Using ALS raster data in forest planning

Abstract

Raster type of forest inventory data with site and growing stock variables interpreted for small square-shaped grid cells are increasingly available for forest planning. In Finland, there are two sources of this type of lattice data: the multisource national forest inventory and the inventory that is based on airborne laser scanning (ALS). In both cases, stand variables are interpreted for 16 m × 16 m cells. Both data sources cover all private forests of Finland and are freely available for forest planning. This study analyzed different ways to use the ALS raster data in forest planning. The analyses were conducted for a grid of 375 × 375 cells (140,625 cells, of which 97,893 were productive forest). The basic alternatives were to use the cells as calculation units throughout the planning process, or aggregate the cells into segments before planning calculations. The use of cells made it necessary to use spatial optimization to aggregate cuttings and other treatments into blocks that were large enough for the practical implementation of the plan. In addition, allowing premature cuttings in a part of the cells was a prerequisite for compact treatment areas. The use of segments led to 5–9% higher growth predictions than calculations based on cells. In addition, the areas of the most common fertility classes were overestimated and the areas of rare site classes were underestimated when segments were used. The shape of the treatment blocks was more irregular in cell-based planning. Using cells as calculation units instead of segments led to 20 times longer computing time of the whole planning process than the use of segments when the number of grid cells was approximately 100,000.

Keywords

Raster data / ALS-based inventory / Spatial optimization / Segmentation / Simulated annealing / Cellular automata

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