空间直观森林景观模型空间不确定性的地统计学模拟

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

为了用尽可能小的蒙特卡罗模拟样本来反映模型模拟结果中的不确定性,把拉丁超几何体采样引入地统计随机模拟的LU分解算法.首先把拉丁超几何体采样与普通随机采样在LU分解算法中的表现进行比较,然后把基于拉丁超几何体采样的LU分解法应用于空间直观森林景观模型LANDIS的模拟.结果表明,与普通随机采样相比,拉丁超几何体采样能捕获更多的不确定性,特别是在蒙特卡罗模拟次数较少时.LANDIS模型的模拟结果表明,由地统计学随机模拟所引入的不确定性在象元尺度上随模拟时间增加而增加,但是在景观尺度上并没有受很大影响.

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 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. 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.