VEGETATION MEASUREMENTS FROM DIGITAL ASTRONAUT PHOTOGRAPHY William L. Stefanov a,b, *, Julie A. Robinson c,e, S. Alan Spraggins d,e a
Department of Geological Sciences, b Center for Environmental Studies, Arizona State University, Tempe, Arizona, 85287-6305, USA –
[email protected] c Lockheed Martin Space Operations, d Hernandez Engineering, e Earth Sciences and Image Analysis Laboratory SX3, NASA Johnson Space Center, Houston, Texas, 77058, USA –
[email protected];
[email protected]
KEY WORDS: Astronaut Photography, ASTER, Vegetation, Biomass, Time Series, Paris, France, Digital Camera, Multispectral ABSTRACT: Astronaut photography of cities collected during Apollo, Skylab, Shuttle, Mir, and International Space Station missions provides a useful dataset for urban analysis that complements the satellite data archive. Recent astronaut photography acquired with digital cameras is now approaching the ground resolutions of commercial satellites such as IKONOS (i.e. less than 6 m/pixel). Astronaut photographs are a relevant source of data for urban analyses, particularly for studies that do not have the resources to purchase commercial-quality data. The CCD image sensors in the cameras currently used for astronaut photography are sensitive to the infrared portion of the electromagnetic spectrum, but infrared signal is filtered out above 700 µm. As such, the digital camera data contain less information on actively synthesizing vegetation than they would with an infrared signal included. We present an analysis of aboveground biomass (i.e. actively photosynthesizing vegetation) derived from astronaut photography of the Paris, France metropolitan area acquired on April 24, 2002 using a Kodak DCS 760C electronic still camera aboard the International Space Station. The accuracy of biomass estimation obtained from the digital camera data is demonstrated by comparison with Advanced Spaceborne Thermal Emission and Reflection Radiometer visible to near infrared data for Paris acquired on April 8, 2002. Correlations of bands between the two instruments allow interpretation of the identified vegetation and soil classes. Collection of astronaut photography over global urban centers is ongoing and planned for future orbital missions, and will be a useful addition to ongoing studies of urban ecosystem change, sustainability, and resilience. 1.
INTRODUCTION*
Typical users of remotely sensed information for urban studies in the academic, public, and private sectors include physical scientists, social geographers, city planners, and developers. Biogeochemical, geobotanical, and mineralogical and spatial analyses are performed using a wide array of multispectral and hyperspectral data acquired by sensors of varying spatial and spectral resolutions such as the Advanced Spaceborne Thermal Emission and Reflection Radiometer, or ASTER; the Landsat and Système pour l’Observation de la Terre, or SPOT, series of sensors; and airborne systems such as the Airborne Visible/Infrared Imaging Spectrometer, or AVIRIS (Ustin et al., 1999; Jensen, 2000). Satellite-based sensors such as those associated with the Landsat, SPOT, and Advanced Very High Resolution Radiometer (AVHRR) programs have provided nearly complete global coverage since the mid-1970s and currently provide invaluable information on global land cover change, urban growth, temperature, and vegetation health. More recently, social scientists have begun to recognize the potential uses of remotely sensed data for the study of sociopolitical dynamics and urbanization (Donnay et al., 2001; Donnay and Unwin, 2001). The majority of the sensors discussed above were built and launched, and are maintained, by various governmental agencies such as the United States National Aeronautics and Space Administration (NASA), the United States Geological Survey (USGS), and the European Space Agency. As such, these data are frequently either free of charge or are subsidized for end users such as scientists and urban planners. Recently, commercial providers have entered the remote sensing arena
*
Corresponding author.
with very high resolution panchromatic to multispectral datasets such as IKONOS. These data tend to be expensive and limited temporal and spatial coverage compared to governmental programs. In order to achieve commercial success, these providers must have limited data licensing and charge higher prices per scene than government-subsidized data. However the cost of the data limits their educational and scientific use by academic users and government agencies. Astronaut photography is a relatively untapped data resource that can help address these concerns and is relevant in moderate and high spatial resolution studies of cities. Astronaut photography (AP) of cities collected during Apollo, Skylab, Shuttle, Mir, and International Space Station missions provides a useful historical and ongoing dataset for urban analysis that can address temporal gaps in the existing satellite data archive. Astronaut photography is variable in look angle, solar illumination, and spatial resolution (Robinson et al., 2002). In some cases the archive predates satellite missions and represents the oldest remotely sensed information on urban growth and land use. In other cases, a time series constructed from Landsat or SPOT archives can benefit from inclusion of additional data at intermediate time points drawn from astronaut photography (Webb et al., 2000). The application of orbital photography for urban studies was first discussed in the 1960s (Wellar, 1969) and usefulness of AP digitized from film for tracking urban growth has been demonstrated by Robinson et al. (2000). Recent astronaut photography acquired with digital cameras is now approaching the ground resolutions of commercial satellites such as IKONOS (i.e. less than 6 m/pixel, Robinson and Evans, 2002). These detailed images from the International Space Station are wildly popular among the general public because of the opportunity to see detailed public domain views
of their cities from orbit (http://eol.jsc.nasa.gov/cities/, Fig. 1). However, astronaut photographs also are a relevant source of data for urban analyses, particularly for studies that do not have the resources to purchase commercial-quality data. High resolution AP data can be used to provide ground truth information for lower resolution satellite data, increasing the validation options without dramatically increasing the data cost. High resolution AP could be used in sophisticated classification approaches (such as expert systems or neural networks, i.e. Stefanov et al., 2001b, in press) and multi-scalar analyses of urban regions.
Three major components of the digital astronaut photography system determine the spectral response underlying the data collection: the spectral response of the CCD (charge-coupled device) sensor in the camera, the effects of optical filters in the camera or lens, and the optical transmissivity of the window through which the photograph is taken. The Kodak DCS 760 electronic still digital camera currently in use aboard the ISS uses a CCD that has a significant response in the near infrared, but this response is limited by a removable infrared filter mounted between the lens and the CCD (Eastman Kodak Company, 1999). In comparing astronaut photographs of cities with ASTER data, a visual correlation between the camera response for vegetation and other data sensitive to near infrared energy (i.e. ASTER) is observed. In this paper we use ASTER data to help understand the vegetation signal in the various bands of AP, and evaluate the use of digital AP photography for multi-temporal vegetation dynamics studies in urban/peri-urban environments. Our purpose is to examine the extent to which cameras currently being used on the International Space Station are multispectral instruments, and lay the foundation for their use in urban studies. 2. METHODOLOGY 2.1 Data Processing
Figure 1. High spatial resolution (6.3 m/pixel) astronaut photography (Houston, USA, partial image, ISS001-E-5535). A. ASTER System Response Functions 1 Band 1 (Green) Band 2 (Red) Band 3 (NIR)
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Review of the available digital AP archive lead us to choose an image of the Paris metropolitan region collected on April 24, 2002 (15:18:37 GMT) for use in our vegetation density comparison analysis. This image (scene ISS004E10414) was selected, even though it does not represent the maximum spatial resolution available from AP, because it contains both a dense urban region as well as extensive agricultural fields and parklands, and has little to no cloud cover. Taken from an altitude of 389 km, spatial resolution of the AP scene is approximately 25 m/pixel. We also chose the image because of the striking yellow fields—this unusual signature has not been seen in other astronaut photographs of urban or agricultural areas around the world. The ASTER sensor collected visible to near infrared (VNIR) data over the same region of metropolitan Paris on April 8, 2002 (10:59:30.3 GMT; granule AST_L1B:003:2006580741). ASTER is in orbit at 705 km with a spatial resolution of 15 m in the VNIR bands.
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The AP image was georeferenced to the ASTER Level 1B (L1B) VNIR dataset using nearest neighbor resampling and a 4th-order polynomial equation with 125 tie points. This produced a registration accuracy of 0.002 pixel (root-meansquare error). Registration of the two images was required in order to assure that the same pixel area in each image was sampled for data number (DN) extraction. No additional corrections (such as atmospheric correction) were applied to either dataset.
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Figure 2. System response functions for visible and near infrared bands of ASTER (NASA JPL, 2003) and astronaut photography (Eastman Kodak Company, 1999; Window transmittance including debris pane, redundant pressure pane, primary pressure pane, and laminated scratch pane, NASA JSC, unpublished).
Qualitative comparison of both datasets suggests that there is significant correspondence (in relative DN values) between the AP red band and ASTER band 3 (sensitive to near infrared wavelengths). The specifications of the AP system (Fig. 2) and inclusion of the IR filter would restrict sensitivity in these wavelengths, however. In order to test the observed qualitative correlations of the AP and ASTER VNIR data it is necessary to select random pixels for comparison. This was achieved by performing a minimum distance classification of the AP data using training regions in several visually defined classes (Fig
3). These classes were chosen as they represent a range of vegetated and non-vegetated land cover types. We focused class selection on vegetated land cover types as these will exhibit the greatest response in the near infrared (Jensen, 2000). Table 1 lists the AP image classes and the corresponding land cover types interpreted from the ASTER data (on the basis of reflectance values in the VNIR bands).
population of 30 to approach the recommended sample size of Congalton and Green (1999). Values of DN were recorded for each checkpoint from both datasets and used to assess the degree of correlation for each class. 250
Yellow (Y) Dark Green1 (DG1) Dark Green2 (DG2) Tan (T) Olive1 (O) White (W)
ASTER Land Cover Interpretation Vegetation, high productivity Vegetation, moderate to low productivity Vegetation (non-canopied) Bare soil Sparsely vegetated soil Light colored soils and materials
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Table 1. Astronaut photograph image classes and corresponding land cover types.
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Figure 3. Subset of AP data (upper) and corresponding ASTER data (lower; bands 321 as RGB) for a portion of the northern Paris metropolitan area. Refer to Table 1 for class identifiers. A total of 50 check points were initially determined for each class using an automated stratified random approach based on the supervised classification results (Congalton and Green, 1999). Each point was checked to insure that it fell within the area of overlap of the ASTER and AP data, and that it belonged to the correct visual AP class. Points that did not satisfy these criteria were removed from the set for that class. It was also desired to spatially distribute the sample points throughout each class as much as possible to capture intraclass variability and avoid oversampling any particular region. Each class checkpoint set was therefore required to have a minimum
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Figure 4. Correlations between ASTER and AP bands (units are digital numbers) for different types of vegetation and soil cover. A: green bands; B: red bands; C: ASTER near infrared band vs. AP red band; D: ASTER red plus near infrared bands vs. AP red band.
2.3
Results and Discussion
Figure 4 presents the correlation results for the ASTER green, red, and near infrared bands with the AP green and red band for the vegetated (Y, DG1, DG2) and soil (T, O, W) classes. Table 2 lists descriptive statistics for the class checkpoints. Inspection of Figure 4 (A and B) indicates that the correlations between the green bands and the red bands are strong (R2 = 0.8347 and R2 = 0.8163, respectively) for all classes except the Yellow (Y) AP image class. In both the red and green bands in the AP image, the Yellow class has overall high DNs, but these do not correspond to high DNs in either ASTER band 1 or 2. The strength of signal for these areas is only in ASTER band 3, the near infrared band (Fig. 4, C).
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187.48 ± 14.75 87.73 ± 12.00 84.00 ± 6.22 163.62 ± 11.28 134.73 ± 13.43 215.45 ± 8.13 ASTER (DN) Band 2 (R)
199.81 ± 14.43 54.40 ± 12.87 65.58 ± 9.90 192.32 ± 8.60 140.83 ± 10.17 225.58 ± 6.78
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56.68 ± 4.37 34.84 ± 3.60 121.81 ± 7.86 53.87 ± 6.02 32.53 ± 6.12 86.43 ± 19.06 52.63 ± 4.37 33.19 ± 5.38 65.78 ± 13.57 92.12 ± 12.97 82.88 ± 12.98 74.70 ± 9.35 76.87 ± 9.29 60.67 ± 12.38 72.13 ± 16.05 145.2 ± 24.14 126.78 ± 23.22 96.60 ± 16.50
Table 2. Class checkpoint means and standard deviations (1σ). Number of samples for each class is indicated by n. NIR is near infrared Interpretation of these results is aided by application of color theory and the spatial context of each class (Fig. 3). Class Y is characterized in the digital camera data as yellow polygonal areas not located within the urban core of Paris. The yellow coloration is caused by high reflectance in the red and green bands. High reflectance in the both the visible green and near infrared wavelengths is typical of actively photosynthesizing vegetation (Ustin et al., 1999). Conversely, reflectance in the visible red wavelengths is low even for actively photosynthesizing vegetation due to chlorophyll absorptions. The geographic location, polygonal geometry, and local field checking for the Y class areas indicate that these are agricultural fields. The unusual color measured by the AP system appears to also be associated with a strong signal in the near infrared, as indicated by the bright red-pink signature (indicative of highly productive vegetation) of these areas in the ASTER VNIR data (Fig. 3). We believe this indicates a correspondence of atypical vegetation, perhaps new growth, a specific phenological stage, or fields in bloom, such that the unusual visible color corresponds to the areas of greatest photosynthesis. The correlation between the AP red band and the ASTER data improves greatly if ASTER band 2 and 3 are combined (Fig. 4, D). Although the general intensity of classes correlates well between the bands, within each class the correlations are
relatively low. We believe this lack of direct correlation is due to the broad spectral response of the AP bands compared to the very sharp response of the ASTER instrument (Fig. 2). Understanding of the correlations observed for the soil classes in the AP visible red band (T, O, and W) is aided by comparison with the ASTER VNIR data. The signature of the T class in the ASTER data is blue-green, which is indicative of negligible reflectance in the near infrared. The majority of the T class areas are interspersed with the Y class and have similar morphology and geographic location, suggesting that these are fallow (non-vegetated) agricultural fields. The O class is somewhat similar to the DG1 and DG2 classes in terms of magnitude and direction of correlation to ASTER band 3 (Fig. 4) but with slightly higher mean DN levels (Table 2). The RGB signatures of these classes are also similar in both the AP and ASTER VNIR imagery (Fig. 2). This suggests that the O, DG1, and DG2 class areas are similar in terms of land cover type, and are most probably a mixture of bare soil and vegetation. The W class exhibits the highest overall DN values in both the AP and ASTER VNIR data. Comparison of these class pixels with the corresponding ASTER data does not suggest that actively photosynthesizing vegetation is present however (Fig. 3; Table 2). This class appears bright white in the AP data due to equally high response in the blue, green, and red wavelengths. Similarly high brightness values are exhibited in the ASTER VNIR data. This suggests a very light-colored surface such as concrete, quartz- or feldspar-rich soil, or white rooftops. Examination of high-resolution RGB digital aerial photographs and field investigation will help to understand the observed spectral characteristics of the W class. Field investigation will also help verify our hypotheses regarding AP image class Y. The clustering of the various AP image classes compared with the ASTER data shows the meaningful classification information present in the AP data. 3.
CONCLUSIONS
The digital cameras being used by astronauts onboard the International Space Station were selected to provide a public domain record of the human spaceflight experience. With the advances that have been made in acquiring high spatial resolution data (Robinson and Evans, 2002), astronaut-acquired photographs of cities become an extremely useful source of remote sensing data. The combination of professional quality digital cameras, and high quality spacecraft viewing ports, allow new quantitative rigor to a historical data archive. This paper has just begun to evaluate the possible spectral analysis of such images. We have focused on discrimination and analysis of vegetation and soil land cover types, but higher resolution AP data promises to be useful for classification of materials within urban core regions as well. Because of the popularity of city images, astronauts continue to make cities a focus of their photographic activities. This will pay large dividends for the urban remote sensing community. High-resolution images are a new source of interpretive data for validation of high to moderate-resolution remote sensing datasets, such as Landsat or ASTER. Digital astronaut photography is being used for this purpose in the Urban Environmental Monitoring Project at Arizona State University (Stefanov et al., 2001a; Ramsey, in press) AP images like this view of Paris gave insights and raised questions about vegetation density that went beyond the information available from ASTER. Field checking to be done in April 2003 will
allow a better understanding of the unique vegetation observations reported here. 4.
REFERENCES
Congalton, R.G., and K. Green, 1999. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. Lewis Publishers, Boca Raton, FL, 137 p. Donnay, J.-P., M.J. Barnsley, and P.A. Longley, 2001. Remote sensing and urban analysis. In: Remote Sensing and Urban Analysis, Taylor and Francis, London, UK, pp. 3-18. Donnay, J.-P., and D. Unwin, 2001. Modelling geographical distributions in urban areas. In: Remote Sensing and Urban Analysis, Taylor and Francis, London, UK, pp. 205-224.
sciences. In: Remote Sensing for the Earth Sciences: Manual of Remote Sensing, Third Edition, Volume 3, John Wiley & Sons, New York, NY, pp. 189-248. Webb, E. L., Ma. A. Evangelista, and J.A. Robinson, 2000. Digital land use classification using Space Shuttle-acquired orbital photographs: A quantitative comparison with Landsat TM imagery of a coastal environment, Chanthaburi, Thailand. Photogrammetric Engineering & Remote Sensing, 66, pp.14391449. Wellar, B. S., 1969. The role of space photography in urban and transportation data series. In: Proceedings of the 6th International Symposium on Remote Sensing of Environment, Vol. 2, University of Michigan, Ann Arbor, WI, pp. 831-854. 5.
Jensen, J.R., 2000. Remote Sensing of the Environment: An Earth Resource Perspective. Prentice Hall, Upper Saddle River, NJ, 544 p. NASA JPL, 2003. Protoflight model: System response functions. http://asterweb.jpl.nasa.gov/instrument/vnirchart.htm (accessed 15 April 2003). Ramsey, M.S., 2003. Mapping the city landscape from space: The Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) Urban Environmental Monitoring Program. In: Earth Sciences in the Cities, American Geophysical Union, in press. Robinson, J. A., and C.A. Evans, 2002. Space Station Allows Remote Sensing of Earth to within Six Meters. Eos, Transactions, American Geophysical Union, 83 (17), pp. 185, 188. Robinson, J. A., D.L. Amsbury, D.A. Liddle, and C.A. Evans, 2002. Astronaut-acquired orbital photographs as digital data for remote sensing: Spatial resolution. International Journal of Remote Sensing, 23, pp. 4403-4438. Robinson, J. A., B. McRay, and K.P. Lulla, 2000. Twenty-eight years of urban growth in North America quantified by analysis of photographs from Apollo, Skylab and Shuttle-Mir. In: Dynamic Earth Environments: Remote Sensing Observations from Shuttle-Mir Missions, John Wiley & Sons, New York, NY, pp. 25-42. Stefanov, W.L., P.R. Christensen, and M.S. Ramsey, 2001a. Remote sensing of urban ecology at regional and global scales: Results from the Central Arizona-Phoenix LTER site and ASTER Urban Environmental Monitoring program. In: Remote Sensing of Urban Areas, Regensburger Geographische Schriften, 35, pp. 313-321 (on supplemental CD-ROM). Stefanov, W.L., M.S. Ramsey, and P.R. Christensen, 2001b. Monitoring urban land cover change: An expert system approach to land cover classification of semiarid to arid urban centers. Remote Sensing of Environment, 77 (2), pp. 173-185. Stefanov, W.L., M.S. Ramsey, and P.R. Christensen, Identification of fugitive dust generation, deposition, and transport areas using remote sensing. Environmental and Engineering Geoscience, in press. Ustin, S.L., M.O. Smith, S. Jacquemoud, M. Verstraete, and Y. Govaerts, 1999. Geobotany: Vegetation mapping for earth
ACKNOWLEDGEMENTS
We thank M. Kacenelenbogen for information on land cover distribution in the Paris metropolitan area and C. A. Evans for reviewing the manuscript. Study of cities is supported by the ISS Program and the NASA JSC Technology Transfer Office.
