Influence of size and shape of forest inventory units on the layout of harvest blocks in numerical forest planning

View/ Open
Issue date
2019Suggested citation
Pascual, Adrián;
Pukkala, Timo;
Miguel Magaña, Sergio de;
Pesonen, Annukka;
Packalen, Petteri;
.
(2019)
.
Influence of size and shape of forest inventory units on the layout of harvest blocks in numerical forest planning.
European Journal of Forest Research, 2019, vol. 138, p. 111-123.
https://doi.org/10.1007/s10342-018-1157-5.
Metadata
Show full item recordAbstract
The purpose of this study was to assess the effect of using alternative types of forest inventory units (FIUs) in multi-objective
forest planning. The research was carried out in a Mediterranean forest area in central Spain. The study area was divided,
alternatively, into pixels (square cells) and segments of two different sizes (small and large), which represented the tested
FIU types. Airborne laser scanning data (ALS) and field sample plots were combined using the area-based approach to
estimate forest attributes for each FIU. Dynamic treatment units were created using cellular automaton optimization aiming
at maximizing timber production during a 60-year plan with periodical even-flow cuttings both with and without the aim of
creating aggregated harvest blocks. The hypothesis was that the use of segments would enhance the clustering of harvests,
as compared to cells, and provide dynamic treatment units more suitable for forestry practice. The results showed that
segment-based planning created compact harvest blocks even without the use of spatial objective variables in optimization.
The spatial layout of the solution for large segments was the most efficient in the absence of spatial objective variables. The
FIU type that performed the best in maximizing timber production was the small segments. For the three tested FIU types,
the inclusion of spatial objective variables further improved the clustering of harvests, especially during the latter half of the
60-year planning period. Segmentation acted as a first-phase clustering that made spatial optimization easier and faster. In
the case of square cells, the clustering of harvests was greatly improved by the inclusion of spatial goals. The forest planning
system and the spatial optimization method proposed in this study maximize the utility of fine-grained ALS data.
Is part of
European Journal of Forest Research, 2019, vol. 138, p. 111-123European research projects
The following license files are associated with this item: