Wildfire can substantially alter forest vertical structure and aboveground biomass, yet commonly used spectral burn-severity indices may not directly represent three-dimensional structural change. Using the 2020 Holiday Farm Fire in Oregon, USA, as a case study, we integrated GEDI observations with Sentinel-1, Sentinel-2, and topographic data to characterize pre- and post-fire canopy height and aboveground biomass density (AGBD). We quantified structural losses, evaluated their relationships with the differenced normalized burn ratio (dNBR), and examined whether these relationships varied with pre-fire forest structure. Mean canopy height and AGBD decreased by 27.0
The scales at which social-ecological processes like fire occur on the landscape are often misaligned with jurisdictional boundaries that artificially divide ecosystems. Thus, effective fire governance requires collaboration across multiple land ownerships with differing institutional areas of responsibility. Across the Western US, there is a need to expand collaborative fire governance between US federal agencies and Tribes to effectively address forest health challenges at meaningful scales, yet Tribal-Federal cross-boundary fuels reduction planning and implementation remains limited. We address this gap through a case study with the Southern Ute Indian Tribe and San Juan National Forest in southwestern Colorado, using a participatory action research approach and a mixed-methods design including interviews and focus groups. Qualitative coding revealed four unique fire-use contexts that motivate Tribal-Federal collaborative fire governance within our case study. We find that barriers and opportunities for collaborative fire governance are diverse and interrelated, identifying 31 types of barriers and 28 types of opportunities. Our findings highlight the need for mechanisms tailored to address the unique circumstances of collaboration between US agencies and Tribes.
The purpose of this paper is to examine the scale and importance of Indigenous cultural burning in precolonial times on the Central California Coast. We begin by reviewing recent advances that help us understand past human–environment interactions and facilitate the detection of ancient cultural burning by Tribes in Western North America. Insights from this recent work, along with Indigenous cultural practices in the study area, are employed to identify expectations for possible cultural prairie stewardship. We then synthesize findings from historical fire ecology and eco-archaeological studies to evaluate if there is evidence for cultural burning on the Central California Coast in Late Holocene times (ca. 2000 BCE-1800 CE). In reviewing available data drawn from fire-scar analyses, pollen and charcoal studies, phytolith soil sampling, and fine-grained eco-archaeological investigations, we find that there is empirical support for frequent cultural burning of coastal prairies taking place along the Central California Coast from at least ca. 700–800 CE that continued into historic times. We argue that lighting fire alone would not produce the abundance and distribution of vegetation types that were/are crucial to the Indigenous people in this area. Our review suggests that the long-term maintenance of coastal prairies and other ecosystems in Tribal lands along the Central California Coast would not have happened with lightning ignition alone. We conclude that it was largely the product of deliberate human intervention undertaken to augment the diversity and availability of culturally important plants and animals dating back at least 1300 years ago.
Wildfire effects on soil can differ among soil properties, soil depths, and fire-severity classes, yet their longer-term trajectories remain poorly resolved in subtropical pine plantations. We evaluated temporal convergence of burned soils toward an unburned reference following an April 2022 wildfire in a Pinus massoniana plantation in central China. Twenty permanent plots representing unburned, low-, moderate-, and high-severity conditions were monitored across eight surveys over 43 months and three soil depths. Severity-associated differences were strongest and most consistent in the 0–10 cm soil layer during the early monitoring period. Under high severity, surface-soil organic carbon (SOC) at the first survey was 19.08 ± 1.49 g kg−1 compared with 38.61 ± 0.59 g kg−1 in the unburned reference, corresponding to a 50.6
Grassland fuels can be patchy across space and time, especially on landscapes where multiple grassland plant communities co-occur. However, the standard fuel models inadequately capture the spatiotemporal variability in grassland fuels for local, strategic planning of fuel treatments. We aimed to better understand the seasonal variation in grassland fuels and fire behavior across grassland types. Here, we developed remote sensing-based grassland fuel maps using multispectral satellite data (10-m resolution) from Sentinel-2, compared seasonal fire behavior across grassland types, and performed a sensitivity analysis of fire behavior models to changes in litter, dead 1 h, and live herbaceous loads, and fuelbed depth estimates. We were able to best predict live herbaceous load (R2 = 0.85), followed by fuelbed depth (R2 = 0.81), dead 1 h fuel load (R2 = 0.76), and litter load (R2 = 0.67) based on remotely sensed predictors. Our spatially predicted grassland fuel maps showed seasonal variability in fuel loads in different places, reflecting the varying timing of curing by grassland types. In particular, we observed more accumulation of dead 1 h load between August and October in early senescing mixedgrass prairies than later senescing tallgrass prairies. We found an overall faster modeled rate of spread in smooth brome-dominated, invasive grasslands and native tallgrass prairies compared to mixedgrass and prairie dog-dominated grasslands. From the sensitivity analysis, we found that modeled fire behavior model is most sensitive to dead 1 h fuel load and least sensitive to litter load in this grassland system. At a time when grassland fires are increasing and resources for fuel management are limited, remote sensing approach to grassland fuel modeling may help land managers make decisions about where and when to prioritize fuel treatments to mitigate the risk of catastrophic wildfires.
