Forest inventory is increasingly producing infor-mation on the locations and sizes of individual trees.This information can be acquired by airborne or terrestrial laser scanning or analyzing photogrammetric data.Howev...Forest inventory is increasingly producing infor-mation on the locations and sizes of individual trees.This information can be acquired by airborne or terrestrial laser scanning or analyzing photogrammetric data.However,all trees are seldom detected,especially in young,dense,or multi-layered stands.On the other hand,the complete size distributions of trees can be predicted with various methods,for instance,kNN data imputation in an area-based LiDAR inventory,predicting the parameters of a distribution func-tion from remote sensing data,field sampling,or using his-togram matching and calibration methods.The predicted distribution can be used to estimate the number and sizes of the non-detected trees.The study’s objective was to develop a method for forest planning that efficiently uses the avail-able tree-level data in management optimization.The study developed a two-stage hierarchical method for tree-level management optimization for cases where only part of the trees is detected or measured individually.Cutting years and harvest rate curves for the non-detected trees are optimized at the higher level,and the cutting events of the detected trees are optimized at the lower level.The study used differ-ential evolution at the higher level and simulated annealing at the lower level.The method was tested and demonstrated in even-aged Larix olgensis plantations in the Heilongjiang province of China.The optimizations showed that optimiz-ing the harvest decisions at the tree level improves the profit-ability of management compared to optimizations in which only the dependence of thinning intensity on tree diameter is optimized.The approach demonstrated in this study pro-vides feasible options for tree-level forest planning based on LiDAR inventories.The method is immediately applicable to forestry practice,especially in plantations.展开更多
The current trends in forestry in Europe include the increased use of continuous cover forestry(CCF)and the increased availability of tree-level forest inventory data.Accordingly,recent literature suggests methodologi...The current trends in forestry in Europe include the increased use of continuous cover forestry(CCF)and the increased availability of tree-level forest inventory data.Accordingly,recent literature suggests methodologies for optimizing the harvest decisions at the tree level.Using tree-level optimization for all trees of the stand is computationally demanding.This study proposed a two-level optimization method for CCF where the harvest prescriptions are optimized at the tree level for only a part of the trees or the first cuttings.The higher-level algorithm optimizes the cutting years and the harvest rates of those diameter classes for which tree-level optimization is not used.The lower-level algorithm allocates the individually optimized trees to different cutting events.The most detailed problem formulations,employing much tree-level optimization,resulted in the highest net present value and longest optimization time.However,restricting tree-level optimization to the largest trees and first cuttings did not significantly alter the time,intensity,or type of first cutting.Computing times could also be shortened by applying accumulated knowledge from previous optimizations,implementing learning aspects in heuristic search,and optimizing the search algorithms for short computing time and good-quality solutions.展开更多
Determining underlying factors that foster deforestation and delineating forest areas by levels of susceptibility are of the main challenges when defining policies for forest management and planning at regional scale....Determining underlying factors that foster deforestation and delineating forest areas by levels of susceptibility are of the main challenges when defining policies for forest management and planning at regional scale. The susceptibility to deforestation of remaining forest ecosystems （shrubland, temperate forest and rainforest） was conducted in the state of San Luis Potosi, located in north central Mexico. Spatial analysis techniques were used to detect the deforested areas in the study area during 1993-2007. Logistic regression was used to relate explana- tory variables （such as social, investment, forest production, biophysical and proximity factors） with susceptibility to deforestation to construct predictive models with two focuses： general and by biogeographical zone In all models, deforestation has positive correlation with distance to rainfed agriculture, and negative correlation with slope, distance to roads and distance to towns. Other variables were significant in some cases, but in others they had dual relationships, which varied in each biogeographi- cal zone. The results show that the remaining rainforest of Huasteca region is highly susceptible to deforestation. Both approaches show that more than 70% of the current rainforest area has high and very high levels of susceptibility to deforestation. The values represent a serious concern with global warming whether tree carbon is released to atmos- phere. However, after some considerations, encouraging forest environ- mental services appears to be the best alternative to achieve sustainableforest management.展开更多
Forest degradation induced by intensive forest management and temperature increase by climate change are resulting in biodiversity decline in boreal forests.Intensive forest management and high-end climate emission sc...Forest degradation induced by intensive forest management and temperature increase by climate change are resulting in biodiversity decline in boreal forests.Intensive forest management and high-end climate emission scenarios can further reduce the amount and diversity of deadwood,the limiting factor for habitats for saproxylic species in European boreal forests.The magnitude of their combined effects and how changes in forest management can affect deadwood diversity under a range of climate change scenarios are poorly understood.We used forest growth simulations to evaluate how forest management and climate change will individually and jointly affect habitats of red-listed saproxylic species in Finland.We simulated seven forest management regimes and three climate scenarios(reference,RCP4.5 and RCP8.5)over 100 years.Management regimes included set aside,continuous cover forestry,business-as-usual(BAU)and four modifications of BAU.Habitat suitability was assessed using a speciesspecific habitat suitability index,including 21 fungal and invertebrate species groups.“Winner”and“loser”species were identified based on the modelled impacts of forest management and climate change on their habitat suitability.We found that forest management had a major impact on habitat suitability of saproxylic species compared to climate change.Habitat suitability index varied by over 250%among management regimes,while overall change in habitat suitability index caused by climate change was on average only 2%.More species groups were identified as winners than losers from impacts of climate change(52%–95%were winners,depending on the climate change scenario and management regime).The largest increase in habitat suitability index was achieved under set aside(254%)and the climate scenario RCP8.5(>2%),while continuous cover forestry was the most suitable regime to increase habitat suitability of saproxylic species(up to+11%)across all climate change scenarios.Our results show that close-to-nature management regimes(e.g.,continuous cover forestry and set aside)can increase the habitat suitability of many saproxylic boreal species more than the basic business-as-usual regime.This suggests that biodiversity loss of many saproxylic species in boreal forests can be mitigated through improved forest management practices,even as climate change progresses.展开更多
Ever since the disastrous floods of 1998, the Chinese government has used the Natural Forest Protection and Sloping Land Conversion Programs to promote afforestation and reforestation as means to reduce runoff, contro...Ever since the disastrous floods of 1998, the Chinese government has used the Natural Forest Protection and Sloping Land Conversion Programs to promote afforestation and reforestation as means to reduce runoff, control erosion, and stabilize local livelihoods. These two ambitious programs have been reported as large-scale successes, contributing to an overall increase in China’s forest cover and to the stated goals of environmental stabilization. A small-scale field study at the project level of the implementation of these two programs in Baiwu Township, Yanyuan County, Sichuan, casts doubt upon the accuracy and reliability of these claims of success; ground observations revealed utter failure in some sites and only marginal success in others. Reasons for this discrepancy are posited as involving ecological, economic, and bureaucratic factors. Further research is suggested to determine whether these discrepancies are merely local aberrations or represent larger-scale failures in reforestation programs.展开更多
The Zagros forests are a treasure of valuable oak forests, but they have been severely degraded from long-term misuse. Geographic information systems （GIS） and multi-criteria decision analysis （MCDA） have been inc...The Zagros forests are a treasure of valuable oak forests, but they have been severely degraded from long-term misuse. Geographic information systems （GIS） and multi-criteria decision analysis （MCDA） have been increasingly used to improve the management of vulnerable ecosystems to prevent further degradation and increase the sustainability of land use. This study presents a methodology to assess land suitability using remote sensing （RS） to obtain wall-to-wall data for the calculations, GIS to analyze the data, and MCDA to rank alternative land uses. The criteria and subcriteria affecting the suitability of land for different uses were identified and weighted using an analytic hierarchy process. Variables used as subcriteria were assessed using satellite data and other sources of information such as existing maps and field surveys. Numerical values for the subcriteria were classified, and each class was given a priority rating according to expert judgments. Based on the ratings and weights of the subcriteria, a priority map was created for each land use using the weighted linear combination method. The priority maps for different land uses were overlaid to obtain a preliminary land use map, which often indicated several simultaneous land uses for the same location. The preliminary map was further edited by removing unrealistic, mutually exclusive land-use combinations. The study tested and demonstrated the potential of integrating RS, G1S and MCDA techniques for solving complicated land allocation problems in forested regions using a scientifically sound and practical approach for efficient and sustainable allocation of forestland for different uses.展开更多
Estimating the carbon storage of forests is essential to support climate change mitigation and promote the transition into a low-carbon emission economy.To achieve this goal,voluntary carbon markets(VCMs)are essential...Estimating the carbon storage of forests is essential to support climate change mitigation and promote the transition into a low-carbon emission economy.To achieve this goal,voluntary carbon markets(VCMs)are essential.VCMs are promoted by a spontaneous demand,not imposed by binding targets,as the regulated ones.In Italy,only in Veneto and Piedmont Regions(Northern Italy),VCMs through forestry activities were carried out.Valle Camonica District(Northern Italy,Lombardy Region)is ready for a local VCM,but carbon storage of its forests was never estimated.The aim of this work was to estimate the total carbon storage(TCS;t C ha^−1)of forest biomass of Valle Camonica District,at the stand level,taking into account:(1)aboveground biomass,(2)belowground biomass,(3)deadwood,and(4)litter.We developed a user-friendly model,based on site-specifi c primary(measured)data,and we applied it to a dataset of 2019 stands extracted from 45 Forest Management Plans.Preliminary results showed that,in 2016,the TCS achieved 76.02 t C ha^−1.The aboveground biomass was the most relevant carbon pool(48.86 t C ha^−1;64.27%of TCS).From 2017 to 2029,through multifunctional forest management,the TCS could increase of 2.48 t C ha^−1(+3.26%).In the same period,assuming to convert coppices stands to high forests,an additional TCS of 0.78 t C ha^−1(equal to 2.85 t CO 2 ha^−1)in the aboveground biomass could be achieved without increasing forest areas.The additional carbon could be certifi ed and exchanged on a VCM,contributing to climate change mitigation at a local level.展开更多
基金supported by the Natural Science Foundation of China (U21A20244 and 32071758)funding provided by University of Eastern Finland (including Kuopio University Hospital)
文摘Forest inventory is increasingly producing infor-mation on the locations and sizes of individual trees.This information can be acquired by airborne or terrestrial laser scanning or analyzing photogrammetric data.However,all trees are seldom detected,especially in young,dense,or multi-layered stands.On the other hand,the complete size distributions of trees can be predicted with various methods,for instance,kNN data imputation in an area-based LiDAR inventory,predicting the parameters of a distribution func-tion from remote sensing data,field sampling,or using his-togram matching and calibration methods.The predicted distribution can be used to estimate the number and sizes of the non-detected trees.The study’s objective was to develop a method for forest planning that efficiently uses the avail-able tree-level data in management optimization.The study developed a two-stage hierarchical method for tree-level management optimization for cases where only part of the trees is detected or measured individually.Cutting years and harvest rate curves for the non-detected trees are optimized at the higher level,and the cutting events of the detected trees are optimized at the lower level.The study used differ-ential evolution at the higher level and simulated annealing at the lower level.The method was tested and demonstrated in even-aged Larix olgensis plantations in the Heilongjiang province of China.The optimizations showed that optimiz-ing the harvest decisions at the tree level improves the profit-ability of management compared to optimizations in which only the dependence of thinning intensity on tree diameter is optimized.The approach demonstrated in this study pro-vides feasible options for tree-level forest planning based on LiDAR inventories.The method is immediately applicable to forestry practice,especially in plantations.
基金supported by the KESTO project (Planning and implementation of the harvesting of climate-resilient continuous cover forests (CCF) using digitalization in North Karelia),Grant Number 41007-00241901funded by the European Regional Development Fund (ERDF)funding provided by University of Eastern Finland (including Kuopio University Hospital)
文摘The current trends in forestry in Europe include the increased use of continuous cover forestry(CCF)and the increased availability of tree-level forest inventory data.Accordingly,recent literature suggests methodologies for optimizing the harvest decisions at the tree level.Using tree-level optimization for all trees of the stand is computationally demanding.This study proposed a two-level optimization method for CCF where the harvest prescriptions are optimized at the tree level for only a part of the trees or the first cuttings.The higher-level algorithm optimizes the cutting years and the harvest rates of those diameter classes for which tree-level optimization is not used.The lower-level algorithm allocates the individually optimized trees to different cutting events.The most detailed problem formulations,employing much tree-level optimization,resulted in the highest net present value and longest optimization time.However,restricting tree-level optimization to the largest trees and first cuttings did not significantly alter the time,intensity,or type of first cutting.Computing times could also be shortened by applying accumulated knowledge from previous optimizations,implementing learning aspects in heuristic search,and optimizing the search algorithms for short computing time and good-quality solutions.
文摘Determining underlying factors that foster deforestation and delineating forest areas by levels of susceptibility are of the main challenges when defining policies for forest management and planning at regional scale. The susceptibility to deforestation of remaining forest ecosystems （shrubland, temperate forest and rainforest） was conducted in the state of San Luis Potosi, located in north central Mexico. Spatial analysis techniques were used to detect the deforested areas in the study area during 1993-2007. Logistic regression was used to relate explana- tory variables （such as social, investment, forest production, biophysical and proximity factors） with susceptibility to deforestation to construct predictive models with two focuses： general and by biogeographical zone In all models, deforestation has positive correlation with distance to rainfed agriculture, and negative correlation with slope, distance to roads and distance to towns. Other variables were significant in some cases, but in others they had dual relationships, which varied in each biogeographi- cal zone. The results show that the remaining rainforest of Huasteca region is highly susceptible to deforestation. Both approaches show that more than 70% of the current rainforest area has high and very high levels of susceptibility to deforestation. The values represent a serious concern with global warming whether tree carbon is released to atmos- phere. However, after some considerations, encouraging forest environ- mental services appears to be the best alternative to achieve sustainableforest management.
基金Open access funding provided by Norwegian University of Life Sciences。
文摘Forest degradation induced by intensive forest management and temperature increase by climate change are resulting in biodiversity decline in boreal forests.Intensive forest management and high-end climate emission scenarios can further reduce the amount and diversity of deadwood,the limiting factor for habitats for saproxylic species in European boreal forests.The magnitude of their combined effects and how changes in forest management can affect deadwood diversity under a range of climate change scenarios are poorly understood.We used forest growth simulations to evaluate how forest management and climate change will individually and jointly affect habitats of red-listed saproxylic species in Finland.We simulated seven forest management regimes and three climate scenarios(reference,RCP4.5 and RCP8.5)over 100 years.Management regimes included set aside,continuous cover forestry,business-as-usual(BAU)and four modifications of BAU.Habitat suitability was assessed using a speciesspecific habitat suitability index,including 21 fungal and invertebrate species groups.“Winner”and“loser”species were identified based on the modelled impacts of forest management and climate change on their habitat suitability.We found that forest management had a major impact on habitat suitability of saproxylic species compared to climate change.Habitat suitability index varied by over 250%among management regimes,while overall change in habitat suitability index caused by climate change was on average only 2%.More species groups were identified as winners than losers from impacts of climate change(52%–95%were winners,depending on the climate change scenario and management regime).The largest increase in habitat suitability index was achieved under set aside(254%)and the climate scenario RCP8.5(>2%),while continuous cover forestry was the most suitable regime to increase habitat suitability of saproxylic species(up to+11%)across all climate change scenarios.Our results show that close-to-nature management regimes(e.g.,continuous cover forestry and set aside)can increase the habitat suitability of many saproxylic boreal species more than the basic business-as-usual regime.This suggests that biodiversity loss of many saproxylic species in boreal forests can be mitigated through improved forest management practices,even as climate change progresses.
文摘Ever since the disastrous floods of 1998, the Chinese government has used the Natural Forest Protection and Sloping Land Conversion Programs to promote afforestation and reforestation as means to reduce runoff, control erosion, and stabilize local livelihoods. These two ambitious programs have been reported as large-scale successes, contributing to an overall increase in China’s forest cover and to the stated goals of environmental stabilization. A small-scale field study at the project level of the implementation of these two programs in Baiwu Township, Yanyuan County, Sichuan, casts doubt upon the accuracy and reliability of these claims of success; ground observations revealed utter failure in some sites and only marginal success in others. Reasons for this discrepancy are posited as involving ecological, economic, and bureaucratic factors. Further research is suggested to determine whether these discrepancies are merely local aberrations or represent larger-scale failures in reforestation programs.
文摘The Zagros forests are a treasure of valuable oak forests, but they have been severely degraded from long-term misuse. Geographic information systems （GIS） and multi-criteria decision analysis （MCDA） have been increasingly used to improve the management of vulnerable ecosystems to prevent further degradation and increase the sustainability of land use. This study presents a methodology to assess land suitability using remote sensing （RS） to obtain wall-to-wall data for the calculations, GIS to analyze the data, and MCDA to rank alternative land uses. The criteria and subcriteria affecting the suitability of land for different uses were identified and weighted using an analytic hierarchy process. Variables used as subcriteria were assessed using satellite data and other sources of information such as existing maps and field surveys. Numerical values for the subcriteria were classified, and each class was given a priority rating according to expert judgments. Based on the ratings and weights of the subcriteria, a priority map was created for each land use using the weighted linear combination method. The priority maps for different land uses were overlaid to obtain a preliminary land use map, which often indicated several simultaneous land uses for the same location. The preliminary map was further edited by removing unrealistic, mutually exclusive land-use combinations. The study tested and demonstrated the potential of integrating RS, G1S and MCDA techniques for solving complicated land allocation problems in forested regions using a scientifically sound and practical approach for efficient and sustainable allocation of forestland for different uses.
基金The study is part of a PhD Research Project funded by the Italian Ministry of Education,University and Research(MIUR).
文摘Estimating the carbon storage of forests is essential to support climate change mitigation and promote the transition into a low-carbon emission economy.To achieve this goal,voluntary carbon markets(VCMs)are essential.VCMs are promoted by a spontaneous demand,not imposed by binding targets,as the regulated ones.In Italy,only in Veneto and Piedmont Regions(Northern Italy),VCMs through forestry activities were carried out.Valle Camonica District(Northern Italy,Lombardy Region)is ready for a local VCM,but carbon storage of its forests was never estimated.The aim of this work was to estimate the total carbon storage(TCS;t C ha^−1)of forest biomass of Valle Camonica District,at the stand level,taking into account:(1)aboveground biomass,(2)belowground biomass,(3)deadwood,and(4)litter.We developed a user-friendly model,based on site-specifi c primary(measured)data,and we applied it to a dataset of 2019 stands extracted from 45 Forest Management Plans.Preliminary results showed that,in 2016,the TCS achieved 76.02 t C ha^−1.The aboveground biomass was the most relevant carbon pool(48.86 t C ha^−1;64.27%of TCS).From 2017 to 2029,through multifunctional forest management,the TCS could increase of 2.48 t C ha^−1(+3.26%).In the same period,assuming to convert coppices stands to high forests,an additional TCS of 0.78 t C ha^−1(equal to 2.85 t CO 2 ha^−1)in the aboveground biomass could be achieved without increasing forest areas.The additional carbon could be certifi ed and exchanged on a VCM,contributing to climate change mitigation at a local level.
