Wade T. Tinkham

Is burn severity related to fire intensity? Observations from landscape scale remote sensing

Biomass burning by wildland fires has significant ecological, social and economic impacts. Satellite remote sensing provides direct measurements of radiative energy released by the fire (i.e. fire intensity) and surrogate measures of ecological change due to the fire (i.e. fire or burn severity). Despite anecdotal observations causally linking fire intensity with severity, the nature of any relationship has not been examined over extended spatial scales. We compare fire intensities defined by Moderate Resolution Imaging Spectroradiometer Fire Radiative Power (MODIS FRP) products with Landsat-derived spectral burn severity indices for 16 fires across a vegetation structure continuum in the western United States. Per-pixel comparison of MODIS FRP data within individual fires with burn severity indices is not reliable because of known satellite temporal and spatial FRP undersampling. Across the fires, 69% of the variation in relative differenced normalized burn ratio was explained by the 90th percentile of MODIS FRP. Therefore, distributional MODIS FRP measures (median and 90th-percentile FRP) derived from multiple MODIS overpasses of the actively burning fire event may be used to predict potential long-term negative ecological effects for individual fires.

A Comparison of Two Open Source LiDAR Surface Classification Algorithms

With the progression of LiDAR (Light Detection and Ranging) towards a mainstream resource management tool, it has become necessary to understand how best to process and analyze the data. While most ground surface identification algorithms remain proprietary and have high purchase costs; a few are openly available, free to use, and are supported by published results. Two of the latter are the multiscale curvature classification and the Boise Center Aerospace Laboratory LiDAR (BCAL) algorithms. This study investigated the accuracy of these two algorithms (and a combination of the two) to create a digital terrain model from a raw LiDAR point cloud in a semi-arid landscape. Accuracy of each algorithm was assessed via comparison with >7,000 high precision survey points stratified across six different cover types. The overall performance of both algorithms differed by only 2%; however, within specific cover types significant differences were observed in accuracy. The results highlight the accuracy of both algorithms across a variety of vegetation types, and ultimately suggest specific scenarios where one approach may outperform the other. Each algorithm produced similar results except in the ceanothus and conifer cover types where BCAL produced lower errors.

Towards a new paradigm in fire severity research using dose–response experiments

Most landscape-scale fire severity research relies on correlations between field measures of fire effects and relatively simple spectral reflectance indices that are not direct measures of heat output or changes in plant physiology. Although many authors have highlighted limitations of this approach and called for improved assessments of severity, others have suggested that the operational utility of such a simple approach makes it acceptable. An alternative pathway to evaluate fire severity that bridges fire combustion dynamics and ecophysiology via dose–response experiments is presented. We provide an illustrative example from a controlled nursery combustion laboratory experiment. In this example, severity is defined through changes in the ability of the plant to assimilate carbon at the leaf level. We also explore changes in the Differenced Normalised Differenced Vegetation Index (dNDVI) and the Differenced Normalised Burn Ratio (dNBR) as intermediate spectral indices. We demonstrate the potential of this methodology and propose dose–response metrics for quantifying severity in terms of carbon cycle processes.

An accuracy assessment of the MTBS burned area product for shrub–steppe fires in the northern Great Basin, United States

Although fire is a common disturbance in shrub–steppe, few studies have specifically tested burned area mapping accuracy in these semiarid to arid environments. We conducted a preliminary assessment of the accuracy of the Monitoring Trends in Burn Severity (MTBS) burned area product on four shrub–steppe fires that exhibited varying degrees of within-fire patch heterogeneity. Independent burned area perimeters were derived through visual interpretation and were used to cross-compare the MTBS burned area perimeters with classifications produced using set thresholds on the Relativised differenced Normalised Burn Index (RdNBR), Mid-infrared Burn Index (MIRBI) and Char Soil Index (CSI). Overall, CSI provided the most consistent accuracies (96.3–98.6%), with only small commission errors (1.5–4.4%). MIRBI also had relatively high accuracies (92.2–97.9%) and small commission errors (2.1–10.8%). The MTBS burned area product had higher commission errors (4.3–15.5%), primarily due to inclusion of unburned islands and fingers within the fire perimeter. The RdNBR burned area maps exhibited lower accuracies (92.9–96.0%). However, the different indices when constrained by the MTBS perimeter provided variable results, with CSI providing the highest and least variable accuracies (97.4–99.1%). Studies seeking to use MTBS perimeters to analyse trends in burned area should apply spectral indices to constrain the final burned area maps. The present paper replaces a former paper of the same title (http://dx.doi.org/10.1071/WF13206), which was withdrawn owing to errors discovered in data analysis after the paper was accepted for publication.

Effects of fire radiative energy density dose on Pinus contorta and Larix occidentalis seedling physiology and mortality

Climate change is projected to exacerbate the intensity of heat waves and drought, leading to a greater incidence of large and high-intensity wildfires in forested ecosystems. Predicting responses of seedlings to such fires requires a process-based understanding of how the energy released during fires affects plant physiology and mortality. Understanding what fire ‘doses’ cause seedling mortality is important for maintaining grasslands or promoting establishment of desirable plant species. We conducted controlled laboratory combustion experiments on replicates of well-watered nursery-grown seedlings. We evaluated the growth, mortality and physiological response of Larix occidentalis and Pinus contorta seedlings to increasing fire radiative energy density (FRED) doses created using natural fuels with known combustion properties. We observed a general decline in the size and physiological performance of both species that scaled with increasing FRED dose, including decreases in leaf-level photosynthesis, seedling leaf area and diameter at root collar. Greater FRED dose increased the recovery time of chlorophyll fluorescence in the remaining needles. This study provides preliminary data on what level of FRED causes mortality in these two species, which can aid land managers in identifying strategies to maintain (or eliminate) woody seedlings of interest.

Replacing time with space: using laboratory fires to explore the effects of repeated burning on black carbon degradation

Soil organic matter plays a key role in the global carbon cycle, representing three to four times the total carbon stored in plant or atmospheric pools. Although fires convert a portion of the faster cycling organic matter to slower cycling black carbon (BC), abiotic and biotic degradation processes can significantly shorten BC residence times. Repeated fires may also reduce residence times, but this mechanism has received less attention. Here we show that BC exposed to repeated experimental burns is exponentially reduced through four subsequent fires, by 37.0, 82.5, 98.6 and 99.0% of BC mass. Repeated burning can thus be a significant BC loss mechanism, particularly in ecosystems where fire return rates are high, relative to BC soil incorporation rates. We further consider loss rates in the context of simulated BC budgets, where 0–100% of BC is protected from subsequent fires, implicitly representing ecosystems with varying fire regimes and BC transport and incorporation rates. After five burns, net BC storage was reduced by as much as 68% by accounting for degradation from repeated burning. These results illustrate the importance of accounting for BC loss from repeated burning, further highlighting the potential conflict between managing forests for increasing soil carbon storage vs maintaining historic fire regimes.

A methodology to characterize vertical accuracies in lidar-derived products at landscape scales

A u g u s t 2 0 1 3 709 (Hudak et al., 2009). These high precision DEMs are already being implemented in numerous applications, including habitat assessment (Martinuzzi et al., 2009), forest succession and volume (Goodwin et al., 2006; Falkowski et al., 2009), snow depth (Hopkinson et al., 2004; Deems et al., 2006), hydrologic modeling (Bowen and Waltermire, 2002; Murphy et al., 2008), carbon sequestration (Asner, 2009), glacial monitoring (Abermann et al., 2010), and fl oodplain assessments (Marks and Bates, 2000). The versatility of lidar is still being explored, and its potential not only reaches across the research and resource management spectrum but into municipal and logistical applications of both the public and private sectors. Although lidar has demonstrated ability to produce high quality DEMs and is useful for assessing terrain, vegetation, and ecosystem characteristics over large areas, there are still errors that must be quantifi ed. This is critical when using the data to develop subsequent products and metrics (Smith et al., 2009; Tinkham et al., 2011), because these errors can have signifi cant impacts on the quality and accuracy of each derived product. For example, errors in forest inventory metrics such as tree height which is used to estimate timber volume can lead to large fi nancial implications (Gatziolis et al., 2011), mischaracterization of fi ne-scale morphology of hydrological features can produce incorrect fl ow predictions, and assessments of objects with low vertical heights (e.g., shrubs ~0.30 m) can be missed in some classifi cations potentially lowering forage availability estimates (Glenn et al., 2011). The impacts of lidar acquisition parameters and environmental conditions on vertical and horizontal accuracy of lidar-derived DEMs have been well studied (Su and Bork, 2006; Bater and Coops, 2009; TriglavČ ekada et al., 2009). Prior studies have investigated the infl uence of vegetation and topographic features within semi-arid shrub-steppe (Tinkham et al., 2011), piedmont (Hodgson and Bresnahan, 2004), and forested (Hyyppä et al., 2005; Tinkham et al., 2012) ecosystems on errors within lidar-derived DEMs at point and plot scales. However, these studies are predominately limited in their spatial inference to where coincident lidar and fi eld surveying data are present. Given DEMs and the products that are subsequently derived from them are generally applied to landscape scale problems, a spatially explicit method for predicting error is needed (Glennie, 2007). Bater and Coops (2009) applied a classifi cation tree approach to a lidar data set and spatially predicted the DEM vertical error based on Abstract Light detection and ranging (lidar) is the premier technology for high-resolution elevation measurements in complex landscapes. Lidar error assessments allow for objective interpretation of Digital Elevation Models (DEMs) and products reliant on these layers. The purpose of this study is to spatially estimate the vertical error of a lidar-derived DEM across seven cover types through modeling of fi eld survey data. We use thirty-four variables and ground-based fi eld survey data in a Random Forest regression to predict elevation error. Four variables captured the variability within the lidar errors, with three variables relevant to the distribution of returns within the vegetation and one relating to the terrain form. Good agreement was observed when comparing the survey against the model predictions (μ = −0.02 m, s = 0.13 m, and RMSE = 0.14 m). With most lidar products reliant upon accurate production of DEMs, providing spatially explicit assessments of uncertainty at the landscape level will increase user confi dence in lidar products.

Accuracy of WAAS-Enabled GPS-RF Warning Signals When Crossing a Terrestrial Geofence.

Geofences are virtual boundaries based on geographic coordinates. When combined with global position system (GPS), or more generally global navigation satellite system (GNSS) transmitters, geofences provide a powerful tool for monitoring the location and movements of objects of interest through proximity alarms. However, the accuracy of geofence alarms in GNSS-radio frequency (GNSS-RF) transmitter receiver systems has not been tested. To achieve these goals, a cart with a GNSS-RF locator was run on a straight path in a balanced factorial experiment with three levels of cart speed, three angles of geofence intersection, three receiver distances from the track, and three replicates. Locator speed, receiver distance and geofence intersection angle all affected geofence alarm accuracy in an analysis of variance (p = 0.013, p = 2.58 × 10−8, and p = 0.0006, respectively), as did all treatment interactions (p < 0.0001). Slower locator speed, acute geofence intersection angle, and closest receiver distance were associated with reduced accuracy of geofence alerts.

Spatial variability of surface fuels in treated and untreated ponderosa pine forests of the southern Rocky Mountains

There is growing consensus that spatial variability in fuel loading at scales down to 0.5 m may govern fire behaviour and effects. However, there remains a lack of understanding of how fuels vary through space in wildland settings. This study quantifies surface fuel loading and its spatial variability in ponderosa pine sites before and after fuels treatment in the southern Rocky Mountains, USA. We found that spatial semivariance for 1- and 100-h fuels, litter and duff following thin-and-burn treatments differed from untreated sites, and was lower than thin-only sites for all fuel components except 1000-h fuels. Fuel component semivariance increased with mean fuel component loading. The scale of spatial autocorrelation for all fuel components and sites ranged from <1 to 48 m, with the shortest distances occurring for the finest fuel components (i.e. duff, litter). Component mean fuel particle diameter strongly predicted (R2 = 0.88) the distance needed to achieve sample independence. Additional work should test if these scaling relationships hold true across forested ecosystems, and could reveal fundamental processes controlling surface fuel variability. Incorporating knowledge of spatial variability into fuel sampling protocols will enhance assessment of wildlife habitat, and fire behaviour and effects modelling, over singular stand-level means.

High resolution mapping of development in the wildland-urban interface using object based image extraction

The wildland-urban interface (WUI), the area where human development encroaches on undeveloped land, is expanding throughout the western United States resulting in increased wildfire risk to homes and communities. Although census based mapping efforts have provided insights into the pattern of development and expansion of the WUI at regional and national scales, these approaches do not provide sufficient detail for fine-scale fire and emergency management planning, which requires maps of individual building locations. Although fine-scale maps of the WUI have been developed, they are often limited in their spatial extent, have unknown accuracies and biases, and are costly to update over time. In this paper we assess a semi-automated Object Based Image Analysis (OBIA) approach that utilizes 4-band multispectral National Aerial Image Program (NAIP) imagery for the detection of individual buildings within the WUI. We evaluate this approach by comparing the accuracy and overall quality of extracted buildings to a building footprint control dataset. In addition, we assessed the effects of buffer distance, topographic conditions, and building characteristics on the accuracy and quality of building extraction. The overall accuracy and quality of our approach was positively related to buffer distance, with accuracies ranging from 50 to 95% for buffer distances from 0 to 100 m. Our results also indicate that building detection was sensitive to building size, with smaller outbuildings (footprints less than 75 m2) having detection rates below 80% and larger residential buildings having detection rates above 90%. These findings demonstrate that this approach can successfully identify buildings in the WUI in diverse landscapes while achieving high accuracies at buffer distances appropriate for most fire management applications while overcoming cost and time constraints associated with traditional approaches. This study is unique in that it evaluates the ability of an OBIA approach to extract highly detailed data on building locations in a WUI setting.