Browsing by Subject "k-NN"

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  • Zhou, P.; Luukkanen, O.; Tokola, T.; Nieminen, Juhana (2008)
  • McInerney, Daniel; Barrett, Frank; McRoberts, Ronald E.; Tomppo, Erkki (2018)
  • Rintarunsala, Juhani (Helsingin yliopisto, 2018)
    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.