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Remote sensing techniques have become pivotal in monitoring algal blooms and population dynamics in freshwater bodies, particularly to assess the ecological risks associated with eutrophication. This study focuses on remote sensing methods for the analysis of 4 Italian lakes with diverse geological origins, leveraging water quality samples and data from the Sentinel-2 and Landsat 5.7–8 platforms. Chl-a, a well-correlated indicator of phytoplankton biomass abundance and eutrophication, was estimated using ordinary least squares linear regression to calibrate surface reflectance with chl-a concentrations. Temporal gaps between sample and image acquisition were considered, and atmospheric correction dedicated to water surfaces was implemented using ACOLITE and those specific to each satellite platform. The developed models achieved determination coefficients higher than 0.69 with mean square errors close to 3 mg/m3 for water bodies with low turbidity. Furthermore, the time series described by the models portray the seasonal variations in the lakes water bodies. [ABSTRACT FROM AUTHOR]

The detection of burnt areas from satellite imagery is one of the most straightforward and useful applications of satellite remote sensing. In general, the approach relies on a change detection analysis applied on pre- and post-event images. This change detection analysis usually is carried out by comparing the values of specific spectral indices such as: NBR (normalised burn ratio), BAI (burn area index), MIRBI (mid-infrared burn index). However, some potential sources of error arise, particularly when near-real-time automated approaches are adopted. An automated approach is mandatory when the burnt area monitoring should operate systematically on a given area of large size (country). Potential sources of errors include but are not limited to clouds on the pre- or post-event images, clouds or topographic shadows, agricultural practices, image pixel size, level of damage, etc. Some authors have already noted differences between global databases of burnt areas based on satellite images. Sources of errors could be related to the spatial resolution of the images used, the land-cover mask adopted to avoid false alarms, and the quality of the cloud and shadow masks. This paper aims to compare different burnt areas datasets (EFFIS, ESACCI, Copernicus, FIRMS, etc.) with the objective to analyse their differences. The comparison is restricted to the Italian territory. Furthermore, the paper aims to identify the degree of approximation of these satellite-based datasets by relying on ground survey data as ground truth. To do so, ground survey data provided by CUFA (Comando Unità Forestali, Ambientali e Agroalimentari Carabinieri) and CFVA (Corpo Forestale e Vigilanza Ambientale Sardegna) were used. The results confirm the existence of significant differences between the datasets. The subsequent comparison with the ground surveys, which was conducted while also taking into account their own approximations, allowed us to identify the accuracy of the satellite-based datasets. [ABSTRACT FROM AUTHOR]

This contribution describes the creation of a support system for the operational activities of detailed perimeter of wooded areas attacked by insects/pathogens and/or forest fires, by means of a SAPR (Airborne System with Remote Piloting) with definition of the trajectory in automation via real-time recognition assisted by an airborne sensor and satellite system. [ABSTRACT FROM AUTHOR]

Accurate fuel mapping is crucial for effectively determining wildfire risk and implementing management strategies. The primary challenge in fuel type mapping lies in the need to develop accurate and efficient methods for identifying and categorizing the various combustible materials present in an area, often on a large scale. In response to this need, this paper presents a comprehensive approach that combines remote sensing data and Convolutional Neural Network (CNN) to discriminate between fire behavior fuel models. In particular, a CNN-based classification approach that leverages Sentinel-2 imagery is exploited to accurately classify fuel types into seven preliminary main classes (broadleaf, conifers, shrubs, grass, bare soil, urban areas, and water bodies). To further refine the fuel mapping results, subclasses were generated from the seven principles by using biomass and bioclimatic maps. These additional maps provide complementary information about vegetation density and climatic conditions, respectively. By incorporating this information, we align our fuel type classification with the widely used Standard Scott and Burgan (2005) fuel classification system. The results are highly promising, showcasing excellent CNN training performance with all three metrics—accuracy, recall, and F1 score—achieving an impressive 0.99%. Notably, the network exhibits exceptional accuracy in a test case conducted in the southern region of Sardinia, successfully identifying Burnable classes in previously unseen pixels: broadleaf at 0.99%, conifer at 0.79%, shrub at 0.76%, and grass at 0.84%. The proposed approach presents a valuable tool for enhancing fire management, contributing to more effective wildfire prevention and mitigation efforts. Thus, this tool could be leveraged by fire management agencies, policymakers, and researchers to improve the determination of wildfire risk and management. [ABSTRACT FROM AUTHOR]

This Special Issue aims at studies covering the uses of AI techniques to interpret data obtained using different UAV sensors, providing references for the application of UAV imagery in various fields. This Special Issue comprises 15 articles, primarily exploring methods to utilize AI techniques for interpreting UAV imagery data. The development of unmanned aerial vehicles (UAV) and artificial intelligence (AI) techniques has drawn increasing interest and started a novel area of research applications. [Extracted from the article]

Natural vegetation provides various benefits to human society, but also acts as fuel for wildfires. Therefore, mapping fuel types is necessary to prevent wildfires, and hyperspectral imagery has applications in multiple fields, including the mapping of wildfire fuel types. This paper presents an automatic semisupervised machine learning approach for discriminating between wildfire fuel types and a procedure for fuel mapping using hyperspectral imagery (HSI) from PRISMA, a recently launched satellite of the Italian Space Agency. The approach includes sample generation and pseudolabelling using a single spectral signature as input data for each class, unmixing mixed pixels by a fully constrained linear mixing model, and differentiating sparse and mountainous vegetation from typical vegetation using biomass and DEM maps, respectively. Then the procedure of conversion from a classified map to a fuel map according to the JRC Anderson Codes is presented. PRISMA images of the southern part of Sardinia, an island off Italy, were considered to implement this procedure. As a result, the classified map obtained an overall accuracy of 87% upon validation. Furthermore, the stability of the proposed approach was tested by repeating the procedure on another HSI acquired for part of Bulgaria and we obtained an overall stability of around 84%. In terms of repeatability and reproducibility analysis, a degree of confidence greater than 95% was obtained. This study suggests that PRISMA imagery has good potential for wildfire fuel mapping, and the proposed semisupervised learning approach can generate samples for training the machine learning model when there is no single go-to dataset available, whereas this procedure can be implemented to develop a wildfire fuel map for any part of Europe using LUCAS land cover points as input. [ABSTRACT FROM AUTHOR]

Abstract: In this paper the detection of components of volcanic eruption has been carried out investigating, in appropriate bands of the electromagnetic spectrum (6.25, 8.7, 10.8, 12μm), the values of the brightness temperature. The analysis has been performed in the Thermal Infrared Region (TIR) studying both the absorption–emission and scattering phenomena related to the interactions between electromagnetic radiation and volcanic emissions. The results have been achieved by means of a combined use of numerical simulations, devoted to examining the behaviour of the atmosphere gases and volcanic components, and remotely sensed satellite images. The proposed methodologies allow an estimate of the amount of gaseous and solid components, of the size of the emitted particles, of the height of the volcanic plume and of the distance of the volcanic components from the crater. The processed images come from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) sensor on board the geo-stationary satellite Meteosat Second Generation (MSG) and take into consideration an eruption of the Etna volcano as a study case (1st of April 2012, 04:30 and 05:30UTC). The procedures are general and may therefore be extended to any other similar case. [Copyright &y& Elsevier]

The purpose of this study was to combine all available information on the state of Lake Pertusillo (Basilicata, Italy), both in the field and published, which included Sentinel-2A satellite data, to understand algal blooms in a lacustrine environment impacted by petroleum hydrocarbons. Sentinel-2A data was retrospectively used to monitor the state of the lake, which is located near the largest land-based oil extraction plant in Europe, with particular attention to chlorophyll a during algal blooms and petroleum hydrocarbons. In winter 2017, a massive dinoflagellate bloom (10.4 × 106 cell/L) of Peridinium umbonatum and a simultaneous presence of hydrocarbons were observed at the lake surface. Furthermore, a recent study using metagenomic analyses carried out three months later identified a hydrocarbonoclastic microbial community specialized in the degradation aromatic and nitroaromatic hydrocarbons. In this study, Sentinel-2A imagery was able to detect the presence of chlorophyll a in the waters, while successfully distinguishing the signal from that of hydrocarbons. Remotely sensed results confirmed surface reference measurements of lacustrine phytoplankton, chlorophyll a, and the presence of hydrocarbons during algal blooms, thereby explaining the presence of the hydrocarbonoclastic microbial community found in the lake three months after the oil spill event. The combination of emerging methodologies such as satellite systems and metagenomics represent an important support methodology for describing complex contaminations in diverse ecosystems. [ABSTRACT FROM AUTHOR]

This paper aims to assess the suitability of remote sensing for enhancing the management of water body resources and for providing an inexpensive way to gather, on a wide area, weed infestation extent and optical parameter linked to the water body status. Remotely sensed satellite images and ancillary ground true data were used to produce land cover maps, trough classification techniques, and water compounds maps, applying radiative transfer models. The study proposed within the framework of the cooperation between Italian Foreign Affair Ministry (through the University of Rome) and Kenyan Authorities has been carried out on the Kenyan part of the Lake Victoria. This lake is one of the largest freshwater bodies of the world where, over the last few years environmental challenges and human impact have perturbed the ecological balance affecting the biodiversity. The objective of this research study is to define the thematic products, retrievable from satellite images, like weed abundance maps and water compound concentrations. These products, if provided with an appropriate time frequency, are useful to identify the preconditions for the occurrence of hazard events like abnormal macrophyte proliferation and to develop an up-to-date decision support system devoted to an apprised territory, environment and resource management. [Copyright &y& Elsevier]

Fires represent one of the main factors of degradation and destruction of the Mediterranean forest heritage. According to fire-fighting agencies, a satellite-based fire-detection system can be considered operationally useful for Mediterranean countries when fires with a minimum extent of 1500 m² can be detected with a temporal resolution of 30 min. In fact, such a system should be able to detect fires at their first stage when it is possible to extinguish them more easily. The Centro di Ricerca Progetto San Marco has been analyzing for several years the possibility of using images acquired by the Spinning Enhanced Visible and Infrared Imager sensor onboard the geostationary satellite Meteosat Second Generation for this purpose. A new processing approach exploiting the increase in both spatial and temporal resolution (15 min) with respect to previous meteosat systems is described in this paper. The idea is based on the use of a change-detection technique to maximize the detection capabilities of the system in spite of its limited spatial resolution. This technique consists of comparing two or more images acquired at 15-min intervals, for which any temperature change can be attributed to fast dynamic phenomena, such as fires, when natural changes are modeled and removed. An assessment of the performances of this algorithm is carried out comparing its results with the report made available by Italian fire-fighting agencies and with fire products based on higher resolution sensors such as the Moderate Resolution Imaging Spectroradiometer. [ABSTRACT FROM AUTHOR]

Mediterranean forests are gravely affected by wildfires, and despite the increased prevention effort of competent authorities in the past few decades, the yearly number of fires and the consequent damage has not decreased significantly. To this end, a number of dynamical methods have been developed in order to produce short-term hazard indices, such as the Fire Probability Index and the Fire Weather Index. The possibility to estimate the fire hazard is based on the observation that there is a relationship between the characteristics of the vegetation (i.e., the fuel), in terms of abundance and moisture content, and the probability of fire insurgence. The density, type, and moisture content of the vegetation are modeled using custom fuel maps, developed using the latest Corine Land Cover, and using a number of indices such as the NDVI (Normalized Difference Vegetation Index), Global Vegetation Moisture Index (GVMI), and the evapotranspiration, derived from daily satellite imagery. This paper shows how the algorithm for the calculation of the Fire Potential Index (FPI) was improved by taking into account the effect of wind speed, topography, and local solar illumination through a simple temperature correction, preserving the straightforward structure of the FPI algorithm. The results were validated on the Italian region of Sardinia using official wildfire records provided by the regional administration. [ABSTRACT FROM AUTHOR]

Assessing the performance of optical imaging systems is crucial to evaluate their capability to satisfy the product requirements for an Earth Observation (EO) mission. In particular, the evaluation of image quality is undoubtedly one of the most important, critical and problematic aspects of remote sensing. It involves not only pre-flight analyses, but also continuous monitoring throughout the operational lifetime of the observing system. The Ground Sampling Distance (GSD) of the imaging system is often the only parameter used to quantify its spatial resolution, i.e., its capability to resolve objects on the ground. In practice, this feature is also heavily influenced by other image quality parameters such as the image sharpness and Signal-to-Noise Ratio (SNR). However, these last two aspects are often analysed separately, using unrelated methodologies, complicating the image quality assessment and posing standardisation issues. To this end, we expanded the features of our Automatic Edge Method (AEM), which was originally developed to simplify and automate the estimate of sharpness metrics, to also extract the image SNR. In this paper we applied the AEM to a wide range of optical satellite images characterised by different GSD and Pixel Size (PS) with the objective to explore the nature of the relationship between the components of overall image quality (image sharpness, SNR) and product geometric resampling (expressed in terms of GSD/PS ratio). Our main objective is to quantify how the sharpness and the radiometric quality of an image product are affected by different product geometric resampling strategies, i.e., by distributing imagery with a PS larger or smaller than the GSD of the imaging system. The AEM allowed us to explore this relationship by relying on a vast amount of data points, which provide a robust statistical significance to the results expressed in terms of sharpness metrics and SNR means. The results indicate the existence of a direct relationship between the product geometric resampling and the overall image quality, and also highlight a good degree of correlation between the image sharpness and SNR. [ABSTRACT FROM AUTHOR]

In the recent years Italian Space Agency has open a couple of calls concerning new space missions and new earth observation sensors. The present paper aims at describing the proposal submitted by the authors having the objective to develop a new concept of MIR/TIR satellite mission capable to overcome some of the limits of the actual optical sensors operating in the thermal region of the electromagnetic spectrum. These limits can be synthetized in:- low spatial resolution;- inaccurate surface temperature estimate;- limited diurnal sampling.At present spatial resolution and LST retrieval are relatively low and relatively inaccurate. Relatively means that even if significant progress has been done on the performances of thermal sensors on board of satellite systems the improvement is still not adequate for many applications become of interest in recent years. In fact, the spatial resolution of presently flying systems is not better than 90 m whereas the surface temperature estimate (Land and Sea Surface Temperature, LST, SST) is limited by the not perfect knowledge of the atmospheric characteristics at the time of the image acquisition. Another problem is represented by the uncertain knowledge of the surface emissivity for some type of coverage.An alternative way to increase image resolution is to design the sensor system to acquire images at a lower resolution and use the sensor system's pointing capabilities to allow collection of multiple low-resolution images within a short time span as the sensor travels over a target area. Then, image processing methods to fuse the multiple low-resolution images into a single high resolution image may be used. However, applying these resolution enhancement algorithms to satellite imagery acquired by rapid pointing maneuvers adds considerable complexity to the problem.All EO missions share the types of orbits used, i.e. a Sun-synchronous orbit (SSO). This is a great advantage in the case of sensors operating in the VIS/NIR/SWIR spectral regions because it minimizes changes in the acquired scene induced by the change in solar illumination conditions. On the other hand, however, this condition may represent a limit in the case of sensors operating in the MIR/LWIR range except for the two typical observations at about 12 hours of difference from the SSO. In fact, from the analysis of the diurnal variation of the surface temperature, in principle it is possible to retrieve the thermal characteristics of the material / soil sub-surface or to detect anomalies at the sub-surface level through the observation of the diurnal variation of the temperature, which is related aside from the surface albedo to the material thermal characteristics (e.g., thermal conductivity and heat capacity) which magnitudes vary with the materials composing the sub-surface layer.Therefore, the paper focuses on proposing an innovative satellite mission capable to contribute to fill an observational gap in existing EO systems. The temporal and spatial sampling characteristics of the proposed mission could represent a significant innovation as such resolutions (spatial and temporal) are excluded from the current panorama of EO missions. [ABSTRACT FROM AUTHOR]

Forest fires are one of the main sources of threat and degradation of vegetated and anthropicareas in the Mediterranean Basin. In the last decade, Italy experienced about 5500 forest firesper year, which burned about 50.000 hectares per year, although a high interannual variabilitycharacterizes this phenomenon. Supporting wildland fire prevention and management is thekey objective of the S2IGI (Sistema Satellitare Integrato Gestione Incendi) project, whichaims to provide a short and medium term forecasting of wildfire danger, an early detection ofwildland fires, a real-time forecast of wildland fire propagation, and an assessment of firedamages, based on the use of advanced technologies as Earth observation (EO) dataexploitation.In more detail, the main goal of S2IGI is the development of added-value products based onsatellite images, weather numerical predictions and in-situ ancillary data, together with adedicated decision support system. Therefore, S2IGI can support the wildland firemanagement activities along the following three phases: (1) forecast and prevention,(2) monitoring, detection and suppression, (3) damage assessment and vegetationrecovery.The products delivered by this project derive from previous EU and national RTD projects, inwhich, for many years, the project partners have been involved; the same products have beenextensively evaluated during the training and the demonstration period of the past projects.Crowd sourcing data will be also tested as additional source of information during theemergency phases.S2IGI project is being developed encompassing the following main principles:a) to define, make and demonstrate a functional architecture, accounting for all types ofoperational scenarios of fire-related emergencies at local, regional and national level,respectively;b) to customize and implement into the system the whole set of unsupervised, fire-orientedEO techniques prioritizing the exploitation of the Copernicus data and products;c) to explore, test, and implement into the system innovative unsupervised techniques andalgorithms for fire detection and burn scar mapping;d) to provide the overall system with relevant, quantitative decision support functionalities, asthe near-real-time straightforward modeling of fires in controlled environments under knownboundary conditions.This paper is devoted to introduce the S2IGI pilot project, and to describe the main productsand the related validation methods. S2IGI is a three years lasting project supported bythe Regional Administration of Sardinia under the POR (Programma OperativoRegionale) FESR (Fondo Europeo di Sviluppo Regionale) Sardegna 2014– 2020. [ABSTRACT FROM AUTHOR]

Thermal Airborne Spectrographic imager (TASI) has been widely used for different research purposes and applications, but only few works have been carried out on Land Surface Temperature (LST) retrievals exploiting the instrument data. LST is a significant parameter in a wide range of scientific disciplines, such as studies regarding evapo-transpiration, global change, and monitoring of water plants stress and drought. In order to solve the inversion problem for LST estimation by remote sensed data, a great variety of algorithms has been published and successfully applied on most popular satellite and airborne data. In this context, we aim to select the best wavelengths combination for TASI channels in order to retrieve LST by its data applying the split window (SW) algorithm suggested by Sobrino and Raissouni (2000) TASI-600 is a thermal sensor with 32 channels covering the spectral range between 8 and 11.5 µm (LWIR) and a spectral resolution of 109.5 nm. With the purpose of selecting the optimal bands combination, numerical analysis has been performed on TASI channels located in the range between 10 and 11.5 µm, which corresponds to the typical SW wavelengths (10 – 12 µm). A radiative transfer experiment has been carried out by using Moderate Resolution Atmospheric Radiance and Transmittance Model (MODTRAN) code. An observer altitude of 1 km, consistent with aerial acquisitions, and profiles and surface characteristics of a training dataset gathered from the database compiled by Borbas et al. have been considered. The SeeBor V5.0 database consists of 15,704 clear-sky profiles of temperature, ozone, total precipitable water (TPW); and other ancillary data such as ground latitude, longitude and elevation, surface pressure, skin temperature, spectral emissivities and the relative International Geosphere-Biosphere Programme (IGBP) classification. As preliminary outcomes, we have obtained that best TASI channels combinations suitable for LST estimation result in channel 18 (10.034 µm) combined with channel 28 (11.024 µm), and channel 29 (11.134 µm) combined with channel 30 (11.354 µm). Simulations of LST retrievals reveal an R2 of 0.999 for both bands combinations and a Root Mean Square Error (RMSE) of 0.485 K for the pair of bands 18 and 28, and a RMSE of 0.521 K for the pair of channels 29 and 30. Nevertheless, TASI channels located after 11 µm present a larger Noise Equivalent Delta Temperature (NEΔT) which increases with wavelength; thus, the involvement of these bands would have negative impact on the outcomes. On the other hand, the spectral variability in the emissivity of the different IGBP classes becomes larger for the bands combination 18 and 28, especially for barren areas (bare soil class), resulting on greater errors on the LST estimation. Hence, a sensitivity analysis has been accomplished evaluating the contribution to the total theoretical error on LST retrieval on the two channels pairs for the different terms involved in the SW algorithm, in particular those concerning the NEΔT and emissivity uncertainty. Sobrino, J. A., & Raissouni, N. (2000). Toward remote sensing methods for land cover dynamic monitoring: Application to Morocco. IJRS, 21(2), 353-366. [ABSTRACT FROM AUTHOR]

The paper aims to present the results obtained in the development of a system allowing for the detection and monitoring of forest fires and the continuous comparison of their intensity when several events occur simultaneously—a common occurrence in European Mediterranean countries during the summer season. The system, called SFIDE (Satellite FIre DEtection), exploits a geostationary satellite sensor (SEVIRI, Spinning Enhanced Visible and InfraRed Imager, on board of MSG, Meteosat Second Generation, satellite series). The algorithm was developed several years ago in the framework of a project (SIGRI) funded by the Italian Space Agency (ASI). This algorithm has been completely reviewed in order to enhance its efficiency by reducing false alarms rate preserving a high sensitivity. Due to the very low spatial resolution of SEVIRI images (4 × 4 km2 at Mediterranean latitude) the sensitivity of the algorithm should be very high to detect even small fires. The improvement of the algorithm has been obtained by: introducing the sun elevation angle in the computation of the preliminary thresholds to identify potential thermal anomalies (hot spots), introducing a contextual analysis in the detection of clouds and in the detection of night-time fires. The results of the algorithm have been validated in the Sardinia region by using ground true data provided by the regional Corpo Forestale e di Vigilanza Ambientale (CFVA). A significant reduction of the commission error (less than 10%) has been obtained with respect to the previous version of the algorithm and also with respect to fire-detection algorithms based on low earth orbit satellites. [ABSTRACT FROM AUTHOR]

The exacerbation of wildfires, attributed to the effects of climate change, presents substantial risks to ecological systems, infrastructure, and human well-being. In the context of the Sustainable Development Goals (SDGs), particularly those related to climate action, prioritizing the assessment and management of the occurrence and intensity of extensive wildfires is of utmost importance. In recent times, there has been a significant increase in the frequency and severity of widespread wildfires worldwide, affecting several locations, including Australia, Italy, and the United States of America. The presence of complex phenomena marked by limited predictability leads to significant negative impacts on biodiversity and human lives. The utilization of satellite-derived data with neural networks, such as convolutional neural networks (CNNs), is a potentially advantageous approach for augmenting the monitoring capabilities of wildfires. This research examines the generalization capability of four neural network models, namely the fully connected (FC), one-dimensional (1D) CNN, two-dimensional (2D) CNN, and three-dimensional (3D) CNN model. Each model's performance, as measured by accuracy, recall, and F1 scores, is assessed through K-fold cross-validation. Subsequently, T-statistics and p-values are computed based on these metrics to conduct a statistical comparison among the different models, allowing us to quantify the degree of similarity or dissimilarity between them. By using training data from Australia and Sicily, the performances of the trained model are evaluated on the test dataset from Oregon. The results are promising, with cross-validation on the training dataset producing mean precision, recall, and F1 scores ranging between approximately 0.97 and 0.98. Especially, the fully connected model has superior generalization capabilities, whilst the 3D CNN offers more refined and less distorted classifications. However, certain issues, such as false fire detection and confusion between smoke and shadows, persist. The aforementioned methodologies offer significant perspectives on the capabilities of neural network technologies in supporting the detection and management of wildfires. These approaches address the crucial matter of domain transferability and the associated dependability of predictions in new regions. This study makes a valuable contribution to the ongoing efforts in climate change by assisting in monitoring and managing wildfires. [ABSTRACT FROM AUTHOR]

The proposed system provides for the development of a SAPR system (Airborne Remote Pilotage System) which can be of assistance to the detailed perimeter, of wooded areas attacked by insects/pathogens and/or by forest fires in order to transmit to the competent authorities the certification of these areas affected by environmental damage, in compliance with current legislation. This activity is normally carried out through the interpretation of satellite image analyzes with spatial resolutions that are not always adequate for the purpose, which often entails the need for re-perimeteration in the field carried out by operators equipped with GNSS handheld systems. [ABSTRACT FROM AUTHOR]

In March 2019, the PRISMA (PRecursore IperSpettrale della Missione Applicativa) hyperspectral satellite was launched by the Italian Space Agency (ASI), and it is currently operational on a global basis. The mission includes the hyperspectral imager PRISMA working in the 400–2500 nm spectral range with 237 bands and a panchromatic (PAN) camera (400–750 nm). This paper presents an evaluation of the PRISMA top-of-atmosphere (TOA) L1 products using different in situ measurements acquired over a fragmented rural area in Southern Italy (Pignola) between October 2019 and July 2021. L1 radiance values were compared with the TOA radiances simulated with a radiative transfer code configured using measurements of the atmospheric profile and the surface spectral characteristics. The L2 reflectance products were also compared with the data obtained by using the ImACor code atmospheric correction tool. A preliminary assessment to identify PRISMA noise characteristics was also conducted. The results showed that: (i) the PRISMA performance, as measured at the Pignola site over different seasons, is characterized by relative mean absolute differences (RMAD) of about 5–7% up to 1800 nm, while a decrease in accuracy was observed in the SWIR; (ii) a coherent noise could be observed in all the analyzed images below the 630th scan line, with a frequency of about 0.3–0.4 cycles/pixel; (iii) the most recent version of the standard reflectance L2 product (i.e., Version 2.05) matched well the reflectance values obtained by using the ImACor atmospheric correction tool. All these preliminary results confirm that PRISMA imagery is suitable for an accurate retrieval of the bio-geochemical variables pertaining to a complex fragmented ecosystem such as that of the Southern Apennines. Further studies are needed to confirm and monitor PRISMA data performance on different land-cover areas and on the Radiometric Calibration Network (RadCalNet) targets. [ABSTRACT FROM AUTHOR]

Abstract: Urban growth and population dynamics are among the most critical information needed for future economic development planning, natural resources allocation and environmental management. In the present work, two methods, the first based on night-time images produced by NOAA and population maps provided by Oak Ridge National Laboratory's (ORNL) LandScan, and the second one on SAR imagery, were used in order to assess the expansion of urban areas surrounding the Winam Gulf (Lake Victoria, Kenya) at different scales. In the time covered by night-time lights imagery, the study highlighted a period of constant growth rate between 2002 and 2006 and a negative trend after 2006 and 2008. This decrease may be related to two main events occurring in the study area between 2006 and 2007: the decline of the Lake Victoria level and the abnormal proliferation of the floating weeds within the Winam Gulf. Meanwhile, the urban feature extraction obtained at a different scale within a particular district from 1997 up to 2008 results in a constant growth rate. Population movements around this zone explain different dynamics that should be studied in detail in order to understand their particular roots. [Copyright &y& Elsevier]

Novel approaches and algorithms to estimate crop physiological processes from Earth Observation (EO) data are essential to develop more sustainable management practices in agricultural systems. Within this context, this paper presents the results of different research activities carried out within the ESA-MOST Dragon 4 programme. The paper encompasses two research avenues: (a) the retrieval of biophysical variables of crops and yield prediction; and (b) food security related to different crop management strategies. Concerning the retrieval of variables, results show that LAI, derived by radiative transfer model (RTM) inversion, when assimilated into a crop growth model (i.e., SAFY) provides a way to assess yields with a higher accuracy with respect to open loop model runs: 1.14 t·ha−1 vs 4.42 t·ha−1 RMSE for assimilation and open loop, respectively. Concerning food security, results show that different pathogens could be detected by remote sensing satellite data. A k coefficient higher than 0.84 was achieved for yellow rust, thus assuring a monitoring accuracy, and for the diseased samples k was higher than 0.87. Concerning permanent crops, neural network (NN) algorithms allow classification of the Pseudomonas syringae pathogen on kiwi orchards with an overall accuracy higher than 91%. [ABSTRACT FROM AUTHOR]

Developing reliable methodologies of data quality assessment is of paramount importance for maximizing the exploitation of Earth observation (EO) products. Among the different factors influencing EO optical image quality, sharpness has a relevant role. When implementing on-orbit approaches of sharpness assessment, such as the edge method, a crucial step that strongly affects the final results is the selection of suitable edges to use for the analysis. Within this context, this paper aims at proposing a semi-automatic, statistically-based edge method (SaSbEM) that exploits edges extracted from natural targets easily and largely available on Earth: agricultural fields. For each image that is analyzed, SaSbEM detects numerous suitable edges (e.g., dozens-hundreds) characterized by specific geometrical and statistical criteria. This guarantees the repeatability and reliability of the analysis. Then, it implements a standard edge method to assess the sharpness level of each edge. Finally, it performs a statistical analysis of the results to have a robust characterization of the image sharpness level and its uncertainty. The method was validated by using Landsat 8 L1T products. Results proved that: SaSbEM is capable of performing a reliable and repeatable sharpness assessment; Landsat 8 L1T data are characterized by very good sharpness performance. [ABSTRACT FROM AUTHOR]

This study validated SNAP-derived LAI from Sentinel-2 and its consistency with existing global LAI products. The validation and inter-comparison experiments were performed on two processing levels, i.e., Top-of-Atmosphere and Bottom-of-Atmosphere reflectances and two spatial resolutions, i.e., 10 m, and 20 m. These were chosen to determine their effect on retrieved LAI accuracy and consistency. The results showed moderate R2, i.e., ~0.6 to ~0.7 between SNAP-derived LAI and in-situ LAI, but with high errors, i.e., RMSE, BIAS, and MAE >2 m2 m–2 with marked differences between processing levels and insignificant differences between spatial resolutions. In contrast, inter-comparison of SNAP-derived LAI with MODIS and Proba-V LAI products revealed moderate to high consistencies, i.e., R2 of ~0.55 and ~0.8 respectively, and RMSE of ~0.5 m2 m–2 and ~0.6 m2 m–2, respectively. The results in this study have implications for future use of SNAP-derived LAI from Sentinel-2 in agricultural landscapes, suggesting its global applicability that is essential for large-scale agricultural monitoring. However, enormous errors in characterizing field-level LAI variability indicate that SNAP-derived LAI is not suitable for precision farming. In fact, from the study, the need for further improvement of LAI retrieval arises, especially to support farm-level agricultural management decisions. [ABSTRACT FROM AUTHOR]

Vulnerability assessment is a vital component of wildfire management. This research focused on the development of a framework to measure and map vulnerability levels in several areas within Mediterranean Europe, where wildfires are a major concern. The framework followed a stepwise approach to evaluate its main components, expressed by exposure, sensitivity and coping capacity. Data on population density, fuel types, protected areas location, roads infrastructure and surveillance activities, among others, were integrated to create composite indices, representing each component and articulated in multiple dimensions. Maps were created for several test areas, in northwest Portugal, southwest Sardinia in Italy and northeast Corsica in France, with the contribution of local participants from civil protection institutions and forest services. Results showed the influence of fuel sensitivity levels, population distribution and protected areas coverage for the overall vulnerability classes. Reasonable levels of accuracy were found on the maps provided through the validation procedure, with an overall match above 72% for the several sites. The systematic and flexible approach applied allowed for adjustments to local circumstances with regards to data availability and fire management procedures, without compromising its consistency and with substantial operational capabilities. The results obtained and the positive feedback of end-users encourage its further application, as a means to improve wildfire management strategies at multiple levels with the latest scientific outputs. [ABSTRACT FROM AUTHOR]

• Five new ensemble models were developed for forest fire susceptibility modeling. • 10 conditioning factors were considered in this study. • RF-FR ensemble model outperformed SVM-FR, MLP-FR, CART-FR and LR-FR models. Forest fire disaster is currently the subject of intense research worldwide. The development of accurate strategies to prevent potential impacts and minimize the occurrence of disastrous events as much as possible requires modeling and forecasting severe conditions. In this study, we developed five new hybrid machine learning algorithms namely, Frequency Ratio-Multilayer Perceptron (FR-MLP), Frequency Ratio-Logistic Regression (FR-LR), Frequency Ratio-Classification and Regression Tree (FR-CART), Frequency Ratio-Support Vector Machine (FR-SVM), and Frequency Ratio-Random Forest (FR-RF), for mapping forest fire susceptibility in the north of Morocco. To this end, a total of 510 points of historic forest fires as the forest fire inventory map and 10 independent causal factors including elevation, slope, aspect, distance to roads, distance to residential areas, land use, normalized difference vegetation index (NDVI), rainfall, temperature, and wind speed were used. The area under the receiver operating characteristics (ROC) curves (AUC) was computed to assess the effectiveness of the models. The results of conducting proposed models indicated that RF-FR achieved the highest performance (AUC = 0.989), followed by SVM-FR (AUC = 0.959), MLP-FR (AUC = 0.858), CART-FR (AUC = 0.847), LR-FR (AUC = 0.809) in the forecasting of the forest fire. The outcome of this research as a prediction map of forest fire risk areas can provide crucial support for the management of Mediterranean forest ecosystems. Moreover, the results demonstrate that these novel developed hybrid models can increase the accuracy and performance of forest fire susceptibility studies and the approach can be applied to other areas. [ABSTRACT FROM AUTHOR]

This work presents the dynamics of fire occurrences, greenhouse gas (GHG) emissions, forest clearing, and degradation in the Brazilian Amazon during the period 2006–2019, which includes the approval of the new Brazilian Forest Code in 2012. The study was carried out in the Brazilian Amazon, Pará State, and the municipality of Novo Progresso (Pará State). The analysis was based on deforestation and fire hotspot datasets issued by the Brazilian Institute for Space Research (INPE), which is produced based on optical and thermal sensors onboard different satellites. Deforestation data was also used to assess GHG emissions from the slash-and-burn practices. The work showed a good correlation between the occurrence of fires in the newly deforested area in the municipality of Novo Progresso and the slash-and-burn practices. The same trend was observed in the Pará State, suggesting a common practice along the deforestation arch. The study indicated positive coefficients of determination of 0.72 and 0.66 between deforestation and fire occurrences for the municipality of Novo Progresso and Pará State, respectively. The increased number of fire occurrences in the primary forest suggests possible ecosystem degradation. Deforestation reported for 2019 surpassed 10,000 km2, which is 48% higher than the previous ten years, with an average of 6760 km2. The steady increase of deforestation in the Brazilian Amazon after 2012 has been a worldwide concern because of the forest loss itself as well as the massive GHG emitted in the Brazilian Amazon. We estimated 295 million tons of net CO2, which is equivalent to 16.4% of the combined emissions of CO2 and CH4 emitted by Brazil in 2019. The correlation of deforestation and fire occurrences reported from satellite images confirmed the slash-and-burn practice and the secondary effect of deforestation, i.e., degradation of primary forest surrounding the deforested areas. Hotspots' location was deemed to be an important tool to verify forest degradation. The incidence of hotspots in forest area is from 5% to 20% of newly slashed-and-burned areas, which confirms the strong impact of deforestation on ecosystem degradation due to fire occurrences over the Brazilian Amazon. [ABSTRACT FROM AUTHOR]

Fusarium head blight in winter wheat ears produces the highly toxic mycotoxin deoxynivalenol (DON), which is a serious problem affecting human and animal health. Disease identification directly on ears is important for selective harvesting. This study aimed to investigate the spectroscopic identification of Fusarium head blight by applying continuous wavelet analysis (CWA) to the reflectance spectra (350 to 2500 nm) of wheat ears. First, continuous wavelet transform was used on each of the reflectance spectra and a wavelet power scalogram as a function of wavelength location and the scale of decomposition was generated. The coefficient of determination R2 between wavelet powers and the disease infestation ratio were calculated by using linear regression. The intersections of the top 5% regions ranking in descending order based on the R2 values and the statistically significant (p-value of t-test < 0.001) wavelet regions were retained as the sensitive wavelet feature regions. The wavelet powers with the highest R2 values of each sensitive region were retained as the initial wavelet features. A threshold was set for selecting the optimal wavelet features based on the coefficient of correlation R obtained via the correlation analysis among the initial wavelet features. The results identified six wavelet features which include (471 nm, scale 4), (696 nm, scale 1), (841 nm, scale 4), (963 nm, scale 3), (1069 nm, scale 3), and (2272 nm, scale 4). A model for identifying Fusarium head blight based on the six wavelet features was then established using Fisher linear discriminant analysis. The model performed well, providing an overall accuracy of 88.7% and a kappa coefficient of 0.775, suggesting that the spectral features obtained using CWA can potentially reflect the infestation of Fusarium head blight in winter wheat ears. [ABSTRACT FROM AUTHOR]