Investigating spatial relationships among fuels, wildfire severity, and post-fire invasion by exotic plant species through linkage of multiphase sampling design and multiscale nested sampling field plots, pre- and post-fire, can be accomplished by integrating spatial information with spatial statistical models. This technique provides useful information and tools for describing ecological and environmental characteristics, including landscape-scale fire regimes, invasive plants, and hotspots of diversity (native and exotic plants) for the Cerro Grande fire site, Los Alamos, New Mexico. To predict the distribution, presence, and patterns of native and exotic species, we used modeling of large- and small-scale variability by integrating field data and spatial information (eight bands of Landsat Thematic Mapper [TM] data, six derived vegetation indices, six bands of tasseled cap transformations, elevation, slope, aspect) and spatial statistics. We present the results of trend surface models that describe the large-scale spatial variability using stepwise multiple regressions based on the Ordinary Least Squares (OLS) method. Models with small variance were selected. In addition, the residuals from the trend surface model based on the OLS estimates were modeled using ordinary kriging for modeling small-scale variability based on a Gaussian semi-variogram. The final surfaces were obtained by combining two models (the trend surface based on the OLS and the kriging surface of residuals). All models were selected based on the lowest values of standard errors, modified Akaike's Information Criterion (AICC) statistics, and high R2. For large-scale spatial variability models using the OLS procedure, R2 values ranged from 10.04% to 58.6% and all variables were significant at a < 0.05 level. When the kriging model was added with the OLS model, R2 values ranged from 60% to 84%. This new technique will help natural resource management teams to identify areas vulnerable to invasion by exotic plant species and predict their consequent potential for wildfire. © 2004, Tall Timbers Research, Inc.