Remote sensing-based prediction of forest fire characteristics

Forest fires are a major ecosystem disturbance at global scale, put pressure on agencies in charge of citizens and infrastructure security and cause unvaluable human losses. Fires are controlled by multiple static and dynamic drivers related to topography, land cover, climate, weather, and anthropic activity. Among these, weather is an active driver of live and dead fuel moisture, which has a direct effect on fire occurrence and behaviour. As a result, in areas experiencing prolonged droughts and heat waves, altered meteorological patterns lead to increased frequency and intensity of forest fires. The operational response of governments, local authorities, forest managers and civil protection agencies in charge of managing forest fires is informed by the assessment of factors controlling fire occurrence and behaviour, often synthesised in maps of fire danger. Danger is defined as the resultant of all factors affecting the inception, spread, and difficulty of control of fires, and it is typically expressed in the form of an index. Key contributors to fire danger are fuel type, amount, and conditions, notably with respect to moisture content. Remote sensing measurements in the shortwave infrared are sensitive to water content of live fuels, while measurements in the thermal infrared allow the detection of vegetation stress conditions due to vapour pressure deficit. In fact, several scholars proved that satellite estimates of vegetation water content and of land surface temperature could be effectively used to predict fire occurrence. Nevertheless, to the best of this author’s knowledge, no research was previously published connecting pre-fire remote sensing measurements to fire behaviour characteristics. This clearly identifies a knowledge gap which needs further investigation and that can be translated in the following research question: to what extent can remote sensing of forest condition be used to predict fire behaviour characteristics and assess the probability of extreme events? The research described in this dissertation aimed at developing methods based on pre-fire optical and thermal remote sensing observations of forests for the prediction of fire behaviour characteristics. The study was carried out in Campania, Italy (13595 km2), one of the most densely populated and fire affected regions in the Mediterranean. Data on all fire events recorded between 2002 and 2011 was provided by Carabinieri (Italian national gendarmerie) forest fire preparedness unit (Nucleo Informativo Antincendio Boschivo, NIAB). The study made use of MODIS land surface temperature (LST) and surface reflectance collection 6 products, which are publicly available on the USGS Land Processes Distributed Active Archive Center (LP DAAC). Approach was probabilistic in nature, trying to relate pre-fire satellite observations of vegetation conditions to the probability distributions of burned area, fire duration and rate of spread. Efforts initially focussed on assessing LST anomaly and its effect on fire behaviour characteristics. LST anomaly is a measure of excess enthalpy stored in fuels. It controls the probability of flames extinction and thus fire duration. First, a climatology of LST was constructed from the longest available time series of daily MODIS LST by means of the Harmonic Analysis of Time Series (HANTS) algorithm. HANTS was then used to construct annual models of daily LST. Finally, the daily LST anomaly was evaluated as the difference between the annual model and the climatology. Fires in the database were then associated with LST anomaly values recorded at their corresponding location on the day prior to the event. Probability distribution functions of log-transformed burned area (normal), log-transformed fire duration (generalised extreme value, GEV) and log-transformed rate of spread (Weibull) where then determined in ten decile bins of LST anomaly. The mean and the standard deviation of the normal distribution of log-transformed burned area showed a clear linear dependence on LST anomaly (r2=0.81, p<0.001 and r2=0.52, p<0.05 respectively), indicating an increase in the probability of large fires with increasing LST anomaly. Similarly, a marked linear dependence on LST anomaly was found for the location (r2=0.78, p<0.001), scale (r2=0.79, p<0.001) and shape (r2=0.87, p<0.001) of the GEV distribution of log-transformed fire duration, favouring longer fire duration with increasing LST anomaly. Conversely, the LST anomaly had a limited effect on the Weibull distribution of log-transformed rate of spread, with scale and shape showing slightly decreasing trends (r2=0.50, p<0.05 and r2=0.54, p<0.05 respectively). A likelihood ratio test showed that the probability models of log-transformed burned area, fire duration and rate of spread conditional to LST anomaly (alternative models) allowed the rejection of the corresponding unconditional models fitting all data (null models), confirming that LST anomaly is a covariate of burned area, fire duration and, to a lesser extent, rate of spread. These results are in line with expectations from models of the combustion process. Following a similar line of reasoning, this study further focussed on remote sensing of live fuel moisture content (LFMC). This vegetation property controls ignition delay, and thus affects flames propagation. The first step was the construction of a novel spectral index, the perpendicular moisture index (PMI), specifically designed to be sensitive to LFMC. The PMI was developed from simulated vegetation spectral data convolved to MODIS bands by noting that in the spectral reflectance subspace of MODIS bands 2 (0.86 µm) and 5 (1.24 µm) isolines of LFMC can be identified, and that these isolines are straight and parallel. By taking as a reference the line corresponding to LFMC=0 (completely dry vegetation), the PMI was calculated as the distance of measured reflectance from the reference line. The PMI is thus a measure of LFMC, and higher values of PMI correspond to higher moisture content. The index was found to be linearly related to LFMC, especially for dense vegetation cover (r2=0.70 when leaf area index is larger than 2, r2=0.87 when larger than 4). When vegetation cover is less dense, the contribution of soil background to the measured reflectance increases, and the PMI underestimates LFMC. PMI maps were produced from the MODIS 8-day composited reflectance product, and fires in the database were associated with the corresponding PMI value at the fire location in the pre-fire compositing period. Using the same approach adopted for LST anomaly, the probability distribution functions of log-transformed burned area, fire duration and rate of spread were determined in ten decile bins of PMI. The mean of the normal distribution of log-transformed burned area showed a clear linear dependence on PMI (r2=0.80, p<0.001), while no trend could be observed for standard deviation. A clear linear dependence on PMI was also found for scale and shape of the Weibull distribution of log-transformed rate of spread (r2=0.97, p<0.001 and r2=0.82, p<0.001 respectively). These results were further confirmed by a likelihood ratio test where the probability models of log-transformed burned area and rate of spread conditional to PMI allowed the rejection of the corresponding unconditional models fitting all data. Location and shape of the GEV distribution of log-transformed fire duration showed no significant linear trend with PMI, whereas scale showed a weak trend (r2=0.55, p<0.05). However, in the likelihood ratio test the probability model of log-transformed fire duration conditional to PMI failed to reject the corresponding unconditional model. These results showed that PMI is a covariate of burned area and rate of spread, as expected from flames propagation models, but not of fire duration. Predictions of fire characteristics based on concurrent observations of LST anomaly and PMI were compared with predictions based on the Fire Weather Index (FWI) System. This fire danger rating tool proved to be effective in several areas worldwide, including Europe. FWI values from weather reanalysis data were associated with fires in the database and were analysed with the same approach adopted for LST anomaly and PMI. It was found that parameters of the probability distribution function of log-transformed burned area and fire duration conditional to FWI System components followed clear linear trends, with increasing danger values leading to higher probabilities of large burned areas and long fire durations. Conversely, FWI System components were unrelated to the rate of spread. Trend analysis (coefficient of determination and p-value of the linear fit, Sen’s slope and Mann-Kendall test) and likelihood ratio tests were used to compare the trends in the parameters of the probability distributions of fire characteristics. It was shown that remote sensing predictions of burned area and fire duration were comparable or better than those from FWI, and that PMI is a good predictor of the rate of spread whereas FWI System components are not. The identified linear trends in the dependence of the parameters of the probability distribution of log-transformed burned area, fire duration and rate of spread on LST anomaly and on PMI allow the prediction of the probability of extreme events, conditional to ignition, as a function of pre-fire remote sensing observations. As both LST anomaly and PMI are good covariates of burned area, these two remote sensing observations of vegetation conditions can be used jointly to improve the prediction of the probability of fires larger than say, the 95th percentile of all events recorded in the study area (30 ha). It was found that the probability of a fire resulting in a burned area larger than 30 ha increases from 0.9% to 9.2% with pre-fire LST anomaly increasing from -2.1 to 4.3 K and increases from 1.8% to 7.4% with pre-fire PMI decreasing from 0.052 to -0.032. When the probability of fires exceeding 30.0 ha is modelled as a function of both LST anomaly and PMI, the probability increases from 0.5% to 12.7%. This confirms that the joint use of LST anomaly and PMI leads to improved predictions. The scientific community showed a consensus on the need to improve fire danger prediction through a more accurate assessment of live fuel condition. Existing fire danger rating systems estimate fuel moisture content from meteorological variables, which results in an undesired approximated solution due to underlying assumptions. Consequently, any direct observation of fuel moisture content has the potential to enable a better evaluation of fire occurrence and fire danger indices. From a remote sensing perspective, these considerations are translated in the research question on the need to understand to what extent can satellite measurements be used to predict forest fire behaviour characteristics. This research showed that remote sensing of vegetation in the optical and thermal domains allows the prediction of the probability distributions of fire behaviour characteristics such as burned area, duration, and rate of spread. These can be further used to evaluate the probability of extreme events, conditional to ignition, as a function of pre-fire remote sensing measurements, contributing to predict danger. It should be noted once more that this result was achieved by using pre-fire remote sensing observations, allowing the prediction of fire characteristics. In perspective, results showed in this dissertation can support the development of operational tools for forest managers and civil protection agencies in their fire preparedness activities.Optical and Laser Remote Sensin

An application of the perpendicular moisture index for the prediction of fire hazard

Various factors contribute to forest fire hazard, and among them vegetation moisture is the one that dictates susceptibility to fire ignition and propagation. The scientific community has developed a number of spectral indices based on remote sensing measurements in the optical domain for the assessment of vegetation equivalent water thickness (EWT), which is defined as the mass of liquid water per unit of leaf surface. However, fire models rely on the live fuel moisture content (LFMC) as a measure of vegetation moisture. LFMC is defined as the ratio of the mass of the liquid water in a fresh leaf over the mass of oven dry leaf, and spectral indices proposed so far fail in capturing LFMC variability. Recently, the perpendicular moisture index (PMI), based on MODIS, was pro-posed to overcome this limitation and provide a direct measure of LFMC. The aim of this research was to understand the potential and limitations of the PMI in predicting fire hazard, towards its ap-plication in a practical context. To this purpose, a data set of more than 7,700 fires recorded in Campania (13,595 km2), Italy, between 2000 and 2008 was compared with PMI derived from MODIS images. Results show that there is no relationship between PMI and fire size, whereas a linear correlation was found between the spectral index and fire rate of spread.Geoscience & Remote SensingCivil Engineering and Geoscience

Remote sensing estimation of vegetation moisture for the prediction of fire hazard (poster)

Geoscience & Remote SensingCivil Engineering and Geoscience

Predicting forest fires burned area and rate of spread from pre-fire multispectral satellite measurements

Operational forest fire danger rating systems rely on the recent evolution of meteorological variables to estimate dead fuel condition. Further combining the latter with meteorological and environmental variables, they predict fire occurrence and spread. In this study we retrieved live fuel condition from MODIS multispectral measurements in the near infrared and shortwave infrared. Next, we combined these retrievals with an extensive dataset on actual forest fires in Campania (13,595 km2), Italy, to determine how live fuel condition affects the probability distribution functions of fire characteristics. Accordingly, the specific objective of this study was to develop and evaluate a new approach to estimate the probability distribution functions of fire burned area, duration and rate of spread as a function of the Perpendicular Moisture Index (PMI), whose value decreases with decreasing live fuel moisture content (LFMC). To this purpose, available fire data was intersected with MODIS 8-day composited reflectance data so to associate each fire event with the corresponding pre-fire PMI observation. Fires were then grouped in ten decile bins of PMI, and the conditional probability distribution functions of burned area, fire duration and rate of spread were determined in each bin. Distributions of burned area and rate of spread vary across PMI decile bins, while no significant difference was observed for fire duration. Further testing this result with a likelihood ratio test confirmed that PMI is a covariate of burned area and rate of spread, but not of fire duration. We defined an extreme event as a fire whose burned area (respectively rate of spread) exceeds the 95th percentile of the frequency distribution of all observed fire events. The probability distribution functions in the ten decile bins of PMI were combined to obtain a conditional probability distribution function, which was then used to predict the probability of extreme fires, as defined. It was found that the probability of extreme events steadily increases with decreasing PMI. Overall, at the end of the dry season the probability of extreme events is about the double than at the beginning. These results may be used to produce frequently (e.g. daily) updated maps of the probability of extreme events given a PMI map retrieved from e.g. MODIS reflectance data.Optical and Laser Remote Sensin

Remote sensing evaluation of fire hazard: Towards operational tools for improving the security of citizens and protecting the environment

Forest fires are a threat for both the environment and the security of citizens. This is particularly relevant in the Mediterranean, where the population density is high, and long dry summers drive vegetation into fireprone conditions. Policy makers underline the key role of prevention over damage reparation, and indeed efforts are conducted at regional, national and international level to develop tools supporting fire managers’ activities. The preventive allocation of intervention resources is based on fire hazard maps; these should be updated frequently throughout the fire season. In this framework satellite remote sensing can play a key role, providing daily measurements in the optical domain for the determination of vegetation water content, a key parameter for the prediction of fire hazard. This paper outlines current practices adopted in Mediterranean Europe, analyses how earth observation data are used, and underlines areas of improvement. Strengths and weaknesses of algorithms for the retrieval of vegetation water content are discussed. Examples are provided with the production of remote sensing maps of vegetation moisture in three different areas of the Mediterranean: Campania (Italy), Tuscany (Italy) and Provence-Alpes-Côte d'Azur (France). Finally, a brief review describes current and forthcoming Earth Observation missions with the potential to support fire prevention activities.Geoscience and EngineeringCivil Engineering and Geoscience

Fire risk assessment: The role of hyperspectral remote sensing

The increasing demand for effective forest fire prevention instruments has faced operational and future Earth observation instruments with the challenge of producing updated and reliable maps of vegetation moisture. Various empirical band-ratio indexes have been proposed so far, based on multispectral remote sensing data, that have been found to be related to vegetation moisture expressed in terms of equivalent water thickness (EWT), which is defined as the weight of liquid water per unit leaf area. More sophisticated retrieval methodologies can be adopted when hyperspectral data are available, e.g. based on spectral curve fitting in selected water absorption bands or radiative transfer model inversion, allowing for better estimates of EWT. Problems arise with the evaluation of fuel moisture content (FMC), which is the percentage weight of water per unit of oven-dried leaf weight, due to its weak signal in vegetation spectrum. FMC is essential in fire models, and it is not interchangeable with EWT. Basing on simulated vegetation spectra, this study aims at demonstrating that hyperspectral images of vegetated areas can be effectively used to evaluate FMC with accuracies not achievable with multispectral data. To this purpose, radiative transfer models PROSPECT and SAILH have been used to simulate canopy reflectance. Vegetation spectra have then been convolved to hyperspectral data basing on the design specifications of a formerly planned ASI-CSA hyperspectral mission (JHM configuration C), similar to those of the forthcoming PRISMA. For comparison against multispectral instruments, measurements from the Operational Land Imager (OLI) have also been simulated. Two retrieval methods have been tested, based on spectral indexes and on partial least squares (PLS) regression. The latter methodology is particularly suited to analyse high-dimensional data. Results confirm that spectral indexes are good predictors of vegetation moisture expressed as EWT, but their performance in evaluating FMC is poor. By using PLS regression on hyperspectral data, a linear model can be built that accurately predicts FMC. No such result is achievable from OLI simulated data.Remote SensingAerospace Engineerin

Stima della pericolosita d'incendio in Campania mediante serie temporali di misure MODIS nell'infrarosso termico

Geoscience & Remote SensingCivil Engineering and Geoscience

Valutazione della pericolosita d'incendio da dati ottici MODIS mediante il perpendicular moisture index

Geoscience & Remote SensingCivil Engineering and Geoscience

Relating spatiotemporal patterns of forest fires burned area and duration to diurnal land surface temperature anomalies

Forest fires are a major source of ecosystem disturbance. Vegetation reacts to meteorological factors contributing to fire danger by reducing stomatal conductance, thus leading to an increase of canopy temperature. The latter can be detected by remote sensing measurements in the thermal infrared as a deviation of observed land surface temperature (LST) from climatological values, that is as an LST anomaly. A relationship is thus expected between LST anomalies and forest fires burned area and duration. These two characteristics are indeed controlled by a large variety of both static and dynamic factors related to topography, land cover, climate, weather (including those affecting LST) and anthropic activity. To investigate the predicting capability of remote sensing measurements, rather than constructing a comprehensive model, it would be relevant to determine whether anomalies of LST affect the probability distributions of burned area and fire duration. This research approached the outlined knowledge gap through the analysis of a dataset of forest fires in Campania (Italy) covering years 2003-2011 against estimates of LST anomaly. An LST climatology was first computed from time series of daily Aqua-MODIS LST data (product MYD11A1, collection 6) over the longest available sequence of complete annual datasets (2003-2017), through the Harmonic Analysis of Time Series (HANTS) algorithm. HANTS was also used to create individual annual models of LST data, to minimize the effect of varying observation geometry and cloud contamination on LST estimates while retaining its seasonal variation. LST anomalies where thus quantified as the difference between LST annual models and LST climatology. Fire data were intersected with LST anomaly maps to associate each fire with the LST anomaly value observed at its position on the day previous to the event. Further to this step, the closest probability distribution function describing burned area and fire duration were identified against a selection of parametric models through the maximization of the Anderson-Darling goodness-of-fit. Parameters of the identified distributions conditional to LST anomaly where then determined along their confidence intervals. Results show that in the study area log-transformed burned area is described by a normal distribution, whereas log-transformed fire duration is closer to a generalized extreme value (GEV) distribution. The parameters of these distributions conditional to LST anomaly show clear trends with increasing LST anomaly; significance of this observation was verified through a likelihood ratio test. This confirmed that LST anomaly is a covariate of both burned area and fire duration. As a consequence, it was observed that conditional probabilities of extreme events appear to increase with increasing positive deviations of LST from its climatology values. This confirms the stated hypothesis that LST anomalies affect forest fires burned area and duration and highlights the informative content of time series of LST with respect to fire danger.Optical and Laser Remote Sensin

Combining multi-spectral and thermal remote sensing to predict forest fire characteristics

Forest fires preparedness strategies require the assessment of spatial and temporal variability of fire danger. While several tools have been developed to predict fire occurrence and behaviour from weather data, it is acknowledged that fire danger models may benefit from direct assessment of live fuel condition, as allowed by Earth Observation technologies. In this study, the performance of pre-fire observations of land surface temperature (LST) anomaly and of the Perpendicular Moisture Index (PMI) in predicting fire characteristics was evaluated against the Canadian Forest Fire Weather Index (FWI) System, a fire danger model adopted in several areas worldwide. To this purpose, a database of forest fires recorded in Campania (13,595 km2), Italy, was combined with MODIS retrievals of LST anomaly and PMI, and with FWI maps from NASA’s Global Fire Weather Database. Fires were grouped in decile bins of LST anomaly, PMI and FWI System components, and probability distribution functions of burned area, fire duration and rate of spread were fitted in each bin. The dependence of probability model parameters on LST anomaly, PMI and FWI System components was assessed by means of trend analysis (coefficient of determination and p-value of the linear fit, Sen’s slope and Mann-Kendall test) and likelihood ratio test versus the corresponding unconditional probability model. Finally, the probability of an extreme event, conditional to ignition, was modelled as a function of LST anomaly and PMI. Results show that the probability distribution function of burned area has a strong dependence on both LST anomaly and PMI, that the probability distribution function of fire duration has a strong dependence on LST anomaly but not on PMI, and that the probability distribution function of rate of spread has a weak dependence on LST anomaly and a strong dependence on PMI. These results are in line with expectations from models of the combustion and flames propagation processes. Trend analyses and likelihood ratio tests showed that the FWI System components are good predictors of burned area and fire duration, but not of rate of spread. They also confirmed that, where LST anomaly and PMI are covariates of the considered fire characteristic, their performance is similar or better than the FWI System components. Finally, the probability of an extreme event in terms of burned area as a joint function of LST anomaly and PMI shows a wider dynamic range than the same probability modelled as a function of these remote sensing variables individually.Optical and Laser Remote Sensin