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Imaging radar has been dramatically developed over the past decades enabling a better understanding of cultural heritage from a microwave perspective. Nonetheless, a dedicated survey and analysis of the performance of such technology in cultural heritage monitoring and management is required. In order to fill this gap, we first review the technology advance of imaging radar, including ground penetration radar, ground-based and airborne/satellite radar, in the focused cultural applications to grasp the development trend of these technologies. We then analyse the performance and limitations of imaging radar technologies based on their respective characteristics to facilitate the technology service in practical applications. Finally, we propose a flexible solution of imaging radar in cultural heritage through technical integration with pilot synergy applications in archaeological prospection and cultural heritage diagnosis and conservation.

In the last two decades, archaeology has benefited from the development of earth observation (EO) technologies, including optical multispectral, LiDAR and synthetic aperture radar (SAR) remote sensing. The latter is gaining the attention of an expanding community of scientists and archaeologists due to the increasing availability of multi-platform, multi-band, multi-polarization and very high-resolution satellite SAR data. It is increasingly becoming an important tool in archaeology owing to specific characteristic of its operational modalities, e.g. all-weather, penetration, polarization and interferometry. However, compared to other EO technologies, SAR is encountering more difficulties in realizing its full potential for archaeological applications due to the greater complexity of data processing and interpretation tools. In this paper, SAR-based approaches for the reconnaissance of archaeological signs and SAR interferometry for the monitoring of cultural heritage sites are discussed. Ways and means to reduce complexity of data processing and interpretation tools are also explored.

Archaeological prospection of earthen buried structures, namely non-fired sun-dried mud bricks mixed with organic material, is a critical challenge to address. In fact, this building material exhibits a very low geophysical contrast compared to its surroundings and, therefore, earthen structures are very complex to be identified using remote sensing. In order to cope with this issue, in this paper, we focus on the evaluation of satellite X-band radar data (COSMO-SkyMed) capability for detecting earthen buried structures in a desert area. The results obtained from satellite radar data have been validated for a test site in Pachacamac (Peru) by using unmanned aerial vehicle (UAV) and geomagnetic techniques. The test site is outside the fenced protected zone of Pachacamac, today in the tentative UNESCO list. This paper is the first attempt made until now in evaluating the detectability of earthen archaeological remains using satellite Synthetic Aperture Radar (SAR) data. Outcomes from our investigations clearly point out that the approach we adopted can be useful applied for preventive archaeology and for the planning of future excavation campaigns.

This study assesses forest-fire susceptibility (FFS) in Fars Province, Iran using three geographic information system (GIS)-based machine-learning algorithms: boosted regression tree (BRT), general linear model (GLM), and mixture discriminant analysis (MDA). Recently, BRT, GLM, and MDA have become important machine-learning algorithms and their use has been enriched by application to various fields of research. A database of historical FFs identified using Landsat-8 OLI and MODIS satellite images (at 358 locations) and ten influencing factors (elevation, slope, topographical wetness index, aspect, distance from urban areas, annual mean temperature, land use, distance from road, annual mean rainfall, and distance from river) were input into a GIS. The 358 sites were divided into two sets for training (70%) and validation (30%). BRT, GLM, and MDA models were used to analyze the spatial relationships between the factors influencing FFs and the locations of fires to generate an FFS map. The prediction success of each modelled FFS map was determined with the help of the ROC curve, accuracy, overall accuracy, True-skill statistic (TSS), F-measures, corrected classify instances (CCI), and K-fold cross-validation (4-fold). The accuracy results of training and validation dataset in the BRT (AUC = 88.90% and 88.2%) and MDA (AUC = 86.4% and 85.6%) models are more effective than the GLM (AUC = 86.6% and 82.5%) model. Also, the outcome of the 4-fold measure confirmed the results from the other accuracy measures. Therefore, the accuracies of the BRT and MDA models are satisfactory and are suitable for FFS mapping in Fars Province. Finally, the well-accepted neural network application of learning-vector quantization (LVQ) reveals that land use, annual mean rainfall, and slope angle were the most useful determinants of FFS. The resulting FFS maps can enhance the effectiveness of planning and management of forest resources and ecological balances in this province.

Up to nowadays, satellite data have become increasingly available, thus offering a low cost or even free of charge unique tool, with a great potential for quantitative assessment of urban expansion and urban sprawl, as well as for monitoring of land use changes and soil consumption. This growing observational capacity has also highlighted the need for research efforts aimed at exploring the potential offered by data processing methods and algorithms, in order to exploit as much as possible this invaluable space-based data source. The work herein presented concerns an application study on the process of urban sprawl conducted with the use of satellite ASTER data. The selected test site is highly significant, being it a coastal zone (with the presence of sand and rocks) characterized by a fragmented ecosystem and small towns, with an increasing rate of urbanization and soil consumption. In order to produce synthetic maps of urban areas, ASTER images were classified using two automatic classifiers, Maximum Likelihood (MLC) and Support Vector Machines (SVMs) applied by changing setting parameters, with the aim to compare their respective performances in terms of robustness, speed and accuracy. All process steps have been developed integrating Geographical Information System and Remote Sensing, and adopting free and open source software. Results pointed out that the SVM classifier with RBF kernel was generally the best choice (with accuracy higher than 90%) among all the configurations compared, and the use of multiple bands globally improves classification. One of the critical elements found in this case study is given by the presence of sand and sand mixed with rocks. The use of different configurations for the SVMs, i.e. different kernels and values of the setting parameters, allowed us to calibrate the classifier also to cope with a specific need, as in our case, to achieve a reliable discrimination of sand from urban area. © 2014 Elsevier B.V. All rights reserved.

The time dynamics of SPOT-VEGETATION Normalized Difference Vegetation Index (NDVI) time series are analyzed by using the statistical approach of the Fisher–Shannon (FS) information plane to assess and monitor vegetation recovery after fire disturbance. Fisher–Shannon information plane analysis allows us to gain insight into the complex structure of a time series to quantify its degree of organization and order. The analysis was carried out using 10-day Maximum Value Composites of NDVI (MVC-NDVI) with a 1 km × 1 km spatial resolution. The investigation was performed on two test sites located in Galizia (North Spain) and Peloponnese (South Greece), selected for the vast fires which occurred during the summer of 2006 and 2007 and for their different vegetation covers made up mainly of low shrubland in Galizia test site and evergreen forest in Peloponnese. Time series of MVC-NDVI have been analyzed before and after the occurrence of the fire events. Results obtained for both the investigated areas clearly pointed out that the dynamics of the pixel time series before the occurrence of the fire is characterized by a larger degree of disorder and uncertainty; while the pixel time series after the occurrence of the fire are featured by a higher degree of organization and order. In particular, regarding the Peloponneso fire, such discrimination is more evident than in the Galizia fire. This suggests a clear possibility to discriminate the different post-fire behaviors and dynamics exhibited by the different vegetation covers.

Archaeological and cultural heritage (ACH), one of the core carriers of cultural diversity on our planet, has a direct bearing on the sustainable development of mankind. Documenting and protecting ACH is the common responsibility and duty of all humanity. It is governed by UNESCO along with the scientific communities that foster and encourage the use of advanced non-invasive techniques and methods for promoting scientific research into ACH and conservation of ACH sites. The use of remote sensing, a non-destructive tool, is increasingly popular by specialists around the world as it allows fast prospecting and mapping at multiple scales, rapid analysis of multisource datasets, and dynamic monitoring of ACH sites and their surrounding environments. The cost of using remote sensing is lower or even zero in practical applications. In this review, in order to discuss the advantages of airborne and spaceborne remote sensing (ASRS), the principles that make passive (photography, multispectral and hyperspectral) and active (synthetic aperture radar (SAR) and light detection and ranging radar (LiDAR)) imaging techniques suitable for ACH applications are first summarized and pointed out; a review of ASRS and the methodologies used over the past century is then presented together with relevant highlights from well-known research projects. Selected case studies from Mediterranean regions to East Asia illustrate how ASRS can be used effectively to investigate and understand archaeological features at multiple -scales and to monitor and assess the conservation status of cultural heritage sites in the context of sustainable development. An in-depth discussion on the limitations of ASRS and associated remaining challenges is presented along with conclusions and a look at future trends.

The application of Earth Observation (EO) techniques has exhibited great potential for archaeological investigations, even if in an experimental stage. It has accounted for a number of important archaeological discoveries and has provided manifold capabilities starting from the detection of cultural features through archaeological prospecting in regional surveys, to palaeo-ecosystem studies and paleo-landscape reconstructions.

The detection of buried earthen structures by using remote sensing techniques is still an open issue and a strategic challenge as crucial as it is complex. It is crucial because earthen archaeological remains are widely present thoughtout the world (in South America, Asia, Africa) and it is complex due to the subtle physical contrast between earthen remains and the surrounding subsoil. This paper presents the results from investigations based on Very High Resolution (VHR) satellite imagery (2002, 2005, 2008), geomagnetic surveys (November 2008) and Ground Probing Radar (GPR) (April 2008), we conducted on a mound named Piramide Naranjada. It is located in Cahuachi (Nasca, Southern Peru), the largest adobe Ceremonial Centre in the World. The studies were performed on two sides of this pyramid: (i) North-Eastern side, not yet excavated, using satellite data and magnetic prospection; (ii) and Eastern side using GPR to support the ongoing excavation activity. In the North-eastern side, results from the analysis of satellite images allowed the identification of shallow and outcropping adobe (earthen material) walls related to terraced platforms which compose the trunk-pyramidal structure of the Piramide Naranjada. These archaeological features were further investigated by high resolution geomagnetic surveys. The gradiometric maps enabled the identification of magnetic anomalies with different shapes: (i) linear shape features linked to buried, shallow and outcropping walls (the latter two already detected from the optical dataset); (ii) and circular shape anomalies linked to tombs and ceremonial offerings. In the Eastern side, where the excavations are in progress, radargrams allowed the discovery of a rich ceremonial offering made up mainly of ceramics, textiles, and painted pumpkins.

The investigation of the temporal fluctuations in forest fires is an important topic in the context of environmental sciences. Time-scaling scale-invariant methodologies have been used to characterize the temporal properties of forest-fire sequences detected in the Italian territory. Our findings reveal that the fire sequences can be considered as fractal processes with a high degree of time-clusterization of the events. The time-clustering phenomenon is clearly visible from timescales of order of days. Furthermore the fire sequences observed with decreasing latitude tend to be more time-clusterized.

Temporal dynamical analysis in fire sequences recorded from 1992 to 2005 in a fire-vulnerable area of Patagonia (Argentina) was performed by using the Allan Factor statistics. Three typologies of fires were investigated: forest, steppe and urban. The obtained results show the presence of annual periodicities, superimposed to a time-scaling behaviour, which characterize the point processes of fires as fractal time processes with a rather high degree of time-clusterization of the events.

For thousands of years, people throughout the world have built homes, communities and temples using earth materials, mainly in arid and semi-arid lands where generally other building materials were quite scarce. In the arid regions of Peru, from the 3rd millennium B.C. to Inca age, several civilizations shaped sand, clay and water into bricks (known as adobe) to build ceremonial centres, pyramids and towns. The detection of adobe archaeological buried remains by means of remote sensing techniques is a challenge as difficult as engaging, since the adobe has a composition quite similar to the neighbouring earth material. In this paper, Very High Resolution (VHR) satellite imagery have been used to identify archaeological features linked to the presence of shallow and outcropping adobe structures in some test sites selected from within the Ceremonial Centre of Cahuachi (Southern Peru). Both vegetated and non-vegetated areas were investigated. The promising results we obtained pointed out that the use of Earth Observation (EO) technologies can open new perspectives to detect adobe settlements.

This paper focuses on the detection and spatial characterization of microtopographic relief linked to archaeological remains using full-waveform (FW) Airborne Laser Scanning (ALS). ALS is an optical measurement technique for obtaining high-precision information on the Earth's surface including basic terrain mapping, such as Digital Terrain Model (DTM) and Digital Surface Model (DSM). In the field of cultural heritage management, ALS can provide detailed information useful for feature extraction, but the detection of archaeological microtopographic relief is still a challenge especially for vegetated and highly sloped areas. The investigation was carried out for the archaeological area of Monte Irsi (Southern Italy) characterized by dense herbaceous cover and complex topographical and morphological features, which make air/space prospection very difficult. Results from our investigations pointed out that ALS is a valuable data source to detect and map cultural features even under dense vegetation.

The fire-induced variability in vegetation dynamics was analyzed by means of a fluctuation analysis applied to a time series of satellite SPOT-VEGETATION normalized difference of vegetation index (NDVI) data from 1998 to 2003. We used the detrended fluctuation analysis (DFA), which allows capturing persistent behavior in nonstationary signals. We analyzed four vegetation sites, two affected and the other two not affected by fires. Our results point out that the persistence of vegetation dynamics is significantly increased by the occurrence of fires.

Spatio-temporal clusters in 1997–2003 fire sequences of Tuscany region (central Italy) have been identified and analysed by using the scan statistic, a method which was devised to evidence clusters in epidemiology. Results showed that the method is reliable to find clusters of events and to evaluate their significance via Monte Carlo replication. The evaluation of the presence of spatial and temporal patterns in fire occurrence and their significance could have a great impact in forthcoming studies on fire occurrences prediction.

The space–time fractality of the forest-fire sequence (1997–2003) occurred in the Tuscany Region (central Italy), one of the most vulnerable to wildfires in Italy, has been approached by using spatial and temporal fractal tools. The fractal exponent α , estimated by the Fano factor method, characterises the time-clustering behaviour of the set of fires, while the correlation dimension D c , calculated by means of the correlation integral method, gives information on the space-clustering behaviour of the sequence of fires. We found that (i) the investigated fire set is globally characterized by space–time clustering behaviour; (ii) α and D c decreases and increases, respectively with the increase of the threshold size of burned area; (iii) the time variation of α shows a tendency towards Poissonian processes in correspondence of the largest events.

The characterization of fuel types is very important for computing spatial fire hazard and risk and simulating fire growth and intensity across a landscape. However, due to the complex nature of fuel characteristic a fuel map is considered one of the most difficult thematic layers to build up. The advent of 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. 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 based on a maximum likelihood (ML) classification algorithm; (III) accuracy assessment for the performance evaluation based on the comparison of ASTER-based results with ground-truth. Results from our analysis showed that the use ASTER data provided a valuable characterization and mapping of fuel types being that the achieved classification accuracy was higher than 90%.

Fuel types are one of the most important factors that should be taken into consideration for computing spatial fire hazard and risk and simulating fire growth and intensity across a landscape. However, fuel mapping is extremely difficult because the description of fuel properties is very complex. In the present study, forest fuel mapping is considered from a remote sensing perspective by the assessment and mapping of general vegetation complexes. The purpose is to delineate forest types by exploring the use of remote sensing QuickBird data. In order to ascertain how well QuickBird data can provide an exhaustive classification of fuel properties, a sample area characterized by mixed vegetation covers and complex topography data was analysed. The selected sample area has an extension at around 60 km 2 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 QuickBird data, were used as ground-truth dataset to assess the results obtained for the considered test area. 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 based on a maximum likelihood (ML) classification algorithm; (III) accuracy assessment for the performance evaluation based on the comparison of QuickBird-based results with ground-truth data. Results from our analysis showed that the use of remotely sensed data at high spatial and spectral resolution provided a valuable characterization and mapping of fuel types being that the achieved classification accuracy was higher than 75%.

The capability of satellite QuickBird imagery for the identification of archaeological crop marks is herein presented and discussed for two test sites located in the South of Italy. The selected sites, dating back to Middle Ages, were buried under surfaces covered by herbaceous plants characterized by a different phenological status (dry/green) when the satellite data were acquired. The methodological approach adopted for the enhancement and extraction of crop marks is mainly based on the use of data fusion and edge detection algorithm. The main remarkable differences found for the two archaeological sites can be suitably linked to the different state of vegetation that caused a different spectral response. In particular, near infrared (NIR) spectral channel was able to better enhance crop marks observed for dry vegetation; whereas, Normalized Difference Vegetation Index (NDVI) was found to be more capable to better enhance crop marks observed for green vegetation.

The spectral capability of satellite QuickBird imagery for the identification of archaeological marks linked to the presence of buried remains is here discussed for two medieval archaeological sites in the South of Italy. The considered test sites present complex topographical and morphological features, typical of many medieval settlements, which make archaeological prospection with any remote sensing technologies difficult. Results from the performed investigations showed that the satellite QuickBird imagery can be a valuable data source for reconstructing the urban shape of buried settlements up to single building scale. Such analyses can be useful for detecting locations and extracting features of archaeological sites especially prior to any excavation work and for increasing the cultural value of historical sites.

Could the dynamics of vegetation covers be significantly influenced by fires? Is it possible to identify and quantify such effect? In order to detect fire-induced variability in vegetational dynamics, we performed a fluctuation analysis of time series of satellite SPOT-VEGETATION normalized difference of vegetation index (NDVI) data from 1998 to 2003 of two shrub-land test sites. We used the detrended fluctuation analysis (DFA), which permits the detection of persistent properties in nonstationary signal fluctuations. We analyzed two shrub-land sites, one in “healthy conditions” (fire-unaffected) and the other in “ill conditions” (fire-affected). Our results point out to the active role played by fires in increasing the persistence of the vegetation dynamics.

In this work, we discuss the use of principal component analysis (PCA) for evaluating the vegetation interannual anomalies. The analysis was preformed on a temporal series (1999–2002) of the yearly Maximum Value Composit of SPOT/VEGETATION NDVI acquired for Sicily Island. The PCA was used as a data transform to enhance regions of localized change in multi-temporal data sets. This is a direct result of the high correlation that exists among images for regions that do not change significantly and the relatively low correlation associated with regions that change substantially. Both naturally vegetated areas (forest, shrub-land, herbaceous cover) and agricultural lands have been investigated in order to extract the most prominent natural and/or man-induced alterations affecting vegetation behavior. Our findings suggest that PCA can provide valuable information for environmental management policies involving biodiversity preservation and rational exploitation of natural and agricultural resources.

The characterization of 1 / f α scaling behaviour in normalized difference vegetation index (NDVI) satellite data of forest ecosystems in southern Italy has been performed using the multiple segmenting method (MSM) and detrended fluctuation analysis (DFA). Our findings point out to the characterization of Mediterranean forest ecosystems as governed by persistent mechanisms, which indicate positive feedbacks tending to destabilize the system under external forces or perturbations.

Temporal regimes in fire data recorded from 1997 to 2003 in an area of central Italy have been detected by using the Allan Factor statistics. Our findings reveal the presence of meteo-climatic-related periodicities; furthermore time-scaling behaviour characterizes the point process of the fire sequence as a fractal process with a high degree of time-clusterization of the events.