Current issue: 54(2)
Simulation and modeling have become more common in forest biomass studies. Dynamic simulation has been used to study the supply chain of forest biomass with numerous different models. A robust predictive multi-year model requires biomass availability data, where annual variation is included spatially and temporally. This can be done by using data from enterprises, but in some cases relevant data is not accessible. Another option is to use forest inventory data to estimate biomass availability, but this data must be processed in the correct form to be utilized in the model. This study developed a method for preparing forest inventory data for a multi-year simulation supply model using the theoretical availability of feedstock. Methods for estimating quality changes during roadside storage are also presented, including a possible parameter estimation to decrease the amount of data needed. The methods were tested case by case using the inventory database “Biomass Atlas” and weather data from a weather station in Mikkeli, Finland. The data processing method for biomass allocation produced a reasonable quantity of stands and feedstock, having a realistic annual supply with variation for the demand point. The results of the study indicate that it is possible to estimate moisture content changes using weather data. The estimations decreased the accuracy of the model and, therefore, estimations should be kept minimal. The presented data preparation method can generate a supply of forest biomass for the simulation model, but the validity of the data must be ensured for correct model behavior.
This study examined the relationships between forest management planning units and patches formed by forest habitat components. The test area used was a part of Koli National Park in North Karelia, eastern Finland. Forest management planning units (i.e. forest compartments) were defined by using a traditional method of Finnish forestry which applies aerial photographs and compartment-wise field inventory. Patches of forest habitat components were divided according to subjective rules by using a chosen set of variables depicting the edaphic features and vegetation of a forest habitat. The spatial distribution of the habitat components was estimated with the kriging-interpolation based on systematically located sample plots. The comparisons of the two patch mosaics were made by using the standard tools of GIS. The results of the study show that forest compartment division does not correlate very strongly with the forest habitat pattern. On average, the mean patch size of the forest habitat components is greater and the number of these patches lower compared to forest compartment division. However, if the forest habitat component distribution had been considered, the number of the forest compartments would have at least doubled after intersection.