Accurate flood mapping is important for both planning activities during emergencies and as a support for the successive assessment of damaged areas. A valuable information source for such a procedure can be remote sensing synthetic aperture radar (SAR) imagery. However, flood scenarios are typical examples of complex situations in which different factors have to be considered to provide accurate and robust interpretation of the situation on the ground. For this reason, a data fusion approach of remote sensing data with ancillary information can be particularly useful. In this paper, a Bayesian network is proposed to integrate remotely sensed data, such as multitemporal SAR intensity images and interferometric-SAR coherence data, with geomorphic and other ground information. The methodology is tested on a case study regarding a flood that occurred in the Basilicata region (Italy) on December 2013, monitored using a time series of COSMO-SkyMed data. It is shown that the synergetic use of different information layers can help to detect more precisely the areas affected by the flood, reducing false alarms and missed identifications which may affect algorithms based on data from a single source. The produced flood maps are compared to data obtained independently from the analysis of optical images; the comparison indicates that the proposed methodology is able to reliably follow the temporal evolution of the phenomenon, assigning high probability to areas most likely to be flooded, in spite of their heterogeneous temporal SAR/InSAR signatures, reaching accuracies of up to 89%.

Multi-sensor, multi-band and multi-temporal remote sensing data can be very useful in precise flood monitoring. In this paper, we describe DAFNE, a Matlab (R)-based, open source toolbox, to produce flood maps from remotely sensed and other ancillary information, through a data fusion approach. DAFNE is based on Bayesian Networks, and is composed of several independent modules, each one performing a different task. Multi-temporal and multi-sensor data can be easily handled, with the possibility of producing time series of output flood maps, and thus follow the evolution of single or recurrent flood events. Here, an application of the toolbox is illustrated to delineate a flood map, close to the peak of inundation occurred in April 2015 on the Strymonas river (Greece), from multi-band optical and SAR data.

The ability of remote-sensing technologies to rapidly deliver data on habitat quantity (e.g., amount, configuration) and quality (e.g., structure, distribution of individual plant species, habitat types and/or communities, persistence) across a range of spatial resolutions and temporal frequencies is increasingly sought-after in conservation management. However, several problematic issues (e.g., imagery correction and registration, image interpretation, habitat type and quality definitions, assessment and monitoring procedures, uncertainties inherent in mapping, expert knowledge integration, scale selection, analysis of the interrelationships between habitat quality and landscape structure) challenge the effective and reliable use of such data and techniques. We discuss these issues, as a contribution to the development of a common language, framework and suite of research approaches among ecologists, remote-sensing experts and stakeholders (conservation managers) on the ground, and highlight recent theoretical and applied advances that provide opportunities for meeting these challenges. Reconciling differing stakeholder perspectives and needs will boost the timely provisioning of reliable information on the current and changing distribution of biodiversity to enable effective conservation management.

At a global level, protected sites have been established for the primary purpose of conserving biodiversity, with survey and monitoring of habitats undertaken largely within their boundaries. However, because of increasing human populations with greater access to resources, there is a need to now consider monitoring anthropic activities in the surrounding landscapes as pressures and disturbances are impacting on the functioning and biodiversity values of many protected sites. Earth Observation (EO) data acquired across a range of spatial and temporal scales offer new opportunities for monitoring biodiversity over varying time-scales, either through direct or indirect mapping of species or habitats. However, Land Cover (LC) and/or Land Use (LU), rather than habitat maps are generated in many national and international programs and, whilst the translation from one classification to the other is desirable, differences in definitions and criteria have so far limited the establishment of a unified approach. Focusing on both natural and non-natural environments associated with Natura 2000 sites in the Mediterranean, this paper considers the extent to which three common LC/LU taxonomies (CORINE, the Food and Agricultural Organisation (FAO) Land Cover Classification System (FAO-LCCS) and the IGBP) can be translated to habitat taxonomies with minimum use of additional environmental attributes and/or in situ data. A qualitative and quantitative analysis based on the Jaccard's index established the FAOLCCS as being the most useful taxonomy for harmonizing LC/LU maps with different legends and dealing with the complexity of habitat description and as a framework for translating EO-derived LC/LU to habitat categories. As demonstration, a habitat map of a wetland site is obtained through translation of the LCCS taxonomy.

The use of remote sensed images in many applications of environmental monitoring,change detection, risks analysis, damage prevention, etc. is continuously growing.Classification of remote sensed images, exploited for the production of land cover maps,involves continuous efforts in the refinement of the employed methodologies. The pixel-wise approach, which considers the spectral information associated to each pixel in theimage, is the standard classification methodology. The continuous improving of spatialresolution in remote sensors requires the focus on what is around a single pixel with theintegration of "contextual" information. In order to produce more reliable land cover mapsfrom the classification of high resolution images, this paper analyzes the effectiveness ofthe integration of contextual information comparing two different pixel-wise techniques forits extraction: 1) the post-classification filtering with a Majority filter applied to the mapproduced by the standard Maximum Likelihood algorithm; 2) the segmentation algorithmSMAP. The results were compared. A GeoEye-1 image, exploited in the framework of theAsi-Morfeo project, was considered.

The objective of this study is to cross-compare three algorithms for retrieving surface soil moisture (SSM) from ESA's Sentinel-1 (S-1) data. The context is provided by the large scientific and application interest in SSM products at high resolution and regional/continental scale that can be retrieved from S-1 data alone or in combination with other missions such as NASA/SMAP and ESA/SMOS. Of the three investigated algorithms, one inverts a scattering model exploiting a Bayesian approach, whereas the other two are change detection approaches. The cross-comparison is carried out by using both simulated and experimental data. Strengths and weaknesses of the three algorithms are identified and discussed.

High-resolution, remotely sensed images of the Earth surface have been proven to be of help in producing detailed flood maps, thanks to their synoptic overview of the flooded area and frequent revisits. However, flood scenarios can be complex situations, requiring the integration of different data in order to provide accurate and robust flood information. Several processing approaches have been recently proposed to efficiently combine and integrate heterogeneous information sources. In this paper, we introduce DAFNE, a Matlab®-based, open source toolbox, conceived to produce flood maps from remotely sensed and other ancillary information, through a data fusion approach. DAFNE is based on Bayesian Networks, and is composed of several independent modules, each one performing a different task. Multi-temporal and multi-sensor data can be easily handled, with the possibility of following the evolution of an event through multi-temporal output flood maps. Each DAFNE module can be easily modified or upgraded to meet different user needs. The DAFNE suite is presented together with an example of its application.

Understanding the influence of surrounding landscape structure on local habitat quality is necessary tocomplement the modelling of empirical relationships between habitat quality and species distributionpatterns. Traditional models explain patterns of biodiversity as a function of 'habitat amount in thelandscape', irrespective of spatially-explicit variation in habitat fragmentation or habitat quality, implicitlyignoring the interdependence between spatial components of land-use change. The contrastinghypothesis-that local habitat units are not interchangeable because their habitat attributes are dependent onvariation in surrounding habitat structure at both patch and landscape levels-was tested using ahierarchical causal modelling approach. Suchmodels are observation-intensive, so we generated fine-grained measures of habitat patch internal heterogeneities (proxies for habitat quality) from very highresolution Earth Observation images over multiple spatial extents. The results demonstrate the causaldependence of local habitat quality on surrounding patch and landscape context, and indicate that theinfluence of landscape pattern on habitat structure can be mediated by the cross-level dependenciesbetween landscape and patch attributes. From thespatial context dependence of habitat quality, weconclude that a substantial degree ofinterdependence among habitat effectsis likely to be the normindetermining theecological consequences of habitat fragmentation.

The objective of this study is to describe a new technique, based on dense time series of Copernicus Sentinel-1 (S-1) and Sentinel-2 (S-2) data, aimed to identify tillage changes at regional/continental scale and at approximately 100 m resolution.

Traditionally, analyses of relationships between amphibians and habitat focused on breeding environ-ments (i.e., pond features) more than on the features of the surrounding environment. Nevertheless,for most amphibians the terrestrial phase is longer than the aquatic phase, and consequently landscapefeatures (i.e., habitat mosaics) may have an important role for modelling amphibian distribution.There were different aims in this analysis. Firstly, we compared the effectiveness of the informationprovided by land cover/use (LC/LU) classes and habitat classes defined according to a new habitat tax-onomy named General Habitat Category (GHC), which is based on the concept of biological forms ofdominant vegetation and class naturalness. The GHC map used was obtained from a pre-existing val-idated LC/LU map, by integrating spectral and spatial measurements from very high resolution Earthobservation data according to ecological expert rules involving concepts related to spatial and temporalrelationships among LC/LU and habitat classes.Then, we investigated the importance for amphibians of the landscape surrounding ponds within theItalian Alta Murgia National Park. The work assessed whether LC/LU classes in pond surrounds are impor-tant for the presence/absence of amphibians in this area, and identified which classes are more importantfor amphibians. The results obtained can provide useful indications to management strategies aiming atthe conservation of amphibians within the study area. An information-theoretic approach was adoptedto assess whether GHC maps allow to improve the performance of species distribution models. We usedthe Akaike's Information Criterion (AICc) to compare the effectiveness of GHC categories versus LC/LUcategories in explaining the presence/absence of pool frogs. AICc weights suggest that GHC categoriescan better explain the distribution of frogs, compared to LC/LU classes.

L'osservazione della Terra da piattaforme spaziali, integrata con misure in situ e con acquisizione da piattaforme aeree, è una tecnologia di riferimento per il monitoraggio di ampie zone del Territorio con una elevata frequenza spaziale. Queste ca-ratteristiche sono essenziali per investigare, da un lato, l'effetto di modifiche indotte da cambiamenti climatici, dall'altro la presenza di situazioni che siano precursori di cambiamenti, in un'ottica di previsione ed allerta. Nel presente lavoro vengono presentati alcuni esempi applicativi in questo scenario.

Global warming has increased the frequency of algal blooms in internal water bodies. The algal blooms are an unpleasant sight and hinder various recreational and economic. The increase in the anthropogenic load of nutrients (eutrophication) has led to an increase in the presence of toxic algae, the blue-green algae in the coastal and internal water bodies. A mature flowering of blue-green algae often emerges on top like a layer of foam containing high concentrations of toxins. Contact with these toxins poses a direct health risk for both humans and animals. Therefore, monitoring the concentration of algae and the occurrence of scum in lakes has become a topic of interest for management and science.Optical remote sensing is a validated tool for sensing, monitoring and developing better understanding of the state of lakes. However, it is highly hindered by clouds. For regions with frequent cloud cover, this means loss of data, which derails the purpose of sensing. This makes difficult to spatially and temporally characterize scum area for a comprehensive ecological analysis. Combining data obtained using different types of sensor can be an option worth investigating, and a good candidate for this purpose is the synthetic aperture radar (SAR), due partly to its capacity to collect data independent of cloudy cover.We use a synergistic approach involving optical and SAR images together with meteorological parameters to monitor algal cyanobacteria blooms over Tai Hu and Chaohu lakes and Curonian Lagoon. The satellite images are provided by the Sentinel 1, 2 and 3 satellites. Meteorological parameters come from in situ stations or from the European Centre for Medium-Range Weather Forecasts (ECMWF) database. With respect to optical data, the scum index was developed using ratio of TOA reflectance in NIR and RED bands exploiting the high difference in backscattering and absorption between water with and without scum. For S1 imagery, a polarimetric index is defined and results able to identify anomalies on the lakes surface. The use of Google Earth Engine helped with the images selection and the time series analysis of the indexes. A preliminary study suggests that this index combined with the knowledge of wheatear variables, such as wind speed and the 2 meters air temperature, can reliably detect the occurrences of algal blooms.

This paper investigates the use of time series of ALOS/PALSAR-1 and COSMO-SkyMed data for the soil moisture retrieval (mv) by means of the SMOSAR algorithm. The application context is the exploitation of mv maps at a moderate spatial and temporal resolution for improving flood/drought monitoring at regional scale. The SAR data were acquired over the Capitanata plain in Southern Italy, over which ground campaigns were carried out in 2007, 2010 and 2011. The analysis shows that the mv retrieval accuracy is 5%-7% m^3/m^3 at L- and X band, although the latter is restricted to a use over nearly bare soil only.

The concentration of some trace gases in cave is documented to have higher values with respect to the outside atmosphere. Partial pressure of carbon dioxide is one of the main parameter in cave due to its control in speleothem deposition and speleogenesis. Its concentration could depend on soil activity in the cover, or disaggregation of organic matter, but mostly is originated by the karst process itself through the nucleation of calcium carbonate. Recently this gas has been proposed as a key in local carbon cycle at short term scale as sinks and sources for atmospheric CO2.With the purpose to understand the anomalous high concentration observed in the Murge of Apulia, this work reports the preliminary results of the CO2 monitoring performed in the underground atmosphere of 5 caves in this karst region, (Castellana, Salto, Calzino, Torre di Mastro, and Abate Eustasio), monitored in January 2018. Air CO2 concentration was measured as mixing ratio using a Non-Dispersive Infra-Red spectrometer NDIR (Zenith), with a range 0÷10,000 ppm and an accuracy of ±50 ppm. As the concentration of this gas in air masses is dependent of its water vapour content, temperature and relative humidity were also recorded. In the Castellana Cave the monitoring was performed continuously for few days using a NDIR datalogger (Perfect Prime: range 0÷10,000 ppm; accuracy ±50 ppm) which recorded CO2 concentration every 10 minutes.The preliminary results show an increase in CO2 concentration with depth. It ranges from typical value of cave environment to level proximal to soil concentration. The partial pressure of CO2 in Torre di Mastro at 40 m in depth from the surface reaches value >10,000 ppm (temperature 12.5 °C - 100%). In Castellana Cave the monitoring lasted for 1 day in a passage close to the public trail, with concentration around 800 ppm, and some peaks during the night. Here the temperature was 14.0 °C and relative humidity never reached saturation (85%) probably due to the artificial ventilation of the cave. Spot measurements were also performed along the touristic path with values ranging between 1,000 to 1530 ppm. The carbon dioxide content in the Salto Cave had typical cave values (from 2000 to 2800 ppm). Only the final shaft showed CO2 concentration > 10,000 ppm (temperature 18.2 °C - 100 %). The lowest value was recorded at the Abate Eustasio Cave with pCO2 ranging between 900 ppm at the bottom of the entrance and 1,000 ppm in the inner cave passages (temperature 16.2 °C - 100%). On the other hand, the Calzino Cave showed the highest and dangerous CO2 concentration that suddenly rose few meter below the entrance (16.2 °C - 100%), reaching 12,700 ppm in the shaft at -10 m and 36,000 ppm further inside at -20 m in depth (a shaft never explored for this reason). This CO2 value seems not related to physical parameter such as temperature and relative humidity. Further investigation could clarify this pattern.

Agricultural terraces represent one of the best ways to prevent land degradation in hilly and mountainous landscapes. However, it is widely recognized that terraced slopes are threatened by agricultural land abandonment. In the literature, very few studies have quantitatively examined the influence of agricultural abandonment on the stability of terraced slopes. The goal of this research is to investigate the relationships between landslide magnitude and land use conditions of agricultural terraced slopes. In particular, LiDAR (Light Detection And Ranging) elevation data, coupled with aerial photo interpretation, were used for the computation of shallow landslide mobilized volumes on terraced slopes affected by an intense rainfall event. We performed the analysis within the Vernazza basin, a small Mediterranean coastal catchment located in the "Cinque Terre" area (Liguria, northwestern Italy), comparing pre-event and post-event LiDAR datasets. The results revealed that abandoned terraced slopes have been affected by a higher amount of mobilized debris volumes than still-cultivated terraces. Furthermore, terraces abandoned for a short time (less than 25-30 years) resulted in the most hazardous land use class, showing erosion rates that were approximately 2 and 3 times higher than terraced slopes abandoned a long time ago (more than 25-30 years) and still-cultivated terraces, respectively. These findings highlight that land abandonment and agricultural mismanagement can intensify the magnitude of rainfall-induced shallow landslides.

We apply high-resolution, X-band, stripmap COSMO-SkyMed data to the monitoring of flood events in the Basilicata region (Southern Italy), where multitemporal datasets are available with short spatial and temporal baselines, allowing interferometric (InSAR) processing. We show how the use of the interferometric coherence information can help to detect more precisely the areas affected by the flood, reducing false alarms and missed identifications which affect algorithms based on SAR intensity alone. The effectiveness of using the additional InSAR information layer is illustrated by RGB composites of various combinations of intensity and coherence data. Analysis of multitemporal SAR intensity and coherence trends reveals complex behavior of various field types, which we interpret through a Bayesian inference approach, based on a manual identification of representative scattering and coherence signatures of selected homogeneous fields. The approach allows to integrate external, ancillary information to derive a posteriori probabilistic maps of flood inundation accounting for different scattering responses to the presence of water. First results of this semiautomated methodology, using simple assumptions for the SAR signatures and a priori information based on the distance from river courses, show encouraging results, and open a path to improvement through use of more complex hydrologic and topo-hydrographic information.

In precision flood monitoring it is important to follow the temporal evolution of an event. Often, however, sufficient temporal coverage of events spanning several days can be attained only by recurring to multi-sensor data, due to different acquisition characteristics and schedules of different types of sensors. We present an example of a successful fusion of data coming from both SAR (COSMO-SkyMed stripmap, 3-m resolution) and optical (RapidEye, multispectral, 5 mresolution) data, covering a flood event in southern Italy. The data fusion is performed through a Bayesian network approach, a reliable means to infer probabilistic infomation from heterogeneous sources. Results show accordance with independent model-based flood mapsreaching accuracies of up to 96%.

In this paper, an algorithm using Sentinel-1 (S-1) and Sentinel-2 (S-2) data to identify changes of tillage over agricultural fields at approximately similar to 100m resolution is presented. The methodology implements a multiscale temporal change detection on S-1 VH backscatter in order to single out VH changes due to agricultural practices only. The algorithm can be applied over bare or scarcely vegetated agricultural fields, which are identified from S-2 NDVI measurements. An initial assessment at farm scale using in situ and S-1 and SPOT5-Take5 data, acquired over the Apulian Tavoliere in southern Italy in 2015, is illustrated. A full validation of the approach is in progress over three European agricultural areas located in Italy, Spain and France. Results will be further reported in the paper.

Soil moisture content is an essential climate variable that is operationally delivered at low resolution (e.g. 36-9 km) by earth observation missions, such as ESA/SMOS, NASA/SMAP and EUMETSAT/ASCAT. However numerous land applications would benefit from the availability of soil moisture maps at higher resolution. For this reason, there is a large research effort to develop soil moisture products at higher resolution using, for instance, data acquired by the new ESA's Sentinel missions. The objective of this study is twofold. First, it presents the validation status of a pre-operational soil moisture product derived from Sentinel-1 at 1 km resolution. Second, it assesses the possibility of integrating Sentinel-2 data and additional ancillary information, such as parcel borders and high resolution soil texture maps, in order to obtain soil moisture maps at "field scale" resolution, i.e. similar to 0.1 km Case studies concerning agricultural sites located in Europe are presented.

In this work, the objective is to discuss a validation strategy for Sentinel-1 (S-1) Surface Soil Moisture (SSM) Product at 1km resolution

In this work the objective is to illustrate a pre-operational surface soil moisture (SSM) product over the Mediterranean basin at 1 km resolution derived from Sentinel-1 (S-1) observationsto provide a comparison against operational SSM products from SMAP, SMOS and ASCAT missions derived at lower resolution (e.g., 625 km2)

The objective of this study is to illustrate a pre-operational near surface soil moisture (SSM) product derived, at 1 km resolution, from Sentinel-1 data over the Mediterranean basin. The high resolution is crucial in this area characterized by small to medium size watersheds (e.g. from 500 km2 to 5000 km2). Indeed, it may allow to resolve SSM patterns related to the landscape characteristics of the watersheds and link them to their hydrologic response.

We present a multi-layer, multi-temporal flood map of the event occurred on December 2013 in Basilicata (southern Italy), documenting the spatial evolution of the inundated areas through time, as well as some ground effects of floodwaters inferred from the imagery. The map, developed within a GIS and consisting of four, 1:20,000 scale, different layers, was prepared using image processing, visual image interpretation and field survey controls. We used two COSMO-SkyMed synthetic aperture radar (SAR) images, acquired during the event, and a Plèiades-1B High-Resolution optical image, acquired at the end of the event. We also used the information derived from the satellite imagery to update some local features of the OpenStreetMap (OSM) geospatial database, and then integrated it within the flood map. A classified multi-temporal dynamic map of inundation and flood effects has been produced in the form of a multi-layer pdf file (Main Map).

This paper discusses the application of the Cross-Correlation Analysis (CCA) technique to multi-spatial resolution Earth Observation (EO) data for detecting and quantifying changes in forest ecosystems in two different protected areas, located in Southern Italy and Southern India. The input data for CCA investigation were elaborated from the forest layer extracted from an existing Land Cover/Land Use (LC/LU) map (time T1) and a more recent (T2, with T2 > T1) single date image. The latter consist of a High Resolution (HR) Landsat 8 OLI image and a Very High Resolution (VHR) Worldview-2 image, which were analysed separately. For the Italian site, the forest layer (1:5000) was first compared to the HR Landsat 8 OLI image and then to the VHR Worldview-2 image. For the Indian site, the forest layer (1:50,000) was compared to the Landsat 8 OLI image then the changes were interpreted using Worldview-2. The changes detected through CCA, at HR only, were compared against those detected by applying a traditional NDVI image differencing technique of two Landsat scenes at T1 and T2. The accuracy assessment, concerning the change maps of the multi-spatial resolution outputs, was based on stratified random sampling. The CCA technique allowed an increase in the value of the overall accuracy: from 52% to 68% for the Italian site and from 63% to 82% for the Indian site. In addition, a significant reduction of the error affecting the stratified changed area estimation for both sites was obtained. For the Italian site, the error reduction became significant at VHR (±2 ha) in respect to HR (±32 ha) even though both techniques had comparable overall accuracy (82%) and stratified changed area estimation. The findings obtained support the conclusions that CCA technique can be a useful tool to detect and quantify changes in forest areas due to both legal and illegal interventions, including relatively inaccessible sites (e.g., tropical forest) with costs remaining rather low. The data obtained through CCA intervention could not only support the commitments undertaken by the European Habitats Directive (92/43/EEC) and the Convention of Biological Diversity (CBD) but also satisfy UN Sustainable Development Goals (SDG).

Periodic monitoring of biodiversity changes at a landscape scale constitutes a key issue for conservation managers. Earth Observation (EO) data offers a potential solution, through direct or indirect mapping of species or habitats. Most national and international programs rely on the use of Land Cover (LC) and/or Land Use (LU) classification systems. Yet, these are not as clearly relatable to biodiversity in comparison to habitat classifications, and provide less scope for monitoring. While a conversion from LC/LU classification to habitat classification can be of great utility, differences in definitions and criteria have so far limited the establishment of a unified approach for such translation between these two classification systems. Focusing on five Mediterranean NATURA 2000 sites, this paper considers the scope for three of the most commonly used global LC/LU taxonomies - CORINE Land Cover (CLC), the Food and Agricultural Organisation (FAO) Land Cover Classification System (LCCS) and the International Geosphere-Biosphere Programme (IGBP) to be translated to habitat taxonomies. Through both quantitative and expert knowledge based qualitative analysis of selected taxonomies, FAO-LCCS turns out to be the best candidate to cope with the complexity of habitat description and provides a framework for EO and in-situ data integration for habitat mapping, reducing uncertainties and class overlaps and bridging the gap between LC/LU and habitats domains for landscape monitoring - a major issue for conservation. This study also highlights the need to modify the FAO-LCCS hierarchical class description process to permit the addition of attributes based on class-specific expert knowledge to select multi-temporal (seasonal) EO data and improve classification. An application of LC/LU to habitat mapping is provided for a coastal Natura 2000 site with high classification accuracy as a resultKey words: Mapping; land cover; land use; habitat; earth observation; taxonomies; Natura 2000; classification schemes

The objective of this study is to present the validation status of a pre-operational soil moisture product derived from Sentinel-1 at 1 km resolution and at large scale. The product is developed within the context of an ESA SEOM study (seom.esa.int/page_project034.php).

Monitoring the status and future trends in biodiversity can be prohibitively expensive using ground-based surveys. Consequently, significant effort is being invested in the use of satellite remote sensingto represent aspects of the proximate mechanisms (e.g., resource availability) that can be related tobiodiversity surrogates (BS) such as species community descriptors. We explored the potential of veryhigh resolution (VHR) satellite Earth observation (EO) features as proxies for habitat structural attributesthat influence spatial variation in habitat quality and biodiversity change. In a semi-natural grasslandmosaic of conservation concern in southern Italy, we employed a hierarchical nested sampling strategyto collect field and VHR-EO data across three spatial extent levels (landscape, patch and plot). Speciesincidence and abundance data were collected at the plot level for plant, insect and bird functional groups.Spectral and textural VHR-EO image features were derived from a Worldview-2 image. Three windowsizes (grains) were tested for analysis and computation of textural features, guided by the perceptionlimits of different organisms. The modelled relationships between VHR-EO features and BS responsesdiffered across scales, suggesting that landscape, patch and plot levels are respectively most appropriatewhen dealing with birds, plants and insects. This research demonstrates the potential of VHR-EO forbiodiversity mapping and habitat modelling, and highlights the importance of identifying the appropriatescale of analysis for specific taxonomic groups of interest. Further, textural features are important in themodelling of functional group-specific indices which represent BS in high conservation value habitattypes, and provide a more direct link to species interaction networks and ecosystem functioning, thanprovided by traditional taxonomic diversity indices.

Modelling the empirical relationships between habitat quality and species distribution patterns is the first step to understanding human impacts on biodiversity. It is important to build on this understanding to develop a broader conceptual appreciation of the influence of surrounding landscape structure on local habitat quality, across multiple spatial scales. Traditional models which report that 'habitat amount' in the landscape is sufficient to explain patterns of biodiversity, irrespective of habitat configuration or spatial variation in habitat quality at edges, implicitly treat each unit of habitat as interchangeable and ignore the high degree of interdependence between spatial components of land-use change. Here, we test the contrasting hypothesis, that local habitat units are not interchangeable in their habitat attributes, but are instead dependent on variation in surrounding habitat structure at both patch- and landscape levels. As the statistical approaches needed to implement such hierarchical causal models are observation-intensive, we utilise very high resolution (VHR) Earth Observation (EO) images to rapidly generate fine-grained measures of habitat patch internal heterogeneities over large spatial extents. We use linear mixed-effects models to test whether these remotely-sensed proxies for habitat quality were influenced by surrounding patch or landscape structure. The results demonstrate the significant influence of surrounding patch and landscape context on local habitat quality. They further indicate that such an influence can be direct, when a landscape variable alone influences the habitat structure variable, and/or indirect when the landscape and patch attributes have a conjoined effect on the response variable. We conclude that a substantial degree of interaction among spatial configuration effects is likely to be the norm in determining the ecological consequences of habitat fragmentation, thus corroborating the notion of the spatial context dependence of habitat quality. (C) 2014 Elsevier B.V. All rights reserved.

The study of biodiversity requires the ability to explores a plurality of genomic and/or observational data under a geospatial, phylogenetic and temporal frameworks. Diversity expressed as the exponential of Shannon Entropy the selected measure unit for this exploration. Building on Chao et al. 2010 entropy was generalized to take in account that entity are differently different following a rooted phylogenetic structure and the contribution to alpha and beta diversity of each node was estimated (Sandionigi et al. 2014). Alpha and Beta diversity contribution was parsed also by sampling locations and exposed in a GEO compliant web services (WPS 1.0 ) that expose GEO compliant file format. The tool presented is a synthesis from previous work on biodiversity analysis services in FP7 project BioVeL by CNR-ITB, IBBE and INFN and word done in the Group of Earth Observation done by CNR-ISSIA and algorithm development by CNR-ITB and ZooPlant lab.