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Land surface temperature (LST) is a manifestation of the surface thermal environment (LSTE) and an important driver of physical processes of surface land energy balance at local to global scales. Tenerife is one of the most heterogeneous islands among the Canaries from a climatological and bio-geographical point of view. We study the surface thermal conditions of the volcanic island with remote sensing techniques. In particular, we consider a time series of Landsat 8 (L8) level 2A images for the period 2013 to 2019 to estimate LST from surface reflectance (SR) and brightness Temperature (BT) images. A total of 26 L8 dates were selected based on cloud cover information from metadata (land cloud cover < 10%) to estimate pixel-level LST with an algorithm based on Radiative Transfer Equations (RTE). The algorithm relies on the Normalized Difference Vegetation Index (NDVI) for estimating emissivity pixel by pixel. We apply the Independent Component Analysis (ICA) that revealed to be a powerful tool for data mining and, in particular, to separate multivariate LST dataset into a finite number of components, which have the maximum relative statistical independence. The ICA allowed separating the land surface temperature time series of Tenerife into 11 components that can be associated with geographic and bioclimatic zones of the island. The first ten components are related to physical factors, the 11th component, on the contrary, presented a more complex pattern resulting possibly from its small amplitude and the combination of various factors into a single component. The signal components recognized with the ICA technique, especially in areas of active volcanism, could be the basis for the space-time monitoring of the endogenous component of the LST due to surface hydrothermal and/or geothermal activity. Results are encouraging, although the 16-day revisit frequency of Landsat reduces the frequency of observation that could be increased by applying techniques of data fusion of medium and coarse spatial resolution images. The use of such systems for automatic processing and analysis of thermal images may in the future be a fundamental tool for the surveillance of the background activity of active and dormant volcanoes worldwide.

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 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.

In this article, we propose an automatic procedure for classification of UAV imagery to map weed presence in rice paddies at early stages of the growing cycle. The objective was to produce a weed map (common weeds and cover crop remnants) to support variable rate technologies for site-specific weed management. A multi-spectral ortho-mosaic, derived from images acquired by a Parrot Sequoia sensor mounted on a quadcopter, was classified through an unsupervised clustering algorithm; cluster labelling into 'weed'/'no weed' classes was achieved using geo-referenced observations. We tested the best set of input features among spectral bands, spectral indices and textural metrics. Weed mapping performance was assessed by calculating overall accuracy (OA) and, for the weed class, omission (OE) and commission errors (CE). Classification results were assessed under an 'alarmist' approach in order to minimise the chance of overestimating weed coverage. Under this condition, we found that best results are provided by a set of spectral indices (OA = 96.5%, weed CE = 2.0%). The output weed map was aggregated to a grid layer of 5 × 5 m to simulate variable rate management units; a weed threshold was applied to identify the portion of the field to be subject to treatment with herbicides. Ancillary information on weed and crop conditions were derived over the grid cells to support precision agronomic management of rice crops at the early stage of growth.

Providing relatively fine spatial resolution multispectral data, Landsat-8, Landsat-7 (L8 and L7, respectively) and Sentinel-2 (S2) from 2013 to 2018 have been used in this study for enabling high-frequency monitoring of water quality of two small (the smaller with an area of 1.6 km2) freshwater dammed reservoirs. Located in Sardinia (Italy) and Crete (Greek), respectively, Mulargia and Aposelemis represent vital resources to supply drinking water in downstream valleys. A total of 400 cloud-free satellite images were turned into information on water quality by using an image processing chain implementing physically based methods for retrieving chlorophyll-a concentration (Chl-a), turbidity, Secchi disk depth (SDD) and surface water temperature. These estimates have been successfully validated (the lower Pearson correlation r was 0.88 for Chl-a) with 23 match-ups of in situ and satellite data. Results of the multi-temporal analyses showed a decrease of SDD due to the increase of Chl-a in Aposelemis or an increase of turbidity in Mulargia. For both freshwater reservoirs, the satellite-derived trophic state index assigned both lakes to mesotrophic conditions. The results finally suggested the effectiveness of S2 and Landsat in increasing, for the latest investigated years, the frequency of observations.

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.

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&rsquo, 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&rsquo, 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

The present short paper describes the functionalities of a desktop tool enabling non-expert users to develop downstream services for the periodic download and processing of data from Sentinel sources.

This paper aims at investigating the use of microwave and optical images for the detection and characterization of fire scars in vegetated areas. To cope with this issue, Sentinel-1 (S-1) C-band synthetic aperture radar (SAR), and multispectral Sentinel-2 (S-2) multispectral data are used. First, a consolidated fuzzy-based methodology, specifically designed for retrieving burned areas, is considered. Then, an investigation based on the analysis of the maps of backscattering coefficients, as obtained by processing stacks of SAR images, is performed. The outcomes obtained with SAR data are validated by comparison with the results obtained with the optical multispectral data. Presented experiments are related to the Soberanes fires, California, which ignited on July 22, 2016, and affected more than 400 square kilometers. In addition, we explore the potential use of stacks of spatial coherence maps generated from pairs of complex-valued SAR images that encompass a fire event to recover new, additional information on the vegetation state and extent of the fire scars. To the purpose, stacks of S-1 SAR images acquired at different polarizations over the Mt. Vesuvius, Italy, in the 2017 summer season has been used. Some preliminary results, which evidence how the variations of the spatial coherence values can be somehow related to a fire and used to recover additional pieces of information, are presented.

In this work a dataset of maps of Chl-a derived from different sensors (MERIS-OLI-MSI-OLCI) satellite images is used to evaluate water quality of the four most important Italian subalpine lakes (Garda, Maggiore, Iseo, Como) in the period 2003-2018. In order to produce Chl-a concentration maps, imagery needs to be processed along a processing chain, which includes radiometric correction (to convert digital numbers into at-satellite-atmospheric radiance), atmospheric correction to obtain Remote sensing reflectance (C2R for MERIS, 6SV for MSI and OLI, Polymer for OLCI), and finally the application of bio-optical models to retrieve chl-a concentration. The results presented in this study show: i) that the seasonal variability of chl-a concentration is particularly pronounced during spring and autumn; ii) Lake Iseo is the one that has a worse judgment than WFD; iii) 2005 and 2016 were quite critical years; iv) trends show a slight tendency to increase Chl-a concentrations during the period considered, particularly for Iseo and Maggiore lakes.

The aim of this paper is to investigate how optical and Synthetic Aperture Radar (SAR) data can be combined in an integrated multi-source framework to identify burned areas at the regional scale. The proposed approach is based on the use of fuzzy sets theory and a region-growing algorithm. Landsat TM and (C-band) ENVISAT Advanced Synthetic Aperture Radar (ASAR) images acquired for the year 2003 have been processed to extract burned area maps over Portugal. Pre-post fire SAR backscatter temporal difference has been integrated with optical spectral indices to the aim of reducing confusion between burned areas and low-albedo surfaces. The output fuzzy score maps have been compared with reference fire perimeters provided by the Fire Atlas of Portugal. Results show that commission and omission errors in the output burned area maps are a function of the threshold applied to the fuzzy score maps; between the two extremes of the greatest producer’s accuracy (omission error < 10%) and user’s accuracy (commission error < 5%), an intermediate threshold value provides errors of about 20% over the study area. The integration of SAR backscatter allowed reducing local commission errors from 65.4% (using optical data, only) to 11.4%, showing to significantly mitigate local errors due to the presence of cloud shadows and wetland areas. Overall, the proposed method is flexible and open to further developments; also in the perspective of the European Space Agency (ESA) Sentinel missions operationally providing SAR and optical datasets.

The triangle method has been applied to derive a weekly indicator of evaporative fraction on vegetated areas in a temperate region in Northern Italy. Daily MODIS Aqua Land Surface Temperature (MYD11A1) data has been combined with air temperature maps and 8-day composite MODIS NDVI (MOD13Q1/MYD13Q1) data to estimate the Evaporative Fraction (EF) at 1 km resolution, on a daily basis. Measurements at two eddy covariance towers located within the study area have been exploited to assess the reliability of satellite based EF estimations as well as the robustness of input data. Weekly syntheses of the daily EF indicator (EFw) were then derived at regional scale for the years 2010, 2011 and 2012 as a proxy of overall surface moisture condition. EFw showed a temporal behavior consistent with growing cycles and agro-practices of the main crops cultivated in the study area (rice, forages and corn). Comparison with official regional corn yield data showed that variations in EFw cumulated over summer are related with crop production shortages induced by water scarcity. These results suggest that weekly-averaged EF estimated from MODIS data is sensible to water stress conditions and can be used as an indicator of crops’ moisture conditions at agronomical district level. Advantages and disadvantages of the proposed approach to provide information useful to issue operational near real time bulletins on crop conditions at regional scale are discussed.

This paper aims at investigating the potential of Sentinel-1 C-band synthetic aperture radar (SAR) observations for detecting fire scars in vegetated areas at regional scale. A comprehensive analysis of the backscattering coefficients is carried out. The experimental analysis is conducted by analyzing the scenario of the Sardinia Island, which is one of the Italian regions most affected by fire events over the summer season. The detection capability of fire scars in such an environment is demonstrated by exploiting information extracted from dual-polarized SAR data. Our results reveal a significant decrease of the VH response over the fire-disturbed forests, thus, highlighting the effectiveness of such cross-polarized observations. In order to prove the validity of the proposed approach for the detection of fire scars in the vegetation layer, the results of the conducted experiments have been suitably compared with burned areas identified by using an existing fuzzy-based algorithm, which has been applied to multispectral Landsat-8 operational land imager data. This investigation opens the way to systematic methods for monitoring fire scars in heterogeneous environments, and in particular in fire-prone Mediterranean ecosystems.