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Many studies have been conducted to quantify the possible ecosystem/landscape response to the anticipated global warming. However, there is a large amount of uncertainty in the future climate predictions used for these studies. Specifically, the climate predictions can be very different based on a variety of global climate models and alternative greenhouse emission scenarios. In this study, we coupled a forest landscape model, LANDIS-II, and a forest process model, PnET-II, to examine the uncertainty (that results from the uncertainty in the future climate predictions) in the forest-type composition prediction for a transitional forest landscape [the Boundary Water Canoe Area]. Using an improved global-sensitivity analysis technique [Fourier amplitude sensitivity test], we also quantified the amount of uncertainty in the forest-type composition prediction contributed by different climate variables including temperature, CO2, precipitation and photosynthetic active radiation (PAR). The forest landscape response was simulated for the period 2000–2400ad based on the differential responses of 13 tree species under an ensemble of 27 possible climate prediction profiles (monthly time series of climate variables). Our simulations indicate that the uncertainty in the forest-type composition becomes very high after 2200ad, which is close to the time when the current forests are largely removed by windthrow disturbances and natural mortality. The most important source of uncertainty in the forest-type composition prediction is from the uncertainty in temperature predictions. The second most important source is PAR, the third is CO2 and the least important is precipitation. Our results also show that if the optimum photosynthetic temperature rises due to CO2 enrichment, the forest landscape response to climatic change measured by forest-type composition may be substantially reduced. [ABSTRACT FROM AUTHOR]

When using remote sensing and geographic information systems, accurately scaling-up spatial data of a variable and their uncertainties from a finer to a coarser spatial resolution is widely required in mapping and managing natural resources and ecological and environmental systems. In this study, four up-scaling methods were derived based on simple and ordinary cokriging estimators and a sequential Gaussian cosimulation algorithm for points and blocks. Taking spatial variability of variables into account in the up-scaling process made it possible to simultaneously and accurately obtain estimates and estimation variances of larger blocks from sample and image data of smaller supports. With the aid of Thematic Mapper imagery, these methods were compared in a case study where overall vegetation and tree covers were sealed up from a spatial resolution of 30 × 30 m² to 90 × 90 m² with a stratification method at 90 × 90 m². The results showed that the methods Point simple coKriging-Point co-Simulation scaling UP (PsK-PSUP) and PsK-Block co-Simulation (PsK-BS) led to smaller errors and better reproduced spatial distribution and variability of the variables than the other methods. Choosing PsK-PSUP or PsK-BS depends on the users' emphasis on accuracy of estimates and variances, computational time, etc. The methods can be applied to multiple continuous variables that have any distribution. It is also expected that the general idea behind the methods can be expanded to scaling-up spatial data for categorical variables. [ABSTRACT FROM AUTHOR]