COMPARISON OF DAY AND NIGHT IMAGERY IN SUPPORT OF THE NORMALIZED DIFFERENCE THERMAL INDEX (NDTI) 1 INTRODUCTION

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1 COMPARISON OF DAY AND NIGHT IMAGERY IN SUPPORT OF THE NORMALIZED DIFFERENCE THERMAL INDEX (NDTI) 1 Michael McInerney, Research Electronics Engineer Robert Lozar, Spatial Analyst U.S. Army Engineer Research and Development Center (ERDC) Construction Engineering Research Laboratory (CERL) P.O. Box 9005 Champaign IL Michael.K.McInerney@usace.army.mil Robert.C.Lozar@usace.army.mil ABSTRACT This paper expands on the authors previous research in the development of a Normalized Difference Thermal Index (NDTI) to distinguish pavements from roofs as part of a remote sensing application. In the previous study, a single daytime data set from the multispectral Advanced Thermal and Land Applications Sensor (ATLAS) system was used to differentiate between urban pavements and roofs on the basis of differences in their thermal infrared (TIR) signatures. In this study, a daytime TIR data set was compared with a nighttime data set of the same geographic area, collected within a 2 day period, to determine whether differences in material heating and cooling rates could help to produce more accurate imagery than by using a single data set alone. Statistical analyses of the data sets supports the viability of the NDTI as a TIR imaging technique for urban infrastructure. INTRODUCTION Remote sensing is the collection of data and information about an object from a distance. The collection methods range from land-based data acquisition to sensors placed on helicopters, planes, and satellites. The majority of sensor technologies used for remote sensing utilize the electromagnetic spectrum. New sensors with increased spatial and spectral resolution hold the potential to allow greater feature delineation. Older sensor platforms, such as the Landsat Thematic Mapper satellite, have a spectral spatial resolution of 30 m per smallest picture element (pixel). Newer platforms are being tested as airborne sensors. The Advanced Thermal and Land Applications Sensor (ATLAS), like the Landsat Thematic Mapper (TM), is a passive multispectral sensor that measures the strength of emitted or reflected electromagnetic radiation. The sensors of both instruments record electromagnetic radiation in the visible portion of the spectrum on Bands 1, 2, and 3, while Bands 4, 5, and 7 sense energy in the reflective-infrared portion of the spectrum. On the TM, Band 6 senses a wide portion of the thermal spectrum (wavelengths from μm). This band has proven useful for vegetation and crop stress detection, heat intensity, insecticide applications, and for locating thermal pollution. It can also be used to locate geothermal activity. However, this single band covers the entire thermal infrared (TIR) spectrum. Finer spectral, as well as spatial resolution is required for distinguishing urban landcover/landuse types in the TIR spectrum. The increase in sensor spectral resolution has promoted remote material identification. Many materials have a distinctive spectrum in the long-wave infrared (LWIR) region. However even using the most advanced hyperspectral remote sensing techniques, it is still not possible to distinguish clearly between pavements and roofs (Herold, 2004). Their hyperspectral spectra are similar because they are made of similar materials. However, because the material use is different, we hypothesize that the thermal emissions of pavements and rooftops will vary throughout the day due to their differing construction applications and thermal masses. For example, the thermal emissions of an asphalt parking lot should vary slowly throughout the day. The earth, which is in direct contact with the asphalt, is a large thermal mass that moderates the solar heat gains and losses of the pavement. Conversely, an asphalt roof should have greater 1 Approved for public release; distribution is unlimited.

2 temperature oscillations because it is insulated by air from such a massive thermal sink. Using heat transfer theory, including conduction, convection, solar, and blackbody radiation, we developed a fundamental thermal heating and cooling model for these infrastructure elements. This differential thermal inertia is the theoretical basis upon which we propose and test the efficacy of the Normalized Difference Thermal Index (NDTI) concept in distinguishing pavements from roofs (McInerney, et. al., 2006; McInerney and Lozar, 2007). The ATLAS sensor provides spatial resolutions as small as 5 meters per pixel. The increased spatial resolution of the ATLAS sensor is crucial for distinguishing between buildings and other characteristics of the urban landscape because the sizes of the urban infrastructure are in this range. In a previous study (McInerney and Lozar, 2008) in which we attempted to use the currently available satellite thermal imagery (from ASTER) to apply the NDTI, we found that at 60 meters per pixel resolution, the smearing of the two land cover types was too great to successfully apply the NDTI to remotely define urban infrastructure. The biggest disadvantage of the ATLAS data is that it is not taken regularly. Because ATLAS is flown on an air platform, each use must be individually funded. The data we have used was gathered over a decade ago and no new flights over the same area have occurred since. So although it is high-quality data, we have no comparative or update capability. BACKGROUND In our study centered on Atlanta, Georgia, we used data taken in May 1997 for the National Aeronautics and Space Administration (NASA) Urban Heat Island Program by the ATLAS sensor system flown onboard a NASA Stennis Learjet. The data for Atlanta were collected at approximately 5032 meters above mean terrain, resulting in a spatial resolution of approximately 10 meters per pixel. Spectral bands 1 to 7 of the ATLAS instrument are similar to those on the Landsat TM satellite. The bands of particular interest in our investigation are the six new TIR bands indicated in Table 1. The thermal bands range from 8.20 µm to 12.2 µm, and provide valuable information about urban landscape characteristics. A GPS location of the acquired data was used to ground-truth the information. The daytime information for Atlanta was corrected for the attenuation effect of the atmosphere. The data were originally recoded in 8-bit format with integer values ranging from 0 to 255. These values were adjusted for transmittance and path radiance variations, along with various calibrations for temperature measurements. This task was completed using the MODTRAN program Table 1. ATLAS Thermal Channel Specifications. Channel Band limits (μm) Thermal Infrared (TIR) Bands developed by the United States Air Force Geophysics Laboratory. A section of the available data was cut out to provide a manageable amount of data to process and store. However the data available did not include the comparable nighttime images we were interested in testing.

3 Figure 1. TIR ATLAS view of the study area. The same thermal bands are used for the upper left (daytime) and lower right (nighttime) ERDC-CERL contracted with NASA Stennis Space Center, Mississippi to reprocess the original flight data into sets of comparable day/night images for several locations 2, the Atlanta study area in this paper being one of those reprocessed data sets. Ortho-rectified aerial imagery of approximately the same timeframe as the ATLAS imagery was located and subdivided. The aerial imagery served as the base map in the Ground Control Point (GCP) selection process. Due to the non-linear and somewhat unstable ATLAS flight data, it was necessary to extract sub areas from each study site in order to attain an acceptable overall RMS error during the control point selection process. In selecting and evaluating the ground control points, the Polynomial Transformation Model was used; selecting approximately 100 points for a study site (~50 points for each sub area). In reaching the desired RMS error of 1 pixel, the polynomial order ranged from 3 images. to 5. A "Rubber Sheeting Model" was applied to rectify most of the data sets. Once the data had been successfully rectified, the resulting sub area products were then mosaicked together. In this study, daytime imagery was compared to nighttime imagery (Figure 1). The hypothesis being tested was if the NDTI could better distinguish the difference between rooftops and pavements using the temporal difference between night and day. A commonly used remote sensing technique is to divide the values of a sensor s different spectral bands at the same spatial location by each other. A small ratio implies a small change, and a large ratio means there is a greater spectral difference. This technique is used for many applications such as sensing minerals in earth ores. However, for more sensitive comparisons, a more sophisticated technique is given in Equation 1 Band( x) Band( y) NDI = Band( x ) + Band( y ) Equation 1 This procedure is termed a normalized difference index and yields values ranging from -1 to +1. The normalization allows for comparison between different bands. The procedure is often applied to the identification of vegetation using the large difference in the absorption of the red and near infrared bands by chlorophyll. Because we posit a thermal inertia difference between roofs and pavements, we believed that a reliable thermal index could be derived. First we define the way we calculate the NDTI. Previously, using only daytime imagery, we used equation 1 where Bands (x) and (y) were in the same image (in this instance, daytime) (McInerney et. al., 2006, 2007, and 2008). For this investigation we needed to redefine the NDTI in order to explore the difference in thermal characteristics between day and night. We modify the equation as follows: 2 The following processing description is courtesy of Dr. James C. Smoot, (SSC-NASA), Stennis Space Flight Center, Mississippi.

4 Band( x ) Band( y ) day night NDTI day-night = Band( xday ) + Band( ynight ) Equation 2 With this modified equation we processed the day and night imagery. METHODOLOGY Procedure and Criteria for the Evaluation of the Normalized Difference Thermal Index (NDTI) First we calculated the NDTI day-night values for all 36 combinations 3 and then determined which of the day/night NDTIs best presented an identifiable roof vs. pavement thermal change. Our images contained Digital Numbers (DN) with values in the range of 0 to 255. To prevent undefined results when both bands are zero valued, the NDTI day-night was assigned a value of zero in these instances. This is consistent with the interpretation that bands of like values indicate no thermal change. Further, for ease of interpretation, the result was multiplied by 100 to expand the range from -1 to +1 to 100 to Our goal in terms of the resultant image was to find the greatest contrast between the roofs and pavements, as shown in Figure 2. In this image most roads are dark and most buildings are light. This is encouraging but the question remains, Which NDTI day-night band combination results in the best discrimination between roads and pavements? Generating a Reliability Test The next step was to compare the NDTI calculations against ground truth locations. An aerial image of the study area was obtained from the USGS archives. The NAPP image 4 was acquired in a year (1999) comparable to the year of our subject ATLAS imagery. The image was georeferenced to our study area 5. From this image, two sets of vector files were generated, one that defined pavements and one that defined rooftops (Figure 3). Digitizing these two types presented unique issues. For the definition of roads (5.8% of the study area), the major issue is that they are long thin areas, which implies there is a large amount of edge. For our purposes, the inclusion of more edge in the analysis leads to more areas with a calculated NDTI day-night that include objects other than pavements in their spectral signature. Interstate 75 is the widest pavement within the study area and was expected to provide the purest pavement signature representation 6. We have assumed that there is little difference in the construction materials between interstate pavements and other pavements in the study area. The main concern with using interstates as ground truth points is that portions are elevated, violating our assumption that all pavements are in direct contact with the earth. 3 Since all values are positive and (Band(xday) - Band(xnight)) is merely the negative of (Band(xnight) - Band(xday)), no additional information is gained by computing the NDTIday-night with the bands reversed. 4 ID: NP0NAPP , Roll: Frame: 191, Date: 1999/2/8 Proj: National Aerial Photography Program (NAPP). 5 Affine Transformation with Total RMS Error of In fact we found that even within the type areas, the central pavements were generally better defined than the road shoulders.

5 Figure 2. NDTI day-night result of Day Band 11 and Night Band 12. On the whole, notice that buildings are light shades and roads are dark shades. Figure 3. The two ground truth locations; yellow for pavements and orange for roofs.

6 A similar problem occurs when dealing with the digitizing of the roofs (2.8% of the study area); roofs are disjointed in their coverage. Again finding large areas of a single roof material was difficult. In addition, the aerial image was flown at such a low altitude that some of the taller buildings were tilted to the side. Therefore the location of the rooftops was actually defined by their ground level footprint rather than the offset location of the rooftops in the image. Also, because of the limited resolution of the ATLAS images (and even that of the NAPP images), only the largest roofs were identified for inclusion in the ground truth collection. We believe that these large rooftops are warehouses, so this sample may not well represent other types of construction such as houses and smaller buildings (e.g. warehouse roofs are usually flat and covered with larger sized gravel, while house roofs are usually sloping with gravel the size of course sand). We also observed that many large fabrications were constructed with interior concrete pavement sections, supposedly so that trucks would have access. This resulted in a building form reminiscent of the teeth on a comb. Since this would confuse the distinction we are looking for, such buildings were not included in the ground truth sample. There is also the possibility that by making the choices indicated we have used up most of the potential pixels of pure signal. Thus it can be expected that other locations that will be identified by the NDTI day-night will exhibit a less pure definition than in the ground truth samples. The two ground truth types were translated into a grid format so that they could be used as a mask to define the type spectral response. To gather the statistical data, first the Roads-grid (followed by the Roofs-grid) was used as a mask to extract the type NDTI day-night statistical data. Statistical Results From the extracted set of data we were looking for two NDTI day-night results (one for roofs, one for pavements) that best separate these two cover types. The criteria for separation (i.e., exhibit the greatest diurnal difference) were: the mean NDTI day-night value should be as distant from 0 as possible, the standard deviation of that same calculation should be as narrow as possible, i.e. the spectral signature is well defined. Table 2 presents a summary of the ranking based on mean and standard deviation. The nomenclature c10d14n, for example, is interpreted as the NDTI day-night calculation for band 10 of the daytime imagery and band 14 of the nighttime imagery. From the rankings in Table 2 only the band 10 day - band 10 night (c10d10n) combination appears in the top 10 of roads for both mean and standard deviation. In contrast, three NDTI day-nights appear in the top 10 for roofs (c14d13n, c14d10n, and c13d11n, in that order based on best mean). It is interesting and possibly unfortunate that the best definition of roads and roofs does not come from a single NDTI day-night calculation. However if one combines the two best NDTI day-night in each category (Figure 4), preserving the low mean values of the roads (mean 9.57) and the high values of the roofs (mean 13.8), this increases the average mean difference between roads and roofs to Within a single band the greatest difference is only (c13d15n) while c13d15n did show up in 10th place in the best means for roads ranking, it had only near average standard deviation for roofs. Note that for roads, for all calculation the average value of the mean was 5.05 and the average value of the standard deviation was 10.84, while for the roofs, the average value of the mean was 8.03 and the average value of the standard deviation was This implies that the definition by mean was more distinct for the roof values but the definition by standard deviation was about two and a half times poorer for roofs in general. Because of this observation, definition by mean is given more weight in cover type identification for the best NDTI day-night.

7 Table 2. Best NDTIday-night rankings. Ranking by Best Mean (Greatest Absolute Value is Best) Ranking by Best Standard Deviation (Smallest Value is Best) Roads Roofs Roads Roofs c10d14n c14d12n c15d10n 8.90 c10d10n c10d15n c15d12n c13d10n 8.95 c13d10n c12d14n c14d11n c14d10n 9.10 c11d10n c12d15n c13d12n c13d11n 9.54 c14d10n c11d14n c14d13n c15d11n 9.57 c12d10n c11d15n c15d11n c10d10n 9.72 c15d10n c10d13n c15d13n c14d11n 9.74 c14d13n c13d14n c11d12n c15d12n 9.79 c13d13n c10d10n c14d10n c13d12n 9.80 c14d15n c13d15n c13d11n c14d12n 9.96 c13d11n To illustrate these results, in Figure 5 we have overlaid the building definition onto the original NNAP image. An examination of Figure 5 results in a few observations. In the downtown Atlanta area (upper right), the definition of roof is so good that the shapes of the buildings are easily seen. On the other hand, in the tree lined residential areas in the bottom center of the image, the buildings are more poorly identified. Interestingly the baseball diamond of Turner Field in the bottom right reads like a rooftop while a similar diamond directly to the south inside the Atlanta stadium does not. For some reason the material that covers the upper diamond has a great deal in common with pavement materials. Further, in the type examples of pavements, two railroad paths were mistakenly categorized as pavements. In spite of this, the NDTI day-night removed these two locations (the curve in the upper left quadrant and the wedge shape protruding up from the bottom center) from their misidentified category and put them in the grouping of roof-like cover. Perhaps the gravel of a railroad bed is the cause, having more in common with rooftop gravel; though it is not clear why its thermal inertia would not be more like that of a pavement.

8 Figure 4. Combined best NDTI day-night. Darker red is surer pavement. Darker yellow is surer roof. Gray is poorly defined. Figure 5. The NDTI day-night building (roofs) definition overlaid onto the original NNAP image.

9 Figure 6. Urban infrastructure refined with "gravel" (gray areas). Compared with Figure 4, both rooftops and pavements seem more sharply defined. Further investigation of this inconsistency led to a serendipitous finding. The fact that the railroads so strongly switched characteristics, from being roads to rooftops, suggested that they might have a unique signature. The NDTI daynight that most strongly identified roofs (i.e., that had both good mean and standard deviation) was c14d13n. This led to the question, For the type example of railroads, what were the comparable statistics? When the c14d13n calculation was performed on only newly redigitized railroad type areas, the mean was (similar to that for roofs and in line with those of the top 10 listings for the roof NDTI day-night ) and the standard deviation was (much better than the other standard deviations, although the sample size was small, only 0.7% of the study area). To enhance the illustration of c14d13n for this new railroad type, we re-centered the lightest locations at this new mean (13.76) and increasingly darkened everything else using ¼ standard deviation intervals from the new mean. The result showed that not only could the other railroad areas be identified, but also differences within the new category could be distinguished. Further inspection showed that the image also exhibited a good deal of noise. After comparing the noise to the NNAP image, it appeared that the mean was not only finding railroads but appeared to represent locations more like parking areas possibly areas covered with larger sized gravel material. In fact, when we compared this new definition with Figure 4 we realized that the gravel category is much the same as the Figure 5 gray areas. Thus, it seems that large gravel is a significant land cover that further defines pavements and rooftops in its thermal inertia characteristics. In fact, in overlaying this new definition with the previous best roofs versus pavements image (Figure 6) tends to refine the definition of both compared to Figure 4. This suggests that with additional investigation it is feasible to identify another distinct cover type using the NDTI as the discrimination tool.

10 DISCUSSION This paper presents the results of an analysis of the hypothesis that the different heating and cooling rates of urban land covers (due to their thicknesses and substrates) may be detectable remotely if thermal imagery sampled at different times of the day are compared, enabling the classification of pavements and rooftops. We found that: The ATLAS thermal bands can be used to distinguish between roofs and pavements by the NDTI day-night technique using day and night imagery. The NDTI is a useful concept because o it is easy to calculate, and o it is inexpensive to produce. NDTI based on a single set of bands was not as accurate as an NDTI that combined the best bands for pavements and another for rooftops. Previous research established the NDTI concept and provided a demonstration of its efficacy. This research develops the techniques to apply the concept to compatible daytime and nighttime imagery sets and identifies possible band combinations for defining a reliable NDTI day-night to define land cover types for pavements, rooftops and possibly a new category that initially we are calling large gravel. We evaluated various daytime versus nighttime band combinations of the ATLAS thermal spectral bands to determine how well we might be able to distinguish roofs from pavements based on their thermal signatures as expressed by a Normalized Difference Thermal Index (NDTI). Previous research proposed two NDTIs be used, one to define pavements and one to define rooftops. This research finds a similar situation is true, but that using Band 10 is the most viable alternative for roads (Band 10 was not previously identified as highly important when using only daytime imagery), while for rooftops combinations of Bands 11, 13 and 14 are most important (Bands 13 and 15 were previously identified as most important when using only daytime imagery). Within this investigation, as in our previous research, the thermal bands identified as most useful to support an NDTI have varied (though all provide a level of merit), but we have conclusively demonstrated that the ATLAS thermal bands (and equivalent bands on other instruments) have great potential in correctly classifying urban infrastructure. CONCLUSION Figure 7 suggests illustrates that the NDTI technique has merit as a significant tool in defining urban infrastructure from remotely sensed thermal spectral data. This 3-D representation was generated solely from NDTI calculations within the Atlanta study area. Although it is not perfect, this image shows the general urban structure well. For pavements, darker indicates greater likelihood of pavement. The NDTI indicated rooftop locations have all been raised the same amount. Here the viewer is flying north above the Interstate 75 corridor. Interstate 20 is the curvilinear form snaking from left to right across the upper third of the image. Circular Turner Stadium appears at lower center with the baseball field directly above. It is easy to see that the streets and structures of central Atlanta are canted at about 45 from the cardinal directions in the top center area while just across Interstate 75 to the right, the roads are well aligned to the compass points. Light gray areas are those not well defined as either roofs or pavements. Figure 8 shows a 3-D representation of the same data, this one from a different perspective and a wider view. Our hypothesis that the thermal emissions of pavements and rooftops will vary throughout the day due to their differing construction applications and thermal masses is supported by our findings. Furthermore, this information can be used to remotely classify these infrastructure components. The statistics and images demonstrate that the NDTI concept is viable.

11 Figure 7. This 3-D representation generated solely from NDTI calculations within the Atlanta study area. Figure 8. More inclusive view of model represented in Figure 7.

12 RECOMMENDATIONS The NDTI is a simple initial step in the process of classifying urban infrastructure through Thermal Remote Sensing. A certain amount of noise still remains in the resulting images. We recommend: That research on other image processing manipulation techniques (e.g., ratioing or principal components) is carried out to determine if an improvement to the NDTI s results can be made. It would be useful to compare the previously developed daylight only NDTIs with these new day-versus-night NDTIs to determine if the addition of the nighttime image makes a major improvement. If not, it would be less costly and easier to determine urban infrastructure with a single image 7. The viability of the NDTI needs to be verified for other urban locations. All of our studies so far have focused on Atlanta, Georgia. The physical explanation of why certain band combinations work well needs to be investigated. This work was beyond the scope of this research. The physical explanation for the NDTIs sister index, the Normalized Vegetation Index, is well documented. The NDTI would be more readily accepted if there was a firstprinciple physical rationale for its viability. ACKNOWLEDGMENTS The authors would like to express their appreciation to Dr. Kenton W. Ross and Dr. James C. Smoot, both at the National Aeronautics and Space Administration (NASA) Stennis Space Flight Center, Mississippi, for reprocessing the original ATLAS imagery into compatible daytime and nighttime scenes. We also would like to thank Dale A. Quattrochi, Ph.D., and Maurice Estes, Jr., both of the National Aeronautics and Space Administration (NASA), Earth and Planetary Science Branch, at the George C. Marshall Space Flight Center, Huntsville, AL, for sharing the original ATLAS data for Atlanta. REFERENCES Herold, M., Spectrometry for urban area remote sensing Development and analysis of a spectral library from 350 to 2400 nm, Remote Sensing of Environment, 91 (2004) Lo, C.P., Quattrochi, D.A. and Luvall, J.C., Application of high-resolution thermal infrared remote sensing and GIS to assess the urban heat island effect, International Journal of Remote Sensing, 18:2, McInerney, M., Trovillion, J., Lozar, R., Abdallah, T., and Majumdar, A., Classifying infrastructure using thermal IR signatures, Proceeding of the 2006 ASPRS Conference, Reno, Nevada. McInerney, M. and Lozar, R.C., Comparison of methodologies to derive a Normalized Difference Thermal Index (NDTI) from ATLAS imagery, Proceedings of the 2007 ASPRS Conference, Tampa, Florida. McInerney, M. and Lozar, R.C., Deriving a Normalized Difference Thermal Index (NDTI) from ASTER satellite imagery, Proceedings of the 2008 ASPRS Conference, Portland, Oregon. Quattrochi, D., A decision support information system for urban landscape management using thermal infrared data, Photogrammetric Engineering & Remote Sensing, Vol. 66, No. 10, October 2000, Wolfe, W.L. and Zissis, G.J. (Editors), The Infrared Handbook, revised edition, Prepared by the Environmental Research Institute of Michigan for the Office of Naval Research, Department of the Navy, Washington, DC, Revised Edition Another advantage of deriving an NDTI from a single image is that the consequences of atmospheric absorption and solar incidence angle are minimized because in a single image these considerations are largely constant.