set of M coordinate points that define the neighborhood of (but do not include) the point (x,y). The operation defined by Equation 5 is re-
iURE 2
quivalence between color and numeric representation'
Numeric representation
I
I
I
Digital image
I
I
1 Image cross-section
Original image
20
Filtered image
80 A'
AI
Environ. Sci. Technol.. VoI. 17. NO.2, 1983
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FIGURE 3
Ozone concentration distributiona
*
12
.2i
PPM (Cailornia)
'Over South Coapt Air Basin on June 26, 1974, 16W (4:W P.M.1 Pacific Slmara Time. Areas exprimcow 020ne concenbaliins BXcBeding me Nafbnal Amblenl Air Qualify Standard. or above a Sage 7 Heslth Ale". can be readily identified.
peated for each pixel in the image. Figure 2 shows the effects of applying a two-dimensional neighborhood averaging filter to an image that has been expanded from a 3 X 3 to a 1 5 X 15 matrix. Applications The particular example chosen to illustrate how image processing might be used in environmental applications represents emissions and air quality distributions over the South Coast Air Basin (SCAB) of Southern California. While the primary focus is an air pollution problem, it is important to keep in mind that the procedures can be applied, with little or no modification, to many other areas. Table 3 summarizes a number of projects that have employed similar image processing techniques for the interpretation and manipulation of environmental data. Large-scale, three-dimensional, time-dependent urban air quality models of the type described in Ref82A
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erence 15 require as inputs large amounts of meteorological, emissions, and air quality data. This information must be prepared at a level of accuracy consistent with the required spatial, temporal, and chemical resolution characteristics of the problem. The major application of these models is to evaluate the air quality impacts of alternative control strategies. For a particular study of air quality patterns in 1974, a grid system composed of 2400 5 X 5-km cells, within a 150 X 400-km rectangle, was superimposed over the major portion of the SCAB. In each cell, emission rates of nitrogen oxides (NO,), carbon monoxide (CO), and reactive hydrocarbons (RHC) were compiled for each hour of the day. Airborne ozone concentration was considered to be the prime indicator of air pollution level. The spatial concentration distribution of ozone was generated by interpolating sparse and discrete measurements from monitoring stations to the same grid system
as that used for the emissions inventory. Approximately half a million data values, representing emissions and ozone air quality, were prepared in a format compatible with both the air quality model and the image processing system. After preparation of the basic information, the next step was todisplay the data fields in a form that would graphically illustrate the spatial variations. The original images were too small for effective visualization on the electronic screen. Thus, each picture element was expanded by a factor of eight to maximize the hardware display capabilities and to describe the spatial gradients over each 25-km2 grid cell more realistically. The expansion process and subsequent digital filtering used the same procedures described earlier. Pseudocolor enhancement was used to transform the gray levels into color-coded images. Since the Universal Transverse Mercator (UTM) coordinate system
FIGURE 4
Image information processing steps Environmental data to be displayed
I Emission fluxes Air qualiry measurements Remotely sensed data
Encoding of information
1
I
Dataprocessing
representation of
geometric distortion
Digital finering Contrast enhancament Image rnanipulahon and analysis 8 Color codmg of
different data se
I
FiGURE 5
Combining two different data sets Spatial dlstrlbution of reactive hydrocarbon emissions
...
1
I
14" ... and the resulting ozone concentration field
N o w Ozone is lormed from complex reactions lnvolvlng rea~tivehydrocarbons. nitrogen oxides. and Itpht. The area of wont air quslity is located far downwind from the region of highest e m l ~ i o n i .
Environ. Sci. Technot., VoI. 17. No. 2. 1983
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was used to define the grid system, it was possible to create additional digital images that contained city names, county boundaries, and other geographic features. These images can be combined to produce composite displays. A color-coded scale (Figure 3) provides a direct means for determining the ozone concentration distribution, for example, as a function of location within the region. The colors for this image were chosen with the idea that cool hues (blues) indicate low concentrations and the warmer tones (reds) denote higher levels. One consideration influencing the selection of the transformation functions for air quality display was that, in addition to indicating spatial gradients, the image should also pro-
vide a clear means for identifying those areas exposed to levels of given pollutants above the ambient air quality standard. A summary of the steps involved in using image processing techniques to display emissions and air quality data is shown in Figure 4. Once the images are prepared, it is possible to exploit the data management capabilities of the image processing system to combine different data types. For example, in Figure 5 the reactive hydrocarbon emissions data and the ozone air quality distribution have been combined intoa single image. One feature that is strikingly apparent from the dual display is that the area of worst air quality is far downwind from the region of highest emissions. Such patterns may not be as
FiGURE 6
Time evolution of ozone concentration distribution
This display iilustrates the combined influence of meteomlogical transpoR and spatial Variations in photochemical oxidant Dmduction in the South.Coasl Air Basin. California.
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easily extracted from large quantities of computer output. The time lapse sequence shown in Figure 6 compactly summarizes both the diurnal and spatial variations of the ozone distribution over the South Coast Air Basin. By interpolating between each hourly image, one can produce either video tapes or films that show the dynamic interaction between meteorological transport and photochemical oxidant production. Moreover, with the point transformation operators (Equation 1) it is a simple task to identify particular features in an image. For example, it is easy to identify those areas experiencing pollutant concentrations that exceed the National Ambient Air Quality Standard for population exposure studies.
Software Because of rapid advances in the fields of microelectronics. display technology, and computer programming, it is now feasible to consider the routine use of image processing techniques. In the past. one of the major practical barriers was the high cost of performing the necessary computations on large computing systems. The emergence of extremely fast, low-cost microprocessors has significantly changed this situation. During the last decade. both processing and storage costs have been substantially lowered to the point where it is now fcasible to manipulate very large data arrays on small minicomputers. These innovations have occurred during a time when there have also been similar evolutions in display hardware. One area of particular interest is thc use of high-resolulion displays based on television tcchnology ( I ) . This format is directly compatible with the requirement of image processing. Given this situation, perhaps the only remaining constraint on the widespread use of the techniques reported in this paper is the availability of suitable software. Fortunately. there have also been steady developments of programming systems that enable people who are not already experienced computer users to accomplish many image processing tasks. I n addition to these systems, there are also many other commercially available packages. Just the beginning The advantages of using a combination of computer-generated color graphics and image processing techniques to display large amounts of environmental information have been successfully demonstrated. I n the particular application described. emissions and air quality data for the South Coast Air Basin of California were processcd to produce color-coded maps that show both the temporal and spatial variations in the data fields. This example provided an illustration of the utility of an image-based approach to information management and manipulation. With the advent of large-scale cnvironmental models these techniques have become important tools for the efficient representation and integration of input and output information. These uses represent just the beginning of thc potential applications in this area. Already there is an emerging capability for mathematically representing textured surfaces, modeling the effects
of light reflection, and shading of solid surfaces. These developments, coupled with the use of advanced image-based information systems, offer great potential for presenting environmental information in a form that aids analysis and interpretation.
Soflware for Commercial and Scicntilic Applicalians: Califurnin lnslitule of Technology. Pasadena. Cdir., 1976.
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Acknowledgment Before publication. this article was reviewed for suitability as an ES&T fcdlure by Robert J . McNeal. program manager. Tropospheric Air Quality, National Aeronautics and Space Administration, Washington, D.C. 20546. References
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Kevin J. 1Iu.we.v (1.) ir phy.rica1peographer with rhr Earrh Kesorircer Applicarions Croup wirhin rhe Imuge Proccssinp Applicarions and Dnrlopnrenr Section or rhe Jer Prripulrion Lahoraror?. H u ~ s qreceicrd hic B.A. and M . A . dcpreer in grography from San 1)irg.o Srarc Unircrsir~.. He hor perfimwd rerearch in rhr area.^ of moniroriny regional popr larion e.vpo.ww 10 airhornp ~onraminanrsand a numher (I/ orher enrironmmmlly relarc~dapp1icarion.v o/ image prows.ring rechnulogy.
Richard Blackwell ( r . ) is o rechnical group .mpe~t'i.wrin rhe linage Procrcsinp Decelopmcnl rind Applicarionr Seerion ar rhe CaliJbrnia lmrirttre of Technology, Jcq Propulsion Lohomrory, Pa.vadena, C a l i / . where he ir is r',.rponsihle/or rhe d e w / opmrnr and opplicarion
