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| Titel |
Integration of airborne optical and thermal imagery for archaeological subsurface structures detection: the Arpi case study (Italy) |
| VerfasserIn |
C. Bassani, R. M. Cavalli, L. Fasulli, A. Palombo, S. Pascucci, F. Santini, S. Pignatti |
| Konferenz |
EGU General Assembly 2009
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 11 (2009) |
| Datensatznummer |
250025871
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| Zusammenfassung |
| The application of Remote Sensing data for detecting subsurface structures is becoming a
remarkable tool for the archaeological observations to be combined with the near surface
geophysics [1, 2]. As matter of fact, different satellite and airborne sensors have been used for
archaeological applications, such as the identification of spectral anomalies (i.e. marks)
related to the buried remnants within archaeological sites, and the management and
protection of archaeological sites [3, 5].
The dominant factors that affect the spectral detectability of marks related to manmade
archaeological structures are: (1) the spectral contrast between the target and background
materials, (2) the proportion of the target on the surface (relative to the background), (3) the
imaging system characteristics being used (i.e. bands, instrument noise and pixel size), and
(4) the conditions under which the surface is being imaged (i.e. illumination and atmospheric
conditions) [4].
In this context, just few airborne hyperspectral sensors were applied for cultural heritage
studies, among them the AVIRIS (Airborne Visible/Infrared Imaging Spectrometer), the
CASI (Compact Airborne Spectrographic Imager), the HyMAP (Hyperspectral MAPping)
and the MIVIS (Multispectral Infrared and Visible Imaging Spectrometer). Therefore, the
application of high spatial/spectral resolution imagery arise the question on which is the
trade off between high spectral and spatial resolution imagery for archaeological
applications and which spectral region is optimal for the detection of subsurface
structures.
This paper points out the most suitable spectral information useful to evaluate the image
capability in terms of spectral anomaly detection of subsurface archaeological structures in
different land cover contexts.
In this study, we assess the capability of MIVIS and CASI reflectances and of ATM and
MIVIS emissivities (Table 1) for subsurface archaeological prospection in different sites of
the Arpi archaeological area (southern Italy). We identify, for the selected sites, three main
land cover overlying the buried structures: (a) photosynthetic (i.e. green low vegetation), (b)
non-photosynthetic vegetation (i.e. yellow, dry low vegetation), and (c) dry bare soil.
Afterwards, we analyse the spectral regions showing an inherent potential for the
archaeological detection as a function of the land cover characteristics. The classified
land cover units have been used in a spectral mixture analysis to assess the land
cover fractional abundance surfacing the buried structures (i.e. mark-background
system).
The classification and unmixing results for the CASI, MIVIS and ATM remote sensing
data processing showed a good accordance both in the land cover units and in the subsurface
structures identification. The integrated analysis of the unmixing results for the three sensors
allowed us to establish that for the land cover characterized by green and dry vegetation
(occurrence higher than 75%), the visible and near infrared (VNIR) spectral regions better
enhance the buried man-made structures. In particular, if the structures are covered by more
than 75% of vegetation the two most promising wavelengths for their detection are the
chlorophyll peak at 0.56μm (Visible region) and the red edge region (0.67 to 0.72μm; NIR
region). This result confirms that the variation induced by the subsurface structures (e.g.,
stone walls, tile concentrations, pavements near the surface, road networks) to the natural
vegetation growth and/or colour (i.e., for different stress factors) is primarily detectable
by the chlorophyll peak and the red edge region applied for the vegetation stress
detection. Whereas, if dry soils cover the structures (occurrence higher than 75%), both
the VNIR and thermal infrared (TIR) regions are suitable to detect the subsurface
structures.
This work demonstrates that airborne reflectances and emissivities data, even though at
different spatial/spectral resolutions and acquisition time represent an effective and rapid tool
to detect subsurface structures within different land cover contexts. As concluding results,
this study reveals that the airborne multi/hyperspectral image processing can be an effective
and cost-efficient tool to perform a preliminary analysis of those areas where large cultural
heritage assets prioritising and localizing the sites where to apply near surface geophysics
surveys.
Spectral
Region Spectral
Resolution (μm )Spectral
Range (μm) Spatial
Resolution (m)IFOV
(deg)
ATM VIS-NIR
SWIR-TIR
(tot 12 ch) variable from 24
to 3100 0.42 - 1150 2 0.143
CASI VNIR (48 ch.) 0.01 0.40-0.94 2 0.115
MIVIS VNIR (28ch.) 0.02 (VIS)
0.05 (NIR) 0.43-0.83 (VIS)
1.15-1.55
(NIR)
6 - 7
0.115
SWIR (64ch.) 0.09 1.983-2.478
TIR (10ch.) 0.34-0.54 8.180-12.700
Table 1. Characteristics of airborne sensors used for the Arpi test area.
1 References
2 [1] Beck, A., Philip, G., Abdulkarim, M. and Donoghue, D., 2007. Evaluation of
Corona and Ikonos high resolution satellite imagery for archaeological prospection in western
Syria. Antiquity, 81: 161-175.
3 [2] Altaweel, M., 2005. The Use of ASTER Satellite Imagery in Archaeological
Contexts. Archaeological Prospection, 12: 151- 166.
4 [3] Cavalli, R.M.; Colosi, F.; Palombo, A.; Pignatti, S.; Poscolieri, M. Remote
hyperspectral imagery as a support to archaeological prospection. J. of Cultural Heritage
2007, 8, 272-283.
5 [4] Kucukkaya, A.G. Photogrammetry and remote sensing in archaeology. J. Quant.
Spectrosc. Radiat. Transfer 2004, 97(1-3), 83-97.
[5] Rowlands, A.; Sarris, A. Detection of exposed and subsurface archaeological remains
using multi-sensor remote sensing. J. of Archaeological Science 2007, 34, 795-803. |
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