Mapping and spatial uncertainty analysis of forest vegetation carbon by combining national forest inventory data and satellite images

Forests play an important role in carbon sinks and mitigation of atmospheric concentrations of carbon dioxide and greenhouse effect. Given that sample plots used for collection of forest carbon observations are often much smaller than the map units of forest carbon at regional, national, and global scales, scientists are currently experiencing two challenges. The first challenge is to produce reliable maps of forest carbon using the data from inconsistent sizes of plots and image pixels. Also, because estimates of forest carbon normally contain uncertainties, the second challenge is to accurately model propagation of uncertainties from input data to output results. In this study, a methodology for mapping and analyzing spatial uncertainty of forest carbon estimates was developed to address these challenges. The methodological framework consisted of two methods. The first one was up-scaling method that combined and scaled up existing national forest inventory plot data and satellite images from smaller sample plots and image pixels to larger map units. The second one was spatial uncertainty analysis and error budget method that entailed modeling propagated uncertainties through a geostatistical mapping system. A case study using 46 permanent national forest inventory plots from Wu-Yuan County, Jiangxi, China, was undertaken to test this methodology. The results showed that this method reproduced not only the spatial distribution of forest carbon but also the spatial pattern of variances of its estimates and was able to quantify the contributions of uncertainties from the field plot data and satellite images to the uncertainties of forest carbon estimates. Thus, this study, to some extent, overcame the gaps that currently exist in the generation and assessment of forest carbon estimation maps. Moreover, the results showed that in this case study, the variation of the band ratio defined as (TM2 + TM3 + TM5)/TM7 contributed more uncertainties to the estimates of forest carbon than the variation of the plot data. In addition, we also found out that the product of the input plot forest carbon variance and the band ratio variance, implying the interaction between these two variables, reduced the uncertainties of the forest carbon estimates.     

Mapping dead wood distribution in a temperate hardwood forest using high resolution airborne imagery

Dead wood, in the form of coarse woody debris and standing dead wood, or snags, is an essential structural component of forest ecosystems. It plays a key role in nutrient cycling, ecosystem functions and provision of habitat for a wide variety of species. In order to manage dead wood in a temperate hardwood forest, an understanding of its availability and spatial distribution is important. This research evaluates airborne digital camera remote sensing for mapping temperate forest dead wood across an area within Gatineau Park, Canada. Two approaches were evaluated: (1) direct detection and mapping of canopy dead wood (dead branches and tall snags) through the combination of three techniques in a hybrid classification: ISODATA clustering, object-based classification, and spectral unmixing, and (2) indirect modelling of coarse woody debris and snags using spectral and spatial predictor variables extracted from the imagery. Indirect modelling did not provide useful results while direct detection was successful with field validation showing 94% accuracy for detected canopy level dead wood objects (i.e. 94% of validation sites with canopy dead wood were detected correctly) and 90% accuracy for control sites (i.e. 90% of validation sites with no canopy level dead wood were identified correctly). The procedures presented in this paper are repeatable and could be used to monitor dead wood over time, potentially contributing to applications in forest carbon budget estimation, biodiversity management, and forest inventory.     

The choice of definition has a large effect on reported quantities of dead wood in boreal forest

A survey was conducted to assess the impact of the choice of definition on reported quantities of dead wood in Swedish forests, which to more than 90% are located in the boreal zone. The data collection was made on a subsample of the permanent plots of the Swedish national forest inventory. The objects included were standing dead trees and snags down to 5-cm diameter at breast height, dead lying stems and branches down to a threshold diameter of 1 cm and stumps down to a threshold diameter of 5-cm at normal stump height. Standing trees, snags and stumps were inventoried on 10-m radius circular plots while the downed objects were inventoried using both circular plots and line intersect sampling; thin objects (diameter 1–5 cm) were assessed only through line intersect sampling. The results showed that the estimated volume of dead wood was as high as 25 m³ ha^?1 when all components were included. With the standard Swedish definition, the corresponding estimate was only 10.9 m³ ha^?1, or 43% of the total value. Since definitions of dead wood vary greatly between countries we conclude that great caution must be exercised when figures are compared in connection with international reporting. For example, adding stumps to the Swedish definition would increase the amounts of dead wood from 10.9 to 15.7 m³ ha^?1, i.e. with 44%.     

Spatial interpolation of in situ data by self-organizing map algorithms (neural networks) for the assessment of carbon stocks in European forests

Self-organizing maps (SOMs) are an advanced neural networks application. SOMs were applied for the spatially explicit estimation of forest carbon stocks for a test region in Thuringia (Germany). The approach utilizes in situ national forest inventory data and satellite remote sensing data (Landsat 7 ETM+) and provides maps showing a high-resolution spatial distribution of forest carbon stocks. The generated maps are compared to alternative estimates obtained by the k-nearest neighbour (kNN) method—a remote sensing based carbon assessment. Beside maps the SOM- and kNN-approaches were utilized to calculate statistical estimates of carbon stock and growing stock. The statistical estimates were validated by calculating bias and mean square errors with reference to in situ assessments.     

End-point diameter and total length coarse woody debris models for the United States

Coarse woody debris (CWD) may be defined as dead and down trees of a certain minimum size that are an important forest ecosystem component (e.g., wildlife habitat, carbon stocks, and fuels). Due to field efficiency concerns, some natural resource inventories only measure the attributes of CWD pieces at their point of intersection with a sampling transect (e.g., transect diameter) although measurements of large-end diameter, small-end diameter, and length are often required by natural resource managers. The goal of this study was to develop a system of empirical models that predict CWD dimensions (e.g., large-end diameter) based on CWD attributes measured at the point of intersection with a sample transect and ancillary data (e.g., ecological province). Results indicated that R-squared (R²) values exceeded 0.60 for most of this study's CWD large-end diameter and small-end diameter with only fair results for the length models. The mean residuals of numerous CWD models were within the measurement tolerance expected of actual field crews. Despite remaining unexplained variation, these CWD models may provide foresters with an alternative to the time-consuming activity of measuring all CWD dimensional attributes of interest during large-scale forest inventories.     

Effect of tree species on carbon stocks in forest floor and mineral soil and implications for soil carbon inventories

Forest soil organic carbon (SOC) and forest floor carbon (FFC) stocks are highly variable. The sampling effort required to assess SOC and FFC stocks is therefore large, resulting in limited sampling and poor estimates of the size, spatial distribution, and changes in SOC and FFC stocks in many countries. Forest SOC and FFC stocks are influenced by tree species. Therefore, quantification of the effect of tree species on carbon stocks combined with spatial information on tree species distribution could improve insight into the spatial distribution of forest carbon stocks.We present a study on the effect of tree species on FFC and SOC stock for a forest in the Netherlands and evaluate how this information could be used for inventory improvement. We assessed FFC and SOC stocks in stands of beech (Fagus sylvatica), Douglas fir (Pseudotsuga menziesii), Scots pine (Pinus sylvestris), oak (Quercus robur) and larch (Larix kaempferi).FFC and SOC stocks differed between a number of species. FFC stocks varied between 11.1 Mg C ha⁻¹ (beech) and 29.6 Mg C ha⁻¹ (larch). SOC stocks varied between 53.3 Mg C ha⁻¹ (beech) and 97.1 Mg C ha⁻¹ (larch). At managed locations, carbon stocks were lower than at unmanaged locations. The Dutch carbon inventory currently overestimates FFC stocks. Differences in carbon stocks between conifer and broadleaf forests were significant enough to consider them relevant for the Dutch system for carbon inventory.     

Ecosystem carbon storage and partitioning in a tropical seasonal forest in Southwestern China

Tropical forests play an important role in the global carbon cycle. Despite an increasing number of studies have addressed carbon storage in tropical forests, the regional variation in such storage remains poorly understood. Uncertainty about how much carbon is stored in tropical forests is an important limitation for regional-scale estimates of carbon fluxes and improving these estimates requires extensive field studies of both above- and belowground stocks. In order to assess the carbon pools of a tropical seasonal forest in Asia, total ecosystem carbon storage was investigated in Xishuangbanna, SW China. Averaged across three 1 ha plots, the total carbon stock of the forest ecosystem was 303 t C ha⁻¹. Living tree carbon stocks (both above- and belowground) ranged from 163 to 258 t C ha⁻¹. The aboveground biomass C pool is comparable to the Dipterocarp forests in Sumatra but lower than those in Malaysia. The variation of C storage in the tree layer among different plots was mainly due to different densities of large trees (DBH > 70 cm). The contributions of the shrub layer, herb layer, woody lianas, and fine litter each accounted for 1–2 t C ha⁻¹ to the total carbon stock. The mineral soil C pools (top 100 cm) ranged from 84 to 102 t C ha⁻¹ and the C in woody debris from 5.6 to 12.5 t C ha⁻¹, representing the second and third largest C component in this ecosystem. Our results reveal that a high percentage (70%) of C is stored in biomass and less in soil in this tropical seasonal forest. This study provides an accurate estimate of the carbon pool and the partitioning of C among major components in tropical seasonal rain forest of northern tropical Asia. Results from this study will enhance our ability to evaluate the role of these forests in regional C cycles and have great implications for conservation planning.     

Floristic diversity and carbon stocks in the periphery of Deng–Deng National Park,Eastern Cameroon

Carbon is continuously being removed from the atmosphere by photosynthesis and stored in carbon pools(live,dead,and soil carbon)of forest ecosystems.However,carbon stock in dead wood and of trees with diameters at breast height(dbh)between 5 and 10 cm is often not considered in many studies carried out in the Congo Basin Forest.The relationship between tree diversity,life-forms and carbon stocks has received little attention.This study was carried out on the outskirts of Deng Deng National Park(DDNP)to determine tree diversity(dominant families,species richness and Shannon index),assess carbon stocks in the five carbon compartments(living tree,understory,fine roots,dead wood and litter)as well as to analyze the relationship between(1)carbon stocks and tree diversity;and,(2)between carbon stock and life-forms.The Shannon index of trees≥10 cm dbh ranged from 2.6 in riparian forest to 4.3 in secondary forest;and for the tree between 5 and 10 cm,it ranged to 1.56 in riparian forest to 3.68 in the secondary forest.The study site housed 16 species,7 genera and 3 families which are only found in trees of dbh between 5 and 10 cm.The average total carbon stock of the five compartments varied from 200.1 t ha-1 in forest residues to 439.1 t ha-1 in secondary forest.Dead wood carbon stock varied from 1.2 t ha-1 in riparian forests to 12.51t ha-1 in agroforests.The above ground carbon stocks for trees with diameter between 5 and 10 cm varied from 0.7 t ha-1 in young fallow fields to 5.02 t ha-1 in old secondary forests.This study reveals a low but positive correlation between species richness and total carbon stocks,as well as a significant positive relationship between life-forms and total carbon stocks.The findings highlight the need for more data concerning carbon content of dead wood,carbon of trees≥5 cm<10 cm dbh and the relationship between carbon stocks and tree diversity from other areas of the Congo Basin for a good understanding of the contribution of tropical forests to climate change mitigation.     

Spatially explicit assessment of carbon stocks of a managed forest area in eastern Germany

The Kyoto-protocol permits the accounting of changes in forest carbon stocks due to forestry. Therefore, forest owners are interested in a reproducible quantification of carbon stocks at the level of forest management units and the impact of management to these stocks or their changes. We calculated the carbon stocks in tree biomass and the organic layer including their uncertainties for several forest management units (Tharandt forest, Eastern Germany, 5,500 ha) spatially explicit at the scale of individual stands by using standard forest data sources. Additionally, soil carbon stocks along a catena were quantified. Finally, carbon stocks of spruce and beech dominated stands were compared and effects of thinning intensity and site conditions were assessed. We combined forest inventory and data of site conditions by using the spatial unions of the shapes (i.e., polygons) in the stand map and the site map. Area weighted means of carbon (C) stocks reached 10.0 kg/m2 in tree biomass, 3.0 kg/m2 in the organic layer and 7.3 kg/m2 in mineral soil. Spatially explicit error propagation yielded a precision of the relative error of carbon stocks at the total studied area of 1% for tree biomass, 45% for the organic layer, and 20% for mineral soil. Mature beech dominated stands at the Tharandt forest had higher tree biomass carbon stocks (13.4 kg/m2) and lower organic layer carbon stocks (1.8 kg/m2) compared to stands dominated by spruce (11.6, 3.0 kg/m2). The difference of tree biomass stocks was mainly due to differences in thinning intensity. The additional effect of site conditions on tree carbon stocks was very small. We conclude that the spatially explicit combination of stand scale inventory data with data on site conditions is suited to quantify carbon stocks in tree biomass and organic layer at operational scale.     

Estimating carbon stock in secondary forests: Decisions and uncertainties associated with allometric biomass models

Secondary forests are a major terrestrial carbon sink and reliable estimates of their carbon stocks are pivotal for understanding the global carbon balance and initiatives to mitigate CO₂ emissions through forest management and reforestation. A common method to quantify carbon stocks in forests is the use of allometric regression models to convert forest inventory data to estimates of aboveground biomass (AGB). The use of allometric models implies decisions on the selection of extant models or the development of a local model, the predictor variables included in the selected model, and the number of trees and species for destructive biomass measurements. We assess uncertainties associated with these decisions using data from 94 secondary forest plots in central Panama and 244 harvested trees belonging to 26 locally abundant species. AGB estimates from species-specific models were used to assess relative errors of estimates from multispecies models. To reduce uncertainty in the estimation of plot AGB, including wood specific gravity (WSG) in the model was more important than the number of trees used for model fitting. However, decreasing the number of trees increased uncertainty of landscape-level AGB estimates substantially, while including WSG had limited effects on the accuracy of the landscape-level estimates. Predictions of stand and landscape AGB varied strongly among models, making model choice an important source of uncertainty. Local models provided more accurate AGB estimates than foreign models, but high variability in carbon stocks across the landscape implies that developing local models is only justified when landscape sampling is sufficiently intensive.     

The inventory of carbon stock in New Zealand’s post-1989 planted forest for reporting under the Kyoto protocol

The United Nations Framework Convention on Climate Change (UNFCCC) requires reporting net carbon stock changes and anthropogenic greenhouse gas emissions, including those related to forests. This paper describes the design and implementation of a nation-wide forest inventory of New Zealand’s planted post-1989 forests that arose from Land Use, Land-Use Change and Forestry activities (LULUCF) under Article 3.3 of the Kyoto Protocol. The majority of these forests are planted with Pinus radiata, with the remainder made up of other species exotic to New Zealand. At the start of the project there was no on-going national forest inventory that could be used as a basis for calculating carbon stocks and meet Good Practice Guidelines.A network of ground-based permanent sample plots was installed with airborne LiDAR (Light Detection and Ranging) for double sampling using regression estimators to predict carbon in each of the four carbon pools of above- and below-ground live biomass, dead wood and litter. Measurement, data acquisition and quality assurance/control protocols were developed specifically for the inventory, carried out in 2007 and 2008. Plots were located at the intersection of a forest with a 4 km square grid, coincident with an equivalent 8 km square grid established over the indigenous forest and “grassland with woody biomass” (Other Wooded Land). Planted tree carbon within a ground plot was calculated by an integrated system of growth, wood density and compartment allocation models utilising the data from measurements of trees and shrubs on the plots. This system, called the Forest Carbon Predictor, predicts past and future carbon in a stand and is conditioned so that the calculated basal area and mean top height equals that obtained by conventional mensuration methods at the time of the plot measurement. Mean per hectare carbon stocks were then multiplied by an estimate of the total area of post 1989 forests obtained from wall to wall mapping using a combination of satellite imagery and ortho-photography.The network of permanent samples plots and LiDAR double sampling methodology was designed to be simple and robust to change over time. In the future, using LiDAR should achieve sampling efficiencies over using ground plots alone and reduces any problems regarding restricted access on the ground. The network is to be remeasured at the end of commitment period 1, 2012, and the carbon stocks re-estimated in order to calculate change.     

Rapid carbon accumulation within an unmanaged,mixed, temperate woodland

Forest carbon stocks have increased in both Europe and North America in recent decades. National forest inventories are often used to indicate recent carbon dynamics, but the data from unmanaged forests are often incomplete. Here we calculate changing biomass carbon stocks for a mixed, unmanaged British woodland with two different management histories: (1) older growth stands untouched since 1902 and (2) younger growth stands clear felled in 1943 but have developed naturally since. Transects in the older growth have been monitored since 1945 and the younger growth since 1977. Separate estimates of tree carbon (C), soil C and dead wood C were obtained to verify how C is apportioned in these stands. Tree biomass C stocks had approximately doubled in the older growth stands since 1945 and 60% of C was stored in tree biomass, 38% was stored in soil and 2% stored in coarse woody debris. This study suggests that natural older growth stands are storing more C than typical managed forests, with tree biomass the most important compartment for C stores. If management is to be shifted from biomass production to increased C stores, due consideration should be given to the role of unmanaged, older growth forests.     

Carbon budget of Ontario's managed forests and harvested wood products, 2001–2100

Forest and harvested wood products (HWP) carbon (C) stocks between 2001 and 2100 for Ontario's managed forests were projected using FORCARB-ON, an adaptation of the U.S. national forest C budget model known as FORCARB2. A fire disturbance module was introduced to FORCARB-ON to simulate the effects of wildfire on C, and some of the model's C pools were re-parameterized using data from Canadian forests. Forest C stocks were estimated using allometric equations that represent the relationships between C and net merchantable volume and forest age based on forest inventory statistics. Other pools were included using results from ecological studies related to forest inventory variables. Data from future forest development projections adopted in approved management plans were used as model input to produce forest C budgets for the province's Crown forest management units. The estimates were extended to other types of managed forests in Ontario: parks, measured fire management zones, and private forest lands. Carbon in HWP was estimated in four categories: wood in use, wood in landfill, wood burned for energy, and C emitted by wood decomposition or burning without energy generation. We projected that the C stocks in Ontario's managed forests and HWP (in use and in landfills) would increase by 465.3 Mt from 2001 to 2100, of which 47.9 Mt is from increases in forest C and 417.4 Mt is from HWP C.     

Biased estimation of forest log characteristics using intersect diameters

Logs are an important structural feature of forest ecosystems, and their abundance affects many resources and forest processes, including fire regimes, soil productivity, silviculture, carbon cycling, and wildlife habitat. Consequently, logs are often sampled to estimate their frequency, percent cover, volume, and weight. The line-intersect method (LIM) is one of the most widely used methods to obtain these estimates and has been shown to produce unbiased estimates of log characteristics. With the traditional LIM the diameters of each log at the point of its intersection with the sampling transect are used to estimate log characteristics. Based on a simulation study and a large set of empirical data, we found that use of intersect log diameters to define size classes provided biased estimates of log characteristics. The bias varied by diameter class. Results from the simulation study showed that log frequency and volume were overestimated in small-diameter log classes and underestimated in large-diameter classes. Similarly, results from our empirical analysis showed a 40% overestimate of log volume in the smallest diameter class (15–25 cm), and a 31% underestimate of volume in the largest diameter class (>50 cm). Just as size classes of snags and trees are best defined by their diameter-at-breast height (DBH), size classes of logs should be defined by their large-end diameters (LEDs). When large-end diameters of logs were used instead of diameters measured at the point of transect intersection, bias was substantially reduced or eliminated. These results indicate that line-intersect sampling could be substantially improved by including measurements of LEDs to estimate log characteristics. Our results have far-reaching implications for estimates of log characteristics, such as estimates of fuel loading and subsequent wildfire risk, carbon source and sink dynamics, silviculture, nutrient cycling, and habitat for wildlife. Without our suggested correction to line-intersect sampling, many forest resources associated with log characteristics will not be estimated accurately, affecting a plethora of log-based management and research programs.     

Relationships between the stocking levels of live trees and dead tree attributes in forests of the United States

There has been little examination of the relationship between the stocking of live trees in forests and the associated attributes of dead tree resources which could inform large-scale efforts to estimate and manage deadwood resources. The goal of this study was to examine the relationships between the stocking of standing live trees and attributes of standing dead and downed dead trees using a national inventory of forests in the United States. Results indicated that from the lowest to the highest class of live tree relative stand density, the mean biomass/ha of live trees increased over 2000% while standing dead and downed dead trees biomass/ha increased 295 and 75%, respectively. Correlations between downed deadwood biomass and stand/site attributes increased as live tree stocking increased. The size/density attributes of standing and downed deadwood exhibited no relationship with standing live stocking possibly due to the confounding factors of decay and breakage. This study proposes a conceptual deadwood stocking model with standing live tree stocking as an axis along which deadwood accretion factors (e.g., disturbance, self-thinning, and senescence) and depletion factors (e.g., decay, harvest, and stagnation) ultimately determine deadwood stocking.     

Additive prediction of aboveground biomass for Pinus radiata (D. Don) plantations

A general and two country-specific systems of additive equations were developed to predict aboveground biomass of Pinus radiata plantations from stand variables that are routinely measured in inventory plots and predicted by conventional growth and yield models. The data for this work consisted of 319 plot-based biomass estimates that were derived from individual tree biomass equations developed in situ. These plot-based biomass estimates were compiled from studies reported in the forestry and ecological literature since 1960 and also from personal communications. They represent more than 60 sites worldwide with a majority in Australia and New Zealand. The systems of additive biomass equations developed from these data provide an alternative and addition to the current methods of estimating the aboveground biomass of P. radiata plantations. They also provide a direct linkage between forest inventory measures, outputs from conventional growth and yield models and biomass and carbon stock estimates at the same spatial scale. This direct linkage provides a new basis for scaling to a remote sensing image from which biomass and carbon stocks across the landscape can be mapped. Comparisons of prediction accuracies between this approach and other methods such as scaling up from individual tree biomass estimates and biomass expansion factors highlighted considerable methodological differences in the estimates of aboveground biomass and associated uncertainties over a range of stand age and conditions. These differences should be carefully evaluated before adopting a particular method to estimate aboveground biomass and carbon stocks of P. radiata plantations at a local, regional or national scale.     

森林粗木质残体的贮量和碳库及其影响因素

粗木质残体(CWD)是森林生态系统重要的组成要素,由于定量研究某特定区域CWD的贮量和碳量的工作量相当大,需要长期的研究才能获得可信的数据,因此有关全球森林生态系统CWD的贮量和碳量仍不太清楚.本文根据国内外研究结果综述全球不同森林中CWD的贮量和碳库情况.结果表明:温带针叶林CWD贮量最高(30～200 t·hm-2),而阔叶林最低(8～50 t·hm-2);全球森林CWD碳贮量范围大致为75～114或157 Pg.各森林CWD贮量值变化大,因林龄、CWD分解阶段和人类经营活动(如疏伐、皆伐和控制火烧)而异.今后需更广泛地开展森林CWD调查,以更深刻理解CWD与林分结构、树种特性及干扰的关系.此外,为了更准确地评价CWD在森林生态系统中的生态价值,建议在更大尺度上对全球各类森林的CWD贮量和碳库进行长期的研究.     

Inventory-based estimates of forest biomass carbon stocks in China: A comparison of three methods

Several studies have reported different estimates for forest biomass carbon (C) stocks in China. The discrepancy among these estimates may be largely attributed to the methods used. In this study, we used three methods [mean biomass density method (MBM), mean ratio method (MRM), and continuous biomass expansion factor (BEF) method (abbreviated as CBM)] applied to forest inventory data to estimate China's forest biomass C stocks and their changes from 1984 to 2003. The three methods generated various estimates of the biomass C stocks: the lowest (4.0–5.9 Pg C) from CBM and the highest (5.7–7.7 Pg C) from MBM, with an intermediate estimate (4.2–6.2 Pg C) from MRM. Forest age class is a major factor responsible for these method-induced differences. MBM overestimates biomass for young-aged forests, but underestimates biomass for old-aged forests; while the reverse is true for MRM. Further, the three methods resulted in different estimates of biomass C stocks for different forest types. For temperate/subtropical mixed forests, MBM generated a 92% higher estimate than CBM and MRM generated a 14% lower than CBM. The degree of the overestimates is closely related with the proportion of young-aged forest within total area of each forest type.     

A statistical thinning model for initialising large-scale ecosystem models

Abstract

Large-scale ecosystem models are important tools for carbon assessment at national scales. Many of these models are not initialised with known field data from any particular time, but simulate the growth of each stand from its estimated germination year up to the present or future. The models will overestimate current-day standing volume or biomass unless historic stand management (biomass removal due to thinning) is taken into account. The full management history of each stand is rarely known, and must be somehow estimated. One possibility is to build statistical thinning models based on data in a National Forest Inventory, which could then be integrated into the ecosystem models. If the harvesting model is constructed using only variables that are also used within the ecosystem model, then the management impacts can be included in the ecosystem model for the entire simulated life of the stand. In the case of most flux dynamics models, this precludes the use of the tree-level data that harvesting models have traditionally relied on. In this article, we develop a novel means to interrogate a subset of the Austrian National Forest Inventory based on deriving probability density functions for particular combinations of stand and site variables. We determine the parameters of a probabilistic model to estimate historic patterns of timber removals and validate it against inventory estimates. Our procedure can establish supportable estimates of historic management regimes suitable as input data for subsequent modelling of national-scale forest carbon stocks, sources and sinks.     

Determining sample size in national forest inventories by cost-plus-loss analysis: an exploratory case study

National forest inventories provide information for strategic decisions in a large number of countries. In general, they cover a wide range of variables, from timber-related features to biodiversity and carbon sequestration. Often, it is difficult to decide the exact scope and design of this type of inventory; especially, it is difficult to decide the appropriate sample size. In planning inventories, trade-offs between cost and precision for core variables frequently are made; however, this approach does not fully acknowledge the fact that data typically are collected to form the basis for decisions. In theory, cost-plus-loss analysis provides a more holistic approach to inventory planning, since both inventory costs and losses due to information deficiencies in the decision-making processes are considered. However, whilst it is normally straightforward to determine cost functions, loss functions are difficult to establish; an important reason is that the linkages between data and decisions must be clearly understood. In this study, we explored the possibilities for using cost-plus-loss analysis in connection with determining the appropriate sample size of a national forest inventory. We used Sweden as a case and restricted the analysis to consider the use of data for determining sustainable harvesting levels. The results indicated that the number of plot clusters in Sweden should be in the order of 1,300–2,400 annually, whereas it is currently about 1,400. However, our main objective of the study was not to determine an exact answer for the case of Sweden, but rather to suggest pathways for how cost-plus-loss analysis could be used to support decisions related to determining the appropriate sample size of national forest inventories.