RESUMEN
Background: Characterization of physical fuel distributions across heterogeneous landscapes is needed to understand fire behavior, account for smoke emissions, and manage for ecosystem resilience. Remote sensing measurements at various scales inform fuel maps for improved fire and smoke models. Airborne lidar that directly senses variation in vegetation height and density has proven to be especially useful for landscape-scale fuel load and consumption mapping. Here we predicted field-observed fuel loads from airborne lidar and Landsat-derived fire history metrics with random forest (RF) modeling. RF models were then applied across multiple lidar acquisitions (years 2012, 2019, 2020) to create fuel maps across our study area on the Kaibab Plateau in northern Arizona, USA. We estimated consumption across the 2019 Castle and Ikes Fires by subtracting 2020 fuel load maps from 2019 fuel load maps and examined the relationship between mapped surface fuels and years since fire, as recorded in the Monitoring Trends in Burn Severity (MTBS) database. Results: R-squared correlations between predicted and ground-observed fuels were 50, 39, 59, and 48% for available canopy fuel, 1- to 1000-h fuels, litter and duff, and total surface fuel (sum of 1- to 1000-h, litter and duff fuels), respectively. Lidar metrics describing overstory distribution and density, understory density, Landsat fire history metrics, and elevation were important predictors. Mapped surface fuel loads were positively and nonlinearly related to time since fire, with asymptotes to stable fuel loads at 10-15 years post fire. Surface fuel consumption averaged 16.1 and 14.0 Mg ha- 1 for the Castle and Ikes Fires, respectively, and was positively correlated with the differenced Normalized Burn Ratio (dNBR). We estimated surface fuel consumption to be 125.3 ± 54.6 Gg for the Castle Fire and 27.6 ± 12.0 Gg for the portion of the Ikes Fire (42%) where pre- and post-fire airborne lidar were available. Conclusions: We demonstrated and reinforced that canopy and surface fuels can be predicted and mapped with moderate accuracy using airborne lidar data. Landsat-derived fire history helped account for spatial and temporal variation in surface fuel loads and allowed us to describe temporal trends in surface fuel loads. Our fuel load and consumption maps and methods have utility for land managers and researchers who need landscape-wide estimates of fuel loads and emissions. Fuel load maps based on active remote sensing can be used to inform fuel management decisions and assess fuel structure goals, thereby promoting ecosystem resilience. Multitemporal lidar-based consumption estimates can inform emissions estimates and provide independent validation of conventional fire emission inventories. Our methods also provide a remote sensing framework that could be applied in other areas where airborne lidar is available for quantifying relationships between fuels and time since fire across landscapes.
Antecedentes: La caracterización de la distribución física de los combustibles a través de paisajes heterogéneos es necesaria para entender el comportamiento del fuego, contabilizar las emisiones de humo, y manejar los ecosistemas para su resiliencia. Las mediciones mediante sensores remotos a varias escalas, aportan mapas para mejorar modelos de fuegos y dispersión de humos. Las mediciones con LIDAR aerotransportados que determinan directamente variaciones en altura y densidad de la vegetación, han probado ser especialmente útiles para el mapeo de la carga y el consumo de combustible a escala de paisaje. Predijimos la carga de combustibles en la planicie de Kaibab en el norte de Arizona, en los EEUU, estimamos el consumo a través de los incendios de Castle e Ikes de 2019, mediante la substracción de la carga de mapas de carga del 2020 menos los de 2019, y examinamos las relaciones entre el mapeo de los combustibles superficiales y años desde el fuego, registrados en la base de datos titulada Monitoreo de las Tendencias de la Severidad de los incendios (MTBS). Resultados: Las correlaciones de R2 entre valores de cargas predichos y aquellos de observaciones de campo fueron 50, 39, 59, y 48% para combustible disponible en el dosel, combustibles de 1 a 1000 h, mantillo y hojarasca por debajo del mantillo (duff), y combustible total superficial (la suma de combustibles de 1 a 1000 h y del mantillo y la hojarasca subyacente), respectivamente. Las medidas del LIDAR que describían la distribución del dosel y su densidad, la densidad del sotobosque, las medidas históricas de fuego provistas por el Landsat y la altura (elevación) fueron predictores importantes. Las cargas de combustibles mapeadas fueron positivamente y no linealmente relacionadas al tiempo desde el fuego, con asíntotas hacia cargas de combustible estables entre 10 y 15 años post fuego. El consumo de la carga de combustibles en superficie promedió 16,1 y 14,0 Mg por ha para los incendios de Castle e Ikes, respectivamente y fue positivamente correlacionada con la diferencia normalizada de la relación de quema (dNBR). Estimamos que el consumo del combustible superficial fue de 125,3 ± 54,6 Gg para el incendio de Castle y 27,6 ± 12,0 Gg para la porción del incendio de Ikes (42%), del cual los datos de LIDAR aerotransportados (pre y post fuego), estaban disponibles. Conclusiones: Demostramos y reforzamos que tanto el dosel como los combustibles superficiales pueden ser predichos y mapeados con una moderada precisión usando datos de LIDAR aerotransportados. Las medidas históricas de fuego provistas por el Landsat ayudaron a determinar la variación espacial y temporal de la carga de los combustibles superficiales y nos permitieron describir tendencias temporales en las cargas de combustible superficiales. Nuestros mapas y métodos de consumo y cargas de combustible son de utilidad para los gestores de recursos e investigadores que necesitan de estimaciones amplias de carga de combustible y emisiones a escala de paisaje. Los mapas de carga de combustibles basados en sensores remotos activos pueden ser usados para informar sobre decisiones de manejo de combustible y determinar metas de estructuras de cargas, promoviendo de esa manera la resiliencia del ecosistema. Las estimaciones de consumo basadas en LIDAR multitemporal pueden informar sobre estimaciones de emisiones y proveer de una validación de inventarios convencionales de emisiones por fuegos. Nuestros métodos también proveen de un marco conceptual de sensores remotos que pueden ser aplicados en otras áreas donde el LIDAR aerotransportado está disponible para cuantificar relaciones entre combustibles y tiempo desde el fuego en diferentes paisajes.
RESUMEN
Wilderness areas are not immune to changes in land use, land cover, and/or climate. Future changes will intensify the balancing act of maintaining ecological conditions and untrammeled character within wilderness areas. We assessed the quantitative and spatial changes in land use, land cover, and climate predicted to occur in and around wilderness areas by (1) quantifying projected changes in land use and land cover around wilderness areas; (2) evaluating if public lands surrounding wilderness areas can buffer future land-use change; (3) quantifying future climate conditions in and around wilderness areas; and (4) identifying wilderness areas expected to experience the most change in land use, land cover, and climate. We used projections of land use (four variables), land cover (five variables), and climate (nine variables) to assess changes for 707 wilderness areas in the contiguous United States by mid-21st century under two scenarios (medium-low and high). We ranked all wilderness areas relative to each other by summing and ranking decile values for each land use, land cover, and climate variable and calculating a multivariate metric of future change. All wilderness areas were projected to experience some level of change by mid-century. The greatest land-use changes were associated with increases in agriculture, clear cutting, and developed land, while the greatest land cover changes were observed for grassland, forest, and shrubland. In 51.6% and 73.8% of wilderness areas, core area of natural vegetation surrounding wilderness was projected to decrease for the medium-low and high scenarios, respectfully. Presence of public land did not mitigate the influence of land-use change around wilderness areas. Geographically, projected changes occurred throughout the contiguous U.S., with areas in the northeast and upper Midwest projected to have the greatest land-use and climate change and the southwestern U.S. projected to undergo the greatest land cover and climate change. Our results provide insights into potential future threats to wilderness areas and the challenges associated with wilderness stewardship and climate adaptation. Despite the high degree of protection and remoteness of wilderness areas, effective management and preservation of these lands must consider future changes in land use, land cover, and climate.