Scientific Exploitation of Operational Missions (SEOM) SY-4Sci Synergy

Land Cover Change Detection and Monitoring Methodologies Based on the Combined Use of Sentinel-1 and Sentinel-2 for Natural Resources and Hazard Management.

The main objective of this R&D activity is to develop and validate novel methodologies for Land EO products based on the joint exploitation of Sentinel-1A SAR data and Sentinel-2A optical imagery. The outcome of the activity is intended to be the prototype implementation of a new change detection methodology for land cover and agricultural monitoring along with the supporting documentation, database and products as required by ESA and as detailed subsequently in the deliverables list of this document.

The new EO methodology will be focused on land cover change detection and monitoring methodologies for natural resources and hazard management. As far as land cover topic is concerned, the contractor has identified some key applications that address land cover related issues, which will be used, to develop and test the new methodology. Thus, the list of proposed applications covered in this project is:

  • Landslides
  • Floods
  • Forest chang
  • Snow
  • Agriculture

  • The resulting prototype methodology will be optimized for these five applications, and will be built up based on different test and validation sites specially determined to cover several land products.


    General Idea

    A framework for the semi-automatic and probabilistic mapping of land cover changes is proposed within this project.

    Methodologies will be tuned to track changes due to: natural hazards such as landslides and floods; changes in land cover that influence natural hazard occurrence, like snow cover changes and forest changes; and finally, changes in agriculture. The framework consists of: (i) a multi-sensor training library of change signatures trapped by the Sentinels in a set-up phase and caused by landslides, floods, snow cover, deforestation and agricultural operations, and (ii) a probabilistic classifier which combines image analysis and temporal and/or susceptibility models to recognize, identify, and map changes.

    The first mandatory step is to prepare a library of spectral changes (i.e. the signatures) due to events occurring between two consecutive Sentinel images of the same type (optical or/and SAR). This step involves the extraction of spectral changes over time using bi-temporal change detection methods like image differencing, spectral angle, independent or principal component analysis as well as time series analysis approaches like the Continuous Change Detection and Classification (CCDC) to map changes using Sentinel-2 data and backscattering coefficient changes for Sentinel-1 data, taking into account the Dual Pol channels and coherence maps. Together with the statistics of the main changes, contextual information will be considered such as the distribution of changes in different geo-environmental contexts. Specific changes associated with landslides, floods, snow, forests, and agriculture will be recognised, mapped and the relative signatures will be extracted.

    Library Concept

    The main innovative concept behind this first part is the creation of a robust library, requiring multiple and time-consuming supervised change detection analyses on couples of Sentinel-1 and Sentinel-2 images. The library can then be easily applied for fast change detection mapping, without the need to perform additional supervised classification and reducing notably the effort dedicated to the classification of new images. Until now most of the change detection methods, especially the ones related to SAR images, mainly concentrated on detecting the changes with robust unsupervised techniques. After the techniques have been applied, a great effort is devoted to labelling the changes based on the specific purpose. The advantage of combining S1 and S2 metrics of change comes from the ability of each sensor to intercept different aspects of the same phenomenon that are widely heterogeneous. The library will take into account that the proposed applications can suffer from changes that are different for spatial and temporal scales as well as for intensity

    The recognition of the different types of changes is important and drives the requirements on the satellite data to be used as an input (such as bi-temporal and multi-temporal change detection).

    The second phase is devoted to the probabilistic semi-automatic recognition and mapping of a new change, i.e. a new landslide or forest change. The algorithm recognizes changes between a new image and the previous one, it queries the library looking for similar signatures (similar changes that occurred in the past in similar geo-environmental conditions), and if found, it will use the signature as a training area to assign the probabilistic class membership of each pixel in the new image. The probabilistic class membership can be coupled to other probabilistic susceptibility models, if available, to condition or to weigh the classification. The procedure can run separately for S1 and S2 and two distinct maps are obtained. In the case that S1 and S2 images are available simultaneously (or with a non-significant delay) for the same specific event, combined S1-S2 signatures can be adopted to solve possible ambiguities present in the single signatures in assigning the probabilistic class membership.

    The expected final result is a methodology for automatic recognition and mapping of changes coded in the library. The mapping is probabilistic: for each pixel inside the satellite image, a probability of change is assigned.

    Expected benefits of combined analysis SAR and optical data

    The expected benefits in the exploitation of combined S1 and S2 can be related to technical aspects:

    • S1 and S2 can build a virtual constellation thus increasing the temporal frequency of data availability; this is crucial for hazard mapping as near-real time data can be needed, as well as for crop monitoring as dense time series can be easily built.
    • S1 and S2 can increase data redundancy, as the same physical parameters are observed under different type of signals.

    On the content side, S1 and S2 provide information of the target applications under a different wavelength domain. This is reflected in the capabilities to detect different features and physical properties. As an example, in agricultural mapping while changes from optical data can reflect variation in the plant status and physiology, the changes in the backscattering coefficients can be determined by changes in the geometry and structure of the plants.


    The temporal signature of agricultural fields is driven by the evolution of the biophysical variables that describe the phenology of each individual crop type. Therefore, the changes in these variables are the ones to be detected and measured by exploiting time series of S1 and S2 images.

    After sowing and germination, and during the whole vegetative phase of the growth cycle, crops grow continuously in terms of aboveground vegetation volume. This increase can be followed by observing changes in backscattering coefficients (both co-pol and cross-pol), to be gathered by S1, or by an increase of NDVI (to be derived from S2 data). The maximum values are reached at the reproductive phase. From that point to the end of the cultivation cycle there is a decrease in NDVI and a radar response basically characterized as a random volume (for most crops), with a stronger influence from the ground (in this case highly dependent on the crop type). Consequently, the proposed methodology will be adapted to identify the successive transitions between phenological stages.

    Regarding crop type mapping, the history of states and signal values (from both S1 and S2) followed by each crop during the cycle constitutes the signature to be exploited for defining the classes.


    S1 and S2 data are combined for near-real time detection of forest changes. The main concept is based on modelling the temporal profiles of optical and radar time series for a past observation period. These models are then used to predict the reflectance or backscatter for each new acquisition. If the difference between actual and predicted value exceeds a threshold predefined for each sensor, a change flag is associated with the pixel. This procedure is repeated for all subsequent S1 and S2 acquisitions and the change flags are concurrently updated for each sensor. Each pixel with at least three subsequent change flags is then labelled as change.

    The models are selected to best represent the temporal signal for each of the Sentinel sensors. For S2 we make use of past studies and apply a harmonic function to capture the phenological development over the year. For S1 we test different functions starting from a simple annual mean but also investigate more complex functions.


    A landslide is a movement of a mass of rock, debris, or earth down a slope, under the influence of gravity. Landslides remove or transport the surface soil layers together with the above and belowground vegetation, exposing soils or rocks with different spectral characteristics.

    The analysis of these spectral changes is then performed combining S1 and S2 images and enables the building of a library of signatures of changes (i.e. functions modelling the frequency distributions in different spectral bands). The application of the library on new images allow for the mapping of new landslide events.


    A flood is a relatively high flow of water that overtops the natural and artificial banks of a river submerging the adjacent floodplain area that is normally dry.

    Flooded areas are easily recognizable from satellite images because water has spectral characteristic quite different from the land around. In the case of SAR images (S1) the low backscatter caused by the smooth water surface reflects the radar radiation away from the sensor resulting in dark tones on radar images. For optical images (S2) the reflectance of water surface in the near-infrared (NIR) band is lower than the reflectance on dry areas allowing the easy detection of floods.

    The analysis of the spectral changes due to floods obtained combining S1 and S2 images enables the building of a library of signatures (i.e. the functions modelling the frequency distributions) that will be used for mapping new flood events.


    Sentinel-1 and Sentinel-2 data provide different view and information regarding the snow-cover. Indeed, based on their intrinsic properties, Sentinel-2 provides accurate information about the snow extension, whereas Sentinel-1 is able to identify the presence of water inside the snow blanket, which indicates the melting process. The combination of these two information allows the identification of the presence/absence of snow and its status i.e., dry or wet.

    In detail, the presence of snow can be followed by observing an increase in the NDSI (derived from Sentinel-2). At the same time, when the snow is dry, the coefficient of backscattering (both co-pol and cross-pol) remains almost unchanged w.r.t. the underlying (frozen) soil. It drops down if wet snow with low surface roughness is present i.e., the snow is melting. When the snow is disappearing leaving space to the underlying ground, the NDSI is decreasing accordingly. Based on these observations, the proposed methodology is adapted to identify the presence/absence of snow and its status combining the observation from Sentinel 1 and 2.



    In general terms, the main outcomes of the COMMONS Project are:

    • the development of a library of specific signatures of changes that occur between two or more Sentinel-1 - Sentinel-2 acquisitions.
    • the exploitation of the library to detect changes of land cover due to landslides and flood occurrence, snow and forest coverage changes, and agriculture evolutions.

    In the following, an specific summary of the main achievements for each application is described.

    Agriculture main results

    We implemented and developed a procedure is to estimate a phenological stage value at a parcel level combining information coming from Sentinel-1 and Sentinel-2 images. The proposed method is based on the exploitation of a library that describes the phenological stage evolution in terms of the radar backscatter and optical NDVI for a parcel crop cycle. This allows detecting spatial and temporal divergences from the “expected behaviour” on the parcel at a particular acquisition date. Figure 1 (a) and (b) shows Radarsat-2 mean and Stdv HV/HH for a particular AgriSAR 2009 parcel along the crop cycle vs (a) Doy and (b) phenological stage with their associated (grey and blue respectively) model.

    The generation of the library is based on the phenological stage ground truth data of the parcel along the crop cycle. To that matter the Agrisar 2009 data was employed, where radarsat-2 and RapidEye images were acquired over different test parcels where ground truth data was acquired. HV/VV ratio and NDVI through a crop cycle over the parcel test were modelled to generate the corresponding magnitude vs. phenological stage pdf (library). The phenological stage of a new image was then calculated as the maximum probability stage. A previous unimodality test is performed on the ratio and the NDVI to detect and discard anomaly spatial behaviour on the parcels.

    The study was extended to ESA’s Sentinel-1 and Sentinel-2 data (Figure 2). Nevertheless, Sentinel analysis was not completely done due to the late availability of a complete set of Sentinel-1 and Sentinel-2 data set and the lack of phenological stage ground truth. The performed tests were done using the day of year referred to the beginning and the end of the crop cycle.

    Agriculture main conclusions

    The procedure performed well for estimating the phenological stage and possible anomalies of the investigated crops at a parcel level. The goodness of the results depends on the temporal sampling of the crop cycle as the natural phenological cycle is not linear. Overall errors on the phenological stage were on the order of the 10% of the phenological stage range. Both radar and optical signatures are complementary. Nevertheless, joint analysis at a pdf simultaneous level required more temporally overlapping images. Sentinel-1 and Sentinel-2 series are sensitive and complementary to parcel’s crop cycle phenology and thus valid for the application of the developed methodology. Even though that spatial resolution and the SNR performances for Sentinel-1 and Sentinel-2 are lower than Radarsat-2 and RapidEye, the done tests show that Sentinel’s characteristics fit the requirements to apply the developed methodology.



    Figure 1. Radarsat-2 mean and Stdv HV/HH for a particular AgriSAR 2009 parcel along the crop cycle vs (a) Doy and (b) phenological stage with their associated model.



    Figure 2. (a) Sentinel-1 VH/VV, VH and VV backscatter coefficient evolution of one AgriSAR 2009 parcel and (b) Sentinel-2 NDVI evolution.

    Forest main results

    The concept of synergistically use Sentinel-1 and Sentinel-2 data for forest change detection has been successfully applied in a complex mountain environment in the Central Alps (South Tyrol, Italy). To setup the library, pixel-based harmonic functions were fitted to each band of the Earth Observation data time series (Sentinel-1 and Landsat-8 as a substitute for Sentinel-2, Figure 3). Model parameters and model accuracy estimates (differences between actual and modeled values as well as integrated differences across all bands and time (RMSE)) were added to the library.


    Figure 3. Examples of modeling the spectral-temporal variability for Sentinel-1A and Landsat timeseries for different polarizations, spectral bands and forest types.

    Based on the library content, backscatter and spectral values were estimated for each new Sentinel acquisition and consequently compared to the actual data. Therefore, model parameters and accuracy estimates were resampled from Landsat-8 data to Sentinel-2 data. A synthetic change detection data set was established which introduced change events by combining time series of forest and grasslands considering different local incidence angles and change event timings (Figure 4). The change detection approach was then applied to the synthetic data set and a defined set of change features (RMSE > 5 times mean of modelling period for 3 consecutive observations).


    Figure 4. Examples of synthetic changes in the Sentinel timeseries and their corresponding RMSE values between actual and predicted values.

    The synergistic use of Sentinel-1 and Sentinel-2 data resulted in an overall change detection accuracy of 73.2% (kappa = 0.64) outperforming the use of Sentinel-2 data only (65.5%, kappa = 0.54) and Sentinel-1 data only (70.6%, kappa = 0.61). With respect to the timeliness of the change detection results were less clear. Sentinel-1 and Sentinel-2 data detected 77% pixels at the time of change event and 89% within 2 consecutive acquisitions. Sentinel-1 data only resulted in a slightly better results (80% of pixels detected at the time of the change event, 95% within 2 consecutive acquisitions) while Sentinel-2 data only were less accurate on the exact timing of the change event (67% of pixels detected) but most accurate in when considering also the 2 consecutive acquisitions (97%).

    Forest main conclusions

    The synergistic use of Sentinel-1 and Sentinel-2 data outperforms the use of Sentinel-1 or Sentinel-2 data alone in detecting forest changes while the accuracy of the change detection timeliness was less clear. Two main aspects need to be highlighted that might have impacted the results and introduced an underestimation of accuracy: the library for Sentinel-2 data was based on Landsat-8 data and a synthetic change detection set was used to validate the results.

    Overall, the underlying concept of two independent time-series fitting modules proves to be a highly flexible system which has the potential for global applicability. The system is:

    • adaptable to Sentinel-1 (A+B) and Sentinel-2 (A+B) data availability and forest phenology.
    • extendable to any other Earth Observation data set (e.g. Landsat8).
    • tuneable to different user needs (sensitivity of change detection capacity vs easily implementable in a cloud processing environment.

    Snow main results

    During the project, the effectiveness of the combination of Sentinel-1 and Sentinel-2 for snow cover and snow status identification has been demonstrated. In detail, the combination of S1 and S2 allows:

    • The improvement of S1 wet snow detection;
    • The improvement of S2 snow cover detection;
    • The derivation of wet and dry snow maps.

    The analysis of obtained results highlight that the high spatial and temporal resolution provides by the Sentinel-1 and Sentinel-2 allows the monitoring of snow evolution in complex terrain with a rich level of detail (Figure 5). Moreover, the high temporal resolution of the Sentinel 1 data allowed the definition of transitions in backscattering that identify precise phases related to the different ground conditions that are: soil freezing/soil moisture fluctuation/wetsnow.

    Figure 5. North East Alps, 29 April 2016: (a) VH-NDSI false color composition (b) result obtained by the proposed approach.

    Snow main conclusions

    The combination of multi-source information under the same ground conditions generally reduces the total coverage of the data. This renders the combination of Sentinel 1 and Sentinel 2 suitable for monitoring snow evolution in small and medium size catchments.

    The proper exploitation of the multisource information provided by the SAR and optical sensors requires that the acquisitions should be performed simultaneously. Nonetheless, this very strict requirement is not feasible using a virtual constellation (i.e., a constellation made up of sensors on board of different spacecraft) and therefore a maximum time delay between the two acquisitions for which the situation on the ground is unchanged has been assumed equal to one day.

    In order to mitigate the possibly high cloud coverage, Landsat-8 has been successfully added to the Sentinels virtual constellation. In detail, the use of Sentinel-1 and 2 in full configuration i.e., A and B and the use of Landsat-8 allows the acquisition of up to 72 multisource images per years over the Alps which is a suitable number of observations for snow monitoring.

    The combination of SAR and optical data requires a high processing effort due to the large size of the data. As shown during the project this drawback can be mitigated by using the proposed fusion approaches i.e., geophysical level fusion supposing that the optical snow cover maps and the wet snow maps are already made available from third party data providers such as

    Landslides main results

    We implemented and delivered a procedure to generate event landslide inventory maps combining information recorded by Sentinel-1 and Sentinel-2 images. The classification method recognizes changes caused by new event landslides querying an a-priori knowledge (a library) of the statistical behaviour of similar changes that occurred in the past in similar geo-environmental conditions.

    The library is prepared using a training dataset of images where changes are measured through the LogRatio (LR) index in consecutive Sentinel-1 images, and through a Principal Component Analysis (PCA), a Spectral Angle (SA) Index and changes in Normalized Differential Vegetation Index (ΔNDVI) in consecutive Sentinel-2 images. The combination of Sentinel-1 and Sentinel-2 images takes place in the library when the changes occurred in the different land cover classes are statistically modelled together in the indexes and put in a covariance matrix that represents the training model for the classification. Using as models the elements of the library, a Montecarlo framework generates synthetic training dataset to iteratively train a supervised Maximum Likelihood classifier and obtain potential classifications of new images once the new stack of changes is obtained (Figure 6 (a)). The final ensemble of classifications allows obtaining a single classification together with an estimation of the uncertainty of it that comes from the use of training datasets that are not selected in the image to classify.

    This approach was primarily implemented with the purpose to train in an automatic way, the Maximum Llikelihood classifier with two hypothesized advantages: (i) avoid to select training areas every time that a new event is to map (this process is very time consuming) making easier the systematic use of the Sentinel images to map changes caused by landslides, (ii) to estimate the error of the final classification.

    To test the procedure we mapped the event landslides occurred in Myanmar (Tozang area) in July 2015, event triggered by strong rainfall (Figure 6 (b)). We compared the obtained map with an inventory map obtained through expert-driven photo-interpretation.

    Figure 6. Event landslide in Tozang, area, Myanmar, July 2015. (a) stack of changes including ΔNDVI, PCA, and LR, (b) in red, polygons mapping landslides obtained using the method proposed in the project.

    Landslides main conclusions

    According to the results of the comparison, the classifier performed numerically well because false positive and false negative in the final landslide map represent respectively 0.59%, and 1.04% of the entire classified area.

    In clear sky conditions, the contribute of the optical component was statistically predominant in the achievement of the final results, while the SAR component contributed in very local and marginal situation, in the face of an increase of the complexity in the preparation of the library. The bi-temporal approach does not fully exploits the potentialities offered by the Sentinel-1 and a combined approach, multi-temporal SAR-based for event identification, and bi-temporal optical-(eventually SAR)-based (Mondini, 2017) promises to increment the capacities of system to provide information about landslide events.

    Floods main results

    The same procedure developed for the landslide was used also for generating maps of flooded areas. Changes were measured through Log-ratio (LR) index in consecutive Sentinel-1 images and through a Principal Component Analysis (PCA) and Normalized Difference Water Index (NDWI) in consecutive Sentinel-2 images. The combination of these indices (LR, PCA and NDWI) within a covariance matrix allows us to determine a training model for the classification. Repeating the procedure used to produce landslide maps, the classification of new images is obtained iteratively training a supervised Maximum Likelihood (ML) classifier through a synthetic dataset of signatures generated in a Montecarlo framework based on the signatures derived by the combination of the three indices. The final outcome is represented by an ensemble of classifications providing the inundated pixels and the uncertainty of them.

    To test the procedure, we mapped the flooded areas associated to the event occurred in Missouri (USA) in December 2015 (Figure 7).

    Since the closest post-event Sentinel-1 image was acquired eight days later the peak when the extension of the flood was already drastically reduced, while the optical images were acquired at the time of the maximum flows, we trained the classification model using only optical information present in the library parcelling out the SAR component.

    The extension of the flooded area obtained by the procedure was compared to a reference flooded map (benchmark) obtained by (i) using another classification method (i.e. Support Vector Machine, SVM), and (ii) through a visual interpretation of the optical image acquired during the event, both filtering the permanent water bodies.

    Figure 7. Classification of change (blu) and no change (green) obtained by the proposed approach for the flood event of the December 2015 in Missouri river (USA).

    Floods main conclusions

    The good performance of the procedure is confirmed by the results obtained in terms of True Negative Rate (0.92 for the SVM benchmark and 0.94 for the heuristic benchmark) and True Positive Rate (0.61 for the SVM benchmark and 0.71 for the heuristic benchmark). The goodness of the result is partially due to the flood event, the time of acquisition and the selected study area.

    The fusion can be applied only if the two sensors observe the same phenomenon at the same time, hence to produce a flood map the satellite images should be acquired during (or close to) the maximum extension of the inundation.

    The separated use of optical and SAR sensors can give good results even if test on multi-temporal SAR images should be carried out.

    Other flood events and study areas should be investigated to test the transferability of the procedure and to validate the conclusions.


    Scientific Communications in Workshops:

    ESA living planet symposium. Library based change detection methodologies combining Sentinel-1 and Sentinel-2 data. Mondini, Alessandro (1); Notarnicola, Claudia (2); Blanco, Pablo (3); Tarpanelli, Angelica (1); Rossi, Mauro (1); Sonnenschein, Ruth (2); Marin, Carlo (2) 1: IRPI, CNR, Italy; 2: EURAC, Italy; 3: Altamira Information, Spain Tarpanelli A., Mondini A., Rossi M. (2015) "FLOOD MAPPING BY COUPLING SAR AND OPTICAL IMAGES: POTENTIAL APPLICATION FOR JOINED USE OF SENTINEL-1 AND SENTINEL-2", Le giornate dell'Idrologia 2015: Idrologia di bacino e rischi naturali: monitoraggio, previsione, prevenzione e mitigazione in un contesto di cambiamenti globali, 6-8 October 2015, Perugia, Italy.

    Tarpanelli A., Mondini A., Rossi M. (2016) "FLOOD MAPPING THROUGH THE JOINED USE OF SENTINEL-1 AND SENTINEL-2", Living Planet Symposium, 9-13 May 2016, Prague, Czech Republic. Land Cover Changes investigation using Sentinel 1 and 2: the project COMMOnS (IGU-IGC Beijing 8.2016)

    Spatial autocorrelation changes in multitemporal SAR images for landslide event detection modelling and early warning of landslides, new methods and technologies (3rd Joint Seminar Korea-Italy Florence 4.2017)

    Marin, C., Callegari, M., Notarnicola, C., “A novel multitemporal approach to wet snow retrieval with Sentinel-1 images”, SPIE Remote Sensing, Edinburgh, United Kingdom, 26 - 29 September 2016

    Marin, C., Callegari, M., Greifeneder, F., Notarnicola, C., Zebisch, M., “Monitoring Alpine Water Resources using Satellite Data“, Virtual Alpine Observatory Symposium 2017, Bolzano, Italy, 28 - 30 March 2017

    Combined use of Sentinel-1, Sentinel-2 and Landsat 8 images for near-time forest change detection. R. Sonnenschein, C. Marin, C. Notarnicola, M. Davidson. Multitemp 2017.


    Measures of Spatial Autocorrelation Changes in Multitemporal SAR Images for event landslides Detection. Submitted to Remote Sensing.

    Carlo Marin, Mattia Callegari, Claudia Notarnicola, and Malcolm Davidson, Combination of SAR and Optical Data for Snow Monitoring During the Melting Season, In preparation.

    Related projects


    The overall structure of the proposed industrial consortium comprises CLS (France) as Prime contractor and sole interface to ESA, with CNR (Italy) and EURAC (Italy) as sub-contractors.


    CLS, established in 1986, is a private company with public shareholders: it is a subsidiary of the French Space Agency (CNES) and the French Research Institute for Exploration of the Sea (IFREMER).

    Its core activities are:

    • the development of added-value applications and services based on satellite remote-sensing data.
    • the commercial operation of satellite and ground systems for positioning, data collection, ocean observation and radar surveillance.

    CLS operates in the following sectors:

    • environmental surveillance (incl. oceanography, wildlife tracking, ground motion monitoring, …).
    • sustainable management of marine resources (incl. control of fisheries).
    • maritime security (incl. oil spill and ship detection, identification of potential source of pollution).
    • offshore oil/gas industry.

    CLS acquired in 2008 the Boost Technologies Company as world specialist in Marine SAR applications derived from satellite SAR (Synthetic Aperture Radar) systems. In 2010, CLS acquired the Company ALTAMIRA INFORMATION, leader of Terrestrial radar application. Experience, skill and experts of the two companies are fully integrated in CLS in Radar Applications Division (DAR).

    The Radar Applications Department of CLS is recognized as a main provider of satellite radar scenes and services based on the analysis of Synthetic Aperture Radar (SAR) scenes, with contracts with governmental agencies in France, Europe, Indonesia, Australia and with oil&gas operators worldwide.

    • The main Marine Radar Applications are: Oil spill detection, Vessel detection, Iceberg detection, Wind field retrieval, Swell field retrieval, Ocean surface current (R&D).
    • The main Terrestrial Applications are: Ground deformation applied to natural hazards (groundwater, tectonics, earthquakes, volcanoes, landslides), infrastructures (tunnelling, sea wall defence, new contractions), oil&gas, insurance, DEM production, Change detection, Water mask and water bodies detection and delineation, biomass (vegetation identification, vegetation height).


    The Italian National Research Council (CNR) is the largest public research institution in Italy, the only one under the Research Ministry performing multidisciplinary activities.

    CNR’s mission is to perform research in its own Institutes, to promote innovation and competitiveness of the national industrial system, to promote the internationalization of the national research system, to provide technologies and solutions to emerging public and private needs, to advice Government and other public bodies, and to contribute to the qualification of human resources. CNR participates with IRPI (Institute of Research on Hydrogeological Hazards) located in Perugia.

    The scientific activities of IRPI focus on geo-hazards and environmental problems exploiting a wide range of technologies, including Earth Observation using remote sensing and in-situ techniques for geo-hydrological applications and risk prediction, prevention, and mitigation. The Institute plays a leading role in the development and promotion of industrial research activities in the field of EO technologies for geo-hazard monitoring and assessment. IRPI is Centres of Competence for the Italian National Civil Protection Department (DPC) and plays a leading role in the development and promotion of industrial research activities in the field of EO technologies for geo-hazard monitoring and assessment.

    The main activities at IRPI are:

    • Technological and scientific expertise in the identification, mapping and monitoring of different types of ground deformations induced by natural processes and manmade causes.
    • Advanced services useful for the detection and monitoring of ground deformations, including the assessment of the damage caused by the deformations, at different temporal and geographical scales.
    • Technological infrastructure analysis and system products design;
    • Acquisition and processing of aerial and ground-based Lidar data;
    • Electromagnetic techniques for soil moisture evaluation;
    • Exploitation of optical EO data for landslide detection, mapping and susceptibility zonation;
    • Design of risk scenarios;
    • Implementation and management of complex systems.

    EURAC Research

    EURAC is an advanced private no-profit research and training centre established in 1992 with headquarters in Bolzano. Its 380 staff members united by shared values of passion for their work and an unwavering commitment to quality, have the opportunity to work in a multicultural environment thanks to a wide diversity of nationalities represented among its team.

    EURAC is internally organized in 11 Research Institutes, supported by 11 Service Departments, performing research activities in different fields from issues related to minority rights protection, federal, regional and local governmental trends and the efficient management of public administrations to studies on renewable energies, promotion of sustainable development and the protection of natural resources.

    EURAC activities include national and international research and training projects as well as direct cooperation with public and private clients. EURAC has a strong international vocation, represented both by employing experts coming from different countries and by the participation (as lead or partner) in 32 EU-funded projects in 2015. This strong participation in the international framework is also enhanced by both the Vienna representing office at the UNEP premises and the Brussels representing office hosted by the Common representation of the Euroregion Tyrol-South Tyrol–Trentino. Moreover, EURAC hosts the Alpine Convention for the protection of the Alps Permanent Secretariat branch office.

    EURAC has invested considerable financial and human resources in improving the quality and performance of the assignments we implement. It has developed a quality control system, in accordance with the standard ISO 9001 and in 2007 obtained the ISO 27001 certificate.

    The contributing body for this project is the Institute for Applied Remote Sensing of the department Mountains. Purpose of this research group is the advanced analysis of Earth Observation data and their integration into products and services, tackling most relevant environmental issues in mountain areas. It is strongly involved in climate change impact assessments and monitoring of water resources in mountain areas and eco-systems.


    In this sections will be available the main documents concerning the COMMONS Project: Algorithm Theoretical Definition Basis (ATBD), Requirements Baseline Document, Product Validation Plan Document, Detailed Processing Model, Product Specification Document and Product Validation Plan.


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