PROPAGATING UNCERTAINTY THROUGH SPATIAL ESTIMATION PROCESSES FOR OLD-GROWTH SUB-ALPINE FORESTS USING SEQUENTIAL GAUSSIAN SIMULATION IN GIS

Based on data from 83 plot locations, the geostatistical Monte Carlo t echnique of sequential Gaussian simulation (s.G.s.) was used to genera te 1000 independent spatially continuous representations of three vari ables. These were then used in a geographic information system analysi s to create maps of relative uncertainty for estimated areas of potent ial old-growth forest conditions across a 121 hectare first-order suba lpine watershed. First, identical selection criteria were applied to e ach of the 1000 three-layer input sets to determine areas that simulta neously satisfied three old-growth forest conditions for mean stem dia meter, percent crown cover, and mean age of overstory stems. This crea ted 1000 equally probable realizations of potential old growth for the study area. An uncertainty image for the potential old-growth forest areas was created by summing these realizations. Cells were selected f rom the image histogram that indicated the highest proportions of old- growth conditions. Spatially, these results followed those obtained fr om a similar analysis using kriging. s.G.s. is recommended as a generi c spatial Monte Carlo technique that can be used to assess stochastic elements in complex integrated ecological predictions. (C) 1997 Elsevi er Science B.V.