This paper introduces a modeling approach for the assessment of policy options within the forest-... more This paper introduces a modeling approach for the assessment of policy options within the forest-based bioeconomy. The feedback between the forestry dynamics model and the economic model of the global forest-based sector of the proposed framework is essential, not only for response analysis as to the development of forest resources and for a correct assessment of future harvesting potentials, but also for the assessment of the impact of different management regimes on wood-based product markets. Test runs of the modeling framework on a Swedish case highlight the necessity of considering timber assortments for a comprehensive integration of forest resources and wood-based commodity market dynamics. Hence, the composition of harvest demand in terms of timber assortment affects the allocation of harvesting activities and, consequently, the development of forest resources (and thus future harvest potentials), as well as the production, trade and consumption of wood-based products.
A general regression model for large areas may have poor statistical properties for smaller sub-r... more A general regression model for large areas may have poor statistical properties for smaller sub-regions. In this study, we test the local indicators of spatial association (LISA) in the selection of localization areas of a general regression model. We present four different LISAs: Moran’s I i , Geary’s c i , G i , and G i *. These indices
ABSTRACT Typically, in forest inventory the volume of tally trees is predicted with a volume mode... more ABSTRACT Typically, in forest inventory the volume of tally trees is predicted with a volume model estimated at national level. Such a global model is not unbiased regionally if there is spatial variation in the tree form due to one or more unknown predictors. This regional bias could be reduced or removed if the models were localized to each region or subarea. The localization is easiest if the area can be divided into homogeneous areas with respect to stem form. This study tested whether the localization results depend on the way the division is made and on the size of the subareas. The study area was divided spatially into homogeneous subareas with residuals of the global model or with the local spatial index, Gi*, or both with classification and regression trees, the leaves of which formed the subareas. In addition, two other spatial divisions were created: an administrative forest centre and spatially equal-sized subarea divisions. The localized models were compared with the global model. The root mean squared errors (RMSEs) of localized models were smaller in median and in mean, but maximum values exceeded the overall global model RMSE. The localization reduced local RMSEs on average by 1-6%. The differences between the spatial divisions were small, although the aggregate standard errors and RMSEs were slightly smaller in regression trees. Only 50 ± 8% of the subareas were spatially homogeneous in regression tree divisions, which suggests that either the division criteria or the division method were inadequate.
ISPRS Journal of Photogrammetry and Remote Sensing, 2013
ABSTRACT Accurate forest biomass mapping methods would provide the means for e.g. detecting bioen... more ABSTRACT Accurate forest biomass mapping methods would provide the means for e.g. detecting bioenergy potential, biofuel and forest-bound carbon. The demand for practical biomass mapping methods at all forest levels is growing worldwide, and viable options are being developed. Airborne laser scanning (ALS) is a promising forest biomass mapping technique, due to its capability of measuring the three-dimensional forest vegetation structure. The objective of the study was to develop new methods for tree-level biomass estimation using metrics derived from ALS point clouds and to compare the results with field references collected using destructive sampling and with existing biomass models. The study area was located in Evo, southern Finland. ALS data was collected in 2009 with pulse density equalling approximately 10 pulses/m2. Linear models were developed for the following tree biomass components: total, stem wood, living branch and total canopy biomass. ALS-derived geometric and statistical point metrics were used as explanatory variables when creating the models. The total and stem biomass root mean square error per cents equalled 26.3% and 28.4% for Scots pine (Pinus sylvestris L.), and 36.8% and 27.6% for Norway spruce (Picea abies (L.) H. Karst.), respectively. The results showed that higher estimation accuracy for all biomass components can be achieved with models created in this study compared to existing allometric biomass models when ALS-derived height and diameter were used as input parameters. Best results were achieved when adding field-measured diameter and height as inputs in the existing biomass models. The only exceptions to this were the canopy and living branch biomass estimations for spruce. The achieved results are encouraging for the use of ALS-derived metrics in biomass mapping and for further development of the models.
A general regression model for large areas may have poor statistical properties for smaller sub-r... more A general regression model for large areas may have poor statistical properties for smaller sub-regions. In this study, we test the local indicators of spatial association (LISA) in the selection of localization areas of a general regression model. We present four different LISAs: Moran’s I i , Geary’s c i , G i , and G i *. These indices
This paper introduces a modeling approach for the assessment of policy options within the forest-... more This paper introduces a modeling approach for the assessment of policy options within the forest-based bioeconomy. The feedback between the forestry dynamics model and the economic model of the global forest-based sector of the proposed framework is essential, not only for response analysis as to the development of forest resources and for a correct assessment of future harvesting potentials, but also for the assessment of the impact of different management regimes on wood-based product markets. Test runs of the modeling framework on a Swedish case highlight the necessity of considering timber assortments for a comprehensive integration of forest resources and wood-based commodity market dynamics. Hence, the composition of harvest demand in terms of timber assortment affects the allocation of harvesting activities and, consequently, the development of forest resources (and thus future harvest potentials), as well as the production, trade and consumption of wood-based products.
A general regression model for large areas may have poor statistical properties for smaller sub-r... more A general regression model for large areas may have poor statistical properties for smaller sub-regions. In this study, we test the local indicators of spatial association (LISA) in the selection of localization areas of a general regression model. We present four different LISAs: Moran’s I i , Geary’s c i , G i , and G i *. These indices
ABSTRACT Typically, in forest inventory the volume of tally trees is predicted with a volume mode... more ABSTRACT Typically, in forest inventory the volume of tally trees is predicted with a volume model estimated at national level. Such a global model is not unbiased regionally if there is spatial variation in the tree form due to one or more unknown predictors. This regional bias could be reduced or removed if the models were localized to each region or subarea. The localization is easiest if the area can be divided into homogeneous areas with respect to stem form. This study tested whether the localization results depend on the way the division is made and on the size of the subareas. The study area was divided spatially into homogeneous subareas with residuals of the global model or with the local spatial index, Gi*, or both with classification and regression trees, the leaves of which formed the subareas. In addition, two other spatial divisions were created: an administrative forest centre and spatially equal-sized subarea divisions. The localized models were compared with the global model. The root mean squared errors (RMSEs) of localized models were smaller in median and in mean, but maximum values exceeded the overall global model RMSE. The localization reduced local RMSEs on average by 1-6%. The differences between the spatial divisions were small, although the aggregate standard errors and RMSEs were slightly smaller in regression trees. Only 50 ± 8% of the subareas were spatially homogeneous in regression tree divisions, which suggests that either the division criteria or the division method were inadequate.
ISPRS Journal of Photogrammetry and Remote Sensing, 2013
ABSTRACT Accurate forest biomass mapping methods would provide the means for e.g. detecting bioen... more ABSTRACT Accurate forest biomass mapping methods would provide the means for e.g. detecting bioenergy potential, biofuel and forest-bound carbon. The demand for practical biomass mapping methods at all forest levels is growing worldwide, and viable options are being developed. Airborne laser scanning (ALS) is a promising forest biomass mapping technique, due to its capability of measuring the three-dimensional forest vegetation structure. The objective of the study was to develop new methods for tree-level biomass estimation using metrics derived from ALS point clouds and to compare the results with field references collected using destructive sampling and with existing biomass models. The study area was located in Evo, southern Finland. ALS data was collected in 2009 with pulse density equalling approximately 10 pulses/m2. Linear models were developed for the following tree biomass components: total, stem wood, living branch and total canopy biomass. ALS-derived geometric and statistical point metrics were used as explanatory variables when creating the models. The total and stem biomass root mean square error per cents equalled 26.3% and 28.4% for Scots pine (Pinus sylvestris L.), and 36.8% and 27.6% for Norway spruce (Picea abies (L.) H. Karst.), respectively. The results showed that higher estimation accuracy for all biomass components can be achieved with models created in this study compared to existing allometric biomass models when ALS-derived height and diameter were used as input parameters. Best results were achieved when adding field-measured diameter and height as inputs in the existing biomass models. The only exceptions to this were the canopy and living branch biomass estimations for spruce. The achieved results are encouraging for the use of ALS-derived metrics in biomass mapping and for further development of the models.
A general regression model for large areas may have poor statistical properties for smaller sub-r... more A general regression model for large areas may have poor statistical properties for smaller sub-regions. In this study, we test the local indicators of spatial association (LISA) in the selection of localization areas of a general regression model. We present four different LISAs: Moran’s I i , Geary’s c i , G i , and G i *. These indices
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Papers by Minna Räty