Mapping fuels in the Chihuahuan Desert borderlands using remote sensing, geographic information systems, and biophysical modeling

Abstract

This study integrated field, geographic information systems, and remotely sensed data to generate spatially explicit fuel maps for Big Bend National Park in Texas and the Maderas del Carmen Protected Area in Coahuila, Mexico. We used hierarchical cluster analysis, and classification and regression trees to (i) identify the dominant fuel types in each of the study areas and (ii) build spatially explicit predictive fuels maps. Four fuel types were identified that differed significantly in their live and dead fuel characteristics. Spectral characteristics, topographic position, soil moisture, and solar radiation were the major influences on fuel distribution patterns. Fine-fuel loads were highest in open woodlands on lower topographic positions that had high grass cover. The highest shrub loadings were found on exposed, upper topographic positions. Timber-type fuel loads with high 1, 10, 100, and 1000 hour fuels loads dominated high-elevation valley bottoms. The error rates of the maps were approximately 16%, which falls within the range of typical fuel mapping misclassification rates. The map products from this study are currently being used as inputs for landscape-scale fire modeling and for guiding fuel-reduction treatments using fire and fire surrogates, such as thinning.Résumé : Cette étude intègre des données de terrain, de systèmes d'information géographique et de télédétection pour générer des cartes de combustibles spatialement explicites pour le parc national Big Bend, au Texas, et l'aire protégée de Maderas del Carmen à Coahuila, au Mexique. Nous avons utilisé la classification hiérarchique ainsi que les arbres de ré-gression et de classification pour (i) identifier les types dominants de combustibles dans chacune des aires d'étude et (ii) construire des cartes prédictives spatialement explicites des combustibles. Quatre types de combustibles dont les caractéris-tiques des combustibles vivants et morts sont significativement différentes ont été identifiés. Les caractéristiques spectrales, la position topographique, la teneur en humidité du sol et le rayonnement solaire étaient les principaux facteurs qui influençaient les patrons de distribution des combustibles. La charge de combustibles légers était la plus élevée dans les boisés ouverts dans les positions topographiques basses. Les charges les plus élevées d'arbustes ont été observées dans les positions topographiques hautes et exposées. Les charges de combustibles dont le type inclus le bois des arbres, avec des temps de séchage élevés de 1, 10, 100 et 1000 h, dominaient le fond des vallées à haute altitude. Le taux d'erreur des cartes était approximativement de 16 %, ce qui se situe dans la gamme des taux de classification erronée typiques de la cartographie des combustibles. Les cartes issues de cette étude sont présentement utilisées comme intrants pour la modéli-sation des incendies à l'échelle du paysage et pour la planification des traitements de réduction des combustibles à l'aide du feu ou de substituts du feu comme l'éclaircie.[Traduit par la...

Use of random forests for modeling and mapping forest canopy fuels for fire behavior analysis in Lassen Volcanic National Park, California, USA

“…The National Elevation dataset for LVNP was used to obtain elevation, slope, aspect, and two measures of topographic position for each pixel in the park. These two measures of topographic position-Topo_Pos_150 (TP150) and Topo_Pos_450 (TP450)-are the difference between a pixel's elevation and the average elevation of surrounding pixels within 150 m and 450 m, respectively (Poulos et al, 2007;Poulos, 2009).…”

Climatic and edaphic gradients predict variation in wildland fuel hazard in south‐eastern Australia

“…In contrast, temperate eucalypt forests often are characterised by low‐frequency, high‐intensity wildfire (Murphy et al ). In these areas, fire behaviour largely is determined by climatic effects on wildland‐fuel moisture, which determines the availability of fuel over large, contiguous areas (Miller and Urban , Bradstock et al , Caccamo et al ).…”

“…Information about spatial variation in fuel structure and associated hazard is required for a range of fire‐management purposes. This fuel information may be obtained by direct measurement in the field, which is costly and labour intensive (Falkowski et al 2005, Arroyo et al ), or by complex modelling techniques (Reeves et al ). Technologies such as remote sensing also are used and have increased the spatial coverage of fuel mapping (Poulos , Duff et al ).…”

Spatial variation and prediction of forest biomass in a heterogeneous landscape

“…Defries 2002), assessment of forest biomass distribution in spatially heterogeneous landscapes continues to present challenges especially in regions with insufficient density of forest inventory plots. Across highly heterogeneous landscapes, spatial variation in forest biomass may be especially complex where variations in plant growth and species composition occur over short distances due to sharp interactions between physiographic (e.g., elevation and solar radiation), ecological (e.g., forest edge effects and fire history), and humanmediated (e.g., past logging and other land-use changes) factors (Anderson et al 2009;Clark and Clark 2000;Poulos 2009). As environments become increasingly complex, approaches that examine landscape heterogeneity and its influence on forest biomass distribution are greatly needed to accurately assess trends in the global carbon balance (Houghton 2005).…”