Impacts of forest fuel treatments and wildfire on forests

Tempel, DJ, RJ Gutiérrez, JJ Battles, DL Fry, Y Su, Q Guo, MJ Reetz, SA Whitmore, GM Jones, BM Collins, SL Stephens, M Kelly, WJ Berigan, and MZ Peery. 2015. Modeling short- and long-term impacts of fuel treatments and wildfire on an old-forest species. Ecosphere 6(12) DOI:10.1890/ES15-00234.1

Fuels-reduction treatments are commonly implemented in the western U.S. to reduce the risk of high-severity fire, but they may have negative short-term impacts on species associated with older forests.   Therefore, we modeled the effects of a completed fuels-reduction project on fire behavior and California spotted owl (Strix occidentalis occidentalis) habitat and demography in the Sierra Nevada to assess the potential short- and long-term trade-offs.  We combined field-collected vegetation data and LiDAR data to develop detailed maps of forest structure needed to parameterize our fire and forest-growth models.  We simulated wildfires under extreme weather conditions (both with and without fuels treatments), then simulated forest growth 30 years into the future under four combinations of treatment and fire: treated with fire, untreated with fire, treated without fire, and untreated without fire.

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Teleconnections between land use and wildfire

Butsic, V., M. Kelly and M. A. Moritz. Land Use and Wildfire: A Review of Local Interactions and Teleconnections. Land 2015, 4(1), 140-156; doi:10.3390/land4010140

Fire is a naturally occurring process of most terrestrial ecosystems as well as a tool for changing land use. Since the beginning of history humans have used fire as a mechanism for creating areas suitable for agriculture and settlement. As fires threaten human dominated landscapes, fire risk itself has become a driver of landscape change, impacting landscapes through land use regulations and fire management. Land use changes also influence fire ignition frequency and fuel loads and hence alters fire regimes. The impact of these changes is often exacerbated as new land users demand alternative fire management strategies, which can impact land cover and management far from where land use change has actually occurred. This creates nuanced land use teleconnections between source areas for fires and economic cores, which demand and fund fire protection.

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A proposed early-warning fire detection system

our graphic of the FUEGO conceptPennypacker, CR, MK Jakubowski, M Kelly, M Lampton, C Schmidt, S Stephens, and R Tripp.  2013. FUEGO—Fire Urgency Estimator in Geosynchronous Orbit—A proposed early-warning fire detection system. Remote Sensing 5(10): 5173-5192

Current and planned wildfire detection systems are impressive but lack both sensitivity and rapid response times. A small telescope with modern detectors and significant computing capacity in geosynchronous orbit can detect small (12 m²) fires on the surface of the earth, cover most of the western United States (under conditions of moderately clear skies) every few minutes or so, and attain very good signal-to-noise ratio against Poisson fluctuations in a second. Hence, these favorable statistical significances have initiated a study of how such a satellite could operate and reject the large number of expected systematic false alarms from a number of sources. Here we present both studies of the backgrounds in Geostationary Operational Environmental Satellites (GOES) 15 data and studies that probe the sensitivity of a fire detection satellite in geosynchronous orbit.

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Capturing forest fuel characteristics with lidar

Jakubowski, M. K., Q. Guo, B. Collins, S. Stephens, and M. Kelly. 2013. Predicting surface fuel models and fuel metrics using lidar and CIR imagery in a dense, mountainous forest. Photogrammetric Engineering and Remote Sensing 79(1):37-49

We compared the ability of several classification and regression algorithms to predict forest stand structure metrics and standard surface fuel models. Our study area spans across a dense, topographically complex Sierra Nevada mixed-conifer forest. We used clustering, regression trees, and support vector machine algorithms to analyze high density (average 9 pulses/m2), discrete return, smallfootprint lidar data, along with multispectral imagery. Stand structure metric predictions generally decreased with increased canopy penetration. While the general fuel types were predicted accurately, specific surface fuel model predictions were poor using all algorithms. These fuel components are critical inputs for wildfire behavior modeling, which ultimately support forest management decisions.

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Interactions among wildland fires in the Sierra Nevada

Collins, B., J. Miller, M. Kelly, J.W. van Wagtendonk, and S. L. Stephens. 2009. Interactions among wildland fires in a long-established Sierra Nevada natural fire area. Ecosystems 12(1): 114-128. DOI: 10.1007/s10021-008-9211-7

We investigate interactions between successive naturally occurring fires, and assess to what extent the environments in which fires burn influence these interactions.

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OBIA for classifying urban environments for fire management

Cleve, C., M. Kelly F. Kearns and M. Moritz. 2008. Classification of urban environments for fire management support: A comparison of pixel- and object-based classifications using high-resolution aerial photography. Computers, Environment and Urban Systems 32: 317–326

We explore the accuracy of pixel-based and object-based classification methods used for mapping in the wildland–urban interface (WUI) with free, readily available, high spatial resolution urban imagery, which is available in many places to municipal and local fire management agencies. Results indicate that an object-based classification approach provides a higher accuracy than a pixel-based classification approach when distinguishing between the selected land-use and land-cover categories. Pdf download. Journal link.

Keywords: Wildland–urban interface . Classification . Image analysis . Object-based methods . Remote sensing

Fire occurrence probability in Spain

Lozano et al. 2008. Remote Sensing of Environment. This research explores the relationships between fire occurrence and several families of environmental factors at different spatial observation scales by means of classification and regression tree models. Predictors accounting for vegetation status (estimated by spectral indices derived from Landsat imagery), fire history, topography, accessibility and vegetation types were included in the models of fire occurrence probability.

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Spatial pattern of fires in the Sierra Nevada

Collins, B. M., M. Kelly, J. W. V. Wagendonk and S. L. Stephens. 2007. Landscape Ecology. Mapped fire severityWe use gridded estimates of fire severity, derived from Landsat ETM+ imagery, to identify the biotic and abiotic factors contributing to the observed spatial patterns of fire severity in two large natural fires. Regression tree analysis indicates the importance of weather, topography, and vegetation variables in explaining fire severity patterns between the two fires.

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