Göran Ståhl

Functions for below-ground biomass of Pinus sylvestris, Picea abies, Betula pendula and Betula pubescens in Sweden

Abstract Accurate estimates of below-ground biomass of trees are important when quantifying the amount of carbon sequestered in forests. Allometric single-tree below-ground biomass functions were developed for Pinus sylvestris, Picea abies and Betula pendula and Betula pubescens in Sweden. The idea was to calibrate an existing comprehensive data set of about 600 trees that only covered the stump and coarse roots against a new data set that covered roots down to 2 mm diameter. The new data set consisted of about 80 trees acquired using the same design as for the existing set, but complemented with a detailed inventory of the fine root fractions remaining in the ground. Checks were made to determine whether the density properties of the two data sets were comparable and it was concluded that they were. This was a prerequisite for calibrating the older data against the new information and further for merging the two data sets. The merged data set was used to derive regression functions for below-ground biomass. For all functions the adjusted R 2 values were always higher than 0.95 and the root mean square errors were always lower than 36% for P. sylvestris and P. abies. Below-ground biomass predicted with the new functions was approximately 11% higher than the values obtained using the existing biomass functions.

Remote sensing, field survey, and long-term forecasting: an efficient combination for local assessments of forest fuels.

Abstract This article describes and evaluates a method for assessing local biofuel potentials. Such assessments are important, for example, in connection with the establishment of heating plants for local communities. A sparse grid of field sample plots from an existing national forest inventory (NFI) is used as reference data for satellite image based estimates of forest condition. The data thus obtained will be in a format that makes them readily available for existing forestry scenario models, in this case the Swedish Hugin system. Thus, forecasts of future harvesting levels and the corresponding amounts of forest fuels—mainly from branches and tops—can be derived in a straightforward manner. The proposed method was evaluated in two test areas in northern Sweden, the communes of Vindeln and Alvsbyn. Besides a base-line scenario for the forecasts, a scenario with geographical restrictions as to the extraction of forest fuels was tested. With a maximum transportation distance of 300 m to road, the available forest fuel potential was shown to decrease by more than 50%. With the proposed method, this kind of geographical restriction is easily implemented.

A framework for evaluating data acquisition strategies for analyses of sustainable forestry at national level

Abstract Analysing sustainable forestry at a national level involves many stakeholder groups and multiple resources; thus, analyses tend to be complicated. To evaluate the consequences of different policy options, scenario analyses often are carried out to assess effects in terms of what goods and services can be obtained from forests. This article concerns the provision of input data to such studies. Owing to the complexity of the analyses and the rapid development in the area of forest inventory methodology it is not straightforward to decide how input data should be acquired. This article provides a framework for how analyses of this type can be addressed as well as a case example to illustrate how different data acquisition methods may be evaluated. The framework comprises two steps: the first is to identify the demand of data connected to the resources included in the analysis; the second step is characterizing the quality of inventory data provided by the data acquisition strategy. As an example, three data acquisition strategies were evaluated; none of them was found to be optimal from all points of views and thus the study concludes with a presentation of an integrated method for data acquisition that is based on data from sampled landscape patches.

Assessing uncertainty: Sample size trade-offs in the development and application of carbon stock models

Many parties to the United Nation’s Framework Convention on Climate Change (UNFCCC) base their reporting of change in Land Use, Land-Use Change and Forestry (LULUCF) sector carbon pools on national forest inventories. A strong feature of sample-based inventories is that very detailed measurements can be made at the level of plots. Uncertainty regarding the results stems primarily from the fact that only a sample, and not the entire population, is measured. However, tree biomass on sample plots is not directly measured but rather estimated using regression models based on allometric features such as tree diameter and height. Estimators of model parameters are random variables that exhibit different values depending on which sample is used for estimating model parameters. Although sampling error is strongly influenced by the sample size when the model is applied, modeling error is strongly influenced by the sample size when the model is under development. Thus, there is a trade-off between which sample sizes to use when applying and developing models. This trade-off has not been studied before and is of specific interest for countries developing new national forest inventories and biomass models in the REDD context. This study considers a specific sample design and population. This fact should be considered when extrapolating results to other locations and populations.