Browsing by Subject "SIMO"

As an internationally important topic for forestry, climate change has long been a topic of concern, as well as the ability of the forests to accumulate carbon. In addition, in Finland, these values have essentially been associated with the economic, cultural and social value of forests. In view of these values, it is important to be able to maintain forest resources at a sustainable level for all the different sectors. As far as sustainability is concerned, knowing the current state of forests is significant. This information is collected through the inventory of forests, and today it is mainly based on different remote sensing methods. In order to support reliable decisionmaking, forest information needs to be up-to-date and accurate. The aim of the thesis was to examine the accuracy of different tree attribute estimates and compare them between themselves and to investigate the accuracy of growth models in producing the estimates. In addition, the aim was to evaluate the effects of the accuracy of the remote sensing estimates on the determination of the timing harvests. The research area was located in boreal coniferous forest zone in Southern Finland, Evo (61.19˚N, 25.11˚E). The area comprised a 5 km x 5 km area, comprising about 2000 hectares of forest treated in different ways. Field measurements, aerial imagery, and airborne laser scanning data were generated using estimates for forest inventory attributes based on three different statistical features derived from the remote sensing data in the preparation of estimates. The forest inventory attributes were volume V, basal area-weighted mean diameter Dg, basal area-weighted mean height, number of the stems per hectare, and basal area G. In the prediction of the forest inventory attributes a non-parametric k-NN method was used, and random forest -algorithm was used in the selection of the nearest neighbors. Growth modeling was carried out using SIMO software. It can be seen from the results that, as a rule, more accurate results are obtained by producing airborne lasers canning estimates than by aerial imagery estimates. In addition, prediction precisions were better in coniferous trees than in deciduous trees. In forest inventory attribute estimates, especially the basal area G and volume V are generally underestimated, which is likely to delay the scheduled timing of harvests. Updating remote sensing estimates with growth models would appear to yield more biased estimates compared to the new remote sensing inventory.

This study investigates the effect of the data input on the forest management plan. The objective was to determine the differences between a forest plan where simulation units were either traditional stand compartments, or alternatively subcompartments delineated around measured sample plots. The simulations were compared with respect to the growth of the compartments as well as timing, income and yield from the first harvet operation suggested. The data was collected from a forest area of 72 hectares in Juuka, Eastern Finland. It consisted of 682 sample plots placed in a 30 m x 30 m grid. Independently of the sample plots, the area was divided into compartments as in normal compartmentwise planning, with the exception that no stand data was collected. Instead, the compartmentwise data was calculated from the systematic sample plot inventory. Three simulations were carried out with a planning package SIMO for a period of ten years and using one-year time step. Sim(I) presented the traditional compartmentwise planning, where the variables on compartment level were aggregated from sample plot data in the beginning of the simulation, and then used as simulation units. The other two simulations used a mosaic of sample-plot-based subcompartments as the simulation unit, and aggregate compartmentwise values were only used to determine the harvest decisions and for the comparison of the simulations. Of these mosaic setups, sim(II) was used to evaluate differences in growth rate and harvest yield to sim(I). In this simulation, the operations were adopted from sim(I) and applied simultaneously for each subcompartment of the respective compartment. The third simulation, mosaic setup sim(III) used the same simulation data as sim(II), but harvested the compartments according to the subcompartmentwise values, although using the compartments as harvest units to enable direct comparison in operation timing. Only compartments where harvests were expected during the simulation period were studied further, resulting in 14 compartments in the study. The simulations resulted in a greater growth rate estimate for sim(I). The difference between sim(I) and sim(II) varied among the compartments from 0.1 m3ha-1a-1 up to 2.0 m3ha-1a-1. The timing differences of harvest operations were 0-3 years. Income estimates were 5-10 % greater in the mosaic simulations, as well as especially the yield estimate of logwood. The differences in pulpwood estimations were more moderate, except on final cuts where mosaic simulations expected a better yield in minor species pulpwood than sim(I) which neglected these almost totally. The most effective single factor behind the differences in the simulations seemed to be the variation of site class within the compartment. The amount of compartments in the study was, however, too small and the variation between the compartment results too large to allow the application of the results elsewhere. Based on this study, the choice of data unit has an effect on the forest plan. Especially the effects of variation in site class are likely to be taken better into account if the spatial information of stand characteristics is maintained in the planning calculations. Still, small units are not necessarily better in describing the forest development, as they may result in biased estimations in the growth models.