Can Kohonen networks delineate forest stands?

ABSTRACT

Forest inventory based on airborne laser scanning produces data for small grid cells. Various segmentation methods are used to cluster the grid cells into spatially continuous larger areas that correspond to forest stands. This study examined the performance of self-organizing map (SOM) in the delineation of forest stands. The SOM is also called Kohonen map or Kohonen network, according to the developer of the method. The data consisted of stand attribute grids in 16-m resolution. Spatiality was integrated in the SOM by using the geographical coordinates as criteria in the clustering. The clusters created by the SOM were spatially distinct and formed continuous areas corresponding to traditional stands when geographical coordinates were given more weight in the clustering compared to other stand attributes such as fertility class and stand basal area. The delineation improved when the attributes of the created stands were used as the starting point of repeated SOM rounds, and removing small stands at the beginning of each round. When small within-stand variation in stand attributes was targeted, the SOM often produced irregular stands consisting of disconnected parts. These delineations could be fine-tuned by applying mode filtering to the grid of stand numbers generated by the SOM algorithm.

KEYWORDS:

• Self-organizing map
• automated stand delineation
• self-organizing system
• Kohonen network
• forest planning

Acknowledgements

No external funding was received.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The analyses were based on open access data. A pseudo-code of the SOM variant developed in this study is available in the appendix.