Next‐Generation Biomass Mapping for Regional Emissions and Carbon Inventories: Incorporating Uncertainty in Wildland Fuel Characterization

Prichard, Susan J.; Kennedy, Maureen C.; Andreu, Anne G.; Eagle, Paige; French, Nancy H. F.; Billmire, Michael

doi:10.1029/2019jg005083

Cited by 27 publications

(20 citation statements)

References 49 publications

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“…They found similar results as the default temporal profile used here, but with an extended tail of fire activity into the evening/nighttime hours for western US forests. This result also illustrates how correcting one component in the smoke modeling calculation stream may not result in overall system improvement, due to compensating issues with other components, such as natural fuel heterogeneity (Drury et al 2014), fuel consumption algorithms (Prichard et al 2019), emission factors (Urbanski 2014, Prichard et al 2020, plume rise and the vertical allocation of emissions (Mallia et al 2018;Wilkins et al 2020), and interaction with the changing/diurnal boundary layer (Larkin et al 2012).…”

Section: Discussionmentioning

confidence: 99%

“…The fires burned through about 10 fuel types ranging from grasslands and shrublands to heavily forested systems. The fuel type has a large effect on the quantity of emissions estimated and can be responsible for wide variability in emissions (Prichard et al 2019, Drury et al 2014. Fuel heterogeneity and variability also mean that acres burned are not necessarily a good proxy for emissions.…”

Section: Fire Emissionsmentioning

confidence: 99%

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A multi-analysis approach for estimating regional health impacts from the 2017 Northern California wildfires

O’Neill

Diao

Raffuse

et al. 2021

Journal of the Air & Waste Management Association

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Smoke impacts from large wildfires are mounting, and the projection is for more such events in the future as the one experienced October 2017 in Northern California, and subsequently in 2018 and 2020. Further, the evidence is growing about the health impacts from these events which are also difficult to simulate. Therefore, we simulated air quality conditions using a suite of remotely-sensed data, surface observational data, chemical transport modeling with WRF-CMAQ, one data fusion, and three machine learning methods to arrive at datasets useful to air quality and health impact analyses. To demonstrate these analyses, we estimated the health impacts from smoke impacts during wildfires in October 8-20, 2017, in Northern California, when over 7 million people were exposed to Unhealthy to Very Unhealthy air quality conditions. We investigated using the 5-min available GOES-16 fire detection data to simulate timing of fire activity to allocate emissions hourly for the WRF-CMAQ system. Interestingly, this approach did not necessarily improve overall results, however it was key to simulating the initial 12-hr explosive fire activity and smoke impacts. To improve these results, we applied one data fusion and three machine learning algorithms. We also had a unique opportunity to evaluate results with temporary monitors deployed specifically for wildfires, and performance was markedly different. For example, at the permanent monitoring locations, the WRF-CMAQ simulations had a Pearson correlation of 0.65, and the data fusion approach improved this (Pearson correlation = 0.95), while at the temporary monitor locations across all cases, the best Pearson correlation was 0.5. Overall, WRF-CMAQ simulations were biased high and the geostatistical methods were biased low. Finally, we applied the optimized PM 2.5 exposure estimate in an exposure-response function. Estimated mortality attributable to PM 2.5 exposure during the smoke episode was 83 (95% CI: 0, 196) with 47% attributable to wildland fire smoke.

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Section: Discussionmentioning

confidence: 99%

Section: Fire Emissionsmentioning

confidence: 99%

A multi-analysis approach for estimating regional health impacts from the 2017 Northern California wildfires

O’Neill

Diao

Raffuse

et al. 2021

Journal of the Air & Waste Management Association

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“…The former will allow us to assess vulnerability of all ecotypes to wildfire and peat consumption and the latter to assess any shifts in post-fire trajectories related to severity and frequency of fire. In addition, the greatest unknown in modeling of carbon emissions is the peat or soil organic layer component (French et al, 2004;Prichard et al, 2019). Thus the ability to monitor soil organic layer severity from space will allow for improvements in spatial outputs of belowground carbon emissions.…”

Section: Discussionmentioning

confidence: 99%

Assessing Boreal Peat Fire Severity and Vulnerability of Peatlands to Early Season Wildland Fire

Bourgeau‐Chavez¹,

Grelik²,

Billmire³

et al. 2020

Front. For. Glob. Change

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Globally peatlands store large amounts of carbon belowground with 80% distributed in boreal regions of the northern hemisphere. Climate warming and drying of the boreal region has been documented as affecting fire regimes, with increased fire frequency, severity and extent. While much research is dedicated to assessing changes in boreal uplands, few research efforts are focused on the vulnerability of boreal peatlands to wildfire. In this case study, an integration of field data collection, land cover mapping of peatland types and Landsat-based fire severity mapping was conducted for four early season (May to mid-June) wildfires where peatlands are abundant in northeastern Alberta Canada. The goal was to better understand if peatlands burn more or less preferentially than uplands in fires and how severely the organic soil layers (peat) of different peatland ecotypes burn. The focus was on early season wildfires because they dominated the research area in the decade of study. To do this, a novel Landsat-5 metric was developed to retrieve fire severity of the organic surface layer. Spatial comparisons and statistical analysis showed that proportionally bogs are more likely to burn in early season Alberta wildfires than other ecosystem types, even fire-prone upland conifer. Although for a small sample, we found that when fire weather conditions for the duff layers are severe, the fens of this study appear to become more susceptible to burning. In addition, overall bogs experienced greater severity of burn to the peat layers than fens. Due to the small sample size of peat loss from fire in uplands and limited geographic area of this case study, we were unable to assess if bogs are burning more severely than uplands. Further analysis and Landsat algorithm development for organic soil fire severity in peatlands and uplands are needed to more fully understand trends in belowground consumption for wildfires of all seasons and boreal ecotypes.

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“…), our work calls for a combination of hyperspectral 440 and EC data together with models that accurately represent radiative transfer, energy balance and photosynthesis (RTM-SVAT). The characterization of functional (and biophysical) traits in these networks could be later used to inform, constrain and evaluate TBM, to inform TMB input uncertainties (Prichard et al, 2019), or to benchmark estimates provided by different models and methods (e.g., Luo et al, (2019), Croft et al, (2017), Zhou et al, (2014) or Xie et al, (2018)). Also, when comprehensive enough, they could be predicted at global scale using data-driven approaches or RS 445 (Serbin et al, 2015).…”

Section: Discussionmentioning

confidence: 99%

Combining hyperspectral remote sensing and eddy covariance data streams for estimation of vegetation functional traits

Pacheco‐Labrador

El‐Madany

Martín

et al. 2020

Preprint

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Abstract. Remote Sensing (RS) has traditionally provided estimates of key biophysical properties controlling light interaction with the canopy (e.g., chlorophyll content (Cab) or leaf area index (LAI)). However, recent and upcoming developments in hyperspectral RS are expected to lead to a new generation of products such as vegetation functional traits that control leaf carbon and water gas exchange. This information is pivotal to improve our understanding and capability to predict biosphere-atmosphere fluxes at global scale. Yet, the retrieval of key functional traits such as maximum carboxylation rate (Vcmax) or the Ball-Berry stomatal sensitivity parameter (m) remains challenging, as they only have a weak and indirect influence on optical reflectance factors. Recently, the assimilation of different observations in coupled soil-vegetation-atmosphere transfer (SVAT) and radiative transfer models (RTM) is allowing Vcmax and m estimates; notably using the Soil Canopy Observation of Photosynthesis and Energy fluxes (SCOPE) model. In this work we assess the potential of airborne and satellite emulated hyperspectral imagery jointly with eddy covariance (EC) data for the retrieval of functional traits. Specifically, we made use of time series of gross primary production (GPP) and thermal irradiance measured with net radiometers, together with 17 hyperspectral airborne images. The potential of satellite-borne sensors was tested with emulated EnMAP imagery from the airborne data. EnMAP was selected because of the availability of the emulator, and because is one of the foreseen hyperspectral satellite missions expected to contribute to a new generation of RS products. We estimated ecosystem functional traits by inverting the senSCOPE model, a novel version of SCOPE adapted to represent partly senescent canopies. The experiment takes place in a Mediterranean tree-grass ecosystem subject of a large scale manipulation experiment with nitrogen and nitrogen plus phosphorus, monitored by three EC towers. Parameter estimates and predicted fluxes were evaluated using both ground observations and pattern-oriented model evaluation approach. The method developed in this study provided robust estimates of functional and biophysical parameters for both airborne and synthetic EnMAP datasets. Cab and Vcmax estimates followed observed relationships with leaf nitrogen concentration; whereas m and predicted underlying water use efficiency showed expected relationships with discrimination of 13C isotope in leaves. Results prove that the inversion of coupled RTM-SVAT models against a combination of hyperspectral imagery (e.g., EnMAP), and time series of GPP and thermal irradiance provides reliable estimates of key functional parameters of vegetation that are robust to several sources of uncertainty. The forthcoming satellite hyperspectral missions combined with ecosystem station networks (e.g. Integrated Carbon Observation System (ICOS), NEON, FLUXNET, etc&#8230;), offers unique possibilities to characterize the spatiotemporal distribution of functional parameters relevant for terrestrial biosphere modeling.

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Next‐Generation Biomass Mapping for Regional Emissions and Carbon Inventories: Incorporating Uncertainty in Wildland Fuel Characterization

Cited by 27 publications

References 49 publications

A multi-analysis approach for estimating regional health impacts from the 2017 Northern California wildfires

A multi-analysis approach for estimating regional health impacts from the 2017 Northern California wildfires

Assessing Boreal Peat Fire Severity and Vulnerability of Peatlands to Early Season Wildland Fire

Combining hyperspectral remote sensing and eddy covariance data streams for estimation of vegetation functional traits

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