Geology topics

Todd Hawbaker

Publications and source records attributed to Todd Hawbaker.

At least 19 recordsLinked to original sources

USGS2025

Modelling and mapping burn severity of prescribed and wildfires across the southeastern United States (2000-2022)

Background The southeastern United States (‘Southeast’) experiences high levels of fire activity, but the preponderance of small and prescribed fires means that existing burn severity products are incomplete across the region. Aims We developed and applied a burn severity model across the Southeast to enhance our understanding of regional burn severity patterns. Methods We used Composite Burn Index (CBI) plot data from across the conterminous US (CONUS) to train a gradient-boosted decision tree model. The model was optimised for the Southeast and applied to the annual Landsat Burned Area product for 2000–2022 across the region. Key results The burn severity model had a root mean square error (RMSE) of 0.48 ( R 2 = 0.70) and 0.50 ( R 2 = 0.37) for the CONUS and Southeast, respectively. The Southeast, relative to CONUS, had lower mean absolute residuals in low and moderate burn severity categories. Burn severity was consistently lower in areas affected by prescribed burns relative to wildfires. Conclusions Although regional performance was limited by a lack of high burn severity CBI plots, the burn severity dataset demonstrated patterns consistent with low-severity, frequent fire regimes characteristic of Southeastern ecosystems. Implications More complete data on burn severity will enhance regional management of fire-dependent ecosystems and improve estimates of fuels and fire emissions.

Southeastern United States↗

USGS2025

Evaluating a simulation-based wildfire burn probability map for the conterminous US

Background Wildfire simulation models are used to derive maps of burn probability (BP) based on fuels, weather, topography and ignition locations, and BP maps are key components of wildfire risk assessments. Aims Few studies have compared BP maps with real-world fires to evaluate their suitability for near-future risk assessment. Here, we evaluated a BP map for the conterminous US based on the large fire simulation model FSim. Methods We compared BP with observed wildfires from 2016 to 2022 across 128 regions representing similar fire regimes (‘pyromes’). We evaluated the distribution of burned areas across BP values, and compared burned area distributions among fire size classes. Key results Across all pyromes, mean BP was moderately correlated with observed burned area. An average of 71% of burned area occurred in higher-BP classes, vs 79% expected. BP underpredicted burned area in the Mountain West, especially for extremely large fires. Conclusions The FSim BP map was useful for estimating subsequent wildfire hazard, but may have underestimated burned areas where input data did not reflect recent climate change, vegetation change or human ignition patterns. Implications Our evaluations indicate that caution is needed when relying on simulation-based BP maps to inform management decisions. Our results also highlight potential opportunities to improve model estimates.

Conterminous United States↗

USGS2024

Classifying plant communities in the North American Coastal Plain with PRISMA spaceborne hyperspectral imagery and the spectral mixture residual

The effort to map terrestrial biodiversity, in recent years limited mostly to the use of broadband multispectral remote sensing at decameter scales, can be greatly enhanced by harnessing hyperspectral imagery. Interpretation of hyperspectral imagery may be aided by the Mixture Residual (MR) spectral preprocessing transformation. MR integrates the benefits of spectral mixture analysis with the absorption peak-enhancing characteristics of continuum removal. MR characterizes each pixel as a linear combination of generic end-members estimating the spectral continuum, from which the residual of each wavelength is computed and treated as a source of additional information. Using Hyperspectral Precursor of the Application Mission (PRISMA) imagery, we tested the ability of MR-transformed reflectance as compared to untransformed surface reflectance (SR) to map plant associations and land cover using ground truthing and random forest classifications across four landscapes within the North American Coastal Plain. We used a forward stepwise selection algorithm to choose bands for each classification and subsequently compared these between SR and MR. Our MR classifications distinguished land cover with 5% greater balanced accuracy on average than the SR-based classifications across all four landscapes. The MR-based classification that integrated data from all landscapes into a unified model encompassing all 21 land cover types achieved a 76% average balanced accuracy over three iterations. Generally, MR utilized the near-infrared region to a greater degree than SR while deemphasizing the green peak. Based on our results, MR improves the accuracy of mapping terrestrial biodiversity, likely extending to other current and planned satellite hyperspectral missions.

JGR Biogeosciences↗

USGS2023

Predicting burn severity for integration with post-fire debris-flow hazard assessment: A case study from the Upper Colorado River Basin, USA

Background: Burn severity significantly increases the likelihood and volume of post-wildfire debris flows. Pre-fire severity predictions can expedite mitigation efforts because precipitation contributing to these hazards often occurs shortly after wildfires, leaving little time for post-fire planning and management. Aim: The aim of this study was to predict burn severity using pre-fire conditions of individual wildfire events and estimate potential post-fire debris flow to unburned areas. Methods: We used random forests to model dNBR from pre-fire weather, fuels, topography, and remotely sensed data. We validated our model predictions against post-fire observations and potential post-fire debris-flow hazard estimates. Key results: Fuels, pre-fire weather, and topography were important predictors of burn severity, although predictor importance varied between fires. Post-fire debris-flow hazard rankings from predicted burn severity (pre-fire) were similar to hazard assessments based on observed burn severity (post-fire). Conclusion: Predicted burn severity can serve as an input to post-fire debris-flow models before wildfires occur, antecedent to standard post-fire burn severity products. Assessing a larger set of fires under disparate conditions and landscapes will be needed to refine predictive models. Implications: Burn severity models based on pre-fire conditions enable the prediction of fire effects and identification of potential hazards to prioritise response and mitigation.

Arizona, Colorado, New Mexico, Utah, Wyoming↗

USGS2023

The global wildland-urban interface

The wildland–urban interface (WUI) is where buildings and wildland vegetation meet or intermingle . 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 diseases . 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 area and wildland vegetation . 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 regions , there is a need to understand housing growth and vegetation patterns as drivers of WUI change.

Nature↗

USGS2023

Changes in wildfire occurrence and risk to homes from 1990 through 2019 in the Southern Rocky Mountains, USA

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 km 2 and 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 and r = 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 compared with change in observed exposure. Increases in risk between the 1990s and 2000s were primarily due to warmer and drier weather conditions and secondarily because of housing growth. However, increases between the 2000s and 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.

Colorado, New Mexico, Wyoming↗

USGS2023

Colorado River Basin Actionable and Strategic Integrated Science and Technology Project—Science strategy

The U.S. Geological Survey (USGS) conducts a wide variety of science that improves understanding of droughts and their effects on ecosystems and society. This work includes data collection and monitoring of aquatic and terrestrial systems; assessment and analysis of patterns, trends, drivers, and impacts of drought; development and application of predictive models; and delivery of information and decision-making tools to stakeholders. Stakeholders, which include Federal, Tribal, State, and local agencies, nongovernmental organizations, and others, use this information to anticipate, assess, react to, and mitigate drought conditions and impacts. There is no obvious near-term solution to reduce the frequency and severity of droughts or to mitigate drought impacts. Multidecadal drought is a “grand challenge” that benefits from integration of existing technologies, data, knowledge, and models across related and disparate disciplines, facilitated by new science and technology. In response, the USGS initiated a new integrated-science approach in the Colorado River Basin in 2020. The Colorado River Basin was specifically selected because of concerns about future drought and its consequences for the region. This document explains how the Colorado River Basin Actionable and Strategic Integrated Science and Technology (ASIST) project extends and enhances the science supported by USGS Mission Areas and Programs and articulates scientific gaps and stakeholder needs to identify and reduce drought risks. The approach seeks to answer complex Earth science questions developed in partnership with stakeholders about severe long-term drought. An integrated approach is required to tackle these complex questions, which any single science discipline cannot answer on its own. In addition to increased understanding of drought and drought effects in complex systems, the Colorado River Basin ASIST project was designed to improve efficiencies through rapid location of a broad array of data sources, assembly of model-ready multidisciplinary data, and delivery of actionable science to stakeholders at the speeds and scales needed for decision making. The project team identified the following actions needed for USGS to implement and advance an integrated science approach in the Colorado River Basin: (1) engage with stakeholders to document their needs and iteratively co-produce science and science delivery tools to address these needs, (2) integrate monitoring and observation systems developed by USGS and other agencies that track droughts and their effects, (3) collect and provide analysis-ready data to support integrated applications, (4) integrate data and model connections to predict multiple drought impacts, (5) conduct multidisciplinary coordination to improve interpretations, (6) leverage the knowledge base across USGS to enhance decision making, and (7) support the development of new integrated science approaches and technologies that provide analysis and management tools that can be used to adapt to the effects of drought in the Colorado River Basin. Proposals for initial short-term use-case projects were solicited, a subset of which was selected for funding to test implementation of these actions. Additionally, the project organized and convened a series of science and technology collaboration workshops in the USGS focused on challenges that were identified and prioritized by the short-term use-case projects and during the initial stakeholder analysis. These workshops were designed to bring together diverse perspectives to discuss science and technology challenges, stakeholder needs, capabilities, and knowledge gaps, with the goal of determining how the USGS can address challenges, identify future opportunities for continued engagement between participants, and inform the next steps for the Colorado River Basin ASIST project. Continuing to collaboratively engage with a wide range of stakeholders using an integrated approach will provide a suitable foundation of data and tools to formulate actionable intelligence for predicting droughts and informing adaptation to the effects of long-term drought in a holistic, timely, and effective manner.

Arizona, California, Colorado, Nevada, New Mexico,↗

USGS2023

Colorado River Basin Actionable and Strategic Integrated Science and Technology (ASIST)

Introduction The U.S. Geological Survey (USGS) is initiating a study approach focused on building cross-disciplinary connections to weave together the scientific knowledge related to drought conditions and effects in the Colorado River Basin. The basin is experiencing the worst drought in recorded history, posing unprecedented new challenges in the basin and in areas relying on water from the basin. Science is continually advancing, and there is an increasing need to interpret the connections between studies to predict the effects of drought and other changes affecting the Earth system. The USGS primarily works in independent disciplines and science centers to provide cutting-edge science to advance research and science applications worldwide. The complexity and volume of research that has been conducted related to drought in the Colorado River Basin is difficult to quantify. To complicate matters, studies, models, and datasets are cataloged and may be available in multiple, unrelated locations, across various internal systems, data repositories, and local offices. Furthermore, there are limited interactions and interfaces between scientists and partners working in different science disciplines; in many cases, individual science products require stakeholders to integrate complex interdisciplinary data across geographical and topical extents. The diverse array of interdisciplinary science and science products produced by the USGS highlights the need for a wide ranging collaborative support structure.

Arizona, California, Colorado, Nevada, New Mexico,↗

USGS2023

Identifying building locations in the wildland–urban interface before and after fires with convolutional neural networks

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.

International Journal of Wildland Fire↗

USGS2023

An aridity threshold model of fire sizes and annual area burned in extensively forested ecoregions of the western USA

Wildfire occurrence varies among regions and through time due to the long-term impacts of climate on fuel structure and short-term impacts on fuel flammability. Identifying the climatic conditions that trigger extensive fire years at regional scales can enable development of area burned models that are both spatially and temporally robust, which is crucial for understanding the impacts of past and future climate change. We identified region-specific thresholds in fire-season aridity that distinguish years with limited, moderate, and extensive area burned for 11 extensively forested ecoregions in the western United States. We developed a new area burned model using these relationships and demonstrate its application in the Southern Rocky Mountains using climate projections from five global climate models (GCMs) that bracket the range of projected changes in aridity. We used the aridity thresholds to classify each simulation year as having limited, moderate, or extensive area burned and defined fire-size distributions from historical fire records for these categories. We simulated individual fires from a regression relating fire season aridity to the annual number of fires and drew fire sizes from the corresponding fire-size distributions. We project dramatic increases in area burned after 2020 under most GCMs and after 2060 with all GCMs as the frequency of extensive fire years increases. Our adaptable model can readily incorporate new observations (e.g., extreme fire years) to directly address the non-stationarity of fire-climate relationships as climatic conditions diverge from past observations. Our aridity threshold fire model provides a simple yet spatially robust approach to project regional changes in area burned with broad applicability to ecosystem and vegetation simulation models.

Ecological Modelling↗

USGS2023

The extent of buildings in wildland vegetation of the conterminous U.S. and the potential for conservation in and near National Forest private inholdings

Development in natural areas is a leading threat to biodiversity. Global conservationists have called for the expansion of protected areas to preserve wildlands that are free from buildings, and in the U.S., the ‘America the Beautiful’ initiative aims to protect 30% of land and water areas by 2030 (known as the ‘30x30’ target). Here, we determined opportunities and limitations for conservation in the conterminous U.S. by assessing the extent of buildings in wildland vegetation. We focused specifically on National Forest lands, as these contain numerous private inholdings where development may occur. Using a newly available building footprint dataset, we determined 1) whether buildings were present and 2) numbers of buildings within three distances of wildland vegetation (100, 250, and 500 m), representing varying magnitudes of ecological impact. Our findings revealed that 29% of wildland vegetation nationwide was within 500 m of a building, 15% was within 250 m, and 5% was within 100 m. National Forest lands were less affected by building disturbance, but a substantial proportion (12%) of wildland vegetation area was within 500 m of a building. Of National Forest lands that were within 500 m of an inholding, 76% was not yet in proximity to a building; consequently, ∼10% of National Forest lands (143,474 km 2 ) are susceptible to impacts from future development on inholdings. We conclude that National Forest inholdings are therefore important opportunity areas for 30x30 conservation goals. Our assessments can inform where conservation efforts can limit impacts from present and future development on biodiversity.

Landscape and Urban Planning↗

USGS2023

Rising wildfire risk to houses in the United States, especially in grasslands and shrublands

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 (2.6 million 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.

Science↗

USGS2022

Contemporary (1984–2020) fire history metrics for the conterminous United States and ecoregional differences by land ownership

Background: Remotely sensed burned area products are critical to support fire modelling, policy, and management but often require further processing before use. Aim: We calculated fire history metrics from the Landsat Burned Area Product (1984–2020) across the conterminous U.S. (CONUS) including (1) fire frequency, (2) time since last burn (TSLB), (3) year of last burn, (4) longest fire-free interval, (5) average fire interval length, and (6) contemporary fire return interval (cFRI). Methods: Metrics were summarised by ecoregion and land ownership, and related to historical and cheatgrass datasets to demonstrate further applications of the products. Key results: The proportion burned ranged from 0.7% in the Northeast Mixed Woods to 74.1% in the Kansas Flint Hills. The Flint Hills and Temperate Prairies showed the highest burn frequency, while the Flint Hills and the Sierra Nevada and Klamath Mountains showed the shortest TSLB. Compared to private, public land had greater burned area (19 of 31 ecoregions) and shorter cFRI (25 of 31 ecoregions). Conclusions: Contemporary fire history metrics can help characterise recent fire regimes across CONUS. Implications: In regions with frequent fire, comparison of contemporary with target fire regimes or invasive species datasets enables the efficient incorporation of burned area data into decision-making.

International Journal of Wildland Fire↗

USGS2022

The wildland-urban interface in the United States based on 125 million building locations

The wildland-urban interface (WUI) is the focus of many important land management issues, such as wildfire, habitat fragmentation, invasive species, and human-wildlife conflicts. Wildfire is an especially critical issue, because housing growth in the WUI increases wildfire ignitions and the number of homes at risk. Identifying the WUI is important for assessing and mitigating impacts of development on wildlands and for protecting homes from natural hazards, but data on housing development for large areas are often coarse. We created new WUI maps for the conterminous U.S. based on 125 million individual building locations, offering higher spatial precision compared to existing maps based on U.S. census housing data. Building point locations were based on a building footprint dataset from Microsoft®. We classified WUI across the conterminous U.S. at 30-m resolution using a circular neighborhood mapping algorithm with a variable radius to determine thresholds of housing density and vegetation cover. We used our maps to (1) determine the total area of the WUI and number of buildings included, (2) assess the sensitivity of WUI area included and spatial pattern of WUI maps to choice of neighborhood size, (3) assess regional differences between building-based WUI maps and census-based WUI maps, and (4) determine how building location accuracy affected WUI map accuracy. Our building-based WUI maps identified 5.6% – 18.8% of the conterminous U.S. as being in the WUI, with larger neighborhoods increasing WUI area but excluding isolated building clusters. Building-based maps identified more WUI area relative to census-based maps for all but the smallest neighborhoods, particularly in the north-central states, and large differences were attributable to high numbers of non-housing structures in rural areas. Overall WUI classification accuracy was 98.0%. For wildfire risk mapping and for general purposes, WUI maps based on the 500-m neighborhood represent the original Federal Register definition of the WUI; these maps include clusters of buildings in and adjacent to wildlands and exclude remote, isolated buildings. Our approach for mapping the WUI offers flexibility and high spatial detail, and can be widely applied to take advantage of the growing availability of high-resolution building footprint datasets and classification methods.

Conterminous United States↗

USGS2022

Determination of optimal set of spatio-temporal features for predicting burn probability in the state of California, USA

Wildfires play a critical role in determining ecosystem structure and function and pose serious risks to human life, property and ecosystem services. Burn probability (BP) models the likelihood that a location could burn. Simulation models are typically used to predict BP but are computationally intensive. Machine learning (ML) pipelines can predict BP and reduce computational intensity. In this work, we tested approaches to reduce the set of input features used in an ML model to estimate BP for the state of California, USA, without loss of predictive performance. We used Principal Component Analysis (PCA) to determine the optimal set of features to use in our ML pipeline. Then, we mapped BP and compared model performance when using the reduced set and when using the whole set of features. Models using optimized input achieved similar prediction performance while using less than 50% of the input features.

California↗

USGS2022

Large fires or small fires, will they differ in affecting shifts in species composition and distributions under climate change?

Climate change is expected to increase fire activity, which has the potential to accelerate climate-induced shifts in species composition and distribution in the boreal-temperate ecotone. Wildfire can kill resident trees, and thus provide establishment opportunities for migrating tree species. However, the role of fire size and its interactions with tree species with varied life-history attributes in driving climate-induced shifts is not understood. Future fire regimes could be characterized by many small fires or a few large fires. Large and small fires create and regulate distinct burn patterns, which may influence tree-species responses and post-fire successional trajectories. Here we investigated the effects of future fire-regime variability on the boreal-temperate ecotone of northeastern China under climate change using a coupled forest dynamic model (LANDIS PRO) and ecosystem process model (LINKAGES). We simulated fire regimes using the LANDIS PRO fire module. We designed two fire scenarios (frequent, small fires and infrequent, large fires) to represent different fire regimes in terms of fire size. Results showed fire-catalyzed, climate-induced transitions from boreal to pioneer and temperate forest communities. Frequent, small fires resulted in 13% and 23% higher increases in pioneer and temperate species respectively, relative to infrequent, large fires. Therefore, species composition shifts were faster following frequent, small fires than infrequent, large fires. The results can help policymakers and forest managers determine tradeoffs among strategies to mitigate or adapt to climate change under altered fire regimes.

Great Xing'an Mountains↗

USGS2021

Mapping wetland burned area from Sentinel-2 across the southeastern United States and its contributions relative to Landsat 8 (2016-2019)

Prescribed fires and wildfires are common in wetland ecosystems across the Southeastern United States. However, the wetland burned area has been chronically underestimated across the region due to (1) spectral confusion between open water and burned area, (2) rapid post-fire vegetation regrowth, and (3) high annual precipitation limiting clear-sky satellite observations. We developed a machine learning algorithm specifically for burned area in wetlands, and applied the algorithm to the Sentinel-2 archive (2016–2019) across the Southeastern US (>290,000 km 2 ). Combining Landsat-8 imagery with Sentinel-2 increased the annual clear-sky observation count from 17 to 46 in 2016 and from 16 to 78 in 2019. When validated with WorldView imagery, the Sentinel-2 burned area had a 29% and 30% omission and commission rates of error for burned area, respectively, compared to the US Geological Survey Landsat-8 Burned Area Product (L8 BA), which had a 47% and 8% omission and commission rate of error, respectively. The Sentinel-2 algorithm and the L8 BA mapped burned area within 78% and 60% of wetland fire perimeters ( n = 555) compiled from state and federal agencies, respectively. This analysis demonstrated the potential of Sentinel-2 to support efforts to track the burned area, especially across challenging ecosystem types, such as wetlands.

Florida, Montana, Oregon↗

USGS2021

Using the Landsat Burned Area products to derive fire history relevant for fire management and conservation in the state of Florida, southeastern USA

Development of comprehensive spatially explicit fire occurrence data remains one of the most critical needs for fire managers globally, and especially for conservation across the southeastern United States. Not only are many endangered species and ecosystems in that region reliant on frequent fire, but fire risk analysis, prescribed fire planning, and fire behavior modeling are sensitive to fire history due to the long growing season and high vegetation productivity. Spatial data that map burned areas over time provide critical information for evaluating management successes. However, existing fire data have undocumented shortcomings that limit their use when detailing the effectiveness of fire management at state and regional scales. Here, we assessed information in existing fire datasets for Florida and the Landsat Burned Area products based on input from the fire management community. We considered the potential of different datasets to track the spatial extents of fires and derive fire history metrics (e.g., time since last burn, fire frequency, and seasonality). We found that burned areas generated by applying a 90% threshold to the Landsat burn probability product matched patterns recorded and observed by fire managers at three pilot areas. We then created fire history metrics for the entire state from the modified Landsat Burned Area product. Finally, to show their potential application for conservation management, we compared fire history metrics across ownerships for natural pinelands, where prescribed fire is frequently applied. Implications of this effort include increased awareness around conservation and fire management planning efforts and an extension of derivative products regionally or globally.

Florida↗