A scalable fire danger index based on sentinel imagery

The incidence of wildfires and megafires and their disastrous consequences is increasing all over the planet. According to the latest European Forest Fire Information System annual fire report, in 2021 alone wildfires burned a surface area more than twice the size of Luxembourg, including more than a thousand square kilometres of Natura 2000 protected areas. In addition, 2022 has registered the highest number of wildfires since 2006, and will also be recorded as one of the driest years on record. Assuming that the most efficient and cost-effective way limit the damage caused by wildfires consists in their prevention, building tools to allow the decision makers to allocate resources using state of the art technology and fresh data is of the utmost importance. To this end, the combined usage of data from weather and satellite platforms capable to provide data on a regional or national scale and at a high temporal frequency provides the optimal solution for assessing and monitoring the state of the vegetation. However, users of fire danger product users often complain about the resolution of the provided products. While moderate- or coarse-resolution products may be adequate to cover the regional or national scale, high-resolution products are required to properly describe the fire danger in relatively small-sized areas of high interest in fire danger modelling, such as wildland-urban interfaces, national parks or protected areas. Using a different fire danger product based on the spatial scale of the target may be impractical and increase the workload and training requirements for the personnel. For this reason, we propose a scalable fire danger index based on Sentinel imagery that is able to cover different spatial scales by exploiting the surface reflectances provided by different Sentinel products (i.e. Sentinel-2 and Sentinel-3). This novel index, named Daily Fire Danger Index, exploits both weather and satellite data to estimate all the main variables of fire danger, such as the amount of dead fuel, moisture of the dead and live fuels, wind speed, evapotranspiration etc, and is calibrated using the historical records of wildfire occurrence in the target region. In particular, the live fuel moisture content is estimated using a state of the art procedure based on the inversion of radiative transfer models of the PROSAIL family. The index was tested in Sardinia, a region well-known for its proneness to wildfires and which is also regularly affected by megafires, and the performance comparison with the Canadian Fire Weather Index shows very significant improvements on the capability to discriminate fire danger even at a moderate resolution. Finally, the 2021 Planargia-Montiferru megafire was selected as a case study to showcase the added value of the high-resolution version of the index

Presenting a Semi-Automatic, Statistically-Based Approach to Assess the Sharpness Level of Optical Images from Natural Targets via the Edge Method. Case Study: The Landsat 8 OLI–L1T Data

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

Review of satellite based support to forest fire environmental impact assessment: the example of Arischia (Italy) forest fire

The paper aims at presenting a review of the information which satellite images can provide to support the assessment of the environmental impact of forest fires. This will be done by considering a specific fire event occurred in Italy in 2020 (L’Aquila province) which burned about 361 ha of forest. The burned area and fire damage severity has been computed by using high and very high spatial resolution satellite images and considering the maps produced by the activation of the Copernicus Emergency Management Service. The active fires have been monitored by using Low Earth Orbit and Geostationary Earth Orbit satellites. Additional information as land cover, above-ground-biomass and tree types maps, have been used to estimate burned biomass and atmosphere emissions

Daily fire hazard index for the prevention and management of wildfires in the region of Sardinia

The purpose of this paper is to show how the process for the calculation of the Daily Fire Hazard Index described in (Laneve, Cadau, 2007) [1] was improved upon by taking into account the effects of wind speed and direction, examining the wildfire insurgence data in the Italian region of Sardinia. The Daily Fire Hazard Index was developed in the context of the S2IGI project with the objective to provide a daily estimate of the likelihood of wildfire insurgence, in order to help coordinate the firefighting activities. Using land cover maps, fuel maps and MODIS satellite imagery, an algorithm was developed to estimate the relative amount of live and dead vegetation. Meteorological data is used to determine the temperature, the relative humidity and the wind speed. After using the FAO Penman-Monteith method (1998) [2] for the determination of the reference evapotranspiration of the vegetation, a simple algorithm was used to correct the surface temperature accounting for the effect of the magnitude of the wind speed. After determining the wind direction using the meteorological forecast data, the correction factor takes into account the fact that in Sardinia, the majority of the wildfires occur in days of strong Mistral winds

Evaluating sentinel-3 viability for vegetation canopy monitoring and fuel moisture content estimation

The main objectives of the Sentinel-3 mission are to support ocean forecasting systems, environmental and climate monitoring. However, the coverage of the visible, near-infrared and short-wave infrared portion of the electromagnetic spectrum with a 300 meter resolution and a revisit period of less than 2 days make it very appealing also for vegetation monitoring. In this paper we explore the possibility of using the Sentinel-3 Synergy surface directional reflectances and the PROSAIL model to reliably estimate biophysical variables in general and live fuel moisture content in particular. The latter is a fundamental variable in fire behaviour models and in fire danger assessment, and consequently of high interest in fire management activities. We performed a Global Sensitivity Analysis to identify the most significant PROSAIL parameters in each Synergy channel, and tested the results by implementing a simple Look-Up Table based retrieval algorithm. The outcome shows the potential of biophysical parameter estimation based on this Sentinel-3 product

On-orbit Image Sharpness Assessment using the Edge Method: Methodological Improvements for Automatic Edge Identification and Selection from Natural Targets

The metrics traditionally used for assessing the sharpness level of optical imagery acquired by spaceborne sensors (e.g., relative edge response, point spread function, full width at half maximum of the line spread function, modulation transfer function) are usually measured before launch using a set of standard simulated inputs. However, vibrations occurring during launch and satellite deployment, as well as sensor degradation through time, may alter the nominal characteristics significantly. Therefore, post-launch assessment analyses are necessary for monitoring data quality. To this end, on-orbit measurement techniques for sharpness assessment-e.g., the edge method (EM)-were developed. Selection of suitable targets to be used for such techniques is a crucial step for carrying out the assessment successfully. The objective of this paper is to describe an automatic method for identification of suitable edges to be used for on-orbit sharpness assessment via the EM by taking into account the widespread presence of natural targets-like agricultural fields-on Earth. The presented approach is expected to ease the continuous monitoring of the sharpness level of optical data acquired by spaceborne sensors

A Fully Automatic Method for on-Orbit Sharpness Assessment: a Case Study Using Prisma Hyperspectral Satellite Images

The main objectives of the Sentinel-3 mission are to support ocean forecasting systems, environmental and climate monitoring. However, the coverage of the visible, near-infrared and short-wave infrared portion of the electromagnetic spectrum with a 300 meter resolution and a revisit period of less than 2 days make it very appealing also for vegetation monitoring. In this paper we explore the possibility of using the Sentinel-3 Synergy surface directional reflectances and the PROSAIL model to reliably estimate biophysical variables in general and live fuel moisture content in particular. The latter is a fundamental variable in fire behaviour models and in fire danger assessment, and consequently of high interest in fire management activities. We performed a Global Sensitivity Analysis to identify the most significant PROSAIL parameters in each Synergy channel, and tested the results by implementing a simple Look-Up Table based retrieval algorithm. The outcome shows the potential of biophysical parameter estimation based on this Sentinel-3 product

Evaluating the potentialities of Copernicus Very High Resolution (VHR) optical datasets for assessing the shoreline erosion hazard in microtidal environments

In the past years, several studies have shown that Earth Observation (EO) data can be successfully used for analysing shoreline evolution trends and assessing coastal erosion hazard/risk. Within this framework, the exploitation of long-term archives of sensors data characterised by moderate spatial resolution (e.g., Landsat) has shown its potential; particularly in higher energy coastal environments (e.g., Oceanic areas) where the magnitude of long-term erosion/accretion processes (e.g., decadal) can be resolved by the abovementioned sensors. However, the spatial resolution of these data may prevent an accurate analysis in microtidal coastal environments (e.g., Mediterranean Sea), especially for analyses focused on a short-term period (e.g., few years). This is mainly due to the high level of uncertainty associated with the occurrence of erosion/accretion processes of lower magnitude detected by EO sensors retaining a moderate spatial resolution. Within this context, this work was conceived to evaluate the potentialities of the Copernicus Very High Resolution (VHR) optical datasets (spatial resolution: 2-4 m) for assessing the shoreline evolution trends in an exemplifying urbanised coastal area of the Mediterranean Sea (i.e., Lido di Ostia, Rome, Italy), over a short-term period (i.e., 4 years). To achieve this objective, an automatic technique of shoreline detection and extraction at subpixel level was tested. Results allowed to: i) detect a shoreline evolution trend coherent with the geomorphological characteristics of the study area; ii) smoothly identify/quantify fine-scale variations of accretion/erosion patterns along the coast. This is extremely important to map the areas most exposed to shoreline erosion hazard/risk

Towards an Integrated Approach to Wildfire Risk Assessment: When, Where, What and How May the Landscapes Burn

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