Modeling forest stand structure using geostatistics, geographic information systems, and remote sensing

This study compared five geostatistical methods of interpolation (ordinary kriging, universal kriging with first-degree trend surface, universal kriging with second-degree trend surface, cokriging, and disjunctive kriging) with three traditional estimation methods (polygonal mapping, inverse distance weighting, and inverse distance weighting squared). These eight techniques were used to spatially interpolate the number of stems, total basal area, and number of seedlings on 82 sample plots in a 121-hectare first-order forest watershed in the USDA Forest Service, Fraser Experimental Forest, Fraser, Colorado. Secondary variables used for cokriging included elevation, a combined value for slope and aspect, and the normalized difference vegetation index (NDVI) from Landsat-TM satellite imagery. The comparison criterion was the mean square error (MSE) calculated by cross validation.; For variable number of stems the MSEs ranged from 44.568 to 49.444 with cokriging being the best estimation method and disjunctive kriging giving the poorest results. However, the differences between the various methods were relatively small. The MSEs for variable total basal area ranged from 3.464 to 4.598. The best results were obtained using polygonal mapping, while the poorest results were given by inverse distance weighting squared. Again, the differences between the various methods were relatively small. Variable number of seedlings had the best estimation results applying inverse distance weighting squared (MSE of 69.881). The worst results were obtained using disjunctive kriging (MSE of 118.995). For this variable, the differences in MSEs for the various interpolation methods were much larger than with the other two variables.; The performance was different from one variable to the other. Overall, however, cokriging performed best, followed by polygonal mapping. Universal kriging with a first- or second-degree trend surface yielded, in general, better results than ordinary kriging. Inverse distance weighting was generally outperformed by the linear kriging methods. The nonlinear kriging method (disjunctive kriging) performed least well. These results indicate that spatially cross-correlated variables substantially improve the estimation capability of cokriging, as compared to the other methods for these data.