DERIVING A NORMALIZED DIFFERENCE THERMAL INDEX (NDTI) … · Remote sensing is the collection of...

ASPRS 2008 Annual Conference Portland, Oregon April 28 –May 2, 2008

DERIVING A NORMALIZED DIFFERENCE THERMAL INDEX (NDTI) FROM ASTER SATELLITE IMAGERY1

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 61826-9005 [email protected]

[email protected]

ABSTRACT This paper addresses current investigations into the development of a new remote-sensing indicator, the Normalized Difference Thermal Index (NDTI), used to distinguish between pavements and rooftops in urban areas. The NDTI is based on the premise that if materials have different thermal spectral responses, then ratioing the significantly distinguishing bands and normalizing those values to a standardized range will provide a sensitive and comparable test of thermal character. In previous work, the concept of an NDTI for urban infrastructure was proposed and demonstrated using thermal band data from the airborne imager instrument ATLAS. The satellite-based ASTER sensor offers images in the same thermal spectral range as the ATLAS instrument but at a much coarser resolution. This work investigated the viability of using the ASTER images as a source for NDTI evaluations. It was found that the ASTER images did not clearly distinguish between pavements and rooftops in urban areas, largely because the normal spatial extent of either pavements or rooftops is much smaller than the 90 meter ASTER resolution. As a result, the noise of other land-cover types within an ASTER pixel was greater than the signal needed to distinguish between pavements and rooftops.

INTRODUCTION

Remote sensing is the collection of information about an object from a distance. Remote sensing is widely used for land and terrain analysis and in such diverse fields as city planning and archaeological investigation. The collection methods range from land-based data acquisition to sensors placed on helicopters, airplanes, and satellites. There are many sensor technologies used for remote sensing, but the majority utilize the electromagnetic spectrum. The emergence of new sensors with increased spatial and spectral resolution holds the potential to allow greater feature delineation. Older sensor platforms, such as the original Landsat Thematic Mapper (TM) satellite, had spatial resolutions of 30 m per smallest image element (pixel), which may not provide enough detailed information for urban applications. Newer platforms, such as the airborne-based Advanced Thermal and Land Applications Sensor (ATLAS), provide spatial resolutions of 8 meters per pixel. The increased spatial resolution is crucial for distinguishing between buildings and other characteristics of the urban landscape.

The increase in sensor spectral resolution has encouraged material identification. Many materials have a distinctive spectrum in the hyperspectral 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 application is different, we hypothesized that the thermal emissions of pavements and rooftops will vary throughout the day due to their differing construction techniques and thermal masses. For example, the thermal emissions of an asphalt parking lot should vary slowly throughout the day because the earth is a large thermal mass that moderates the solar heating of the pavement. Conversely, an asphalt roof should have greater temperature swings because it is insulated from a thermal sink. This differential thermal inertia is the theoretical basis upon which we propose and test the efficacy of a thermal-based concept in distinguishing pavements from roofs. 1 Approved for public release; distribution is unlimited.


A commonly used remote sensing image processing (IP) technique is to divide the values of two different spectral bands by each other. A small ratio implies small change, while a large ratio means there is a greater spectral difference. This technique is used for many applications, such as identifying minerals in earth ores. However, for more sensitive comparisons, a more sophisticated technique is shown in Equation 1.

Equation 1. The general Normalized Difference Index equation

This process is called a “normalized index,” and results in values ranging from -1 to +1. The normalization

allows for comparison of many different spectral bands. The procedure is used to identify vegetation based on the large difference in its absorption of the red and near-infrared bands. Because we posit a thermal inertia between roofs and pavements, we hypothesized that a reliable normalized difference thermal index could be derived.

BACKGROUND

The new remote-sensing indicator, the Normalized Difference Thermal Index (NDTI), is used to distinguish between pavements and rooftops in urban areas. The NDTI is based on the premise that materials have different spectral responses, so ratioing significantly distinguishing bands will provide a sensitive and comparable test of thermal character. In previous work, the concept of an NDTI was proposed based on research using the ATLAS airborne imager thermal bands. It was found that two bands of all the possible NDTI combinations of spectral bands provided the best results for both pavements and rooftops.

A recent literature review (Lozar, 2007) showed that most classical airborne and satellites sensors extend to only the Near Infrared (NIR) portion of the electromagnetic spectrum (i.e., they are largely visual sensing instruments). Among the Long Wave Infrared (LWIR) thermal multispectral instruments is the Japanese ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer). It is currently in operation on the American Earth Observation System (EOS) platform. Although ASTER’s 90 meter (~300 ft) resolution is much coarser than the 8 meter ATLAS resolution, it was hoped that ASTER could nevertheless be useful in classifying urban infrastructure. This is important because ASTER imagery is available for anywhere in the world on a frequent basis, while the ATLAS instrument has been flown only as an airborne configuration covering a few locations. So this study was undertaken to answer the question, “Can we use the ASTER Thermal Infrared (TIR) bands to reliably generate a distinction between roofs and pavements?”

SPECTRAL RESPONSES OF ROOFTOPS AND PAVEMENTS

Because we are interested in distinguishing between rooftops and pavements, it is advisable to examine the spectral characteristics of these materials. Unfortunately, the first reality we need to face is that the generic terms “rooftops” and “pavements” can imply a plethora of materials of varying composition. Even worse is that this abundance of materials can be and often is used in either of these two land coverings. In general, however, the most common materials for pavements are concrete (with a coarse aggregate), asphalt, and coarse stone/gravel, versus, for rooftops, asphalt with a finely crushed gravel overlay and membranes with gravel overlays. We examine the Long Wave Infrared (LWIR) or thermal infrared (TIR) section of the spectrum in the range of 8.5 to 12 µm, with emphasis on the 10 to 12 µm range.


Figure 1. Comparison of roofing (left) and concrete (right) materials in the LWIR Range (from Wolfe and Zissis, 1985).

Figure 1 graphs compare concrete and roofing materials in the LWIR range. They show that both materials

exhibit a nearly constant spectral apparent temperature near 310 degrees K. Therefore, to separate these two types of land covers, a sensitive discriminator will be required.

COMPARISON OF ATLAS AND ASTER THERMAL SENSORS

Thermal multispectral instruments are classed under the technical heading of Long Wave Infrared (LWIR) sensors. A LWIR sensor covers the thermal portion of the electromagnetic spectrum, 10.40 – 12.50 µm. Previous research (McInerney and Lozar, 2007) demonstrated that an NDTI based on ALTLAS bands 13 and 14, and bands 13 and 15, provide the best tradeoff between correctly identifying pavements and roofs and distinguishing between them.

Figure 2 compares the ATLAS and ASTER TIR bands. Although there is a relatively good visual agreement, it is noted that one of the critical NDTI ATLAS bands, band 13, has no equivalent ASTER band.

Figure 2. Graphical comparison of the ATLAS and ASTER TIR bands (scale in µm).

Otherwise:

ATLAS BAND 10 matches ASTER BAND 10 ATLAS BAND 11 matches ASTER BAND 11 ATLAS BAND 12 matches ASTER BAND 12 ATLAS BAND 14 matches ASTER BAND 13 ATLAS BAND 15 matches ASTER BAND 14


ROOFS, PAVEMENTS AND THE NORMALIZED DIFFERENCE THERMAL INDEX (NDTI)

In previous work (McInerney et. al., 2006), the concept of an NDTI was proposed, and initial research using

only the ATLAS thermal bands demonstrated the viability of the NDTI. Various image processing techniques were applied to the imagery, identifying in the TIR region the most advantageous combination of bands. The present work investigates whether the ASTER TIR data can also be used to support an NDTI. Procedure and Criteria for the Evaluation of the Normalized Difference Thermal Index (NDTI)

The Normalized Difference Thermal Index (NDTI) was calculated for each of the 10 unique combinations of the ASTER thermal bands. The desire was to have the rooftops and pavements show as distinctly separate polygons. To objectively evaluate how well each of the NDTI band combinations do in distinguishing pavements versus roofs, we focused in on a detailed area of downtown Atlanta, Georgia, the same area where we successfully distinguished pavements and roofs using the NDTI and ATLAS data. At the ASTER scale of 90 meters resolution, we expected it would be feasible only to separate coarse characteristics. This coarse resolution suggests that wide pavement areas, such as interstate highways, might be recognizable, while rooftops at 90 meters would likely be mixed with many other land covers. The most densely “rooftopped” land cover would be in the downtown Atlanta area. Thus, we looked for greatest distinction in the most urbanized areas of the images. To be as sensitive as possible, we concentrated on just the downtown Atlanta area (Figure 3).

Figure 3. On the left is the ATLAS image showing the extent and features included in the study area (yellow boundary), on the right is the ASTER TIR image of the exact same area.

Within the study area, the two target land covers were defined individually by hand digitizing them using the

ATLAS images as the source for ground truth. These land cover definitions were also used in the statistical evaluation. Pavements were easy to pick out because they are long, linear features. The major problem with the identification of pavements is that pavements have a very thin line width. If 90 meter resolution data would be able to pick out these thin features was one of the concerns of this investigation. Rooftops were limited to large buildings that usually appeared bright in the image. Also, larger building cast a shadow, making it easier to distinguish bright building rooftops from parking lots. Rooftops of smaller buildings were avoided because vegetation (trees) often appeared in the same area and would, therefore, present a multi-land cover signature within the 90 meter pixel and confuse the ASTER NDTI results. The result of the digitizing is presented in Figure 4. We also assumed that these land covers have not changed between the date of the ATLAS image (1997) and that of the ASTER image (October 25, 2004).


Figure 4. Distribution of "ground truth" locations for rooftops and pavements within the study area.

To objectively evaluate how well each of the NDTIs do in distinguishing pavements and rooftops, we carried

out the following procedure: • For each NDTI we wish to evaluate, mask out all cells except those that are of the type in question (i.e.

either pavement or roof based on our ground truth areas). • Apply an algorithm developed within our Geographical Information System (GIS) to consider only NDTIs

that exist at our ground control points. • From those of that particular type (either pavement or roof), find the range, the mean, and the standard

deviation. • Pick out the best NDTI for each evaluation and extend the characteristics of ground truth locations to the

whole study area. • Evaluate the whole study area coverage NDTI character in representing the rooftops and/or pavements.


EVALUATION OF THE NORMALIZED DIFFERENCE THERMAL INDEX (NDTI) FOR THE ASTER STUDY AREA DATA

General Observations

A cursory examination of all the NDTIs calculated indicated an interesting and striking pattern: all the means are negative and, with one exception, the entire range of all NDTIs is negative. This indicates that the pattern used to derive the NDTIs (a longer wavelength subtracted from a shorter wavelength) is strongly significant. As a point of departure it suggests that there exists a consistent influence that is proportional to the wavelength in the LWIR area. It initially suggests that if this proportionality exists, then the absence of a particular band (in this case, the significant band 13 in the ATLAS imagery) could potentially be overcome by careful mathematical manipulation. Rank by Mean

For the “ground truth” locations, the highest-ranked NDTI resulted from bands 10 and 14 for both pavements and rooftops. We extended this set of results from the “ground truth cells” to the full study area and assigned reds to values near the pavement mean and blues to values near the rooftop mean (and excluded assigning any color to NDTI values not near either). Figure 5 shows the results.

Figure 5. Full coverage of the study area for the NDTI for bands 10 and 14, with reds for values near the pavement

mean and blues for values near the rooftop mean (and excluding any color to NDTI values not near either).


Figure 5 shows that the means for the pavements and rooftops are so close together that even when ½ standard deviation categories are used, the NDTI categories quickly begin to overlap. Therefore this is not an adequate method to separate pavements and rooftops. Rank by Smallest Standard Deviation and Range

The highest-ranked NDTI by minimum standard deviation resulted from bands 13 and 14 for both pavements and rooftops within the study area. Unfortunately, those ratios at the top of the “rank by smallest standard deviation” list are roughly at the bottom of the list for “rank by mean” analysis. We extended the NDTI for bands 13 and 14 to the full study area and assigned reds for NDTI values near the pavements mean and blues to values near the rooftop mean (and did not assign a color to NDTI values not near either mean). Figure 6 below shows the results.

Figure 6. NDTI results applied to the study area for bands 13 and 14 with reds for pavements and blues rooftops

(and excluding NDTI values not near either mean).

We found that although their ranges are tightly defined, the difference between the pavement and rooftop means is so small that separating the two is difficult. In fact, even though we used 12 categories (with a width of ¼ standard deviation), the means were still barely separated. Figure 6 shows how indistinct the resulting distribution is. Rank by Largest Difference in Mean for same NDTI

Our analysis for the greatest difference between means for a single NDTI showed that even though there is a


tenfold difference between the greatest and least values, the absolute difference is very small. The NDTI for bands 11 and 14 ranked best in this evaluation (“Rank by Largest Difference in Mean for same NDTI”) and also ranked second in the evaluation of the means when pavements and rooftops were considered separately. However, when considering the tightness of the standard deviation, it ranked poorly. So we expected a good deal of overlap between pavements and rooftops.

We applied the results for the best NDTI for bands 11 and 14 over the full extent of the study area. The results are displayed in Figure 7 with reds assigned to pavement values near the pavement mean, blues assigned to rooftop values near the rooftop mean, and no color assignments to NDTI values not near either mean.

Figure 7. NDTI for the best separation of means (bands 11 and 14) displayed over the full extent of the study area with reds for values near the pavement mean, blues for values near the rooftop mean, and white for all other values. Rank by Largest Difference in Mean for same NDTI

Figure 7 does not define the urban structure any better than the other evaluations. In fact, in all the previous evaluations, a pattern emerged. Each analysis was based on “ground truth” locations, and therefore the analysis discerned pavements from rooftops with some fidelity. But when that representation was extended to the rest of the cells within the study area, a much lower fidelity resulted. One would expect the “ground truth” locations to be reasonably well represented, as they were chosen as the locations of greatest “purity” of type, and all others are less “pure.” Therefore, when the findings are extended to the rest of the study area, the more “impure” nature of these


pixels is reflected. Greater resolution will result in a “purer” result, as has been previously demonstrated with the higher-resolution ATLAS data.

Evaluation by Confusion Matrix

To examine more closely the results of “Largest Difference in Mean for same NDTI,” we imported several files based on the 2000 census data that gave in detail the location and type of roads within the study area. Figure 8 shows how heterogeneous the study area is when displayed at the resolution of the ASTER data. Almost every ASTER pixel contains a portion of some road or street. The ASTER pixels are roughly the size of a city block; therefore one would expect both pavements and rooftops to be well represented within each pixel. Based on the additional data of road locations, one could develop a “Pavement Intensity Index” (PII) for each pixel and compare that with the relative weightings derived from an ASTER NDTI evaluation.

Figure 8. Band 11 and 14 NDTI showing that within the study area almost every cell

contains a portion of some road or street.

To begin to make this comparison we first developed the PII layer. This was accomplished by clipping a portion of the road file to the study area, reprojecting it to the ASTER projection, and converting it to a raster form at five times greater detail than the ASTER grid. Each location that had a road was assigned a value of 1. Finally, the data were aggregated up to the ASTER resolution by summing each occurrence of road from the detailed grid to the coarser grid. The ASTER resolution layer could have a cell value as high as 25 if all the smaller cells contained


roads; or a cell value as low as 0 if no roads occurred in the original road layer. The resulting PII is shown in Figure 9. Inspection suggests that this image represents the situation well – areas of larger roads (e.g., interstate highways) are whiter, while more “residential areas” tend to appear darker. This is not a direct measurement of pavement coverage (e.g., it completely misses parking lots), but it is an independent measurement that will likely include most pavements. Because of the omissions, a positive occurrence with a NDTI pavement is significant while a negative occurrence with an NDTI determined pavement is not.

We compared this PII with the NDTI that best represented pavements (bands 10 and 14). First we congregated the four classes that best represented pavements into a single category of “yes pavement” and assigned a value of 1. Similarly, we simplified the PII to a single category of “yes pavement” and assigned a value of 2. We displayed these “yes pavement” layers in a grid format such that the cell edges on the two layers exactly matched. The congregations were done so both layers would have about the same number of positive occurrences (roughly 150). When the two layers are added together (Figure 10), the result preserves the source of the individual pixels. The two layers can now be compared using a confusion matrix.

Figure 10. The Pavement Intensity Index (PII) layer plus the NDTI pavement layer yields

a comparison pavement layer used in the confusion matrix analysis.

The resulting confusion matrix has four possible values for each cell based on the truth of the input cells (i.e., true-true, true-false, false-true, and false-false). We desire a high percent of positives (e.g., pavements) being correctly identified as positive (pavements) and a high percentage of negatives (e.g., not pavements) being correctly identified as negatives (not pavements). The other two possibilities are positives being incorrectly identified as negatives and negatives being incorrectly identified as positives. Our desire is that these latter two be as small a percentage as possible.

Analysis of the data shows that of all locations identified as a pavement location, only about 20% of the total, were identified as roads by both the PII and the NDTI techniques. Both the PII and the NDTI techniques were about twice as likely to identify other locations as roads. Thus, only 20% of all the possibilities were consistently roads. Because there are four possible values for each cell, a random assignment of these four values throughout the confusion matrix layer would assign about 25% of the layer to each value. This indicates that even the best ASTER NDTI pavement assignment is no better than random.

Based on this work, it is believed that to discriminate between pavements and rooftops, the resolution of the imagery must to be finer (probably by about a factor of two) than the land cover types under investigation. For our land types; pavements would be about 30 meters wide and rooftops for city center buildings would be about 15 meters. Thus, imagery in roughly the 10 meter per pixel range is the coarsest that is likely to produce reliable results. This is why the 8 meter resolution ATLAS data can discriminate the two land covers. This was a concern at the onset of the research. That concern proved to be well founded because the resolution of ASTER’s TIR bands is too coarse to discriminate rooftops and pavements in the urban environment.


FINDINGS

A comparison of the ATLAS and ASTER thermal sensors shows that while they cover the same broad spectral range, the band found to produce the best rooftops versus pavements discrimination using the ATLAS imagery was missing from the ASTER sensors (i.e., ATLAS band 13 from 9.60-10.2 µm was not available in the ASTER imagery).

A cursory examination of all ASTER NDTIs shows an interesting pattern: all the means are negative and with one exception the entire range of NDTIs is negative. This indicates that the pattern used to derive the NDTIs (a longer wavelength subtracted from a shorter wavelength) is strongly significant. As a point of departure, it suggests that there exists a consistent influence that is proportional to the wavelength in the LWIR area. It initially suggested that if this proportionality exists, then the absence of a specific band (in this case the significant band 13 in the ATLAS imagery) could potentially be overcome by careful mathematical manipulation.

The NDTIs were evaluated using four methods: by rooftops and pavements, both separately and by mean and standard deviation; together by separation of means; and by an independent road network for pavements only.

• The results of the evaluation of rooftops and pavements separately by mean showed exactly the same pattern exists for both pavements and rooftops even though the pattern does NOT coordinate by wavelength. Thus, there is something inherently good about the NDTI derived from a bands 10 and 14 ratio that is much better than the NDTI derived from a bands 10 and 11 ratio, and this occurs in exactly the same manner for pavements and for rooftops and for all combinations. The fact that the means are not greatly separated suggests that both rooftops and pavements are very similar in the ASTER images.

• The evaluation of rooftops and pavements separately with the smallest (i.e., best) NDTI standard deviation showed that the order of the best rank by mean is roughly the reverse of the order of the best rank by standard deviation.

• For evaluation by rooftops and pavements together by separation of NDTI means, the data show that although there is a tenfold difference between the greatest and least values, even the greatest difference for a single NDTI (band 11 and band 14), the absolute amount of separation is very small. Further, when considering the tightness of the standard deviation, the NDTI for band 11 and band 14 ranked poorly. So although the means are as desirable as available within the group, a good deal of overlap was expected to occur between the definition of pavements and rooftops.

• From a highly detailed vector road file a “Pavement Intensity Index” (PII) was generated. Similarly the four classes in the NDTI for pavements from bands 10 and 14 that best represented pavements were aggregated into a single category (yes for pavement). These two files were compared to see how well a pavement NDTI represented the road network using the concept of a confusion matrix. The analysis showed that of all locations identified as pavement locations, only about 20% of the total was identified as roads by both the PII and the NDTI techniques. Both the PII and the NDTI techniques were about twice as likely to identify another location as a road. Because in this confusion matrix there are four possibilities and a random distribution would assign about 25% to each category, this indicates that even the best ASTER NDTI pavement assignment is no better than random.

One would expect the “ground truth” locations to represent the situation reasonably well, as they were chosen as the locations of greatest “purity” of type. All others are less “pure,” and so when the findings are extended to the rest of the study area, the results reflect the more “impure” nature of these additional pixels. Greater resolution will result in a purer result (as has been previously demonstrated with the high-resolution ATLAS images). This suggests that the major issue here is that the resolution of the ASTER pixels is not great enough to reflect the character of our target land cover types. In fact, the ASTER 90 meter resolution pixels are larger than most of the pavement or rooftop areas available in the image. Even though the study area was chosen to optimize the larger size of the pure pavement or rooftop pixels, few actually occurred in the study area, and as a result the noise of other spectral characteristics quickly overwhelmed the signal we were trying to find.

CONCLUSIONS

This research conclusively shows that the ASTER thermal images are not useful in distinguishing urban structure by differentiating pavements from rooftops. Although the ASTER instrument generates imagery in


roughly the correct spectral range, the spatial domain appears to be inappropriate to the task. ATLAS imagery is in the 8 meters per pixel range while ASTER imagery is 90 meters per pixel. In itself, a degradation of an order of magnitude would be a difficult handicap to overcome, but the spatial extent of the target land cover categories of pavements and rooftops was usually less than 90 meters. At 90 meter resolution, so few cells exhibited a clear signal for either pavements or rooftops that the result is to be considered primarily noise.

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