Microclimates hold the key to spatial forest planning under climate change: Cyanolichens in temperate rainforest

2021 ◽  
Author(s):  
Christopher J. Ellis ◽  
Sally Eaton
Keyword(s):  
Climate Change ◽  
Forest Planning ◽  
Temperate Rainforest ◽  
Spatial Forest Planning

scholarly journals Climate change implications in the northern coastal temperate rainforest of North America

2015 ◽  
Vol 130 (2) ◽  
pp. 155-170 ◽  
Author(s):  
Colin S. Shanley ◽  
Sanjay Pyare ◽  
Michael I. Goldstein ◽  
Paul B. Alaback ◽  
David M. Albert ◽  
...  
Keyword(s):  
Climate Change ◽  
North America ◽  
Temperate Rainforest

The use of cellular automaton approach in forest planning

2007 ◽  
Vol 37 (11) ◽  
pp. 2188-2200 ◽  
Author(s):  
Tero Heinonen ◽  
Timo Pukkala
Keyword(s):  
Cellular Automaton ◽  
Objective Function ◽  
Net Present Value ◽  
Optimization Method ◽  
Forest Planning ◽  
Present Value ◽  
Objective Function Values ◽  
Spatial Forest Planning ◽  
Planning Problems ◽  
Lp Solutions

This study presents an optimization method based on cellular automaton (CA) for solving spatial forest planning problems. The CA maximizes stand-level and neighbourhood objectives locally, i.e., separately for different stands or raster cells. Global objectives are dealt with by adding a global part to the objective function and gradually increasing its weight until the global targets are met to a required degree. The method was tested in an area that consisted of 2500 (50 × 50) hexagons 1 ha in size. The CA was used with both parallel and sequential state-updating rules. The method was compared with linear programming (LP) in four nonspatial forest planning problems where net present value (NPV) was maximized subject to harvest constraints. The CA solutions were within 99.6% of the LP solutions in three problems and 97.9% in the fourth problem. The CA was compared with simulated annealing (SA) in three spatial problems where a multiobjective utility function was maximized subject to periodical harvest and ending volume constraints. The nonspatial goal was the NPV and the spatial goals were old forest and cutting area aggregation as well as dispersion of regeneration cuttings. The CA produced higher objective function values than SA in all problems. Especially, the spatial objective variables were better in the CA solutions, whereas differences in NPV were small. There were no major differences in the performance of the parallel and sequential cell state-updating modes of the CA.

scholarly journals Spatial Forest Planning for Optimized Harvest Scheduling

2019 ◽  
Vol 26 (1) ◽  
Author(s):  
Mariana Bussolo Stang ◽  
Julio Eduardo Arce ◽  
Sebastião do Amaral Machado ◽  
Pedro Henrique Belavenutti ◽  
Luan Demarco Fiorentin
Keyword(s):  
Forest Planning ◽  
Harvest Scheduling ◽  
Spatial Forest Planning

Accounting for climate change in a forest planning stochastic optimization model

2016 ◽  
Vol 46 (9) ◽  
pp. 1111-1121 ◽  
Author(s):  
Jordi Garcia-Gonzalo ◽  
Cristóbal Pais ◽  
Joanna Bachmatiuk ◽  
Andrés Weintraub
Keyword(s):  
Climate Change ◽  
Forest Harvesting ◽  
Forest Growth ◽  
Growth And Yield ◽  
Forest Planning ◽  
Planning Problem ◽  
Climate Scenarios ◽  
Present Value ◽  
Eucalyptus Forest ◽  
Climate Change Uncertainty

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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