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.@en

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.@en

Many dryland rivers are terminal systems, with small channels undergoing prominent downstream size reductions before ending on channelless floodplains, in wetlands, or at playa margins. Spaceborne Digital Elevation Models (DEMs) provide potential for assessing subtle topographic and hydrodynamic changes in these low-gradient, low-relief settings, but challenges are posed by limitations in vertical and horizontal accuracy. This study evaluates the use of different spaceborne DEMs for topographic characterisation and flood flow modelling of the low-gradient (<0.0006 m m⁻¹) Río Colorado terminal system, Bolivia. A comparison between DEM and field dGPS elevation data (1290 measurement points) reveals that the TanDEM-X DEM 12 m (TDX-12 m) has a RMSE of 0.47 m, far less than those of other frequently used spaceborne DEMs such as ALOS RTC (4.58 m) and SRTM (6.02 m). For hydrodynamic modelling, TDX-12 m data were smoothed (adaptive filter and feature-preserving DEM smoothing) and upscaled. The smoothed TDX-12 m data were mosaiced with a dGPS data-derived river path, surveyed along a reach (mean width <30 m) with a prominent downstream size decrease. The methods enabled effective de-noising of the TDX-12 m data (RMSE 0.29 m) and resulted in a high linear regression correlation coefficient (0.75). HEC-RAS 2D modelling reveals that in the selected reach, overbank flooding starts in the downstream part when discharge is <18 m³/s, with flow initially spreading through crevasse channels and levee topographic lows. As discharge increases, flow spreads farther across the floodplain, ultimately forming connected floodplain flow in distal topographic lows. Satellite imagery and a derived water index indicate the same floodplain flow patterns as the modelling (critical success index 0.77). Wider use of DEMs based on TDX-12 m data for topographic characterization and flood flow modelling along relatively small, low-gradient terminal dryland rivers will result in many scientific and applied benefits.

@en

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.

@en

In some internally-draining dryland basins, ephemeral river systems terminate at the margins of playas. Extreme floods can exert significant geomorphological impacts on the lower reaches of these river systems and the playas, including causing changes to flood extent, channel-floodplain morphology, and sediment dispersal. However, the characterization of these impacts using remote sensing approaches has been challenging owing to variable vegetation and cloud cover, as well as the commonly limited spatial and temporal resolution of data. Here, we use Sentinel-2 Multispectral Instrument (MSI) data to investigate the flood extent, flood patterns and channel-floodplain morphodynamics resulting from an extreme flood near the non-vegetated terminus of the Río Colorado, located at the margins of the world's largest playa (Salar de Uyuni, Bolivia). Daily maximum precipitation frequency analysis based on a 42-year record of daily precipitation data (1976 through 2017) indicates that an approximately 40-year precipitation event (40.7 mm) occurred on 6 January 2017, and this was associated with an extreme flood. Sentinel-2 data acquired after this extreme flood were used to separate water bodies and land, first by using modified normalized difference water index (MNDWI), and then by subsequently applying independent component analysis (ICA) on the land section of the combined pre- and post-flood images to extract flooding areas. The area around the Río Colorado terminus system was classified into three categories: water bodies, wet land, and dry land. The results are in agreement with visual assessment, with an overall accuracy of 96% and Kappa of 0.9 for water-land classification and an overall accuracy of 83% and Kappa of 0.65 for dry land-wet land classification. The flood extent mapping revealed preferential overbank flow paths on the floodplain, which were closely related to geomorphological changes. Changes included the formation and enlargement of crevasse splays, channel avulsion, and the development of erosion cells (floodplain scour-transport-fill features). These changes were visualized by Sentinel-2 images along with WorldView satellite images. In particular, flooding enlarged existing crevasse splays and formed new ones, while channel avulsion occurred near the river's terminus. Greater overbank flow on the floodplain led to rapid erosion cell development, with changes to channelized sections occurring as a result of adjustments in flow sources and intensity combined with the lack of vegetation on the fine-grained (predominantly silt, clay) sediments. This study has demonstrated how ICA can be implemented on Sentinel-2 imagery to characterize the impact of extreme floods on the lower Río Colorado, and the method has potential application in similar contexts in many other drylands.

@en

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.@en

Satellite observations of the terrestrial biosphere cover a period of time sufficiently extended to allow the calculation of a reliable climatology. The latter is particularly relevant for studies of vegetation response to climate variability. Observations from space of the land surface are hampered by clouds at shorter wavelength and affected by water in the atmosphere in the microwave range. Both polar orbiting and geostationary satellites have a revisit frequency high enough to allow for some redundancy relative to the processes being observed, so that time series where a fraction of observations are removed and the resulting gaps filled are still very useful to monitor land surface processes. Two examples illustrate this concept in two different spectral regions: Thermal Infrared (TIR) and observations of land surface temperature to study the thermal behavior of the land surface in response to weather and climate and 37 GHz observations of the polarization difference in brightness temperature to retrieve the fractional abundance of watersaturated soil. Three applications of time series of land surface temperature are presented: (a) monitoring of spectral thermal admittance of the land surface; (b) estimation and mapping of air temperature and (c) monitoring of thermal load to assess the risk of forest fires. Two methods were applied to identify and remove anomalous observations (outliers) and to fill the resulting gaps: Harmonic ANalysis of Time Series (HANTS) and theMultichannel Singular Spectrum Analysis (M-SSA). The HANTS algorithm has been widely used to reconstruct time series of Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), Land Surface Temperature (LST) as well as the polarization difference brightness temperature (PDBT) during the past 20 years to remove random noise or eliminate cloud/snow contamination. The M-SSA, an advanced methodology for time series analysis, was utilized to reconstruct gap-free LST time series using both the spatial and the temporal information content in the data set.

@en