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.
Using Geographic Information Systems and Multi-Criteria Decision Analysis to Address Spatially Explicit Problems in Healthcare Policy Decision Making
