Spatial Analysis For Conservation and Sustainability
Fire
Fire is now lacking in many ecosystems that require it, yet fire is also a dire threat to lives, homes, and livelihoods. Due to climate and land use change, fire frequency has risen in recent decades, and we study how societies and communities can better live with fire, and where to restore fire regimes.
Understandingof the vulnerabilityof populationsexposedto wildfires is limited.We usedan index from the U.S.Centersfor DiseaseControl and Preventionto assessthe socialvulnerabilityof populationsexposedto wildfirefrom 2000–2021in California,Oregon,and Washington,whichaccountedfor 90%of exposures in the westernUnitedStates. The numberof peopleexposedto fire from 2000–2010to 2011–2021increasedsubstantially, withthe largest increase,nearly250%,for peoplewithhighsocialvulnerability. In Oregonand Washington,a higherpercentageof exposedpeoplewere highlyvulnerable (>40%)thanin California(~8%).Increasedsocialvulner-abilityof populationsin burnedareas was the primarycontributorto increasedexposure of the highlyvulner-able in California,whereas encroachmentof wildfires on vulnerable populationswas the primarycontributorinOregonand Washington.Our resultsemphasizethe importanceof integrating the vulnerabilityof at-riskpop-ulationsin wildfire mitigation and adaptation plans.
The wildland – urban interface (WUI) is the zone where human settlements are in or near areas of fire-prone wildland vegetation. The WUI is widespread and expanding, with detrimental consequences to human lives, property, and neighboring ecosystems. While the WUI has been mapped in many regions, Europe does not have a high resolution WUI map to date. Moreover, while most WUI research has been focused on quantifying spatial and temporal patterns, little is known about the relationship between the WUI and the socioeconomic conditions that drive its formation. Here, we present the first high-resolution map of the European WUI and provide the first macro-scale analysis of the relationship between the WUI and some of its potential drivers. We found that the WUI covers about 7.4 % of Europe, but its extent varies considerably both across and within countries, with subnational WUI cover varying from nearly zero to almost 90 %. WUI cover is significantly related to socioeconomic variables such as GDP per capita, the proportion of the population above 65 years old, population density, road density, and the proportion of protected areas, but these effects are complex and interactive. This suggests that WUI drivers are likely to differ across and within countries, and hints about the importance of both top-down and local socioeconomic processes in driving the WUI. Our new WUI map can facilitate local as well as regional-scale wildfire risk and ecological assessments that inform policy and management decisions aimed at reducing the detrimental outcomes of the WUI in Europe.
Wildfires and housing development have increased since the 1990s, presenting unique challenges for wildfire management. However, it is unclear how the relative influences of housing growth and changing wildfire occurrence have altered risk to homes, or the potential for wildfire to threaten homes. We used a random forests model to predict burn probability in relation to weather variables at 1-km resolution and monthly intervals from 1990 through 2019 in the Southern Rocky Mountains ecoregion. We quantified risk by combining the predicted burn probabilities with decadal housing density. We then compared the predicted burn probabilities and risk across the study area with observed values and quantified trends. Finally, we evaluated how housing growth and changes in burn probability influenced risk individually and combined. Fires burned 9055 km2and exposed more than 8500 homes from 1990 to 2019.Observed burned area increased 632% from the 1990s to the 2000s, which combined with housing growth, resulted in a 1342% increase in homes exposed.. Increases continued in the 2010s but at lower rates; burned area by 65% and exposure by 32%. The random forests model had excellent fit and high correlation with observations (AUC=0.88 andr=0.9). Observed values were within the 95% uncertainty interval for all years except 2016 (burned area) and 2000(exposure). However, our model overpredicted in years with low observed burned area and underpredicted in years with high observed burned area. Overpredictions in risk resulted in lower rates of change in predicted risk com-pared with change in observed exposure. Increases in risk between the 1990sand 2000s were primarily due to warmer and drier weather conditions and secondarily because of housing growth. However, increases between the 2000sand 2010s were primarily due to housing growth. Our modeling approach identifies spatial and temporal patterns of wildfire potential and risk, which is critical information to guide decision-making. Because the drivers behind risk shift over time, strategies to mitigate risk may need to account for multiple drivers simultaneously.
Background: Wildland–urban interface (WUI) maps identify areas with wildfire risk, but they are often outdated owing to the lack of building data. Convolutional neural networks (CNNs) can extract building locations from remote sensing data, but their accuracy in WUI areas is unknown. Additionally, CNNs are computationally intensive and technically complex, making them challenging for end-users, such as those who use or create WUI maps, to apply.
Aims: We identified buildings pre- and post-wildfire and estimated building destruction for three California wildfires: Camp, Tubbs and Woolsey.
Methods: We evaluated a CNN-based building dataset and a CNN model from a separate commercial vendor to detect buildings from high-resolution imagery. This dataset and model represent to end-users the state of the art of what is readily available for potential WUI mapping.
Key results: We found moderate accuracies for the building dataset and the CNN model and a severe underestimation of buildings and their destruction rates where trees occluded buildings. The CNN model performed best post-fire with accuracies ≥73%.
Conclusions: Existing CNNs may be used with moderate accuracy for identifying individual buildings post-fire and mapping the extent of the WUI. The implications are, however, that CNNs are too inaccurate for post-fire damage assessments or building counts in the WUI.
Wildfire risks to homes are increasing, especially in the wildland-urban interface (WUI), where wildland vegetation and houses are in close proximity. Notably, we found that more houses are exposed to and destroyed by grassland and shrubland fires than by forest fires in the United States. Destruction was more likely in forest fires, but they burned less WUI. The number of houses within wildfire perimeters has doubled since the 1990s because of both housing growth (47% of additionally exposed houses) and more burned area (53%). Most exposed houses were in the WUI, which grew substantially during the 2010s (2.6million new WUI houses), albeit not as rapidly as before. Any WUI growth increases wildfire risk to houses though, and more fires increase the risk to existing WUI houses.
Background. Drivers of fire regimes vary among spatial scales, and fire history reconstructions are often limited to stand scales, making it difficult to partition effects of regional climate forcing versus individual site histories.
Aims. To evaluate regional-scale historical fire regimes over 350 years, we analysed an extensive fire-scar network, spanning 240 km across the upper Great Lakes Region in North America.
Methods. We estimated fire frequency, identified regionally widespread fire years (based on the fraction of fire-scarred tree samples, fire extent index (FEI), and synchronicity of fire years), and evaluated fire seasonality and climate–fire relationships.
Key results. Historically, fire frequency and seasonality were variable within and among Great Lakes’ ecoregions. Climate forcing at regional scales resulted in synchronised fires, primarily during the late growing season, which were ubiquitous across the upper Great Lakes Region. Regionally significant fire years included 1689, 1752, 1754, 1791, and 1891. Conclusions. We found significant climate forcing of region-wide fire regimes in the upper Great Lakes Region.
Implications. Historically, reoccurring fires in the upper Great Lakes Region were instrumental for shaping and maintaining forest resilience. The climate conditions that helped promote widespread fire years historically may be consistent with anticipated climate–fire interactions due to climate change.
Human activity is recognized as a major driver of changes in land cover, land use, and fire regimes that influence and disrupt ecosystems. The Wildland-Urban Interface (WUI) defines areas where low-density settlements overlap with high amounts of wildland vegetation cover and are a focal point for wildfire hazard risks. WUI land uses are expanding and cause many biotic and abiotic implications for the environment and ecosystem functionality, such as declining biodiversity, loss, and fragmentation of habitat. Therefore, we require spatial and temporal information to investigate the rate and extent of the WUI growth and assess its structure and composition for preventing wildfire hazard risk and monitoring its impacts on ecosystems. Point-based WUI maps for South Australia, California (Carlson et al 2022), and Portugal. WUI reference datasets: Australia: Microsoft Building Footprint (~2018) & DEA Land Cover 2018, California: Microsoft Building Footprint (~2015) & NLCD (2016), Portugal: Microsoft Building Footprint (2014-2021) & COS Land Cover (2018).
Especially in the Mediterranean-climate biome, fire has an important ecological role, which put humans living within the Wildland-Urban Interface at high risk, and fire hazards in these regions have achieved media attention in recent years. Mapping the WUI is essential to quantify the extent of this increasing settlement in wildland vegetation-dominated areas. Standard land cover maps often do not include this widespread settlement type, while differentiating only either into high-density urban land uses or vegetation cover.
So far, the extent of the Wildland-Urban Interface and its growth has only been mapped for a few countries or regions based on national land cover products and census or build-location datasets. However, these datasets are only limited available, and therefore, limit our ability to consistently capture WUI area growth, its structures, and drivers. Wildfire in the close proximity of Adelaide (Australia) in 2020 (Landsat-8 from 2nd January 2020, RGB-vizualization: NIR-RED-GREEN, source USGS)
Kira Pfoch is conducting a study aiming at filling this gap in data availability. Kira’s main objective is to map WUI growth within the Mediterranean-climate biome with the Landsat Archive. She investigates reasons for WUI growth that is likely to be associated with expanding human activities that lead to increasing settlements in natural landscapes. Kira further investigates the relation between WUI growth and changes in wildfire activities and characteristics, to assess whether increasing human influences affect fire activity within these landscapes. Finally, the impact and interaction of human activity, climate change, and wildfires affect the natural vegetation, and therefore, Kira studies vegetation type conversion concerning WUI growth across different regions within the Mediterranean climate biome.
In conclusion, Kira’s research will help determine to identify growing human-environment conflict and its impacts on fire and vegetation. Wildfire activity in Portugal in 2017 ((left image) pre-fire image 10th July 2017, (right image) fire image from the 26th July 2017, source USGS)
The boreal forests of Russia, Canada, Alaska, and Scandinavia, collectively known as the circumboreal region, contain 25% of global forests and 50% of the world’s soil carbon pool. Wildfire is an important disturbance agent within the circumboreal region and plays a key role in facilitating its nutrients cycling and succession. Climate change is warming the circumboreal region at twice the rate of the global average creating longer, hotter, and drier wildfire seasons. This in turn has warmed and dried the circumboreal growing season, stressed vegetation, and may have contributed to the severe 2019-2021 wildfire seasons. The specter of increasing burned area extent within the circumboreal region is concerning because it may volatilize the region’s enormous soil carbon pool, exacerbating climate warming.
Ground-based mapping of burned area within the circumboreal region is challenging due to its remote and inaccessible nature. Instead, burned area is typically mapped using satellite-based remotely sensed datasets. Mapping burned area within the Eurasian boreal region prior to the year 2000 is difficult due to a lack of available Landsat and MODIS data. Only the coarse resolution AVHRR archive provides data capable of exhaustively mapping wildfire throughout the circumboreal region over multi-decadal timescales. Mapping wildfire with AVHRR data is complicated by sensor-based limitations that, if unaccounted for, will degrade the quality of resulting burned area maps. An advantage of statistical models over machine learning algorithms typically used to map burned area is that they allow known sources of variation to be explicitly quantified. This makes statistical models well suited for wildfire detection with AVHRR data as the archive’s sources of noise are well documented. As such, we developed a methodology which uses autoregressive timeseries analysis to map circumboreal burned area with AVHRR data (Figure 1). Figure 1: Annual circumboreal wildfire extent mapped using data from the NOAA POES and ESA Metop-B AVHRR archive (1983-2020).
High wildfire years within the circumboreal region are usually facilitated by unseasonably hot and dry summertime conditions. Climate change is predicted to increase the severity of wildfire weather within the circumboreal region, which in turn is expected to increase annual burned area. However, it is unclear which circumboreal ecoregions are currently experiencing worsening wildfire weather, and to what degree this is affecting burned area extent within those ecoregions. We tested for long term trends in ecoregion level severe wildfire weather by conducting a timeseries analysis on circumboreal annual (1983-2020) growing season (Mar.-Nov.) 95th percentile Canadian Fire Weather Index value (Figure 2). We performed this timeseries analysis using a methodology known as remotePARTS which accounts for spatial and temporal autocorrelation in remotely sensed datasets. We then determined the impact of trends in severe wildfire weather on annual ecoregion level burned using autoregressive timeseries analysis. Figure 2: Long term trends in circumboreal 95th percentile growing season (March – November) Fire Weather Index value (1983-2020). Severe wildfire weather is worsening throughout the Eurasian boreal region, but shows greater spatial heterogeneity within the North American boreal region.
We found no significant long-term trends in severe wildfire weather within North American boreal ecoregions. However, wildfire weather appears to be worsening more rapidly in northern latitudes at the continental scale. Conversely, wildfire weather is worsening across all Eurasian boreal ecoregions with the Trans-Baikal conifer forests experiencing the greatest change. Trends in severe wildfire weather were also shown to influence interannual variability in burned area with the Northern Canadian Shield Taiga (50%) and Trans-Baikal Conifer Forests (30%) most sensitive to this relationship (Figures 3, 4). Figure 3: Percentage of interannual variability in North American boreal forest annual (1983-2020) growing season (March-November) burned area accounted for by growing season 95th percentile Fire Weather Index Value. Figure 4: Percentage of interannual variability in Eurasian boreal forest annual (1983-2020) growing season (March-November) burned area accounted for by growing season 95th percentile Fire Weather Index Value.
Climate change is causing the circumboreal wildfire season to become longer, drier, and warmer. Our findings highlight the ecoregion level differences in the intensity of this change. While no significant ecoregion level trends in burned area have yet been detected, the sensitivity of circumboreal wildfire regimes to wildfire weather makes them vulnerable to changing fire seasons. In particular, fire regimes of Eastern Eurasian boreal forests which are experiencing worsening wildfire weather are particularly vulnerable. Further study of climate and wildfire interaction within these ecoregions is required to understand how these fire regimes are currently changing, and how they may change in the future.
The Wildland-Urban Interface (WUI) is where houses and wildland vegetation meet or intermingle. This makes that area a place of potentially severe human-environmental conflict, where settlements are exposed to wildfire, habitat is fragmented, and the risk of spreading zoonotic diseases is increased. While there is evidence for WUI across Australia, Europe, and North America, the worldwide distribution of the WUI is yet unknown.
A group of SILVIS researchers and external collaborators recently presented a global map of the 2020 WUI at 10 m resolution using a globally consistent approach based on remote sensing-derived datasets of building area and wildland vegetation, and identified hotspots of wildfire hazard in the WUI. The total global WUI area in 2020 was 6.4 million km2, or 4.9% of global land area, which is about twice the size of India. Globally, 3.3 billion people live in the WUI, of which two thirds live in landscapes dominated by forests, shrubland, and wetland, and one third in landscapes dominated by grassland. While WUI hotspots could be identified across all continents, Europe and North America have the highest land area share of WUI (12% and 6%). Patterns are highly diverse on a country level with regard to WUI land area share, population living in the WUI and dominant land cover types (Fig. 1). Fig. 1: Wildland-Urban Interface area and population share by world region (top) and for selected countries (bottom). Some US states were also included for better comparability to previous research. F/S/W: Forest/Shrubland/Wetland, Grass.: Grassland.
Wildfires are of increasing concern across the globe, as their frequency, intensity and season-length has increased due to various factors, including climate change, more human ignitions, and rising fuel loads. Wildfires are a particular problem in the WUI, as they cause substantial losses of homes and lives there. Indeed, nearly two thirds of all people affected by wildfires since 2001 lives in the WUI, while only a small share (2% – 10% across regions) of all global wildfire occurrences in that period were in the WUI. Differences among world regions and countries persist here. In North America, 85% of the population affected by wildfire lives in the WUI, but in Africa only 50% does (Fig. 2). Fig. 2: Wildfire and the Wildland-Urban Interface (WUI). A: Share of area affected by wildfire within and outside of the WUI. B: Share of population that experienced wildfire within 1 km of their home within and outside of the WUI. Africa (AF), Asia (AS), Europe (EU), North America (NA), Oceania (OC), and South America (SA).
As fire-prone areas expand globally, a global perspective on WUI data can help guide proactive actions to prepare for future wildfire in the WUI. This is particularly useful in areas where there is a high probability of becoming areas of increased fire hazard towards the middle of the 21st century, such as Temperate Broadleaf and Mixed Forests and (Sub-Tropical) Moist Broadleaf Forests, two biomes that represent 61% of the global population. The global Wildland-Urban Interface. Percent area of the Wildland-Urban Interface (WUI) in ca. 2020 per hexagon with 50 km diagonal length.
The wildland–urban interface (WUI) is where buildings and wildland vegetation meet or intermingle1,2. It is where human–environmental conflicts and risks can be concentrated, including the loss of houses and lives to wildfire, habitat loss and fragmentation and the spread of zoonotic diseases3. However, a global analysis of the WUI has been lacking. Here, we present a global map of the 2020 WUI at 10 m resolution using a globally consistent and validated approach based on remote sensing-derived datasets of building area4 and wildland vegetation5. We show that the WUI is a global phenomenon, identify many previously undocumented WUI hotspots and highlight the wide range of population density, land cover types and biomass levels in different parts of the global WUI. The WUI covers only 4.7% of the land surface but is home to nearly half its population (3.5 billion). The WUI is especially widespread in Europe (15% of the land area) and the temperate broadleaf and mixed forests biome (18%). Of all people living near 2003–2020 wildfires (0.4 billion), two thirds have their home in the WUI, most of them in Africa (150 million). Given that wildfire activity is predicted to increase because of climate change in many regions6, there is a need to understand housing growth and vegetation patterns as drivers of WUI change.
