This paper presents a review of concepts related to wildfire risk assessment, including the determination of fire ignition and propagation (fire danger), the extent to which fire may spatially overlap with valued assets (exposure), and the potential losses and resilience to those losses (vulnerability). This is followed by a brief discussion of how these concepts can be integrated and connected to mitigation and adaptation efforts. We then review operational fire risk systems in place in various parts of the world. Finally, we propose an integrated fire risk system being developed under the FirEUrisk European project, as an example of how the different risk components (including danger, exposure and vulnerability) can be generated and combined into synthetic risk indices to provide a more comprehensive wildfire risk assessment, but also to consider where and on what variables reduction efforts should be stressed and to envisage policies to be better adapted to future fire regimes. Climate and socio-economic changes entail that wildfires are becoming even more a critical environmental hazard; extreme fires are observed in many areas of the world that regularly experience fire, yet fire activity is also increasing in areas where wildfires were previously rare. To mitigate the negative impacts of fire, those responsible for managing risk must leverage the information available through the risk assessment process, along with an improved understanding on how the various components of risk can be targeted to improve and optimize the many strategies for mitigation and adaptation to an increasing fire risk.

Over the past 2 decades, several global burned area products have been produced and released to the public. However, the accuracy assessment of such products largely depends on the availability of reliable reference data that currently do not exist on a global scale or whose production require a high level of dedication of project resources. The important lack of reference data for the validation of burned area products is addressed in this paper. We provide the Burned Area Reference Database (BARD), the first publicly available database created by compiling existing reference BA (burned area) datasets from different international projects. BARD contains a total of 2661 reference files derived from Landsat and Sentinel-2 imagery. All those files have been checked for internal quality and are freely provided by the authors. To ensure database consistency, all files were transformed to a common format and were properly documented by following metadata standards. The goal of generating this database was to give BA algorithm developers and product testers reference information that would help them to develop or validate new BA products. BARD is freely available at https://doi.org/10.21950/BBQQU7 (Franquesa et al., 2020).

This paper presents and evaluates multitemporal LAI estimates derived from Sentinel-2A data on rice cultivated area identified using time series of Sentinel-1A images over the main European rice districts for the 2016 crop season. This study combines the information conveyed by Sentinel-1A and Sentinel-2A into a high-resolution LAI retrieval chain. Rice crop was detected using an operational multi-temporal rule-based algorithm, and LAI estimates were obtained by inverting the PROSAIL radiative transfer model with Gaussian process regression. Direct validation was performed with in situ LAI measurements acquired in coordinated field campaigns in three countries (Italy, Spain and Greece). Results showed high consistency between estimates and ground measurements, revealing high correlations (R² > 0.93) and good accuracies (RMSE < 0.83, rRMSEm < 23.6% and rRMSEr < 16.6%) in all cases. Sentinel-2A estimates were compared with Landsat-8 showing high spatial consistency between estimates over the three areas. The possibility to exploit seasonally-updated crop mask exploiting Sentinel-1A data and the temporal consistency between Sentinel-2A and Landsat-7/8 LAI time series demonstrates the feasibility of deriving operationally high spatial-temporal decametric multi-sensor LAI time series useful for crop monitoring.

The ever increasing need for accurate burned area mapping has led to a number of studies that focus on improving the mapping accuracy and effectiveness. In this work, we investigate the influence of derivative spectral and spatial features on accurately mapping recently burned areas using VHR IKONOS imagery. Our analysis considers both pixel and object-based approaches, using two advanced image analysis techniques: (a) an efficient feature selection method based on the Fuzzy Complementary Criterion (FuzCoC) and (b) the Support Vector Machine (SVM) classifier. In both cases (pixel and object-based), a number of higher-order spectral and spatial features were produced from the original image. The proposed methodology was tested in areas of Greece recently affected by severe forest fires, namely, Parnitha and Rhodes. The extensive comparative analysis indicates that the SVM object-based scheme exhibits higher classification accuracy than the respective pixel-based one. Additionally, the accuracy increased with the addition of derivative features and subsequent implementation of the FuzCoC feature selection (FS) method. Apart from the positive effect in the classification accuracy, the application of the FuzCoC FS method significantly reduces the computational requirements and facilitates the manipulation of the large data volume. In both cases (pixel and objet) the results confirmed that the use of an efficient feature selection method is a prerequisite step when extra information through higher-order features is added to the classification process of VHR imagery for burned area mapping.

The ERMES agromonitoring system for rice cultivations integrates EO data at different resolutions, crop models, and user-provided in situ data in a unified system, which drives two operational downstream services for rice monitoring. The first is aimed at providing information concerning the behavior of the current season at regional/rice district scale, while the second is dedicated to provide farmers with field-scale data useful to support more efficient and environmentally friendly crop practices. In this contribution, we describe the main characteristics of the system, in terms of overall architecture, technological solutions adopted, characteristics of the developed products, and functionalities provided to end users. Peculiarities of the system reside in its ability to cope with the needs of different stakeholders within a common platform, and in a tight integration between EO data processing and information retrieval, crop modeling, in situ data collection, and information dissemination. The ERMES system has been operationally tested in three European rice-producing countries (Italy, Spain, and Greece) during growing seasons 2015 and 2016, providing a great amount of near-real-time information concerning rice crops. Highlights of significant results are provided, with particular focus on real-world applications of ERMES products and services. Although developed with focus on European rice cultivations, solutions implemented in the ERMES system can be, and are already being, adapted to other crops and/or areas of the world, thus making it a valuable testing bed for the development of advanced, integrated agricultural monitoring systems.

This paper proposes the use of a genetic fuzzy-rule-based classification system for land cover classification from hyperspectral images. The proposed classifier, namely, Feature Selective Linguistic Classifier, is constructed through a three-stage learning process. The first stage produces a preliminary fuzzy rule base in an iterative fashion. During this stage, a local feature selection scheme is employed, designed to guide the genetic evolution, through the evaluation of deterministic information about the relevance of each feature with respect to its classification ability. The structure of the model is then simplified in a subsequent postprocessing stage. The performance of the classifier is finally optimized through a genetic tuning stage. An extensive comparative analysis, using an Earth Observing-1 Hyperion satellite image, highlights the quality advantages of the proposed system, when compared with nonfuzzy classifiers, commonly employed in hyperspectral classification tasks.