ATHENA is an on-going Horizon 2020 Twinning project aiming to promote remote sensing technologies for cultural heritage (CH) applications in Cyprus. ATHENA project brings together the Eratosthenes Research Center (ERC) of the Cyprus University of Technology (CUT) with two internationally leading institutions of Europe, namely the National Research Council of Italy (CNR) and the German Aerospace Centre (DLR). The project’s scope is to position the ERC regionally and stimulate future cooperation through placements at partner institutions and enhance the research and academic profile of all participants. The scientific strengthening and networking achieved through the ATHENA project could be of great benefit not only for Cyprus but for the entire Eastern Mediterranean, bearing a plethora of archaeological sites and monuments urgently calling for monitoring and safeguarding.
The preservation of CH and landscape comprises a strategic priority not only to guarantee cultural treasures and evidence of the human past to future generations, but also to exploit them as a strategic and valuable economic asset. The objective of this paper is to present knowledge transfer examples achieved from the ATHENA project through intense training activities. These activities were also designed to enhance the scientific profile of the research staff and to accelerate the development of research capabilities of the ERC. At the same time the results from the training activities were also exploited to promote earth observation knowledge and best practices intended for CH. The activities included active and passive remote sensing data used for archaeological applications, Synthetic Aperture Radar (SAR) image analysis for change and deformation detection, monitoring of risk factors related to cultural heritage sites including archaeological looting etc.
ATHENA is an on-going Horizon 2020 Twinning project aiming to promote remote sensing technologies for cultural heritage (CH) applications in Cyprus. ATHENA project brings together the Eratosthenes Research Center (ERC) of the Cyprus University of Technology (CUT) with two internationally leading institutions of Europe, namely the National Research Council of Italy (CNR) and the German Aerospace Centre (DLR). The project’s scope is to position the ERC regionally and stimulate future cooperation through placements at partner institutions and enhance the research and academic profile of all participants. The scientific strengthening and networking achieved through the ATHENA project could be of great benefit not only for Cyprus but for the entire Eastern Mediterranean, bearing a plethora of archaeological sites and monuments urgently calling for monitoring and safeguarding.
The preservation of CH and landscape comprises a strategic priority not only to guarantee cultural treasures and evidence of the human past to future generations, but also to exploit them as a strategic and valuable economic asset. The objective of this paper is to present knowledge transfer examples achieved from the ATHENA project through intense training activities. These activities were also designed to enhance the scientific profile of the research staff and to accelerate the development of research capabilities of the ERC. At the same time the results from the training activities were also exploited to promote earth observation knowledge and best practices intended for CH. The activities included active and passive remote sensing data used for archaeological applications, Synthetic Aperture Radar (SAR) image analysis for change and deformation detection, monitoring of risk factors related to cultural heritage sites including archaeological looting etc.
Remote sensing science is increasingly being used to support archaeological and cultural heritage research in various ways. Satellite sensors either passive or active are currently used in a systematic basis to detect buried archaeological remains and to systematic monitor tangible heritage. In addition, airborne and low altitude systems are being used for documentation purposes. Ground surveys using remote sensing tools such as spectroradiometers and ground penetrating radars can detect variations of vegetation and soil respectively, which are linked to the presence of underground archaeological features.
Education activities and training of remote sensing archaeology to young people is characterized of highly importance. Specific remote sensing tools relevant for archaeological research can be developed including web tools, small libraries, interactive learning games etc. These tools can be then combined and aligned with archaeology and cultural heritage. This can be achieved by presenting historical and pre-historical records, excavated sites or even artifacts under a “remote sensing” approach. Using such non-form educational approach, the students can be involved, ask, read, and seek to learn more about remote sensing and of course to learn about history.
The paper aims to present a modern didactical concept and some examples of practical implementation of remote sensing archaeology in secondary schools in Cyprus. The idea was built upon an ongoing project (ATHENA) focused on the sue of remote sensing for archaeological research in Cyprus. Through H2020 ATHENA project, the Remote Sensing Science and Geo-Environment Research Laboratory at the Cyprus University of Technology (CUT), with the support of the National Research Council of Italy (CNR) and the German Aerospace Centre (DLR) aims to enhance its performance in all these new technologies.
The landscape of Cyprus is characterized by transformations that occurred during the 20th century, with many of such changes being still active today. Landscapes’ changes are due to a variety of reasons including war conflicts, environmental conditions and modern development that have often caused the alteration or even the total loss of important information that could have assisted the archaeologists to comprehend the archaeo-landscape.
The present work aims to provide detailed information regarding the different existing datasets that can be used to support archaeologists in understanding the transformations that the landscape in Cyprus undergone, from a remote sensing perspective. Such datasets may help archaeologists to visualize a lost landscape and try to retrieve valuable information, while they support researchers for future investigations. As such they can further highlight in a predictive manner and consequently assess the impacts of landscape transformation -being of natural or anthropogenic cause- to cultural heritage.
Three main datasets are presented here: aerial images, satellite datasets including spy satellite datasets acquired during the Cold War, and cadastral maps. The variety of data is provided in a chronological order (e.g. year of acquisitions), while other important parameters such as the cost and the accuracy are also determined. Individual examples of archaeological sites in Cyprus are also provided for each dataset in order to underline both their importance and performance. Also some pre- and post-processing remote sensing methodologies are briefly described in order to enhance the final results. The paper within the framework of ATHENA project, dedicated to remote sensing archaeology/CH, aims to fill a significant gap in the recent literature of remote sensing archaeology of the island and to assist current and future archaeologists in their quest for remote sensing information to support their research.
Two are the risks associated to archaeological heritage. The first one is economic and it is related to the costs needed to perform field survey in all the territory. The second one is scientific, that is the risk to lose artifacts and, consequently, witnesses of the human past [1], in particular in case of large-scale infrastructure works and looting linked to the illicit trade of antiquities. Predictive models are useful to archaeological research to look for the right compromise between the reduction of costs and effectiveness of results. It enables to identify the site locational behavior [2], more in particular environmental site location preferences [3]. From the eighties the development of more user friendly GIS softwares and the increased easiness of relative tools allowed the diffusion of predictive models, that were improved thanks to the greater availability of remotely sensed data and the image processing routines, which are effective for the detection of archaeological features. The proposal of this paper is to make a brief review of existing predictive models and to propose a new model that takes in count spatial properties of archaeological datasets to predict neolithic settlements in Tavoliere in the Apulian region (Southern Italy), already investigated by preventive archaeological methods including geophysics and remote sensing [4].
The application of space technology to archaeological research has been paid great attention worldwide, mainly because
the current availability of very high resolution (VHR) satellite imagery, such as, IKONOS (1999) and QuickBird (2001),
provide valuable data for searching large areas to find potential archaeological sites. Data from VHR satellite can be very
useful for the identification, management and documentation of archaeological resources. Archaeological investigation
based on the use of VHR satellite images may take benefits from the integration and synergic use of both panchromatic
and multispectral data. This can be achieved by using pansharpening techniques, which allow multispectral and
panchromatic images to be merged. The two basic frameworks of pansharpening techniques are Component Substitution
(CS), such as Intensity-Hue-Saturation (IHS) Gram-Schmidt (GS), and multiresolution analysis (MRA), such as wavelets
and Laplacian pyramids (LP). In this paper, both Gram-Schmidt and Laplacian pyramids with context adaptive (CA)
detail injection models were used. QB images were processed for a relevant archaeological area in Southern Italy, the
ancient Siris-Heraclea, a very significant test area because it is characterized by the presence of both surface and
subsurface ancient remains. Outcomes of different pansharpening techniques have been qualitatively evaluated for both
surface and subsurface remains. The visual inspection clearly suggests that the quantitative evaluation of the fusion
performance for archaeological applications is a critical issue, and "ad hoc" local (i.e. context-adaptive) indices need to
be developed.
The restoration of artistic and architectural heritage represents a bench mark of the cultural development of a society. To
this end it is necessary to develop a suitable methodology for the analysis of the material and building components which
are usually brittle and in a poor state of conservation. The paper outlines the advantages and the drawbacks in the use of
Non-Destructive Testing (NDT) techniques and the need to integrate them in order to obtain a reliable reconstruction of
the internal characteristics of the building elements as well as the detection of defects. In the study case we used Ground
Penetrating Radar (GPR), infrared thermography (IRT), sonic and ultrasonic tests to analyze a 13th century precious rose
window in Southern Italy, affected by widespread decay and instability problems. The theoretical capabilities and
limitations of NDT are strictly related to the frequency content of the signals used by the different techniques. Therefore,
integrating several physical methods and using different frequency bands allowed as a comprehensive, multi-scale
approach to the restoration problem. This revealed to be a proper strategy in order to get high-resolution information on
the building characteristics and the state of decay which could support a careful structural restoration.
The application of Very High Resolution (VHR) satellite imagery to archaeological prospection can furnish useful
information for the identification of archaeological features, related to ancient land use patterns, irrigation networks,
paleo-hydrological systems, roads, walls and buildings. These archaeological features could be enhanced by using data
fusion techniques which are able to merge the complementary characteristics of panchromatic and multispectral images.
The quantitative evaluation of the quality of the fused images is one the most crucial aspects in the context of data
fusion. This issue is particularly relevant in the case of the identification of archaeological features, because data fusion
could enhance or lose the small spatial and spectral details which are generally linked with the presence of buried
archaeological remains.
This study is focused on the evaluation of data fusion algorithms applied to Quickbird images for the enhancement of
archaeological features. Three different data fusion techniques, Gram-Schimdt, PCA, and wavelet, were applied to a
study case located in the South of Italy. Focusing on the archaeological features, the evaluation process was performed
by using two different protocols with and without a reference image. Results obtained from the two protocols showed
that the best performance was obtained from the wavelet data fusion.
A reliable mapping of fuel types is very important for computing fire hazard and risk and simulating fire growth and
intensity across a landscape. Due to the complex nature of fuel characteristic a fuel map is considered one of the most
difficult thematic layers to build up especially for large areas. The advent of satellite sensors with increased spatial
resolution may improve the accuracy and reduce the cost of fuels mapping. The objective of this research is to evaluate
the accuracy and utility of imagery from the Advanced Spaceborne Thermal Emission and Reflection Radiometer
(ASTER) satellite imagery. In order to ascertain how well ASTER data can provide an exhaustive classification of fuel
properties a sample area characterized by mixed vegetation covers was analysed. The selected sample areas has an
extension at around 60 km2 and is located inside the Sila plateau in the Calabria Region (South of Italy). Fieldwork fuel
type recognitions, performed before, after and during the acquisition of remote sensing ASTER data, were used as
ground-truth dataset to assess the results obtained for the considered test area. Results from our analysis showed that the
use ASTER data provided a valuable characterization and mapping of fuel types with a classification accuracy higher
than 78%.
The dynamics of vegetation covers in burned and unburned areas can be monitored by using satellite data, which provide
a wide spatial coverage and internal consistency of data sets. Several indices based on satellite data can be used for this
aim. In particular, NDVI (Normalized Difference Vegetation Index) is the most widely used index for vegetation
monitoring based on remote sensing. This paper aims to perform a dynamical characterization of burned and unburned
vegetation covers, using time series of remotely sensed data of some fire-affected and fire-unaffected sites. For this
purpose, we used the Detrended Fluctuation Analysis (DFA), which permits the detection of persistent properties in
nonstationary signals. Our results point out that the persistence of vegetation dynamics is significantly increased by the
occurrence of fires.
This study aims to ascertain how well remote sensing data can characterize fuel type at different spatial scales in
fragmented ecosystems. For this purpose, multisensor and multiscale remote sensing data such as, hyperspectral
(Multispectral Infrared and Visible Imaging Spectrometer) MIVIS and Landsat- Temathic Mapper (TM) acquired
in 1998 were analysed for a test area of Southern Italy characterized by mixed vegetation covers and complex
topography. Fieldwork fuel type recognition, performed at the same time as remote sensing data acquisitions, were
used to assess the results obtained for the considered test areas..
The method comprised the following three steps: (I) adaptation of Prometheus fuel types for obtaining a
standardization system useful for remotely sensed classification of fuel types and properties in the considered
Mediterranean ecosystems; (II) model construction for the spectral characterization and mapping of fuel types; (III)
accuracy assessment for the performance evaluation based on the comparison of satellite-based results with ground-truth.
Two different approaches have been adopted for fuel type mapping: the well-established classification techniques
and spectral mixture analysis. Results from preliminary analysis have showed that the use of unmixing techniques
allows an increase in accuracy at around 7% compared to the accuracy level obtained by applying a widely used
classification algorithm.
Can the satellite QuickBird data detect buried archaeological remains and provide an effective spatial
characterization of them? To answer this question, the capability of satellite QuickBird imagery for the
identification of archaeological marks is herein tested for two test sites located in the South of Italy. The
investigations were performed using a semi-automatic algorithm specifically developed for the detection of linear
alignments.
Results from our analyses showed that QuickBird images can represent a valuable data-source for archaeological
investigations, ranging from small details to synoptic view. In particular, landscape archaeology can especially
benefit from satellite images because such data can place local field studies within a regional context, can be
promptly updated at low cost for large area, and can be directly imported into a GIS environment.
KEYWORDS: Vegetation, Short wave infrared radiation, Satellites, Reflectivity, Near infrared, Data analysis, Statistical analysis, Agriculture, Spatial resolution, Data acquisition
SPOT-Vegetation (SPOT-VGT) data at full spatial resolution were used in order to assess the potentiality and feasibility of using low resolution optical satellite data for the estimation of time/space dynamics of surface moisture content. The seasonal trend of diverse parameters (single channels or spectral indices) suitable/or
specifically designed for moisture estimation have been analyzed in some test sites of southern Italy. The investigations were performed by using imagery selected from a long time series of ten-day compositions of VGT data acquired from 1998 to 2002. The preliminary results indicate that the use of the VGT data for obtaining
information on the time/space dynamics of surface moisture content can be useful.
Over the years, several satellite-based indicators have been developed for Fire Susceptibility Estimation (FSE) most of which are based on AVHRR data. Unfortunately, these indicators yield different results when applied to different ecosystems or geographic regions and this creates confusion concerning their effectiveness. This work aims at evaluating the performance of the existing AVHRR-based FSE methods. Such evaluation was performed in the Basilicata and Calabria Region by using NOAA-12,-14 summery imagery selected from a long time series acquired from 1995 to 1999. Fire susceptibility maps
obtained from the considered methods were compared to fire archives provided by the Italian National Forestry Service. The most satisfactory results were obtained from methods based on the cross analyses of NDVI (Normalized Difference Vegetation Index) with thermal channels.
Physical parameters related to Earth surface and atmosphere show different behaviors when observed at different space-time scales by using both remote sensing or traditional ground based techniques. The main aim of this project was to investigate the information content degradation which results moving from the use of observations obtained by direct-punctual (ground-based), higher spectral/spatial resolution (airborne sensors), higher time-resolution, low cost and low spatial resolution (satellites), in the context of the activities related to natural and environmental risks monitoring in protected natural areas. Several observational techniques have been contemporary used during two fields campaigns in the Pollino National Park (Southern Italy): a) from ground by direct measurements of near surface parameters (from - 70cm of depth up to 200cm of height) as well as by radiosonde and radiometric measurements of surface and atmospheric parameters; b) using hyperspectral (MIVIS) and photographic aerial observations; c) from LANDSAT-TM, NOAAA/AVHRR and ADEOS/AVIRIS satellite sounders. Campaign data have been integrated on a GIS (including high resolution cartographic layers) and long term evolutionary trends (up to 20 years) also considered after the analysis of available historical, LANDSAT and NOAA, satellite records. This paper will present the main achievements of the project with special emphasis on the trade-off between expected performances and economical sustainability of different environmental monitoring strategies in an operational context.
The main objective of this work has been to evaluate the potential of integration of satellite data and topographic factor, in order to achieve improved performance in forest fire danger estimation. Existing AVHRR-based fire danger estimation methods (a review is specifically made) aim at obtaining fire susceptibility classification exploiting, mainly, the temporal evolution of NDVI, and Surface Temperature (Ts). In this work fire danger estimation has been performed integrating satellite data with fuel type and topographic factors. In order to evaluate the reliability of the estimated indices, the time-space distribution of actual forest fires, provided by the Italian Forestry service, has been used. Preliminary results are very promising; they have shown that in the summer of 1996, a large number of forest fires occurred in the estimated higher danger areas.
The most commonly used fire detection methods based on AVHRR (Advanced Very High Resolution Radiometer aboard NOAA satellites) observations have been applied, in this work, to the Italian Peninsula, in order to assess their effectiveness and robustness in an operational scheme for fire monitoring in different areas. The analysis developed so far shows that unsuccessful results obtained from existing detection methods mainly depend on the use of generalized, fixed threshold values, in the fire-detection tests. A new fire-detection technique, based on the most general RAT (Robust AVHRR Technique) approach, is proposed in this work. It seems able to join good performances, typically associated with techniques based on locally tuned thresholds, with high operational exportability, achieved by an automated implementation scheme completely based on AVHRR data hands. Improvements achievable by using this new approach over the Italian Peninsula, have been evaluated (for several forest- fire events and different observational conditions) by comparison with the historical records of the Italian Forestry Service.
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