Carl Seielstad

FireWxNet: a multi-tiered portable wireless system for monitoring weather conditions in wildland fire environments

In this paper we present FireWxNet, a multi-tiered portable wireless system for monitoring weather conditions in rugged wildland fire environments. FireWxNet provides the fire fighting community the ability to safely and easily measure and view fire and weather conditions over a wide range of locations and elevations within forest fires. This previously unattainable information allows fire behavior analysts to better predict fire behavior, heightening safety considerations. Our system uses a tiered structure beginning with directional radios to stretch deployment capabilities into the wilderness far beyond current infrastructures. At the end point of our system we designed and integrated a multi-hop sensor network to provide environmental data. We also integrated web-enabled surveillance cameras to provide visual data. This paper describes a week long full system deployment utilizing 3 sensor networks and 2 web-cams in the Selway-Salmon Complex Fires of 2005. We perform an analysis of system performance and present observations and lessons gained from our deployment.

Deriving Fuel Mass by Size Class in Douglas-fir (Pseudotsuga menziesii) Using Terrestrial Laser Scanning

Requirements for describing coniferous forests are changing in response to wildfire concerns, bio-energy needs, and climate change interests. At the same time, technology advancements are transforming how forest properties can be measured. Terrestrial Laser Scanning (TLS) is yielding promising results for measuring tree biomass parameters that, historically, have required costly destructive sampling and resulted in small sample sizes. Here we investigate whether TLS intensity data can be used to distinguish foliage and small branches (≤0.635 cm diameter; coincident with the one-hour timelag fuel size class) from larger branchwood (>0.635 cm) in Douglas-fir (Pseudotsuga menziesii) branch specimens. We also consider the use of laser density for predicting biomass by size class. Measurements are addressed across multiple ranges and scan angles. Results show TLS capable of distinguishing fine fuels from branches at a threshold of one standard deviation above mean intensity. Additionally, the relationship between return density and biomass is linear by fuel type for fine fuels (r 2 = 0.898; SE 22.7%) and branchwood (r 2 = 0.937; SE 28.9%), as well as for total mass (r 2 = 0.940; SE 25.5%). Intensity decays predictably as scan distances increase; however, the range-intensity relationship is best described by an exponential model rather than 1/d 2 . Scan angle appears to have no systematic effect on fine fuel discrimination, while some differences are observed in density-mass relationships with changing angles due to shadowing.

Using Laser Altimetry-based Segmentation to Refine Automated Tree Identification in Managed Forests of the Black Hills, South Dakota

The success of a local maximum (LM) tree detection algorithm for detecting individual trees from lidar data depends on stand conditions that are often highly variable. A laser height variance and percent canopy cover (PCC) classification is used to segment the landscape by stand condition prior to stem detection. We test the performance of the LM algorithm using canopy height model (CHM) smoothing decisions and crown width estimation for each stand condition ranging from open savannah to multi-strata stands. Results show that CHM smoothing improves stem predictions for low density stands and no CHM smoothing better detects stems in dense even-aged stands, specifically dominant and co-dominant trees (R 2 � 0.61, RMSE � 20.91 stems with smoothing; R 2 � 0.85, RMSE � 46.02 stems with no-smoothing; combined smoothed CHM for low density and unsmoothed CHM for high density stands R 2 � 0.88, RMSE � 28.59 stems). At a threshold of approximately 2,200 stems ha � 1 , stem detection accuracy is no longer obtainable in any

Development and validation of fuel height models for terrestrial lidar – RxCADRE 2012

Terrestrial laser scanning (TLS) was used to collect spatially continuous measurements of fuelbed characteristics across the plots and burn blocks of the 2012 RxCADRE experiments in Florida. Fuelbeds were scanned obliquely from plot/block edges at a height of 20 m above ground. Pre-fire blocks were scanned from six perspectives and four perspectives for post-fire at ~2 cm nominal spot spacing. After processing, fuel height models were developed at one meter spatial resolution in burn blocks and compared with field measurements of height. Spatial bias is also examined. The resultant fuel height data correspond closely with field measurements of height and exhibit low spatial bias. They show that field measurements of fuel height from field plots are not representative of the burn blocks as a whole. A translation of fuel height distributions to specific fuel attributes will be necessary to maximise the utility of the data for fire modelling.

Automated integration of lidar into the LANDFIRE product suite

Accurate information about three-dimensional canopy structure and wildland fuel across the landscape is necessary for fire behaviour modelling system predictions. Remotely sensed data are invaluable for assessing these canopy characteristics over large areas; lidar data, in particular, are uniquely suited for quantifying three-dimensional canopy structure. Although lidar data are increasingly available, they have rarely been applied to wildland fuels mapping efforts, mostly due to two issues. First, the Landscape Fire and Resource Planning Tools (LANDFIRE) program, which has become the default source of large-scale fire behaviour modelling inputs for the US, does not currently incorporate lidar data into the vegetation and fuel mapping process because spatially continuous lidar data are not available at the national scale. Second, while lidar data are available for many land management units across the US, these data are underutilized for fire behaviour applications. This is partly due to a lack of local personnel trained to process and analyse lidar data. This investigation addresses these issues by developing the Creating Hybrid Structure from LANDFIRE/lidar Combinations (CHISLIC) tool. CHISLIC allows individuals to automatically generate a suite of vegetation structure and wildland fuel parameters from lidar data and infuse them into existing LANDFIRE data sets. CHISLIC will become available for wider distribution to the public through a partnership with the U.S. Forest Service’s Wildland Fire Assessment System (WFAS) and may be incorporated into the Wildland Fire Decision Support System (WFDSS) with additional design and testing. WFAS and WFDSS are the primary systems used to support tactical and strategic wildland fire management decisions.

Using Simulated 3D Surface Fuelbeds and Terrestrial Laser Scan Data to Develop Inputs to Fire Behavior Models

Abstract Understanding fine-scale variability in understory fuels is increasingly important as physics-based fire behavior models drive needs for higher-resolution data. Describing fuelbeds 3Dly is critical in determining vertical and horizontal distributions of fuel elements and the mass, especially in frequently burned pine ecosystems where fine-scale fuels arrangement drives fire intensity and resulting fire effects. Here, we describe research involving the use of highly resolved 3D models. We create fuelbeds using individual grass, litter, and pinecone models designed from field measurements. These fuel models are distributed throughout the fuelbed to replicate fuel distribution in rectified nadir photography taken for each plot. The simulated fuelbeds are converted into voxel arrays and biomass is estimated from calculated surface area between mesh vertices for each voxel. We compare field-based fuel depth and biomass with simulated estimates to demonstrate similarities and differences. Biomass distributions between simulated fuel beds and terrestrial laser scan data correlated well using Weibull shape parameters (r = 0.86). Our findings indicate that integration of field, simulated, and terrestrial laser scanner data will improve characterization of fuel mass, type, and spatial allocations that are important inputs to physics-based fire behavior models.

Hillslope topography mediates spatial patterns of ecosystem sensitivity to climate: Forest Productivity in Complex Terrain

Understanding how hillslope topography modulates ecosystem dynamics across topoclimatic gradients is critical for predicting future climate change impacts on vegetation function. We examined the influence of hillslope topography on ecosystem productivity, structure, and photosynthetic activity across a range of water and energy availability using three independent methods in a forested watershed (Montana, USA): 308 tree cores; light detection and ranging quantification of stem density, basal area, foliar biomass, and total biomass; and the enhanced vegetation index (EVI; 1984–2012). Multiple linear regression analysis across three conifer species revealed significant increases in measured basal area increment growth rates (from 56 to 2,058 mm/yr) with increasing values of the topographic wetness index and decreases in the climatic water deficit. At the watershed scale, we observed strong gradients in total biomass (e.g., 52 to 75 Mg/ha), which increased from ridgelines to convergent hollows. The most predominant topographic organization of forest biomass occurred along locations of climatically driven water limitations. Similarly, an analysis of growing season EVI indicated enhanced photosynthetic activity and a prolonged growing season in convergent hillslope positions. Collectively, these analyses confirm that within water-limited landscapes, meter-scale differences in topographic position can mediate the effects of the local energy balance and contribute to large differences in local hydrometeorological processes that are a necessary consideration for quantifying spatial patterns of ecosystem productivity. Further, they suggest that local topography and its topology with regional climate may become increasingly important for understanding spatial patterns of ecosystem productivity, mortality, and resilience as regional climates become more arid.