Sammanfattning

This thesis presents planning approaches adapted for high spatial resolution data from remote sensing and evaluate whether such approaches can enhance the provision of ecosystem services from forests. The presented methods are compared with conventional, stand-level methods. The main focus lies on the planning concept of dynamic treatment units (DTU), where treatments in small units for modelling ecosystem processes and forest management are clustered spatiotemporally to form treatment units realistic in practical forestry. The methodological foundation of the thesis is mainly airborne laser scanning data (raster cells 12.5x12.5 m2), different optimization methods and the forest decision support system Heureka. Paper I demonstrates a mixed-integer programming model for DTU planning, and the results highlight the economic advances of clustering harvests. Paper II and III presents an addition to a DTU heuristic from the literature and further evaluates its performance. Results show that direct modelling of fixed costs for harvest operations can improve plans and that DTU planning enhances the economic outcome of forestry. The higher spatial resolution of data in the DTU approach enables the planning model to assign management with higher precision than if stand-based planning is applied. Paper IV evaluates whether this phenomenon is also valid for ecological values. Here, an approach adapted for cell-level data is compared to a schematic approach, dealing with stand-level data, for the purpose of allocating retention patches. The evaluation of economic and ecological values indicate that high spatial resolution data and an adapted planning approach increased the ecological values, while differences in economy were small. In conclusion, the studies in this thesis demonstrate how forest planning can utilize high spatial resolution data from remote sensing, and the results suggest that there is a potential to increase the overall provision of ecosystem services if such methods are applied.

Nyckelord

cellular automata; forest decision support systems; forest ecosystem services; heuristics; mixed-integer programming; remote sensing; retention forestry; spatial optimization

Publicerad i

Acta Universitatis Agriculturae Sueciae
2023, nummer: 2023:6
ISBN: 978-91-8046-064-4, eISBN: 978-91-8046-065-1
Utgivare: Swedish University of Agricultural Sciences

SLU författare

• Wilhelmsson, Pär

  • Institutionen för skoglig resurshushållning, Sveriges lantbruksuniversitet
    

UKÄ forskningsämne

Skogsvetenskap


Publikationens identifierare

DOI: https://doi.org/10.54612/a.4h25q0pofl

Permanent länk till denna sida (URI)

https://res.slu.se/id/publ/120330