Search

The development of allometric biomass models is important process in biomass estimation because the reliability of forest biomass and carbon estimations largely depends on the accuracy and precision of such models. National Forest Inventories (NFI) are detailed assessments of forest resources at national and regional levels that provide valuable data for forest biomass estimation. However, the lack of biomass allometric equations for each tree species in the NFI currently hampers the estimation of national-scale forest biomass. The main objective of this study was to develop allometric biomass regression equations for each tree species in the NFI of China based on limited biomass observations. These equations optimally grouped NFI and biomass observation species according to their phylogenetic relationships. Significant phylogenetic signals demonstrated phylogenetic conservation of the crown-to-stem biomass ratio. Based on phylogenetic relationships, we grouped and matched NFI and biomass observation species into 22 categories. Allometric biomass regression models were developed for each of these 22 species categories, and the models performed successfully (R2 ​= ​0.97, root mean square error (RMSE) ​= ​12.9 ​t·ha–1, relative RMSE ​= ​11.5%). Furthermore, we found that phylogeny-based models performed more effectively than wood density-based models. The results suggest that grouping species based on their phylogenetic relationships is a reliable approach for the development and selection of accurate allometric equations.

Abstract Background National and international institutions periodically demand information on forest indicators that are used for global reporting. Among other aspects, the carbon accumulated in the biomass of forest species must be reported. For this purpose, one of the main sources of data is the National Forest Inventory (NFI), which together with statistical empirical approaches and updating procedures can even allow annual estimates of the requested indicators. Methods Stand level biomass models, relating the dry weight of the biomass with the stand volume were developed for the five main pine species in the Iberian Peninsula (Pinus sylvestris, Pinus pinea, Pinus halepensis, Pinus nigra and Pinus pinaster). The dependence of the model on aridity and/or mean tree size was explored, as well as the importance of including the stand form factor to correct model bias. Furthermore, the capability of the models to estimate forest carbon stocks, updated for a given year, was also analysed. Results The strong relationship between stand dry weight biomass and stand volume was modulated by the mean tree size, although the effect varied among the five pine species. Site humidity, measured using the Martonne aridity index, increased the biomass for a given volume in the cases of Pinus sylvestris, Pinus halepensis and Pinus nigra. Models that consider both mean tree size and stand form factor were more accurate and less biased than those that do not. The models developed allow carbon stocks in the main Iberian Peninsula pine forests to be estimated at stand level with biases of less than 0.2 Mg∙ha− 1. Conclusions The results of this study reveal the importance of considering variables related with environmental conditions and stand structure when developing stand dry weight biomass models. The described methodology together with the models developed provide a precise tool that can be used for quantifying biomass and carbon stored in the Spanish pine forests in specific years when no field data are available.

Abstract Background The local pivotal method (LPM) utilizing auxiliary data in sample selection has recently been proposed as a sampling method for national forest inventories (NFIs). Its performance compared to simple random sampling (SRS) and LPM with geographical coordinates has produced promising results in simulation studies. In this simulation study we compared all these sampling methods to systematic sampling. The LPM samples were selected solely using the coordinates (LPMxy) or, in addition to that, auxiliary remote sensing-based forest variables (RS variables). We utilized field measurement data (NFI-field) and Multi-Source NFI (MS-NFI) maps as target data, and independent MS-NFI maps as auxiliary data. The designs were compared using relative efficiency (RE); a ratio of mean squared errors of the reference sampling design against the studied design. Applying a method in NFI also requires a proven estimator for the variance. Therefore, three different variance estimators were evaluated against the empirical variance of replications: 1) an estimator corresponding to SRS; 2) a Grafström-Schelin estimator repurposed for LPM; and 3) a Matérn estimator applied in the Finnish NFI for systematic sampling design. Results The LPMxy was nearly comparable with the systematic design for the most target variables. The REs of the LPM designs utilizing auxiliary data compared to the systematic design varied between 0.74–1.18, according to the studied target variable. The SRS estimator for variance was expectedly the most biased and conservative estimator. Similarly, the Grafström-Schelin estimator gave overestimates in the case of LPMxy. When the RS variables were utilized as auxiliary data, the Grafström-Schelin estimates tended to underestimate the empirical variance. In systematic sampling the Matérn and Grafström-Schelin estimators performed for practical purposes equally. Conclusions LPM optimized for a specific variable tended to be more efficient than systematic sampling, but all of the considered LPM designs were less efficient than the systematic sampling design for some target variables. The Grafström-Schelin estimator could be used as such with LPMxy or instead of the Matérn estimator in systematic sampling. Further studies of the variance estimators are needed if other auxiliary variables are to be used in LPM.

Abstract Background The increasing availability of remotely sensed data has recently challenged the traditional way of performing forest inventories, and induced an interest in model-based inference. Like traditional design-based inference, model-based inference allows for regional estimates of totals and means, but in addition for wall-to-wall mapping of forest characteristics. Recently Light Detection and Ranging (LiDAR)-based maps of forest attributes have been developed in many countries and been well received by users due to their accurate spatial representation of forest resources. However, the correspondence between such mapping and model-based inference is seldom appreciated. In this study we applied hierarchical model-based inference to produce aboveground biomass maps as well as maps of the corresponding prediction uncertainties with the same spatial resolution. Further, an estimator of mean biomass at regional level, and its uncertainty, was developed to demonstrate how mapping and regional level assessment can be combined within the framework of model-based inference. Results Through a new version of hierarchical model-based estimation, allowing models to be nonlinear, we accounted for uncertainties in both the individual tree-level biomass models and the models linking plot level biomass predictions with LiDAR metrics. In a 5005 km2 large study area in south-central Sweden the predicted aboveground biomass at the level of 18 m ×18 m map units was found to range between 9 and 447 Mg ·ha −1. The corresponding root mean square errors ranged between 10 and 162 Mg ·ha −1. For the entire study region, the mean aboveground biomass was 55 Mg ·ha −1 and the corresponding relative root mean square error 8%. At this level 75% of the mean square error was due to the uncertainty associated with tree-level models. Conclusions Through the proposed method it is possible to link mapping and estimation within the framework of model-based inference. Uncertainties in both tree-level biomass models and models linking plot level biomass with LiDAR data are accounted for, both for the uncertainty maps and the overall estimates. The development of hierarchical model-based inference to handle nonlinear models was an important prerequisite for the study.

Abstract Background Forests are an important component of the global carbon (C) cycle and can be net sources or sinks of CO2, thus mitigating or exacerbating the effects of anthropogenic greenhouse gas emissions. While forest productivity is often inferred from national-scale yield tables or from satellite products, forest C emissions resulting from dead organic matter decay are usually simulated, therefore it is important to ensure the accuracy and reliability of a model used to simulate organic matter decay at an appropriate scale. National Forest Inventories (NFIs) provide a record of carbon pools in ecosystem components, and these measurements are essential for evaluating rates and controls of C dynamics in forest ecosystems. In this study we combine the observations from the Swiss NFIs and machine learning techniques to quantify the decay rates of the standing snags and downed logs and identify the main controls of dead wood decay. Results We found that wood decay rate was affected by tree species, temperature, and precipitation. Dead wood originating from Fagus sylvatica decayed the fastest, with the residence times ranging from 27 to 54 years at the warmest and coldest Swiss sites, respectively. Hardwoods at wetter sites tended to decompose faster compared to hardwoods at drier sites, with residence times 45–92 and 62–95 years for the wetter and drier sites, respectively. Dead wood originating from softwood species had the longest residence times ranging from 58 to 191 years at wetter sites and from 78 to 286 years at drier sites. Conclusions This study illustrates how long-term dead wood observations collected and remeasured during several NFI campaigns can be used to estimate dead wood decay parameters, as well as gain understanding about controls of dead wood dynamics. The wood decay parameters quantified in this study can be used in carbon budget models to simulate the decay dynamics of dead wood, however more measurements (e.g. of soil C dynamics at the same plots) are needed to estimate what fraction of dead wood is converted to CO2, and what fraction is incorporated into soil.

Abstract Background Forest management aims at obtaining a sustainable production of wood to be harvested to generate products or energy. However, the quantitative influence of forest management and of removals by harvest on biomass stocks has rarely been analysed on a large scale based on measurements. Two hypotheses prevail: management induces a reduction of wood stocks due to cuttings, versus no impact because of increased growth of the remaining trees. Using data collected for 2840 permanent plots across Romania from the National Forest Inventory representing ~ 2.5 Mha, we have tested to what extent different management types and treatments can influence the biomass stock and productivity of beech forests, and attempt to quantify these effects both on the short and long terms. Three main types of beech forest management are implemented in Romania with specific objectives: intensive wood production in production forests, protection of ecosystem services (e.g. watersheds, avalanche protection) in protection forests, and protection of the forest and its biodiversity in protected forests. Production forests encompass two treatments differing according to the stand regeneration method: the age class rotation management and the group shelterwood management. Results We show that forest management had little influence on the biomass stocks at a given stand age. The highest stocks at stand age 100 were observed in production forests (the most intensively managed forests). Consequences of early cuttings were very short-termed because the increase in tree growth rapidly compensated for tree cuttings. The cumulated biomass of production forests exceeded that of protected and protection forests. Regarding the treatment, the group shelterwood forests had a markedly higher production over a full rotation period. The total amount of deadwood was primarily driven by the amount of standing deadwood, and no management effect was detected. Conclusions Given the relatively low-intensity management in Romania, forest management had no negative impact on wood stocks in beech forests biomass stocks at large scale. Stand productivity was very similar among management types or treatments. However cumulated biomass in production forests was higher than in protection or protected forests, and differed markedly according to treatments with a higher cumulated biomass in shelterwood forests.

Abstract Background Over the last decades, many forest simulators have been developed for the forests of individual European countries. The underlying growth models are usually based on national datasets of varying size, obtained from National Forest Inventories or from long-term research plots. Many of these models include country- and location-specific predictors, such as site quality indices that may aggregate climate, soil properties and topography effects. Consequently, it is not sensible to compare such models among countries, and it is often impossible to apply models outside the region or country they were developed for. However, there is a clear need for more generically applicable but still locally accurate and climate sensitive simulators at the European scale, which requires the development of models that are applicable across the European continent. The purpose of this study is to develop tree diameter increment models that are applicable at the European scale, but still locally accurate. We compiled and used a dataset of diameter increment observations of over 2.3 million trees from 10 National Forest Inventories in Europe and a set of 99 potential explanatory variables covering forest structure, weather, climate, soil and nutrient deposition. Results Diameter increment models are presented for 20 species/species groups. Selection of explanatory variables was done using a combination of forward and backward selection methods. The explained variance ranged from 10% to 53% depending on the species. Variables related to forest structure (basal area of the stand and relative size of the tree) contributed most to the explained variance, but environmental variables were important to account for spatial patterns. The type of environmental variables included differed greatly among species. Conclusions The presented diameter increment models are the first of their kind that are applicable at the European scale. This is an important step towards the development of a new generation of forest development simulators that can be applied at the European scale, but that are sensitive to variations in growing conditions and applicable to a wider range of management systems than before. This allows European scale but detailed analyses concerning topics like CO2 sequestration, wood mobilisation, long term impact of management, etc.

Background Information on above-ground biomass (AGB) is important for managing forest resource use at local levels, land management planning at regional levels, and carbon emissions reporting at national and international levels. In many tropical developing countries, this information may be unreliable or at a scale too coarse for use at local levels. There is a vital need to provide estimates of AGB with quantifiable uncertainty that can facilitate land use management and policy development improvements. Model-based methods provide an efficient framework to estimate AGB. Methods Using National Forest Inventory (NFI) data for a ~1,000,000 ha study area in the miombo ecoregion, Zambia, we estimated AGB using predicted canopy cover, environmental data, disturbance data, and Landsat 8 OLI satellite imagery. We assessed different combinations of these datasets using three models, a semiparametric generalized additive model (GAM) and two nonlinear models (sigmoidal and exponential), employing a genetic algorithm for variable selection that minimized root mean square prediction error (RMSPE), calculated through cross-validation. We compared model fit statistics to a null model as a baseline estimation method. Using bootstrap resampling methods, we calculated 95 % confidence intervals for each model and compared results to a simple estimate of mean AGB from the NFI ground plot data. Results Canopy cover, soil moisture, and vegetation indices were consistently selected as predictor variables. The sigmoidal model and the GAM performed similarly; for both models the RMSPE was ~36.8 tonnes per hectare (i.e., 57 % of the mean). However, the sigmoidal model was approximately 30 % more efficient than the GAM, assessed using bootstrapped variance estimates relative to a null model. After selecting the sigmoidal model, we estimated total AGB for the study area at 64,526,209 tonnes (+/− 477,730), with a confidence interval 20 times more precise than a simple design-based estimate. Conclusions Our findings demonstrate that NFI data may be combined with freely available satellite imagery and soils data to estimate total AGB with quantifiable uncertainty, while also providing spatially explicit AGB maps useful for management, planning, and reporting purposes.