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Process flow chart: the GIS manages the processed remote sensing data and the archaeological research data.
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... this point the interpretation of the features can start. The nature and char- acteristics of each element are determined and its causes investigated, also in relationship with the surrounding ele- ments of the landscape and with published information and archaeological thematic cartography, in order to separate the traces due to modern landscape alterations from the ones related to the ancient landscape exploitation (Figure 1). ...
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The use of historical maps in coordination with GIS aids scholars who are approaching a geographical study in which an historical approach is required or is interested in the geographical relationships between different historical representations of the landscape in cartographic document. Historical maps allow the comparison of spatial relationship...
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... The models built and assessed below include variables relating to spectral reflectance measurements gathered by satellite-borne instruments. Such measurements are plausibly associated with soil composition and vegetation qualities associated with the presence or absence of archaeological material (Doneus et al., 2014;Oonk et al., 2009;Traviglia, 2007). The relevance of these variables to archaeological pursuits is somewhat different from that of the variables, such as slope, elevation, and aspect, more traditionally used in APMs, which are included in predictive models not necessarily for their association with anthropogenic changes to the environment but for their possible relation to a particular area's suitability for certain kinds of human activity. ...
... The capabilities of AHI for determining surface properties has proved its interest in multiple earth-observation applications such as geological mapping, environmental monitoring, agriculture and forestry management, atmospheric characterization, biological and chemical detection, or disaster assessment (see Jia et al. (2020) for a recent review of applications). In archaeology, three decades after the first assessments of multispectral images to push back the limits of cropmark identification using near-infra red bands (Hampton, 1974;Hampton et al., 1977), the use of hyperspectral effectively emerged in the 2000s with the evaluation on MIVIS sensor operating in the VNIR, SWIR and TIR domains (Emmolo et al., 2004;Traviglia, 2006b). ...
... AHI for archaeology was most uniquely dedicated to the identification and documentation of cropmarks in agricultural areas (Aqdus et al., 2008(Aqdus et al., , 2012Bennett et al., 2013;Emmolo et al., 2004;Pascucci et al., 2010;Traviglia, 2006aTraviglia, , 2006b, so not surprisingly the red (~650nm) and near-infra red (~750nm) portions of the spectrum were particularly interesting to identify variations of vegetation conditions. Naturally, the computation of vegetation indices from surface reflectance became a common approach for archaeological prospection, like the normalized difference vegetation (NDVI) introduced by Rouse et al. (1973) and defined as a function of the reflectance ( ) in the red and near-infrared wavelength, by : ...
... Manolakis et al., 2003), geology (van der Meer et al., 2012) and coastal mapping (Dekker et al., 2011). For archaeological applications, airborne hyperspectral data have been greatly valuable for terrestrial mapping (Aqdus et al., 2012;Cavalli et al., 2013Cavalli et al., , 2007Cerra et al., 2018;Emmolo et al., 2004;Savage et al., 2012;Traviglia, 2006aTraviglia, , 2006bG. J. Verhoeven, 2017), but to our knowledge, no studies have yet assessed AHI in a submerged context. ...
Menacé par des pressions naturelles et anthropiques croissantes, le patrimoine archéologique fait l’objet d’enjeux de connaissances scientifiques et de mesures de protection. Or les prospections archéologiques sont très difficiles à mener dans des environnements forestiers ou immergés. Dans ce contexte, cette thèse vise à évaluer l’apport des données LiDAR et hyperspectrales aéroportées pour la détection et la caractérisation de structures archéologiques, ces données ayant montré leur intérêt pour accéder à des informations inédites sous la canopée ou sous l’eau. Pour répondre à cet objectif, nous avons développé de nouvelles approches de visualisation et de détection automatique basées notamment sur le deep learning. Nous avons d’abord exploité des données LiDAR topographiques afin de détecter et caractériser des structures archéologiques datant principalement de la période mégalithique, en contexte émergé sur la région de Carnac (Morbihan). Puis nous avons évalué l’imagerie hyperspectrale en contexte immergé sur le site mégalithique d’Er Lannic (Morbihan) et sur l’archipel de Molène (Finistère). Les résultats ont montré l’intérêt des approches d’analyse multi-échelles et d’apprentissage automatique appliquées aux modèles numériques dérivés des données LiDAR, en particulier sous couvert forestier. Nous avons aussi montré l’apport original de l’imagerie hyperspectrale pour la détection et la caractérisation de structures en zone de petits fonds, ouvrant ainsi de nouvelles perspectives quant à l’exploration archéologique de paysages submergés.
... The capabilities of AHI for determining surface properties has proved its interest in multiple earth-observation applications such as geological mapping, environmental monitoring, agriculture and forestry management, atmospheric characterization, biological and chemical detection, or disaster assessment (see Jia et al. (2020) for a recent review of applications). In archaeology, three decades after the first assessments of multispectral images to push back the limits of cropmark identification using near-infra red bands (Hampton, 1974;Hampton et al., 1977), the use of hyperspectral effectively emerged in the 2000s with the evaluation on MIVIS sensor operating in the VNIR, SWIR and TIR domains (Emmolo et al., 2004;Traviglia, 2006b). ...
... AHI for archaeology was most uniquely dedicated to the identification and documentation of cropmarks in agricultural areas (Aqdus et al., 2008(Aqdus et al., , 2012Bennett et al., 2013;Emmolo et al., 2004;Pascucci et al., 2010;Traviglia, 2006aTraviglia, , 2006b, so not surprisingly the red (~650nm) and near-infra red (~750nm) portions of the spectrum were particularly interesting to identify variations of vegetation conditions. Naturally, the computation of vegetation indices from surface reflectance became a common approach for archaeological prospection, like the normalized difference vegetation (NDVI) introduced by Rouse et al. (1973) and defined as a function of the reflectance ( ) in the red and near-infrared wavelength, by : ...
... For archaeological applications, airborne hyperspectral data have been greatly valuable for terrestrial mapping (Aqdus et al., 2012;Cavalli et al., 2013Cavalli et al., , 2007Cerra et al., 2018;M. Doneus et al., 2014;Emmolo et al., 2004;Savage et al., 2012;Traviglia, 2006aTraviglia, , 2006bG. J. Verhoeven, 2017), but to our knowledge, no studies have yet assessed AHI in a submerged context. ...
Threatened by increasing natural and anthropic pressures, the archaeological heritage is the subject of scientific knowledge and protection measures. However, archaeological surveys are very difficult - if not impossible - to carry out in forest or submerged environments. In this context, this thesis aims to evaluate the contribution of airborne LiDAR and hyperspectral data for the detection and characterization of archaeological structures, as these data have shown their interest in providing new information under the canopy or in shallow waters. For that purpose, we have developed new visualization approaches, and automatic detection methods based on deep learning. First, we used topographic LiDAR data to detect and characterize archaeological structures dating mainly from the megalithic period, in a terrestrial context in the Carnac region (Morbihan). Then, we evaluated hyperspectral imagery in a submerged context in the megalithic site of Er Lannic (Morbihan) and in the Molène archipelago (Finistère). The results showed the interest of multi-scale analysis and machine learning approaches applied to numerical models derived from LiDAR data, in particular under forest cover. We also demonstrated the original contribution of hyperspectral imagery for the detection and characterization of structures in shallow waters, thus opening up new perspectives for the archaeological exploration of submerged landscapes.
... The use of multispectral imagery for mapping buried archaeological remains is well established [21], exploiting reflected spectral regions such as near-infrared (NIR~700 to 1100 nm) and red-edge (~700 nm) for detection of features [7,9,[22][23][24]. While multispectral imagery can show crop responses as a true colour (TC) image, false colour composites (FCC) and vegetation indices (VIs) are widely used as an effective way of showing contrasts in vegetation health. ...
In intensively cultivated landscapes, many archaeological remains are buried under the ploughed soil, and detection depends on crop proxies that express subsurface features. Traditionally these proxies have been documented in visible light as contrasting areas of crop development commonly known as cropmarks. However, it is recognised that reliance on the visible electromagnetic spectrum has inherent limitations on what can be documented, and multispectral and thermal sensors offer the potential to greatly improve our ability to detect buried archaeological features in agricultural fields. The need for this is pressing, as ongoing agricultural practices place many subsurface archaeological features increasingly under threat of destruction. The effective deployment of multispectral and thermal sensors, however, requires a better understanding of when they may be most effective in documenting archaeologically induced responses. This paper presents the first known use of the FLIR Vue Pro-R thermal imager and Red Edge-M for exploring crop response to archaeological features from two UAV surveys flown in May and June 2019 over a known archaeological site. These surveys provided multispectral imagery, which was used to create vegetation index (VI) maps, and thermal maps to assess their effectiveness in detecting crop responses in the temperate Scottish climate. These were visually and statistically analysed using a Mann Whitney test to compare temperature and reflectance values. While the study was compromised by unusually damp conditions which reduced the potential for cropmarking, the VIs (e.g., Normalised Difference Vegetation Index, NDVI) did show potential to detect general crop stress across the study site when they were statistically analysed. This demonstrates the need for further research using multitemporal data collection across case study sites to better understand the interactions of crop responses and sensors, and so define appropriate conditions for large-area data collection. Such a case study-led multitemporal survey approach is an ideal application for UAV-based documentation, especially when “perfect” conditions cannot be guaranteed.
... Due to its ability to acquire highly detailed spectral information, airborne hyperspectral imagery (AHI) has been used for various types of earth observation: land-cover/land-use mapping [17,18], target detection [19], geology [20] and coastal mapping [21]. For archaeological applications, airborne hyperspectral data have been greatly valuable for terrestrial mapping [22][23][24][25][26][27][28][29][30][31], but to our knowledge, no studies have yet assessed AHI in a submerged context. Using it for underwater mapping requires addressing challenges related to the complexity of (i) the data (including high dimensionality and signal-to-noise ratio), (ii) the object of study (degraded and partially documented structures) and (iii) the environment, especially the complex light-matter interactions in water, affected by multiple environmental factors such as water constituents, surface conditions and benthic composition. ...
... Its objective is to decrease computational burden (i.e., reduce the number of bands), remove spectrally redundant information or noise and highlight informative spectral variation in the imagery. For remote-sensing hyperspectral data, for which interband correlation is high and noise omnipresent, dimensionality reduction algorithms are used to enhance visual interpretation or as pre-processing before other procedures, such as classification [30]. These algorithms include Principal Component Analysis (PCA) and Minimum Noise Fraction (MNF) [41]. ...
Nearshore areas around the world contain a wide variety of archeological structures, including prehistoric remains submerged by sea level rise during the Holocene glacial retreat. While natural processes, such as erosion, rising sea level, and exceptional climatic events have always threatened the integrity of this submerged cultural heritage, the importance of protecting them is becoming increasingly critical with the expanding effects of global climate change and human activities. Aerial archaeology, as a non-invasive technique, contributes greatly to documentation of archaeological remains. In an underwater context, the difficulty of crossing the water column to reach the bottom and its potential archaeological information usually requires active remote-sensing technologies such as airborne LiDAR bathymetry or ship-borne acoustic soundings. More recently, airborne hyperspectral passive sensors have shown potential for accessing water-bottom information in shallow water environments. While hyperspectral imagery has been assessed in terrestrial continental archaeological contexts, this study brings new perspectives for documenting submerged archaeological structures using airborne hyperspectral remote sensing. Airborne hyperspectral data were recorded in the Visible Near Infra-Red (VNIR) spectral range (400–1000 nm) over the submerged megalithic site of Er Lannic (Morbihan, France). The method used to process these data included (i) visualization of submerged anomalous features using a minimum noise fraction transform, (ii) automatic detection of these features using Isolation Forest and the Reed–Xiaoli detector and (iii) morphological and spectral analysis of archaeological structures from water-depth and water-bottom reflectance derived from the inversion of a radiative transfer model of the water column. The results, compared to archaeological reference data collected from in-situ archaeological surveys, showed for the first time the potential of airborne hyperspectral imagery for archaeological mapping in complex shallow water environments.
... In archaeology, AIS is considered to have a huge potential for airborne prospection, because it is assumed to overcome the deficits of conventional and multispectral imagery and enhance the visibility of soil color differences and plant stress. Several studies have demonstrated the advantage of this imaging technique (e.g., [25][26][27][28][29][30][31][32][33][34][35]). ...
... In archaeology, AIS is considered to have a huge potential for airborne prospection, because it is assumed to overcome the deficits of conventional and multispectral imagery and enhance the visibility of soil color differences and plant stress. Several studies have demonstrated the advantage of this imaging technique (e.g., [25][26][27][28][29][30][31][32][33][34][35]). ...
Imaging spectroscopy acquires imagery in hundreds or more narrow contiguous spectral bands. This offers unprecedented information for archaeological research. To extract the maximum of useful archaeological information from it, however, a number of problems have to be solved. Major problems relate to data redundancy and the visualization of the large amount of data. This makes data mining approaches necessary, as well as efficient data visualization tools. Additional problems relate to data quality. Indeed, the upwelling electromagnetic radiation is recorded in small spectral bands that are only about ten nanometers wide. The signal received by the sensor is, thus quite low compared to sensor noise and possible atmospheric perturbations. The often small, instantaneous field of view (IFOV)—essential for archaeologically relevant imaging spectrometer datasets—further limits the useful signal stemming from the ground. The combination of both effects makes radiometric smoothing techniques mandatory. The present study details the functionality of a MATLAB®-based toolbox, called ARCTIS (ARChaeological Toolbox for Imaging Spectroscopy), for filtering, enhancing, analyzing, and visualizing imaging spectrometer datasets. The toolbox addresses the above-mentioned problems. Its Graphical User Interface (GUI) is designed to allow non-experts in remote sensing to extract a wealth of information from imaging spectroscopy for archaeological research. ARCTIS will be released under creative commons license, free of charge, via website (http://luftbildarchiv.univie.ac.at).
... [26][27][28] In spite of the low resolution and the necessary and sometimes cumbersome post-processing of data, the use of spectral sensors on board satellites and planes has shown to be useful in archaeological research. 29 Only recently has hyperspectral imaging been applied for archaeological purposes. Alexakis et al. used data from four sensors, including Hyperion, which contains 220 spectral bands, to detect Neolithic settlements in a low relief region in Greece. ...
This preliminary work comprises examples where near infrared (NIR) hyperspectral imaging has been applied to identify animal bone material in complex sieved soil-sediment matrices from an archaeological excavation at a Stone Age site in northern Scandinavia. NIR hyperspectral image analysis has been performed, as a fast and non-destructive technique, on whole bone and tooth samples, as well as on soil from the excavation containing fragmented skeletal material in order to identify fragmented bones, to provide information about the skeletal material's chemistry-mineralogy within the site and the different layers as well as studying the possibility of describing their different state of preservation.
... Los sensores hiperespectrales, que registran imágenes en una gran gama de bandas del espectro electromagnético visible (VIS), infrarrojo cercano (NIR), de onda corta (SWIR) y térmico (TIR), permiten obtener datos de alta resolución espectral de amplias regiones en secuencias temporales continuas. Podemos medir parámetros biofísicos y calcular anomalías espectrales y térmicas que puedan ser indicativas de presencia de estructuras antrópicas, o de patologías que afectan a éstas (Traviglia, 2006;Rejas et al., 2007). A su vez, nos aportan información sobre sinergias entre usos de la tierra, geomorfología, mineralogía y vegetación como producto de causas naturales o afectadas por el cambio climático. ...
... Los sensores hiperespectrales, que registran imágenes en una gran gama de bandas del espectro electromagnético visible (VIS), infrarrojo cercano (NIR), de onda corta (SWIR) y térmico (TIR), permiten obtener datos de alta resolución espectral de amplias regiones en secuencias temporales continuas. Podemos medir parámetros biofísicos y calcular anomalías espectrales y térmicas que puedan ser indicativas de presencia de estructuras antrópicas, o de patologías que afectan a éstas (Traviglia, 2006;Rejas et al., 2007). A su vez, nos aportan información sobre sinergias entre usos de la tierra, geomorfología, mineralogía y vegetación como producto de causas naturales o afectadas por el cambio climático. ...
