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Latin hypercube sampling and geostatistical modeling of spatial uncertainty in a spatially explicit forest landscape model simulationAuthor(s): Chonggang Xu; Hong S. He; Yuanman Hu; Yu Chang; Xiuzhen Li; Rencang Bu
Source: Ecological Modelling 185 (2005) 255-269
Publication Series: Scientific Journal (JRNL)
Station: North Central Research Station
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DescriptionGeostatistical stochastic simulation is always combined with Monte Carlo method to quantify the uncertainty in spatial model simulations. However, due to the relatively long running time of spatially explicit forest models as a result of their complexity, it is always infeasible to generate hundreds or thousands of Monte Carlo simulations. Thus, it is of great importance to generate a relatively small set of conditional realizations capturing most of the spatial variability. In this study, 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 (simple random sampling) in LU decomposition simulation. Then it is applied to the investigation of uncertainty 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. 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 exhaustive species age cohort information at each cell is not available.
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CitationXu, Chonggang; He, Hong S.; Hu, Yuanman; Chang, Yu; Li, Xiuzhen; Bu, Rencang. 2005. Latin hypercube sampling and geostatistical modeling of spatial uncertainty in a spatially explicit forest landscape model simulation. Ecological Modelling 185 (2005) 255-269
KeywordsLatin hypercube sampling, uncertainty analysis, geostatistical stochastic simulation, LU decomposition, forest landscape model
- Spatial interpolation of forest conditions using co-conditional geostatistical simulation
- A spatial stochastic programming model for timber and core area management under risk of fires
- Spatial simulation of forest succession and timber harvesting using LANDIS
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