Geostatistical Modeling of Spatial Uncertainty in a Spatially Explicit Forest Landscape Model Simulation

doi:10.7523/j.issn.2095-6134.2005.4.007

Abstract

Abstract:

We introduced an effective sampling method (Latin Hypercube sampling) into a stochastic simulation algorithm (LU decomposition simulation). Latin Hypercube sampling is first compared with a common sampling procedure (random simple sampling) in LU decomposition simulation. Then it is applied to the investigation ofuncertainty in the simulation results of a spatially explicit forest model, LANDIS. Results showed that Latin Hypercube sampling can capture more variability in the sample space than simple random sampling especially when the number of simulations is small. Simple as the application is, it gives us general insights about which model results are robust given the uncertainty introduced by interpolation. Application results showed that LANDIS simulation results at the landscape level (species percent area and their spatial pattern measured by an aggregation index) is not sensitive to the uncertainty in species age cohort information at the cell level produced by geostatistical stochastic simulation algorithms. This suggests that LANDIS can be used to predict the forest landscape change at broad spatial and temporal scales even if exhaust species age cohort information at each cell is not available.

Key words: uncertainty, Kriging interpolation, geostatistical stochastic simulation, LU decomposition, LatinHypercube sampling, spatially explicit forest landscape model

CLC Number:

S718.5

XU Chong-Gang, HU Yuan-Man, CHANG Yu, LI Xiu-Zhen, BU Ren-Cang, HE Hong-Shi. Geostatistical Modeling of Spatial Uncertainty in a Spatially Explicit Forest Landscape Model Simulation[J]. , 2005, 22(4): 436-446.

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