No abstract

Prescribed fires in forest ecosystems can negatively impact human health and safety by transporting smoke downwind into nearby communities. Smoke transport to communities is known to occur around Bend, Oregon, United States of America (USA), where burning at the wildland–urban interface in the Deschutes National Forest resulted in smoke intrusions into populated areas. The number of suitable days for prescribed fires is limited due to the necessity for moderate weather conditions, as well as wind directions that do not carry smoke into Bend. To better understand the conditions leading to these intrusions and to assess predictions of smoke dispersion from prescribed fires, we collected data from an array of weather and particulate monitors over the autumn of 2014 and spring of 2015 and historical weather data from nearby remote automated weather stations (RAWS). We characterized the observed winds to compare with meteorological and smoke dispersion models using the BlueSky smoke modeling framework. The results from this study indicated that 1–6 days per month in the spring and 2–4 days per month in the fall met the general meteorological prescription parameters for conducting prescribed fires in the National Forest. Of those, 13% of days in the spring and 5% of days in the fall had “ideal” wind patterns, when north winds occurred during the day and south winds did not occur at night. The analysis of smoke intrusions demonstrated that dispersion modeling can be useful for anticipating the timing and location of smoke impacts, but substantial errors in wind speed and direction of the meteorological models can lead to mischaracterizations of intrusion events. Additionally, for the intrusion event modeled using a higher-resolution 1-km meteorological and dispersion model, we found improved predictions of both the timing and location of smoke delivery to Bend compared with the 4-km meteorological model. The 1-km-resolution model prediction fell within 1 h of the observed event, although with underpredicted concentrations, and demonstrated promise for high-resolution modeling in areas of complex terrain.

Monitoring and understanding the spatio-temporal variations of forest aboveground biomass (AGB) is a key basis to quantitatively assess the carbon sequestration capacity of a forest ecosystem. To map and update forest AGB in the Greater Khingan Mountains (GKM) of China, this work proposes a physical-based approach. Based on the baseline forest AGB from Landsat Enhanced Thematic Mapper Plus (ETM+) images in 2008, we dynamically updated the annual forest AGB from 2009 to 2012 by adding the annual AGB increment (ABI) obtained from the simulated daily and annual net primary productivity (NPP) using the Boreal Ecosystem Productivity Simulator (BEPS) model. The 2012 result was validated by both field- and aerial laser scanning (ALS)-based AGBs. The predicted forest AGB for 2012 estimated from the process-based model can explain 31% (n = 35, p < 0.05, RMSE = 2.20 kg/m2) and 85% (n = 100, p < 0.01, RMSE = 1.71 kg/m2) of variation in field- and ALS-based forest AGBs, respectively. However, due to the saturation of optical remote sensing-based spectral signals and contribution of understory vegetation, the BEPS-based AGB tended to underestimate/overestimate the AGB for dense/sparse forests. Generally, our results showed that the remotely sensed forest AGB estimates could serve as the initial carbon pool to parameterize the process-based model for NPP simulation, and the combination of the baseline forest AGB and BEPS model could effectively update the spatiotemporal distribution of forest AGB.

We investigate the spatiotemporal patterns of prescribed fire and wildfire within Washington State, USA using records from the state’s Department of Natural Resources (DNR). Spatiotemporal comparisons of prescribed fire and wildfire area burned revealed that (1) fire activity broadly differed between the eastern and western portion of the state in terms of total area and distribution of burn sources, (2) over the 2004–2019 period, wildfire largely replaced prescribed fire as the predominant source of burning, and (3) wildfire and prescribed fire occur during distinct months of the year. Spatiotemporal variation in prescribed fire activity at regional levels were measured using five parameters: total area burned, total biomass burned, burn days, burn approval rates, and pile burn frequency. Within-region spatial variability in prescribed fire parameters across land ownership categories and bioclimatic categories were often detectable. Regression models of the annualized prescribed fire parameters suggested that prescribed fire activities have been declining in multiple administrative regions over the 2004–2019 period. A descriptive analysis of seasonal trends found that prescribed fire use largely peaked in the fall months, with minor peaks usually occurring in the spring. Lastly, we described how area burned, biomass burned, and pile burn frequency differed between prescribed fires approved and denied by the DNR, and found that approved prescribed fires were typically smaller and burned less biomass than denied fires.

The Composite Burn Index (CBI) is commonly linked to remotely sensed data to understand spatial and temporal patterns of burn severity. However, a comprehensive understanding of the tradeoffs between different methods used to model CBI with remotely sensed data is lacking. To help understand the current state of the science, provide a blueprint towards conducting broadscale meta-analyses, and identify key decision points and potential rationale, we conducted a review of studies that linked remotely sensed data to continuous estimates of burn severity measured with the CBI and related methods. We provide a roadmap of the different methodologies applied and examine potential rationales used to justify them. Our findings largely reflect methods applied in North America -particularly in the western USA -due to the high number of studies in that region. We find the use of different methods across studies introduces variations that make it difficult to compare outcomes. Additionally, the existing suite of comparative studies focuses on one or few of many possible sources of uncertainty. Thus, compounding error and propagation throughout the many decisions made during analysis is not well understood. Finally, we suggest a broad set of methodological information and key rationales for decision-making that could facilitate future reviews.