The Life MODERn NEC project has been created thank to the directive "NEC" (National Emission Ceiling, 2016/2284) of the European Union. The objective of the project is to monitor emissions of atmospheric pollutants (sulphur, nitrogen, organic compound, ammonia, and particulate matter) and to assess the impact on water and terrestrial ecosystems. Monitoring of terrestrial ecosystems is carried out by in situ sampling of indicators for air quality, atmospheric deposition, crown condition and phenology, ecosystem chemistry, ground vegetation, tree growth, meteorological variables, ozone injury, and soil solution. The project has identified six sites in Italy where in situ data are systematically collected. The six sites are part of the ICP forests (International Co-operative Programme on Assessment and Monitoring of Air Pollution Effects on Forests) level II plots, that comprises a total of 31 sites distributed over the Italian forests. In this work, all sites (31) were analysed thanks to the advantages offered by remote sensing technologies able to provide synoptic view over large territories. We exploited time series of Copernicus Sentinel-2 (S2) multi-spectral satellite images to estimate phenological metrics of the investigated sites. Phenological metrics are very important parameters to determine the health status of the forests and to identify changes induced by pollutants; specifically, we aim at pointing out changes in the timing and vigour of the plant's annual cycle. Monitoring vegetation phenology with field surveys can be time and manpower consuming because the operator has to visit sites several times during the year to collect data and observations. The use of remote sensing technologies could reduce the effort involved in field measurements and the synergy between remote and field data could increase the accuracy of the metrics. The satellite Sentinel-2 constellations provide multi-spectral images with high spatial resolution and short revisit time which is very important to observe the phenological phases in fast-changing environments. We focused on the 31 sites and used time series of spectral indices derived from Sentinel-2 imagery for the period 2016-2022. Since sites are distributed in all of Italy, downloading and processing all S2 tiles to extract time series of the indices could be quite a resource-intensive process. In order to reduce processing time, spectral indices were extracted from S2 images in Google Earth Engine (GEE). Every site is identified by a set of two coordinates of the central point of the plot that is object of field surveys and measurements in the LIFE MODERn (NEC) project. Methods to identify the phenological metrics (green-up, maturity, senescence and dormancy) are based on a double sigmoid function that was fitted to the time series of the daily vegetation index. From the sigmoid, the metrics were calculated using the derivatives of the curve. Processing is done using the R package "sen2rts" (Ranghetti, 2012). This package takes as input S2 time series, it reduces the noise that could be present in the time series and then fits a double logistic curve and extracts metrics. In the data preparation phase, the pixels under cloud or shadow condition have been removed based on quality layer of the S2 Level 2A product, and then the smoothing parameter has to be regulated based on the index and the annual oscillation. The first index we tested was NDVI which is widely recognized as a suitable indicators of the vegetative annual cycle of plants. Thanks to the red and NIR (near infrared) bands, it is possible to track the increment of biomass and photosynthesis activity, which can be translated into the phenological status of the plants. This is more evident for deciduous broadleaved vegetation, that is characterized by a clear intra-annual seasonality of NDVI; instead, seasonal cycles of evergreen vegetation might be more problematic to identify. These plants retain their leaves for more than one year and their photosynthetic activity is strictly regulated but chlorophyll is always present; therefore, the seasonal variability of NDVI values is less evident. The photosynthetic activity is regulated during the year by pigments like the carotenoids in the evergreens, so it could be tracked using an index specifically designed to be sensitive to them. The result of this work is a dataset of the phenological metrics (expressed as dates-DOY Day Of the Year) for the analysed forest sites. Results were evaluated based on available databases of phenology metrics and/or by photointerpretation. The estimated metrics and their change through the years, could be used to evaluate the difference in the annual cycle of plants eventually attributable to pollutants. The tested methodology and tools could be exploited to expand to the Italian level I sites of the ICP project. The level I plots are more than 250, so this remote sensing-based approach could allow an affordable and fast way to monitor a large number of sites through time. The processing time and memory usage on local machines are limited thanks to the download of NDVI time series from GEE, however, the double logistic fitting steps is still quite demanding and, for this reason, we foresee for future applications over large areas the full implementation of the process chain in GEE environment, to make feasible processing of a large number of points/sites thanks to cloud computing resources.

Forest fires are a keystone ecosystem process in the evolution and maintenance of the Mediterranean biome. However, the expected increase in fire activity under future climate projections poses a growing ecological and socio-economic threat to the Euro-Mediterranean region. The dynamic mapping of fuel types and models assumes relevance to wildland fire risk prevention and management across multiple spatial and temporal scales due to the tight dependence of fire ignition, spread, and growth on vegetation characteristics. Thematic maps of fuel types and models already exist at a global and continental scale, but the spatiotemporal variability of fuel characteristics in the Euro-Mediterranean region highlight the need for further improvements in the identification of techniques and methodologies based on the integration of field observations with remotely sensed data that allow for a periodic update of high resolution thematic maps. This research proposes a methodology for generating a fuel type map for a pilot site located in Sardinia, Italy, compliant with the first level scheme of the hierarchical classification system recently proposed within the context of the EU project FirEUrisk; the classification system has been chosen for its adaptability to different scales of investigation and to different geographical contexts in the Euro-Mediterranean regions. The adopted methodology applies supervised learning algorithms for the fuel type classification from time series of multispectral images of the Sentinel-2 (S2) missions of the Copernicus program. The classification methodology is applied to S2 time series of spectral indices for the period 2020-2021 after preliminary cloud cover masking and compositing. Remotely sensed information is integrated with auxiliary information derived from institutional datasets available on a continental scale, such as the CORINE Land Cover system, and on a local scale, such as the digital elevation model and the mosaic of high resolution orthophotos for the year 2018. Due to the lack of field data uniformly distributed in the pilot site, an ad hoc dataset is generated by photointerpretation for the supervised classification model training and testing. To this aim, a web application is designed and developed to support a consistent data collection within 1 ha regions of interest (ROIs). The application allows the labelling of each 10x10 meter S2 pixel within each ROI selected with a stratified random sampling strategy. A gradient boosting ensemble model is trained for a pixel-level classification that integrates spectral metrics, textural metrics, and other geo-environmental descriptive metrics. Finally, the classifier is applied to derive a 10 m resolution thematic fuel type map for the pilot site in Sardinia. Preliminary results show an overall accuracy greater than 0.9 and calculated as the number of pixels correctly classified by the model out of the total number of pixels included in the different cross-validation datasets. The classification specificity is greater than 0.7 for most of the fuel type classes. Future activities will be focused on a robust validation of the thematic fuel type map by selecting suitable sites based on the possibility of carrying out ad hoc field surveys. If the preliminary results are confirmed, the methodology could be extended to other pilot sites in the Euro-Mediterranean basin and the obtained thematic fuel type map could be processed to obtain a fuel model map to be used for fire management purposes, such as the simulation of fire spread and growth using simulators based on the Rothermel mathematical model.

Coarse resolution sensors are not very sensitive at detecting small fire patches, making current estimations of global burned areas (BA) very conservative. Using medium or high-resolution sensors to generate BA products becomes then a priority, particularly in areas where fires tend to be small and frequent. Building on previous work that developed a small fire dataset (SFD) for Sub-Saharan Africa for 2016, this paper presents a new version of the dataset for 2019 using the two Sentinel-2 satellites (A and B) and VIIRS active fires. Total estimated BA was 4.8 Mkm2. This value was much higher than estimations from two global, coarser-spatial resolution BA products based on MODIS data for the same area and period: 80 % greater than estimates from FireCCI51 (based on MODIS 250 m bands) and 120 % larger than MCD64A1 (based on MODIS 500 m bands). The main differences were observed in those months with higher fire occurrence (November to January for the Northern Hemisphere regions and June to September for the Southern Hemisphere ones). Accuracy assessment of the SFD product was based on a novel sampling strategy designed to obtain independent fire reference perimeters. Validation results showed remarkable high accuracy values comparing to existing global BA products. Overall omission errors (OE) were estimated as 8.5 %, commission errors (CE) as 15.0 %, with a Dice Coefficient of 87.7 %. All of these estimations implied significant improvements over the global, coarser spatial resolution BA products (OE > 50 % and CE > 20 % for the same area and period), as well as over the previous SFD product for 2016 of the same area, generated from a single Sentinel-2 satellite and MODIS active fires (OE = 26.5 % and CE = 19.3 %). Temporal accuracies greatly increased as well with the new product, with 92.5 % of fires detected within the first 10 days of occurrence.

Fires devastated Europe during the summer of 2021, with hundreds of events burning across the Mediterranean, causing unprecedented damage to people, properties, and ecosystems. Remote sensing (RS) is widely recognized as a key source of data for monitoring wildfires [1], exploiting both optical/multi-spectral and microwave satellite sensors [2]. Optical/multi-spectral and microwave satellite observations can provide information on areas affected by fires as well as on fire severity, which is the damage that affects vegetation. The major advantage of RS technology is the consistent and operative availability of data over large areas; these data can also be provided in near real-time for a fast assessment of fire damage. In this work, we exploit both Sentinel-1 (S1) and Sentinel-2 (S2) data from the Sicily region, Italy, to map and monitor the burned areas of the summer 2021 season. Coherent/incoherent change detection approaches have been applied to extract areas where the RS signal has registered a significant change that could have been induced by the occurrence of fire. Cross-comparison analyses between the results obtained using optical and microwave images have been carried out to characterize the performance of the exploited RS methods. To this aim, the fire perimeters available from the European Forest Fire Information System (EFFIS) were used.

This report first introduces the case study area with a focus on the forest fires in Siberia region (section 2). A second section follows with the explanation of the rationale and the material and methods applied (section 3). The results section (section 4) is divided in three sub-sections. Firstly, burned area time-series, water quality variables (i.e., chlorophyll-a and turbidity) and precipitation are reported (section 4.1). Then the results of the regression analysis between fire, meteo-climatic variables and water quality data is presented (section 4.2). Finally, a spatio-temporal analysis of water quality variables, burned areas and precipitation is carried out on selected years (section 4.3). All these results are followed by a discussion with key conclusions of the work (section 5).

Lakes are integrators of environmental and climatic changes occurring within their contributing basins. Factors driving lake condition vary widely across space and time, and lakes, in turn, play an important role in local and global climate regulation, with positive and negative feedback depending on the catchment. Understanding the complex behaviour of lakes in a changing environment is essential to effective water resource management and mitigation of climate change effects. Lakes integrate responses over time and studies of globally distributed lakes can capture different aspects of climate change. A globally harmonized observation approach is needed to identify climate signals in lake physical, hydrological and biogeochemical change and to feed lake state into numerical models. The objective of Lakes_cci is to exploit satellite Earth Observation data to create the largest and longest possible consistent, global record of the five lake climate variables: Lake Water Level (LWL), Lake Water Extent (LWE), Lake Surface Water Temperature (LSWT), Lake Water-Leaving Reflectance (LWLR), and Lake Ice Cover (LIC). Lakes_cci represents a unique framework to provide consistent and homogenous data to the multiple communities of lake scientists. The project actively engages with this community to assess the utility and future improvement of Lakes_cci products. The first phase of the project has recently been completed with the release of the last version (v2.0.2) of the dataset, which is comprised of processed satellite observations at the global scale, over the period 1992-2020, for over 2000 inland water bodies, where and when data quality appropriate for climate studies can be achieved. Phase 2 of the project started in July 2022, with the inclusion of the Lake Ice Thickness variable. One scope of the project is the integration of different satellite-derived products across ESA CCI projects (e.g., Fire, Land). In this study potential of the CCI dataset is explored in three different case studies in the Eurasian region.

The 2021 European summer season has been one of the most intense for Mediterranean regions that experienced a heatwave in August, determining the onset of several fire events across regions in southern Europe [...]

Lakes are already responding rapidly to climate change and in coming decades it is projected that global warming will have a more persistent and stronger effects on hydrology, nutrient cycling, and biodiversity (Cardoso et al. 2009; Carpenter et al. 2011). Recent projections estimate that lakes will get warmer for longer periods, with heatwaves potentially spreading across multiple seasons (Woolway et al. 2021). In some regions, heatwaves can add to existing pressure from drought which can lower lake levels and areal extent resulting from reduced inflows, increased evaporation and extraction for anthropogenic purposes (M?ynski et al. 2021; Zhao et al. 2022). The double heatwave event that occurred in the summer of 2019 in Europe was one of the top five warmest summers since 1500 (Sousa et al. 2020). In lakes, intense phytoplankton blooms might be the result of consecutive heatwave (e.g., Søndergaard et al. 2003; Free et al. 2021). When high temperatures are combined with low humidity, low rainfall, dry vegetation there is an increased risk of wildfire in case there is a source of ignition. Wildfires can have a significant hydro-geomorphological impact on watersheds in relation to post-fire rainfall events that can trigger erosion and transport processes leading to potential alteration of water quality (Smith et al., 2011). Despite the increased concern on the impact of wildfires on lake water quality, an uneven coverage of their geographical distribution has been observed (Shakesby and Doerr, 2006). Moreover, the topic has mainly been addressed at small scales while there remains a poor understanding at larger scales. The European Space Agency (ESA) Climate Change Initiative (cci) products could fill these gaps by providing long term, global coverage of both fire and water quality satellite-derived data also for the remote regions. The Lakes_cci project develops products covering Lake Water Level (LWL), Lake Water Extent (LWE), Lake Surface Water Temperature (LSWT), Lake Ice Cover (LIC) and Lake Water-Leaving Reflectance (LWLR) with the overarching objective to produce and validate a consistent long term dataset. The first phase of the project has recently been completed with the release of the last version (v2.0.2) of the dataset, including about 2000 lakes for the period 1992-2020. The dataset (netCDF file format) is hosted at the Centre for Environmental Data Analysis (https://catalogue.ceda.ac.uk/uuid/a07deacaffb8453e93d57ee214676304). A third user survey was conducted to collect user feedback on the data exploitation (https://climate.esa.int/en/projects/lakes/news-and-events/news/a-new-survey-for-users/). Phase 2 of the project started in July 2022. One scope of the project is the integration of different satellite-derived products across ESA CCI projects. For this reason, a study on wildfire and lakes is ongoing aiming to investigate the relationship between fires and lakes water quality over a wide range of geographical regions and fire regimes. The Fire_cci project, already at phase 2, focuses on several issues relating to fire disturbance including analysing and specifying scientific requirements relating to climate, production of burned area datasets, and product validation and product assessment. In this study potential of the CCI dataset is explored in three different case studies in the Eurasian region. I.Long-term trends in the ECV "Lakes" The dataset was explored for two Italian lakes and one Swedish lake of different depth and trophic state. The lakes are part of the Long-Term Ecosystem Research (LTER) network. In situ data from the LTER dataset were used to compare and integrate satellite products. Time-series of satellite data were then explored to examine trends in the context of key meteo-climatic variables. LSWT, chlorophyll-a (Chl-a), turbidity and ice cover data covering 16 years (2003-2018) were extracted. Daily climate data (wind, air temperature, precipitation) were obtained from ERA5. North Atlantic Oscillation (NAO) daily values were obtained from NOAA-CPC. The analyses revealed variations in the water quality variables, including a significant alteration in the concentrations of Chl-a in the lakes under study. Another aspect highlighted in the study is the variation in response to climate change in lakes in different geographical regions and with different trophic and morphological characteristics, when comparing northern Europe to southern Europe. For example, in Lake Trasimeno (shallow-eutrophic) Chl-a was higher with more positive values of the NAO, lower lake levels and warmer temperatures; in Lake Garda (deep oligotrophic) a shift was found in the timing of the traditional Chl-a peak (Fig. 1); the Erken lake time series indicated a significant increase in Chl-a and air temperature. II. Heatwave and storm events impact on lakes Chl-a data were examined for any potential responses during the 2019 double heatwave period for 36 European lakes, evaluating how the response varies depending on latitude, total concentration of phosphorus and the average depth of the lake. The Chl-a concentrations for summer 2019 were extracted from Lakes_cci dataset (v1.1). Data on total phosphorus and lake mean depth were obtained from Waterbase, the European Environment Agency database on water quality (https://www.eea.europa.eu/data-and-m aps/data/waterbase-water-quality-icm-1) or from the Environmental data MVM database for Swedish lakes (https://miljodata.slu.se/mvm/) or from published literature. The results show that the timing and magnitude of the response to the heatwave events depends on lake depth and nutrients (Fig. 2). Deeper lakes respond sooner probably because of higher temperatures leading to stronger stratification thereby improving the light climate but with the response strength dependent on nutrient status (e.g. Maggiore and Geneva). In contrast, shallower lakes and lakes at lower latitudes showed more asynchrony with a greater response after the heatwave event probably as a result of internal and external loading (e.g. Razim and Balaton). III.Effects of wildfires on Lake Baikal Fire_cci and Lakes_cci datasets were explored to highlight any spatial-temporal relationships of burned area, meteo-climatic and water quality parameters for three sub-basins in correspondence to the inflows of the major tributaries into Lake Baikal. Burning vegetation in the basin could cause an increase in erosion and surface transport process, and a subsequent increase in turbidity and/or concentration of Chl-a in the lake waters. The products were extracted for the period 2003-2019. The first analyses focused on trends in the time series observed independently. Results showed a trend towards an increase in burned area, chlorophyll-a and turbidity in the summer months, over the 16-year period, probably related to climate change. The time series of precipitation were analysed through the use of the standardized precipitation index (SPI). It was observed that SPI assumes significantly negative values and drought indicators for the years 2003 and 2015 in which numerous fires were also observed. A non-parametric multiplicative regression model showed that the temporal variable (seasonal and annual) is the main predictor of turbidity and Chl-a. The predictive role of the burned area and wind were limited in the investigated study area. Effectively, on long-time series over a such a deep, pristine and large lake, data did not show clear effects of wildfires on water parameters except for local effects. For example, the spatial-temporal analysis conducted in some years (2003, 2006, 2011 and 2018) of significant interest, because they represent extreme conditions of fires and precipitation, showed an increase in both Chl-a and turbidity following fire events that resulted in a significant burned area and without significant precipitation events observed in the central portion of the lake for the year 2003 (Fig. 3).

The paper proposes a multi-criteria and data driven fusion approach whose semantics can be explained in terms of attitude towards decisions. It is exemplified to assess environmental status from remote sensing images in order to identify hot spot of critical situations and anomalies induced by wildfires, floods, desertification, erosion etc. by fusing multiple factors defined by experts knowledge. The fusion function is an Ordered Weighted Averaging (OWA) operator, whose behaviour is here characterized by degrees of pessimism and democracy. The paper proposes to explain the semantics of the fusion function learnt from few ground truth data available, i.e., the OWA operator, by computing its degrees of pessimism/optimism and democracy/monarchy, which are defined as semantic interpretations of both orness and dispersions respectively. Pessimism indicates if the fused map is more prone to commission (overestimation) or omission (underestimation) errors, while democracy indicates how many factors contribute to the generation of the map. The approach is exemplified to map the flooded areas from remote sensing by considering different models based on distinct spectral indexes and domain experts.

The availability of high-resolution reference datasets representing in space and time and with high accuracy areas affected by fires is strategic for the validation of remotely-sensed Burned Area (BA) products. This paper proposes a methodology designed to build a burned area reference dataset from Sentinel-2 (S2) images at continental scale by implementing a stratified random sampling scheme. Representative sample units are selected across biomes and regions with high/low fire activity; each unit covers the extent of a S2 tile (~10 000 km) where image time series are classified with a supervised Random Forest algorithm to extract fire perimeters by exploiting visible to near and short-wave infrared S2 wavebands at 10 to 20 m spatial resolution. Time series have to satisfy requirements on maximum cloud cover, maximum time interval between consecutive images and minimum length to be suitable for being selected and processed. The proposed methodology was applied to Sub-Saharan Africa for the year 2019 to select 50 S2 sample units where time series were processed to deliver fire reference perimeters for accuracy assessment of regional BA products. Average series length is 140 days with the longest series in the savanna biome (maximum length is 355 days, 29 consecutive S2 images) and a total of 695 S2 images were processed to build the 2019 reference dataset. This dataset was compared to burned areas derived from very-high resolution Planetscope images over five S2 tiles obtaining 15.5% omission and 11.6% commission errors. To exemplify the use of this reference dataset, S2 perimeters were used to validate the NASA MCD64A1 Collection 6 and the ESA FireCCI51 BA products. The reference dataset has been added to the Burned Area Reference Database (BARD) (Franquesa et al., 2020) and is publicly available at https://doi.org/10.21950/VKFLCH.

The paper proposes a fully automatic algorithm approach to map burned areas from remote sensing characterized by human interpretable mapping criteria and explainable results. This approach is partially knowledge-driven and partially data-driven. It exploits active fire points to train the fusion function of factors deemed influential in determining the evidence of burned conditions from reflectance values of multispectral Sentinel-2 (S2) data. The fusion function is used to compute a map of seeds (burned pixels) that are adaptively expanded by applying a Region Growing (RG) algorithm to generate the final burned area map. The fusion function is an Ordered Weighted Averaging (OWA) operator, learnt through the application of a machine learning (ML) algorithm from a set of highly reliable fire points. Its semantics are characterized by two measures, the degrees of pessimism/optimism and democracy/monarchy. The former allows the prediction of the results of the fusion as affected by more false positives (commission errors) than false negatives (omission errors) in the case of pessimism, or vice versa; the latter foresees if there are only a few highly influential factors or many low influential ones that determine the result. The prediction on the degree of pessimism/optimism allows the expansion of the seeds to be appropriately tuned by selecting the most suited growing layer for the RG algorithm thus adapting the algorithm to the context. The paper illustrates the application of the automatic method in four study areas in southern Europe to map burned areas for the 2017 fire season. Thematic accuracy at each site was assessed by comparison to reference perimeters to prove the adaptability of the approach to the context; estimated average accuracy metrics are omission error = 0.057, commission error = 0.068, Dice coefficient = 0.94 and relative bias = 0.0046.

Sentinel-2 (S2) multi-spectral instrument (MSI) images are used in an automated approach built on fuzzy set theory and a region growing (RG) algorithm to identify areas affected by fires in Mediterranean regions. S2 spectral bands and their post- and pre-fire date (?post-pre) difference are interpreted as evidence of burn through soft constraints of membership functions defined from statistics of burned/unburned training regions; evidence of burn brought by the S2 spectral bands (partial evidence) is integrated using ordered weighted averaging (OWA) operators that provide synthetic score layers of likelihood of burn (global evidence of burn) that are combined in an RG algorithm. The algorithm is defined over a training site located in Italy, Vesuvius National Park, where membership functions are defined and OWA and RG algorithms are first tested. Over this site, validation is carried out by comparison with reference fire perimeters derived from supervised classification of very high-resolution (VHR) PlanetScope images leading to more than satisfactory results with Dice coefficient > 0.84, commission error < 0.22 and omission error < 0.15. The algorithm is tested for exportability over five sites in Portugal (1), Spain (2) and Greece (2) to evaluate the performance by comparison with fire reference perimeters derived from the Copernicus Emergency Management Service (EMS) database. In these sites, we estimate commission error < 0.15, omission error < 0.1 and Dice coefficient > 0.9 with accuracy in some cases greater than values obtained in the training site. Regression analysis confirmed the satisfactory accuracy levels achieved over all sites. The algorithm proposed offers the advantages of being least dependent on a priori/supervised selection for input bands (by building on the integration of redundant partial burn evidence) and for criteria/threshold to obtain segmentation into burned/unburned areas.

Sentinel-2 Multi-Spectral Instrument (MSI) (S-2) images have been used for mapping burned areas within the borders of the Vesuvio National park, Italy, severity affected by fires during summer 2017. A fuzzy algorithm, previously developed for Mediterranean ecosystems and Landsat data, have been adapted and applied to S-2 images. Major improvements with respect to the previous algorithm characteristics are i) the use of S-2 band reflectance in post-fire images and as temporal difference (delta pre- and post-fire) and ii) the definition of fuzzy membership function based on statistics (percentiles) of reflectance as derived from training areas.The following input bands were selected based on their ability to discriminate burned vs. unburned areas: post-fire NIR (Near Infrared, S-2 band 8), post-fire RE (Red Edge, S-2 bands 6 and 7) and temporal difference (delta post-pre fire) of the same bands and additionally of SWIR2 (ShortWave Infrared, S-2 band 12). For each input, a sigmoid function has been defined based on percentiles of the unburned and burned histogram distributions, respectively, derived from training data. In this way, and with respect to previous formulation of the algorithm, membership function can be defined in an automated way when ancillary layer are provided for extracting statistics of burned and unburned surfaces.Input membership degrees for the selected bands have been integrated to derived pixel-based synthetic scores of burned likelihood with Ordered Weighted Averaging (OWA) operators. Different operators were tested to represent different attitudes/needs of the stakeholders between pessimistic (the maximum extent of the phenomenon to minimise the chance of underestimating) and optimistic (minimise the chance of overestimating).Output score maps provided as continuous values in the [0,1] domain have been segmented to extract burned/unburned areas; the performance of the combined threshold and OWA operator has been evaluated by comparison with Copernicus fire damage layers from the Emergency Management Service (EMS) (https://emergency.copernicus.eu/). Error matrix, F-score and omission and commission error metrics have been analysed.Finally, the correlation between fuzzy score derived by applying OWA operators has been analysed by comparison with Copernicus EMS fire damage layers as well as fire severity computed as temporal difference of the NBR index. Results show satisfactory accuracy is achieved for the identification of the most severely affected areas while lower performance is observed for those areas identified as slightly damage and probably affected by fires of lower intensity. Moreover, some discrepancies have been observed between different layers of fire severity due to the non-unique definition of the criteria used for assessing the impact of fires on the vegetation layer.

The paper proposes a scalable fuzzy approach for mapping the status of the environment integrating several distinct models exploiting geo big data. The process is structured into two phases: the first one can exploit products yielded by distinct models of remote sensing image interpretation defined in the scientific literature, and knowledge of domain experts, possibly ill-defined, for computing partial evidence of a phenomenon. The second phase integrates the partial evidence maps through a learning mechanism exploiting ground truth to compute a synthetic Environmental Status Indicator (ESI) map. The proposal resembles an ensemble approach with the difference that the aggregation is not necessarily consensual but can model a distinct decision attitude in between pessimistic and optimistic. It is scalable and can be implemented in a distributed processing framework, so as to make feasible ESI mapping in near real time to support land monitoring. It is exemplified to map the presence of standing water areas, indicator of water resources, agro-practices or natural hazard from remote sensing by considering different models.

The paper proposes a transparent approach for mapping the status of environmental phenomena from multisource information based on both soft computing and machine learning. It is transparent, intended as human understandable as far as the employed criteria, and both knowledge and data-driven. It exploits remote sensing experts' interpretations to define the contributing factors from which partial evidence of the environmental status are computed by processing multispectral images. Furthermore, it computes an environmental status indicator (ESI) map by aggregating the partial evidence degrees through a learning mechanism, exploiting volunteered geographic information (VGI). The approach is capable of capturing the specificities of local context, as well as to cope with the subjectivity of experts' interpretations. The proposal is applied to map the status of standing water areas (i.e., water bodies and rivers and human-driven or natural hazard flooding) using multispectral optical images by ESA Sentinel-2 sources. VGI comprises georeferenced observations created both in situ by agronomists using a mobile application and by photointerpreters interacting with a geographic information system (GIS) using several information layers. Results of the validation experiments were performed in three areas of Northern Italy characterized by distinct ecosystems. The proposal showed better performances than traditional methods based on single spectral indexes.

In this work we propose an approach for mapping flooded areas from Sentinel-2 MSI (Multispectral Instrument) data based on soft fuzzy integration of evidence scores derived from both band combinations (i.e. Spectral Indices - SIs) and components of the Hue, Saturation and Value (HSV) colour transformation. Evidence scores are integrated with Ordered Weighted Averaging (OWA) operators, which model user's decision attitude varying smoothly between optimistic and pessimistic approach. Output is a map of global evidence degree showing the plausibility of being flooded for each pixel of the input Sentinel-2 (S2) image. Algorithm set up and validation were carried out with data over three sites in Italy where water surfaces are extracted from stable water bodies (lakes and rivers), natural hazard flooding, and irrigated paddy rice fields. Validation showed more than satisfactory accuracy for the OR-like OWA operators (F-score > 0.90) with performance slightly decreased (F-score < 0.75) over heterogeneous conditions (e.g. rice fields). The algorithm was applied with no changes and/or tuning to independent sites from the Copernicus Emergency Management Service (EMS) activations to simulate operational conditions. Over these sites, the proposed approach achieved greater, more consistent and robust mapping accuracy compared to traditional approaches based on the segmentation of single input features. Moreover, OWA operators offer an appealing way of combining and aggregating multiple information in decision making by modelling uncertainty in decision process.

Lodging is a major yield-reducing factors in wheat, causing reductions up to 80%. Timely detection of lodging can reduce its impacts and support proper decisions regarding expected yield, crop price or its insurance. Since the incidence of lodging is heterogeneous within a field, very high-resolution remote sensing data can be viable for accurate and prompt spatio-temporal assessment of lodging severity. As such unmanned aerial vehicles (UAVs) provide a versatile and cost-effective solution to monitor crops on a small scale with sub-centimetre spatial resolution. In this study, we analysed the spectral variability between different grades of lodging severity (non-lodged (NL), moderate (ML), severe (SL) and very severe (VSL)) and classified them using high-resolution UAV data. Multispectral orthomosaic UAV images with 5cm resolution and nine bands (covering the VIS-NIR spectrum with Sentinel-2 filters) were acquired in May 2018 for two wheat fields in Bonifiche Ferraresi farm, Jolanda di Savoia, Italy. Concurrent to the time of image acquisition, a field campaign was carried out in which crop characteristics and lodging related parameters were collected. The results showed that reflectance magnitude increased with lodging severity and demonstrated that the red-edge and NIR bands can be used to clearly discriminate between NL and lodged (all grades) wheat and to some extent between different lodging classes (ML, SL and VSL). The nearest neighbourhood classification performed using an object-based segmentation yielded optimal results with an overall accuracy of 90%, thus demonstrating the use of multispectral UAV data as a promising tool for wheat lodging assessment.

Rice mapping products were derived from Sentinel-1A and Landsat-8 OLI multi-temporal imagery over Northern Italy at the early stages of the 2015 growing season. A rule-based algorithm was applied to synthetic statistical metrics (TSDs-Temporal Spectra Descriptors) computed from temporal datasets of optical spectral indices and SAR backscattering coefficient. Temporal series are available up to the tillering/full canopy cover stage which is identified as the optimum timing for delivering in-season information on rice area (i.e. mid July). The approach relies on a-priori knowledge on crop dynamics to adapt time horizons for TSD computation and thresholds to local conditions. Output products consist of maps of rice cultivated areas, rice seeding techniques (dry and flooded rice) and flooding practices. Validation showed rice mapping overall accuracy to be 87.8% with commission and omission errors of 3.5% and 24.7%, respectively. Mapping of rice seeding technique showed good agreement with farmer declarations aggregated at the municipality scale (dry rice r  = 0.71 and flooded rice r  = 0.91). Finally, flood maps have an overall accuracy above 70%. Geo-products on rice areas and flooding occurrence are relevant information for water management at regional scale especially during summer in presence of multiple crops and water shortage.

In this study we exploit UAV data for estimating Fractional Vegetation Cover (FVC) of maize crop at the early stages of the growing season. UAV survey with a MicaSense RedEdge multispectral sensor was carried out on July 13th, 2017 over a maize field in Italy; simultaneous RGB in situ pictures were collected to build a reference dataset of FVC over 15 ESU (Elementary Sampling Units) distributed over the field under investigation. The approach proposed for classification of UAV data is based on local contrast enhancement techniques applied to a vegetation index (NDVI-Normalized Difference Vegetation Index) to capture signal from small plants at the early development stage. The output fc map is obtained over grid cells over 70 &times; 70 cm size. The approach proposed here, based on contextual analysis, reduced artefacts due to illumination conditions by better enhancing signal from vegetation compared to, for example, simple band combination such as vegetation index alone (e.g. NDVI). Validation accomplished by a point comparison between estimated (from UAV) and in situ measured FVC values provided R2 Combining double low line 0.73 and RMSE Combining double low line 6%.

Il contributo, dopo aver analizzato i fattori che influenzano la capacità di affrontare e superare efficacemente situazioni di emergenza legate a rischi naturali e/o antropici, propone una soluzione implementata nell'ambito del progetto "SIMULATOR_ADS: un Sistema Integrato ModULAre per la gesTione e prevenziOne dei Rischi - Arricchito con Dati Satellitari", una piattaforma ICT originale, con un'architettura basata su servizi Web, a supporto degli operatori e delle autorità locali di Protezione Civile nelle fasi di preparazione e di gestione delle emergenze.