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Browsing by Subject "paikkatietoanalyysi"

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  • Mustola, Marjo (2021)
    The loss of forest biodiversity is a global issue. In Finland, there are many measures aiming at preserving the forest biodiversity, for example, protection of the forest habitats defined in the Forest Act and in the Nature Conservation Act; protection of threatened species; the nature management methods in commercially utilized forests and the forest certification. Information of forest resources is collected mainly by remote sensing methods, but also field inventories are carried out, especially when preservation of the areas of high biodiversity value needs to be verified and monitored. Metsäteho Oy has developed an automated method for delineating harvested stands based on harvester location data. The method can be a beneficial tool for providing up-to-date forest resource information. The objective of this study was to research, if harvester location data can be utilized in verifying preservation of areas of high biodiversity value, and in recognizing potential areas, and how that could be implemented in practice. The harvester data used in this study was collected from geographically diverse areas in Finland, and the data contains stem-wise coordinates of harvester while cutting the tree. The delineations of operated areas were generated from harvester location data using the automated method developed by Metsäteho Oy. After the stand delineation was generated, the automated method was utilized in recognizing non-harvested areas left inside and between the harvested stands. Both harvested stands and non-harvested areas were compared to open forest data (including the data of the protected habitats according to the Forest Act and METSO Programme; other protected areas; habitats of threatened species; the Topographic database) using spatial data analysis. The aim was to investigate, if the known areas of high biodiversity value were delimited outside of the harvested stands or if they were left non-harvested within the harvested stand area. In addition, the aim was to research why the automatically recognized non-harvested areas were left without harvesting, and if the non-harvested areas could be potential areas of high biodiversity value. After the spatial data analysis was completed, also field surveys were carried out. Based on the spatial data analysis and the field surveys, the known areas of high biodiversity value were mainly delimited out-side of the harvested stands. The cases in which they were left without harvesting within the harvested stands, were possible to recognize through spatial data analysis. According to the spatial data analysis, part of the automatically recognized non-harvested areas were potential areas of high biodiversity value. Recognized non-harvested areas can be utilized also in recognizing retention tree groups with certain limitations. According to the results, the recognition method for high biodiversity value areas, based on harvester location data, can be utilized when verifying preservation of the high biodiversity value areas, and also other areas that are recorded in spatial data. Based on the observations of this study, it is possible to develop an automated recognition method for high biodiversity value areas, when spatial analysis of datasets in vector format is automated. The positioning accuracy of harvester and the automated method for delineating harvested stands are still causing some challenges when interpreting the results. Also, timeliness and accuracy of the available data of high biodiversity value areas affect on the results of the automated method. To combine different data sources effectively, a data platform is needed in order to use the automated method fluently. The recognition method for high biodiversity value areas can be utilized, for example, when reporting the quality of harvesting work. In addition, the method can be utilized in targeting and minimizing the amount of field inventories when verifying new areas of high biodiversity value. The method enables collecting information and automated monitoring of how the nature management has been integrated into forest management operations in practice. That information contributes to the utilization of forests in economically and ecologically sustainable way.