Coupling terrestrial laser scanning with 3D fuel biomass sampling for advancing wildland fuels characterization

Rowell, Eric; Loudermilk, E. Louise; Hawley, Christie; Pokswinski, Scott; Seielstad, Carl; Queen, Lloyd P.; O’Brien, Joseph J.; Hudak, Andrew T.; Goodrick, Scott L.; Hiers, J. Kevin

doi:10.1016/j.foreco.2020.117945

Cited by 44 publications

(45 citation statements)

References 51 publications

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“…This work will also be important for the development of tools for pre-suppression planning for the mitigation of wildland fire, informing management objectives on the implementation of prescribed fire and for the evaluation of wildland effects and prescribed fire effectiveness. While ALS data has proven to be a good tool for landscape scale studies, these same CHP ideas may also transfer to both space-born LiDAR systems such as GEDI [55] and small-scale TLS data collections [56]. The power of these measurements and their application will be amplified as more spatially coincident collections are made which will allow for the examination of disturbance interactions and long-term trajectories across landscapes, as we now see coming to fruition with the Landsat data archive.…”

Section: Discussionmentioning

confidence: 99%

Decomposing the Interactions between Fire Severity and Canopy Fuel Structure Using Multi-Temporal, Active, and Passive Remote Sensing Approaches

Skowronski

Gallagher

Warner

2020

Fire

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Within the realms of both wildland and prescribed fire, an understanding of how fire severity and forest structure interact is critical for improving fuels treatment effectiveness, quantifying the ramifications of wildfires, and improving fire behavior modeling. We integrated high resolution estimates of fire severity with multi-temporal airborne laser scanning data to examine the role that various fuel loading, canopy shape, and other variables had on predicting fire severity for a complex of prescribed fires and one wildfire and how three-dimensional fuels changed as a result of these fires. Fuel loading characteristics were widely variable, and fires were ignited using a several techniques (heading, flanking, and backing), leading to a large amount of variability in fire behavior and subsequent fire effects. Through our analysis, we found that fire severity was linked explicitly to pre-fire fuel loading and structure, particularly in the three-dimensional distribution of fuels. Fire severity was also correlated with post-fire fuel loading, forest structural heterogeneity, and shifted the diversity and abundance of canopy classes within the landscape. This work demonstrates that the vertical distribution of fuel is an important factor and that subtle difference has defined effects on fire behavior and severity.

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Section: Discussionmentioning

confidence: 99%

Decomposing the Interactions between Fire Severity and Canopy Fuel Structure Using Multi-Temporal, Active, and Passive Remote Sensing Approaches

Skowronski

Gallagher

Warner

2020

Fire

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“…At the time of this work, techniques for converting terrestrial LIDAR data into high-resolution fuel input conditions were not sufficiently mature to be used. Terrestrial LIDAR techniques [41] are expected to simplify the fuel bed development process in the future.…”

Section: Fuelsmentioning

confidence: 99%

Modeling Low Intensity Fires: Lessons Learned from 2012 RxCADRE

et al. 2021

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Coupled fire-atmosphere models are increasingly being used to study low-intensity fires, such as those that are used in prescribed fire applications. Thus, the need arises to evaluate these models for their ability to accurately represent fire spread in marginal burning conditions. In this study, wind and fuel data collected during the Prescribed Fire Combustion and Atmospheric Dynamics Research Experiments (RxCADRE) fire campaign were used to generate initial and boundary conditions for coupled fire-atmosphere simulations. We present a novel method to obtain fuels representation at the model grid scale using a combination of imagery, machine learning, and field sampling. Several methods to generate wind input conditions for the model from eight different anemometer measurements are explored. We find a strong sensitivity of fire outcomes to wind inputs. This result highlights the critical need to include variable wind fields as inputs in modeling marginal fire conditions. This work highlights the complexities of comparing physics-based model results against observations, which are more acute in marginal burning conditions, where stronger sensitivities to local variability in wind and fuels drive fire outcomes.

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“…According to the remote sensing-based IPCC method, a canopy fuel map can be derived using an ALS canopy height model by segmenting every single tree, using, for example, mathematical morphology-based watershed segmentation [26][27][28][29], Multilevel Morphological Active Contour (MMAC) [30] or Multilevel Slicing And Coding (MSAC) techniques [31]. Moreover, the latest development of lidar sensing enables precise inventories of surface fuel and canopy fuel using mobile terrestrial lidar instruments [32][33][34][35]. To overcome the high cost of high-density point cloud ALS data, alternate methods for surface fuel loading (SFL) estimation can be based on mathematical/empirical models using inventory data such as vegetation/species maps and related environmental factors [18,36], satellite fullwaveform lidar data [37,38], and photon lidar data [39].…”

Section: Introductionmentioning

confidence: 99%

Generating a Baseline Map of Surface Fuel Loading Using Stratified Random Sampling Inventory Data through Cokriging and Multiple Linear Regression Methods

Lin

Huang

et al. 2021

Remote Sensing

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Surface fuel loading is a key factor in controlling wildfires and planning sustainable forest management. Spatially explicit maps of surface fuel loading can highlight the risks of a forest fire. Geospatial information is critical in enabling careful use of deliberate fire setting and also helps to minimize the possibility of heat conduction over forest lands. In contrast to lidar sensing and/or optical sensing based methods, an approach of integrating in-situ fuel inventory data, geospatial interpolation techniques, and multiple linear regression methods provides an alternative approach to surface fuel load estimation and mapping over mountainous forests. Using a stratified random sampling based inventory and cokriging analysis, surface fuel loading data of 120 plots distributed over four kinds of fuel types were collected in order to develop a total surface fuel loading model (lntSFL-BioTopo model) and a fine surface fuel model (lnfSFL-BioTopo model) for generating tSFL and fSFL maps. Results showed that the combination of topographic parameters such as slope, aspect, and their cross products and the fuel types such as pine stand, non-pine conifer stand, broadleaf stand, and conifer–broadleaf mixed stand was able to appropriately describe the changes in surface fuel loads over a forest with diverse terrain morphology. Based on a cross-validation method, the estimation of tSFL and fSFL of the study site had an RMSE of 3.476 tons/ha and 3.384 tons/ha, respectively. In contrast to the average loading of all inventory plots, the estimation for tSFL and fSFL had a relative error of 38% (PRMSE). The reciprocal of estimation bias of both SFL-BioTopo models tended to be an exponential growth function of the amount of surface fuel load, indicating that the estimation accuracy of the proposed method is likely to be improved with further study. In the regression modeling, a natural logarithm transformation of the surface fuel loading prevented the outcome of negative estimates and thus improved the estimation. Based on the results, this paper defined a minimum sampling unit (MSU) as the area for collecting surface fuels for interpolation using a cokriging model. Allocating the MSUs at the boundary and center of a plot improved surface fuel load prediction and mapping.

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Coupling terrestrial laser scanning with 3D fuel biomass sampling for advancing wildland fuels characterization

Cited by 44 publications

References 51 publications

Decomposing the Interactions between Fire Severity and Canopy Fuel Structure Using Multi-Temporal, Active, and Passive Remote Sensing Approaches

Decomposing the Interactions between Fire Severity and Canopy Fuel Structure Using Multi-Temporal, Active, and Passive Remote Sensing Approaches

Modeling Low Intensity Fires: Lessons Learned from 2012 RxCADRE

Generating a Baseline Map of Surface Fuel Loading Using Stratified Random Sampling Inventory Data through Cokriging and Multiple Linear Regression Methods

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