An approach is proposed for incorporating the variations in timber growth and yield due to climate change uncertainty into the forest harvesting decision process. A range of possible climate scenarios are transformed by a forest growth and yield model into tree growth scenarios, which in turn are integrated into a multistage stochastic model that determines the timber cut in each future period so as to maximize net present value over the planning horizon. For comparison purposes, a deterministic model using a single average climate scenario is also developed. The performance of the deterministic and stochastic formulations are tested in a case study of a medium-term forest planning problem for a Eucalyptus forest in Portugal where climate change is expected to severely impact production in the coming years. Experiments conducted using 32 climate scenarios demonstrate the stochastic model's superior results in terms of present value, particularly in cases of relatively high minimum timber demand. The model should therefore be useful in supporting forest planners' decisions under climate uncertainty

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The wildland-urban interface (WUI) is the spatial manifestation of human communities coupled with vegetated ecosystems. Spatial delineation of the WUI is important for wildfire policy and management, but is typically defined according to spatial relationships between housing development and wildland vegetation without explicit consideration of fire risk. A fire risk-based definition of WUI can enable a better distribution of management investment so as to maximize social return. We present a novel methodological approach to delineate the WUI based on a fire risk assessment. The approach establishes a geographical framework to model fire risk via machine learning and generate multi-scale, variable-specific spatial thresholds for translating fire probabilities into mapped output. To determine whether fire-based WUI mapping better captures the spatial congruence of houses and wildfires than conventional methods, we compared national and subnational fire-based WUI maps for Chile to WUI maps generated only with housing and vegetation thresholds. The two mapping approaches exhibited broadly similar spatial patterns, the WUI definitions covering almost the same area and containing similar proportions of the housing units in the area under study (17.1% vs. 17.9%), but the fire-based WUI accounted for 13.8% more spatial congruence of fires and people (47.1% vs. 33.2% of ignitions). Substantial regional variability was found in fire risk drivers and the corresponding spatial mapping thresholds, suggesting there are benefits to developing different WUI maps for different scales of application. We conclude that a dynamic, multi-scale, fire-based WUI mapping approach should provide more targeted and effective support for decision making than conventional approaches.

Due to the long time horizon typically characterizing forest planning, uncertainty plays an important role when developing forest management plans. Especially important is the uncertainty related to recently human-induced global warming since it has a clear impact on forest capacity to contribute to biogenic and anthropogenic ecosystem services. If the forest manager ignores uncertainty, the resulting forest management plan may be sub-optimal, in the best case. This paper presents a methodology to incorporate uncertainty due to climate change into forest management planning. Specifically, this paper addresses the problem of harvest planning, i.e., defining which stands are to be cut in each planning period in order to maximize expected net revenues, considering several climate change scenarios. This study develops a solution approach for a planning problem for a eucalyptus forest with 1000 stands located in central Portugal where expected future conditions are anticipated by considering a set of climate scenarios. The model including all the constraints that link all the scenarios and spatial adjacency constraints leads to a very large problem that can only be solved by decomposing it into scenarios. For this purpose, we solve the problem using Progressive Hedging (PH) algorithm, which decomposes the problem into scenario sub-problems easier to solve. To analyze the performance of PH versus the use of the extensive form (EF), we solve several instances of the original problem using both approaches. Results show that PH outperforms the EF in both solving time and final optimality gap. In addition, the use of PH allows to solve the most difficult problems while the commercial solvers are not able to solve the EF. The approach presented allows the planner to develop more robust management plans that incorporate the uncertainty due to climate change in their plans.