A Hierarchical Approach for Scaling Forest Inventory and Fuels Data from Local to Landscape Scales in the Davis Mountains, Texas, USA
Authors: Helen M. Poulos, Ann E. Camp, Richard G. Gatewood, Lynn Loomis
Published: March 12, 2007, Forest Ecology and Management 244 (2007) 1–15
This study combined hierarchical cluster analysis and classification and regression tree algorithms to quantify vegetation and fuel characteristics and to generate spatially explicit vegetation and fuels maps for forest and fire management in the Davis Mountains of west Texas, USA. We used field data, landscape metrics derived from digital elevation models, and spectral information from remotely sensed imagery to (1) determine recent changes in forest stand structure in relation to historical fire exclusion, (2) quantify the effects of fire exclusion on fuel accumulation patterns, and (3) develop predictive vegetation and fuels maps for our study area. Results from this study will be used to implement forest and fire management activities directed toward ecosystem restoration and maintenance.
This study combined hierarchical cluster analysis and classification and regression tree algorithms to quantify vegetation and fuel characteristics and to generate spatially explicit vegetation and fuels maps for forest and fire management in the Davis Mountains of west Texas, USA. We used field data, landscape metrics derived from digital elevation models, and spectral information from remotely sensed imagery to (1) determine recent changes in forest stand structure in relation to historical fire exclusion, (2) quantify the effects of fire exclusion on fuel accumulation patterns, and (3) develop predictive vegetation and fuels maps for our study area. Results from this study will be used to implement forest and fire management activities directed toward ecosystem restoration and maintenance.
