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Open AccessArticle

Improved Cellular Automaton for Stand Delineation

Key Laboratory of Sustainable Forest Ecosystem Management-Ministry of Education, School of Forestry, Northeast Forestry University, Harbin 150040, China
School of Forest Sciences, Joensuu Campus, Faculty of Science and Forestry, University of Eastern Finland, P.O. Box 111, 80101 Joensuu, Finland
Author to whom correspondence should be addressed.
Forests 2020, 11(1), 37;
Received: 22 November 2019 / Revised: 11 December 2019 / Accepted: 21 December 2019 / Published: 25 December 2019
(This article belongs to the Section Forest Inventory, Quantitative Methods and Remote Sensing)
Airborne laser scanning (ALS) is becoming common in forest inventories. The data obtained by laser scanning contain the locations of the echoes of laser pulses. If these data are used in forest management, they need to be segmented into spatially continuous stands that are homogeneous in terms of stand attributes. Prior to segmentation, the laser pulse data can be processed into canopy height model, which shows the distance of canopy surface from the ground. This study used a cellular automaton with a canopy height model for the delineation of tree stands, considering three criteria: homogeneity of the stand in terms of growing stock attributes, stand area, and stand shape. A new method to consider stand shape in cellular automaton was presented. This method had a clear beneficial effect on the stand delineation result. Increasing weight of the shape criterion led to more roundish and less irregular stand shapes. Also, increasing weight of the stand area improved the shape of the stands. The cellular automaton led to average stand areas of 1–1.7 ha, depending on cell size and the parameters of the automaton. The cellular automaton explained 84.7–94.2% of the variation in maximum canopy height when 5 m × 5 m cells were used. Cell sizes of 5–10 m were found to result in the best stand delineation results. View Full-Text
Keywords: self-organizing system; forest map; grid data; laser scanning; ALS inventory; segmentation self-organizing system; forest map; grid data; laser scanning; ALS inventory; segmentation
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Jia, W.; Sun, Y.; Pukkala, T.; Jin, X. Improved Cellular Automaton for Stand Delineation. Forests 2020, 11, 37.

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