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

Abstract

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.

Using ALS raster data in forest planning

“…From the interpretation point of view, the ideal size of the interpretation unit is the same as the area of the field plots that are used in area-based interpretation (Pascual et al 2018). Therefore, stand attributes interpreted for grid cells may be regarded as better estimates than attributes interpreted for larger areas.…”

“…An obvious solution to the first problem is to aggregate the cells into homogeneous segments before planning calculations (Mozgeris 2009;Dechesne et al 2017;Pascual et al 2018). The second problem can be alleviated by using spatial optimization to aggregate treatments (Heinonen et al 2018).…”

“…The second problem can be alleviated by using spatial optimization to aggregate treatments (Heinonen et al 2018). Pre-planning segmentation is also a partial solution to the second problem (Pascual et al 2018). However, if the segments produced before planning calculations (i.e., before simulation and optimization) are small, it is possible to increase the aggregation of cuttings and other treatments by using spatial optimization.…”

Improved Cellular Automaton for Stand Delineation

“…It was assumed that large cell size leads to the mixed cell problem, where narrow artefact stands are delineated at stand borders [9]. However, the results showed that the mixed stand problem was not serious, most probably because the shape parameter prevented the creation of long and narrow stands.…”

“…These methods are often called segmentation methods, since the created areas do not necessarily correspond to traditional stand compartments. They might be too small for the implementation of forest management actions, which means that the segments must be further aggregated to obtain large enough continuous treatment blocks [9,10].…”