Improving our understanding of individual wildfires by combining satellite data with fire spread modelling

Doutoramento em Engenharia Florestal e dos Recursos Naturais - Instituto Superior de AgronomiaWildfires pose real threats to life and property. In Portugal, the recent year of 2017 had the largest burnt area extent and number of casualties. A knowledge gap still exists in wildfire research related with better understanding individual wildfires, which has important implications for fire suppression, management, and policies. Wildfire spread models have been used to study individual wildfires, however, associated uncertainties and the lack of systematic evaluation methods hamper their capability for accurately predicting their spread. Understanding how fire spread predictions can be improved is a critical research task, as they will only be deemed useful if they can provide accurate and reliable information to fire managers. The present Thesis proposes to contribute to improve fire spread predictions by: i) Developing a methodology to systematically evaluate fire spread predictions ii) Thoroughly characterizing input data uncertainty and its impact on predictions; iii) Improving predictions using data-driven model calibration. The spread of large historical wildfires were studied by combining satellite data and models. The major findings of the present Thesis were: i) Satellite data accurately contributed to provide accurate fire dates and ignition information for large wildfires. ii) The evaluation metrics were very useful in identifying areas and periods of low/high spatio-temporal agreement, highlighting the strong underprediction bias and poor accuracy of the predictions. iii) Uncertainties in wind speed and direction, fuel model assignment and typology, location and timing of ignitions, had a major impact on prediction accuracy. iv) Predictions iii) Uncertainties in wind speed and direction, fuel model assignment and typology, location and timing of ignitions, had a major impact on prediction accuracy. iv) Predictions were improved by ‘learning’ from past wildfires, significantly reducing the impact of data uncertainty on the accuracy of fire spread predictions Overall, the work contributed to advance the body of knowledge regarding individual wildfires and identified future research steps towards a reliable operational fire system capable of supporting more effective and safer fire management decisions with the aim of reducing the dramatic impacts of wildfiresN/

MODELING FIRE OBSERVATIONS, IGNITION SOURCES, AND NOVEL FUELS TO UNDERSTAND HUMAN IMPACTS ON FIRE REGIMES ACROSS THE U.S.

Fire is a natural, and necessary, component of many ecosystems. However, people are changing the spatial and temporal distribution of wildfires in the U.S. at great economic and ecological costs. My dissertation addresses the impacts of humans on U.S. fires both through the introduction of ignition sources and flammable grasses. Further, I evaluate fire datasets that are widely used to investigate these phenomena over large spatial and temporal scales. Finally, I create an aboveground carbon map that can be used to estimate the potential carbon loss consequences in western U.S. ecosystems most at risk to fire. My work shows that humans ignited more than 77% of fires in seven western U.S. ecoregions, and when modeling human ignited fires, I found that the importance of ignition proxies varied considerably among ecoregions. In 21 ecoregions across the U.S., I found that eight species of non-native invasive grasses increased rates of fire occurrence by 27%-230%, and six species increased rates of fire frequency by 24%-150%. I also quantified differences in commonly used satellite derived and agency recorded fire records and found they were disparate across the U.S., suggesting that great care should be taken when deciding which fire database to use when analyzing human impacts on fire regimes. Finally, the new estimates I provide for aboveground carbon in semi-arid western U.S. ecosystems are roughly double that of previous estimates; indicating that potential carbon losses from fire in these ecosystems are much larger than originally thought. I conclude that fire ignitions from human sources, and the alteration of fuels through the introduction of non-native, invasive grasses, have already dramatically impacted fire regimes across the U.S. These impacts are presently and will continue to be compounded by climate change. My dissertation suggests that we must consider human impacts on ignitions, vegetation, and their interaction with climate to most effectively manage, predict, and live with fire

Deciphering the impact of uncertainty on the accuracy of large wildfire spread simulations

Predicting wildfire spread is a challenging task fraught with uncertainties. ‘Perfect’ predictions are unfeasible since uncertainties will always be present. Improving fire spread predictions is important to reduce its negative environmental impacts. Here, we propose to understand, characterize, and quantify the impact of uncertainty in the accuracy of fire spread predictions for very large wildfires. We frame this work from the perspective of the major problems commonly faced by fire model users, namely the necessity of accounting for uncertainty in input data to produce reliable and useful fire spread predictions. Uncertainty in input variables was propagated throughout the modeling framework and its impact was evaluated by estimating the spatial discrepancy between simulated and satellite-observed fire progression data, for eight very large wildfires in Portugal. Results showed that uncertainties in wind speed and direction, fuel model assignment and typology, location and timing of ignitions, had a major impact on prediction accuracy.We argue that uncertainties in these variables should be integrated in future fire spread simulation approaches, and provide the necessary data for any firemodel user to do soinfo:eu-repo/semantics/publishedVersio

Spatiotemporal analysis of forest fire risk models : a case study for a greek island

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesForest fires are a natural phenomenon which might have severe implications on natural and anthropogenic ecosystems. Consequently, the integrated protection of these ecosystems from forest fires is of high priority. The aim of the project lies in the development of two preventive models which will act in synergy in order to effectively protect the most critical natural resource of the island, namely, the abundant forests. Thus, fire risk modeling is combined with visibility analysis, so that we may primarily protect the most susceptible territory of the study area. The corner stone of the methodology is primarily relied on the multi-criteria decision analysis. This framework applied not only for the fire risk estimation and the corresponding evolution in a context of 20 years, but for visibility analysis as well, determining the most suitable locations for the establishment of a minimum number of watchtowers. The fire risk map for 2016 indicated that 34% of the entire study area is covered by territory of low fire risk; 27% of moderate risk; 34% of high and very high risk, while there is a 6% of the island which is characterized by extremely fire risk. Similar conclusions can be drawn for 1996, since no significant changes have been observed, especially on the land cover types and their spatial arrangement. Based on the visibility results, more than 40% of the entire island is visible from the selected location scheme consisting of just 8 watchtowers. The intense topography constituted the most critical barrier in increasing this percentage. Some good practices to counterbalance the relative small percentage of visibility could include; the extensive patrols in unmonitored regions through the intense road network of the island; the adoption of drones covering the aforementioned areas, especially when extreme meteorological conditions are expected

Data Fusion for Decision Support

This thesis demonstrates the utility of fusing data from multiple sources, including remote sensing data, in a Geographic Information System (GIS) for decision support by designing a new method of assessing wildfire risk in the wilderness urban interface (WUI) to facilitate better informed land management decisions and reduce mission impacts of wildfires on the military. Information from remote sensing systems has been used for decades to support decisions. Today, data are time and location tagged, making it possible to correlate and fuse disparate sources in a GIS, from which data can be stored, analyzed, and the resulting information shared. The GIS, relating data based on spatial attributes, has become an ideal fusion platform and decision support tool. In demonstration, decades of work in fire science were put to work, applying the Fire Susceptibility Index (FSI) on a new, 30 m scale with Landsat 8 data. Eight data sources were fused in a GIS to identify high-risk patches of wildland by calculating the FSI and preparing it for meaningful analysis and sharing. The initial results, qualitatively validated with wildfire behavior basics, appear promising, providing a view of fire danger in the landscape not seen in the current state of practice

CHARACTERIZING BURN SEVERITY OF BEETLE-KILLED FOREST STANDS LEVERAGING GOOGLE EARTH ENGINE-DERIVED NORMALIZED BURN RATIOS

Following numerous studies, a general consensus on burn severity in forests affected by bark beetle outbreaks has not yet been achieved. The purpose of this study is to characterize burn severities in forest stands affected by mountain pine beetle (MPB) outbreaks, especially in relation to “time since outbreak”, vegetation cover, and topographic factors. This study focuses on wildfires that occurred in the northern Rocky Mountains of Idaho and Montana during the 2012 fire season within forested areas that had previously experienced prior MPB outbreaks. Remote sensing techniques were used to quantify and compare the burn severities of MPB-outbreak stands with those of unaffected lodgepole pine; the role of fire weather was not accounted for in this study. The results indicate time since outbreak and existing vegetation cover were more important influences on burn severity when compared to topographic factors. Initial expectations were that red stage stands would exhibit the highest burn severity. These findings indicate though that 5+ year time since outbreak forest stands experienced higher burn severities compared to unaffected stands and those that were more recently affected by MPB. Increased torching potential may be attributed to increased surface fuel loads from needle fall. Statistical modeling and spatial autocorrelation were not significant but should be considered by future researchers

Application of remote sensing to selected problems within the state of California

There are no author-identified significant results in this report

UNDERSTANDING ENVIRONMENTAL FACTORS DRIVING WILDLAND FIRE IGNITIONS IN ALASKAN TUNDRA

Wildland fire is a dominant disturbance agent that drives ecosystem change, climate forcing, and carbon cycle in the boreal forest and tundra ecosystems of the High Northern Latitudes (HNL). Tundra fires can exert a considerable influence on the local ecosystem functioning and contribute to climate change through biogeochemical and biogeophysical effects. However, the drivers and mechanisms of tundra fires are still poorly understood. Research on modeling contemporary fire occurrence in the tundra is also lacking. This dissertation addresses the overarching scientific question of “What environmental factors and mechanisms drive wildfire ignition in Alaskan tundra?” Environmental factors from multiple aspects are considered including fuel type and state, fire weather, topography, and ignition source. First, to understand the spatial distribution of fuel types in the tundra, multi- year satellite observations and field data were used to develop the first fractional coverage product of major fuel type components across the entire Alaskan tundra at 30 m resolution. Second, to account for the primary ignition source of fires in the HNL, an empirical-dynamical modeling framework was developed to predict the probability of cloud-to-ground (CG) lightning across Alaskan tundra, through the integration of Weather Research and Forecast (WRF) model and machine learning algorithm. Finally, environmental factors including fuel type distribution, fuel moisture state, WRF simulated ignition source and fire weather, and topographical features, were combined with empirical modeling methods to understand their roles in driving wildland fire ignitions across Alaskan tundra from 2001 to 2019. This work demonstrates the strong capability for accurate prediction of CG lightning and wildland fire probabilities, by incorporating dynamic weather models, empirical methods, and satellite observations in data-scarce regions like the HNL. The developed models present a novel component of fire danger modeling that can considerably strengthen the current capability to forecast fire occurrence and support operational fire management agencies in the HNL. In addition, the insights gained from this research will allow for more accurate representation of wildfire ignition probabilities in studies focused on assessing the impact of the projected climate change in HNL tundra which has largely absent in previous modeling efforts