 Research
 Open Access
 Published:
Delineating forest stands from grid data
Forest Ecosystems volume 7, Article number: 13 (2020)
 The Correction to this article has been published in Forest Ecosystems 2020 7:29
Abstract
Background
Forest inventories are increasingly based on airborne laser scanning (ALS). In Finland, the results of these inventories are calculated for small grid cells, 16 m by 16 m in size. Use of grid data in forest planning results in the additional requirement of aggregating management prescriptions into large enough continuous treatment units. This can be done before the planning calculations, using various segmentation techniques, or during the planning calculations, using spatial optimization. Forestry practice usually prefers reasonably permanent segments created before planning. These segments are expected to be homogeneous in terms of site properties, growing stock characteristics and treatments. Recent research has developed methods for partitioning grids of ALS inventory results into segments that are homogeneous in terms of site and growing stock characteristics. The current study extended previous methods so that also the similarity of treatments was considered in the segmentation process. The study also proposed methods to deal with biases that are likely to appear in the results when grid data are aggregated into large segments.
Methods
The analyses were conducted for two datasets, one from southern and the other from northern Finland. Cellular automaton (CA) was used to aggregate the grid cells into segments using site characteristics with (1) growing stock attributes interpreted from ALS data, (2) predicted cutting prescriptions and (3) both stand attributes cutting prescriptions. The CA was optimized for each segmentation task. A method based on virtual stands was used to correct systematic errors in variable estimates calculated for segments.
Results
The segmentation was rather similar in all cases. The result is not surprising since treatment prescriptions depend on stand attributes. The use of virtual stands decreased biases in growth prediction and in the areas of different fertility classes.
Conclusions
Automated stand delineation was not sensitive to the type of variables that were used in the process. Virtual stands are an easy method to decrease systematic errors in calculations.
Background
The use of airborne laser scanning (ALS) is increasing rapidly in forestry (Hyyppä et al. 2008; Vauhkonen et al. 2014). Airborne laser scanning can be done from airplanes or unmanned aerial vehicles. The scanning produces a cloud of return pulses, which can be used to interpret forest variables (Hyyppä et al. 2008). In a normal case, the x, y and z coordinates of the return points are recorded, as well as the intensity of the return pulse (Roncat et al. 2014). It is also known whether the pulse is reflected from ground or vegetation. Differences in the z coordinates between ground pulses and highest vegetation pulses constitute a canopy height model, which can be used to estimate tree locations and heights, and delineate tree crowns (Koch et al. 2014).
The treebased interpretation approach uses the canopy height model to interpret tree locations and heights (Koch et al. 2014; Wing et al. 2018). Crown diameters can also be interpreted. Tree diameter can be predicted from tree height, crown diameter and other variables calculated from the pulse data. The pulse data can also be used to calculate variables that describe crown shape. These variables can be used to interpret tree species (Vauhkonen et al. 2009). The fact that small trees are often hided by the crowns of large trees has decreased the accuracy of forest inventories based on individual tree detection. However, new methods and algorithms are being developed which mitigate this problem (e.g., Kansanen et al. 2016).
The ALS data can also be interpreted with the areabased approach (Næsset 2014). In this method, the stand variables can be interpreted for any spatial units such as existing stand compartments, micro segments or grid cells. The method requires a set of field plots. The idea is to calculate the same variables from the ALS pulse data for the interpretation units and field plots. This makes it possible to find the most similar field plots for every interpretation unit. The measured stand variables of the most similar field plots are used to derive stand characteristics for the interpretation units. Also regression analysis can be used to predict the stand characteristics (Hyyppä et al. 2008). Some of the methods developed recently, such as distribution matching, cannot be easily classified to represent individual tree or areabased approach (Vauhkonen and Mehtätalo 2015; Kansanen et al. 2019).
In Finland, the areabased approach has been used to interpret forest variables for both existing stands and grid cells of 16 m by 16 m (http://www.metsaan.fi/paikkatietoaineistot). Both interpretation results cover all private forests of Finland and are freely available for forest planning. The use of standlevel interpretation results is straightforward and does not require new methodological development in forest planning (Pukkala 2019a).
The situation is different for the grid data. It is possible to treat the grid cells as calculation units in forest planning, and use spatial optimization to create continuous and large enough treatment units for the implementation of prescribed treatments (Heinonen and Pukkala 2007; Packalén et al. 2011; Pukkala 2019a). The spatial units created in this way are temporary, and the whole forest is never systematically divided into stand compartments. Due to their ephemeral nature, the treatment aggregations created by spatial optimization are often called as dynamic treatment units (Pukkala et al. 2014).
Dynamic treatment units may be a good approach when aiming at feasible treatment units and efficient utilization of forest resources (Heinonen et al. 2007). However, forestry practice often prefers fixed and reasonably permanent stand delineations. The prevailing ways to store, maintain and display forest data relies on stands (also called stand compartments or subcompartments). Stands are also the basic units to simulate stand development and implement treatments.
Traditionally, the stand boundaries are visually demarcated on aerial photographs (Mustonen et al. 2008; Olofsson and Holmgren 2014). The stand boundaries are subjective and they may also be obsolete if new compartment inventories do not update stand borders. Stand variables interpreted from ALS data for grid cells offer possibilities to automatize, systemize and even optimize the stand delineation. An additional reason for using the ALS results interpreted for grid cells, instead of using variables interpreted for existing stands, is the fact that the results of areabased inventory are the most accurate if the area of the interpretation unit is approximately the same as the area of the field plots (Pascual et al. 2018). The interpretation grid of 16 m by 16 m results in 256m^{2} cells, which correspond to circular field plots of 9 m radius.
The stand compartments should be homogeneous in terms of site and growing stock characteristics, future stand development and treatments (Mustonen et al. 2008). In Finland, the most relevant site characteristics are soil type (e.g., peatland vs. mineral soil) and fertility class. It is especially important to delineate peatland forests and mineral soil forests into separate stands since they differ significantly in terms of accessibility and treatments. Mineral soil stands can be harvested all year round whereas peatland forest are accessible only when the soil is frozen. Site fertility dictates the growth rate or trees and is therefore an important variable if the stand delineation aims at homogeneous future development within the stand.
In addition to the homogeneity of site, growing stock and treatments, there are also other requirements for stand delineation. Stand shape should be “simple”, and irregular stand shapes are usually avoided. Stands should not be very small, but very large stands are also problematic. Stands are often regarded as undividable units, but too large stands might lead to the need to divide them into two or more parts when aiming for instance at the same harvested volume in every year.
Previous studies have developed methods to create stands or smaller segments from grid data (Baatz and Schäpe 2000; Mustonen et al. 2008; Wulder et al. 2008; Koch et al. 2009; Wu et al. 2013; Olofsson and Holmgren 2014; Pukkala 2019a, b). The suggested methods have used either various segmentation methods (e.g., Mustonen et al. 2008) or cellular automata (e.g., Pukkala 2019b). The segmentation or stand delineation can be done based on the ALS pulse data (Mustonen et al. 2008) or using variables interpreted for cells from the pulse data (Pukkala 2019a).
Similarity of treatment prescriptions has not been used in previous studies on automated, preplanning stand delineation. Previous studies (e.g. Pukkala 2018) have shown that stand basal area and mean tree diameter correlate strongly with the financial maturity of the stand for cutting. The study of Kangas et al. (2011) suggests that errors in mean tree diameter may cause even greater inoptimality losses than errors in stand basal area. Mean diameter correlates with the relative value increment of trees, and therefore predicts the financial maturity of the trees for cutting.
Therefore, for the similarity of treatments within the stand it seems important to use stand basal area and mean tree diameter as the criteria of stand delineation, in addition to soil type and site fertility. However, also direct indicators for cutting maturity and cutting type have been developed. For example, Pukkala (2018) developed formulas that indicate the probability that cutting the stand immediately is the optimal decision. Other formulas were developed for the probability that partial cutting is optimal, thinning type and diameter at which thinning intensity is 50%. The predictions of these formulas can be used as additional criteria for stand delineation, to improve the results in terms of similarity of treatments.
The aim of the current study was to compare automated stand delineations based on (1) growing stock variables, (2) predicted treatments (3) or both, in addition to soil type and site fertility. The method used in stand delineation was cellular automaton (von Neuman 1966; Wolfram 2002) adapted from an earlier study (Pukkala 2019a). The data source was the ALSbased forest inventory where the stand variables are interpreted for grid cells. The data are available at http://www.metsaan.fi/paikkatietoaineistot. The study also proposed a new method to use grid data to deal with withinstand variation in planning calculations.
Materials
Site and growing stock variables interpreted for grid cells were used as the basis of stand delineation. The data are available at http://www.metsaan.fi/paikkatietoaineistot in packages of 375 × 375 cells. Since the size of the cell is 16 m × 16 m = 256 m^{2}, one package covers 3600 ha (375 × 375 × 256 m^{2} = 36,000,000 m^{2} = 3600 ha). Two randomly selected 3600ha grids were downloaded from the web site, one representing southern Finland (x and y coordinates of the lower left corner 326,000 m and 6,756,000 m, respectively, in the GRS Transverse Mercator system) and the other representing northern Finland (590,000 m, 7,326,000 m).
The following variables (layers) were used for stand delineation: land category, soil type, fertility class, mean diameter, mean height, stand basal area. The growing stock variables are available for the total growing stock (all species combined) and separately for pine, spruce and broadleaf species. Variables interpreted for the total growing stock were used for stand delineation in this study.
From the fertility class layer and speciesspecific layers for stand basal area and mean diameter it can be concluded that both areas represented typical managed Finnish forests where. Many stands were mixtures of two or more tree species. Finnish forests are usually managed according to the principles of evenaged forestry and younger stands are often plantations. Despite this, the stands could have large withinstand variation in tree diameter, due to the gradual advance regeneration of various species, especially spruce. In the southern case study forest, mesic sites were the most common, followed by subxeric sites. In the north, subxeric and xeric sites covered most of the area.
The land category layer indicates whether the cell represents productive forest, stunted forest, waste land, agricultural land, water, road, etc. This layer was used as a mask to filter out all cells that did not represent productive forest. The area of productive forest was 2977 ha in the southern case study area and 2140 ha in the northern area. Lakes, nonproductive peatlands etc., fragmented the productive forest clearly more in the north. All the other layers (two site variables and three growing stock variables) were used in stand delineation. The soil type layer indicates whether the cell belongs mineral soil or peatland, which is subdivided into spruce mire, pine bog and open bog (no trees). The fertility class indicates whether the cell is mesotrophic, herb rich, mesic, subxeric, xeric or barren heath site.
Methods
Treatment variables
The site and growing stock variables available for the grid cells were used to calculate additional variables for each cell. These variables describe the probability of cutting (pCut), probability of thinning (pThin), thinning type (tType) and diameter at which thinning intensity is 50% (d50). These additional variables were calculated with the models of Pukkala (2018). The first model predicts the probability that cutting the stand immediately is the optimal decision. The second model predicts the probability that partial cutting is optimal in case of cutting. The model for thinning type indicates the degree to which the optimal partial cutting is thinning from above. The fourth model gives the diameter class for which the thinning intensity should be 50%. In thinning from above, most harvested trees are larger than this diameter whereas the opposite is true in thinning from below.
The equations for the cutting variables are as follows (Pukkala 2018):
(with)
where D is basalareaweighted mean diameter of trees (cm), G is stand basal area (m^{2}·ha^{− 1}), G_{pulp} is the basal area of pulpwoodsized trees (dbh 8–18 cm) in m^{2}·ha^{− 1}, TS is temperature sum (degree days > 5 °C), R is discount rate (%), FS is indicator variable for fertile growing sites (mesic or better) and VT is indicator variable for subxeric site.
The above cutting variables depend on mean tree diameter, stand basal area, fertility class, temperature sum and discount rate. The temperature sum of the region was obtained from a temperature sum map (https://ilmatieteenlaitos.fi/terminenkasvukausi), and the discount rate was taken as 3%. All other predictors of the cutting models were obtained from the ALSbased inventory results interpreted for the grid cells.
Cellular automaton
The cellular automaton (CA) of Pukkala (2019a) was adapted for the purposes of the current study. The automaton was modified so that the four additional cutting variables could also be used in stand delineation. The used CA first divides the forest into squareshaped initial stands (3 ha in this study). Then, the “most suitable” stand number is given to each cell for several iterations. A cell gets the stand number of one of its adjacent stands, which are eight in number (stand number of the cell to the east, west, north and south, and four corner cells). Figure 1 illustrates the evolution of the stand delineation during a short CA run.
The stand number given to a cell depends on a score that is calculated to its eight neighbor cells:
where S_{ij} is the score of assigning the number of stand j to cell i, B_{ij} is the proportion of common border between cell i and stand j, A_{j} is the area of stand j, and D_{ij} is the difference in stand characteristics between cell i and stand j. Increasing stand area and common border increase the score, and increasing difference in stand variables between the cell and the stand decreases the score. If the weight of the difference in stand characteristics (a_{3}) is high, the stands tend to be small and irregular with little withinstand variation in stand variables whereas high weight for common border (a_{1}) results in compact and roundshaped stands.
The area of the final stands is controlled through weight a_{2} and function s_{2}, which indicate the effect of stand area on the score. If the purpose of stand delineation is to avoid both small and very large stands, a sigmoid type of function is preferred (Fig. 2). The same applies for function s_{1}, which describes the effect of the proportion of common border between cell i and stand j on the score of stand j (Fig. 2). The “proportion of common border” is the average of the weights of the 8 neighboring cells in such a way that the cell to east, west, north and south gets a weight equal to one and a corner cell gets a weight smaller than one.
The functions that describe the effect of the proportion of common border (s_{1}) and stand area (s_{2}) on the score were as follows:
The difference between stand characteristics in cell i and stand j was calculated from:
where w_{n} is the weight of stand variable n (Σw_{n} = 1), q_{ni} is the value of standardized variable n in cell i and q_{nj} is the mean value of the same variable in stand j.
In this study, the number of variables used to calculate the difference (N) was 5, 6 or 9 (two site variables and 3, 4 or 7 other variables). When the delineation was based on stand variables (case 1), the growing stock variables used to measure the similarity of the cell and an adjacent stand were mean tree diameter, mean tree height and stand basal area. When the delineation was based on forecasted cuttings these variables were replaced by probability of cutting, probability of thinning, thinning type and diameter at which thinning intensity is 50% (case 2). When the delineation was based on both growing stock variables and treatments (case 3), all the above variables were used to measure similarity. The two site variables used in all cases were soil type and fertility class. All variables included in the calculations were standardized to mean zero and standard deviation 1. This removed the effect of different units of variables.
Functions 6 and 7 have two parameters. In Fig. 2, the red curves show the effect of stand area and common border with the mean values of the allowed ranges of the parameters and the black curves show the effect with the most common values when the parameters were optimized for different stand delineation tasks. The dashed curve on the righthandside diagram describes the effect of average stand area on the objective function when the parameters of the cellular automaton were optimized (see Eq. 10 below).
A stand might disappear completely during a CA run. A stand may also become divided into two or more nonconnected parts. Since this was not a wanted outcome, nonconnected parts were given unique stand numbers after every five iterations. Therefore, the number of stands may decrease or increase during a CA run. The total number of iterations in one CA run was 17 (Pukkala 2019a).
Optimizing the cellular automaton
The performance of the CA depends on its parameters. The comparison of different stand delineation cases is the most objective if the parameters of the CA are optimized for each stand delineation case. The parameters affecting the performance of the CA were as follows (allowed ranges shown in square brackets):

Weight of the corner cell in the calculation of common border [0, 0.3]

Weight of common border in Eq. 5 (a_{1}) [0.4, 0.7]

Weight of stand area in Eq. 5 (a_{2}) [0.1, 0.4]

Weight of similarity of stand variables in Eq. 5 (a_{3}) [0.2, 0.5]

Weights of 5, 6 or 9 stand variables in Eq. 8

Two parameters of Eq. 6 that describes the effect of common border (s_{1})

Two parameters of Eq. 7 that describes the effect of stand area (s_{2})
When five or six variables were used to measure the similarity of stand variables in the cell and an adjacent stand, the weights of these variables (w_{n} in Eq. 8) were allowed to range from 0.1 to 0.6. When similarity was measured with nine variables, the allowed range of each weight was 0.05–0.5. The allowed ranges of parameters b_{1}, b_{2}, c_{1} and c_{2} (Eqs. 6 and 7) were [− 25, − 5], [0.4, 0.8], [− 3, − 1] and [1, 5], respectively.
The total number of CA parameters was either 13 (case 1), 14 (case 2) or 17 (case 3). Their optimal values were found by using particle swarm optimization in the same way as explained in detail in Pukkala (2019b). The baseline objective variable maximized in optimization was the average degree of variance explained by the stand delineation. The R2 of a certain variable was calculated from:
where SSE is the sum of squared deviations from stand mean and SST is the sum of squared deviations from the overall mean. However, this baseline objective variable was modified by the mean area of the delineated stands and the proportion of very small stands. The modified objective function was as follows:
where \( \overline{R2} \) is the average degree of explained variance of the 5, 6 or 9 stand variables used in delineation, \( \overline{A} \) is the average area of the obtained stands, s_{2} is a function that describes the effect of \( \overline{A} \) on objective function (Fig. 2, dashed line), and P_{small} is the proportion of stands smaller than 0.1 ha (i.e. smaller than 4 cells since one cell is 0.0256 ha). The weights of the criteria were set subjectively based on preliminary optimizations.
If the degree of explained variance (R2) is maximized without any constraints or other objectives, the CA produces delineations consisting of a high number of small and irregularly shaped stands. Since this kind of delineation would be hardly acceptable for forestry practice, parameter optimization was controlled by restricting the ranges of the parameters and adding the mean stand area and the proportion of small stands to the objective function (Eq. 10). The parameters of the CA were optimized separately for all stand delineation tasks, i.e., six times (cases 1, 2 and 3 for southern and northern Finland).
Results
Optimal CAparameters
The optimal weight of the corner cell was equal or close to its maximum allowed value (0.3) in most optimization cases (Table 1). Of the three criteria that determined the stand to which a cell was joined, similarity of stand variables had the smallest weight in northern Finland while stand area had the smallest weight in the south. The weight of the proportion of common border was often equal to its lowest allowed value (0.4) because this variable, which contributes to round and compact stand shape, contradicts with the criteria of the objective function that was maximized in parameter optimization (Eq. 10). In the north, much weight was given to stand area because it was otherwise difficult to reach large mean stand area in the fragmented forest landscape of the northern case study area. Productive forest of the northern area was more fragmented by lakes, wasteland (e.g. unproductive peatland) etc., which made it more difficult to create large and homogeneous stands in the north.
Of the stand variables used for segmentation, mean height had often the lowest allowed weight, or when the weight of mean height was high (case 3 in northern Finland), the weight of mean diameter was low. This result suggests that it is sufficient to use either mean diameter or mean height as a criterion in automated stand delineation.
Of the variables that describe cutting treatments, the thinning type variable had always the lowest allowed weight, suggesting that this variable might not be needed in stand delineation. This variable describes the degree to which a partial cutting is thinning from above. The study of Pukkala (2018) suggests that the optimal thinning is usually thinning from above, implying that there might be little variation in the optimal thinning type, making it unnecessary to use it in stand delineation.
In northern Finland, the weights of all cutting variables were equal to their smallest allowed values when the delineation was based on both growing stock cutting variables (case 3). The result suggests that cutting variables may not be important for stand delineation in northern Finland when maximizing Eq. 10. The parameters of the functions for the effect of stand area and proportion of common border (b_{1}, b_{2}, c_{1}, c_{2}) were also often on the limit of the allowed range (black curves in Fig. 2).
Degree of explained variation
The degree of explained variance of stand variables was high, 0.777–0.953, for soil type and reasonably high (0.695–0.893) also for fertility class (Table 2). When the stand delineation was based on growing stock variables (cases 1 and 3) the R2 of these variables was, with one exception, higher than in the case (case 2) where basal area, mean diameter and mean height were not used in delineation. The degree of explained variance in cutting variables was usually lower when these variables were not used in stand delineation. The R2 of a certain variable correlated positively but weakly with the weight of the variable (w_{n} in Eq. 8) in stand delineation (Fig. 3), suggesting that if the stands need to be homogeneous in terms of a particular variable, this variable should have a high weight in stand delineation.
In the southern case study area, the average R2 of all nine stand variables was the highest when both growing stock and cutting variables were used to measure the similarity of a cell and an adjacent stand (Table 2). In northern Finland, the highest overall R2 was obtained when the stand delineation did not use growing stock variables. However, a high degree of explained variance was associated with small average stand area and high proportion of small stands. If the quality of the delineation is measured with the objective function used in parameter optimization (Eq. 10), it can be concluded that the quality of delineation was the lowest when stand variables (basal area, mean diameter, mean height) were not used as criteria (case 2, lowest line in Table 2). The other cases, namely using only growing stock variables alone or with cutting variables resulted in rather similar objective function values.
Similarity of stand delineations
Visual inspection of the stand boundaries reveals that different CA runs resulted in fairly similar stand boundaries (Figs. 4 and 5). Especially cases 1 and 3, where the delineation employed growing stock variables with or without cutting variables, produced stands that were often practically identical (yellow and red borders in Figs. 4 and 5). Stand boundaries that were based on predicted cutting variables (light blue borders in Figs. 4 and 5) differed more from the other delineations but also in this case many boundaries coincided with the boundaries of the other delineations. In southern Finland, the average stand area was clearly larger and the proportion of small stands lower when only cutting variables were used. The opposite was true for northern Finland.
Complications
Previous research and theoretical reasoning (Pukkala 1990; Pukkala and Kolström 1991) have shown that increasing withinstand variation in stand density and tree size decreases the volume increment of the stand, compared to homogeneous stands with the same average stand basal area and mean diameter. When the growing stock is described only with the average stand balsa area and mean diameter, it can be expected that growth predictors will be overestimated, the most in the most heterogeneous stands.
Another consequence of condensing the cell level information to a single stand level record is the underestimation of the areas of rare fertility classes (Pukkala 2019a). In a normal case, the most common fertility class of the cells that constitute a stand is given to the whole stand. Underestimated areas of rare sites are consequences of the fact that these sites often occur as small spots within more common fertility classes. If large stands are pursued, these small spots are not demarcated as separate stands.
In the southern case study forest, the proportion of herb rich site was underestimated by 26%, and in the northern forest, the proportion of mesic site was underestimated by 9%, when compared to proportions calculated from cells (Table 3). The “missing area” of rare fertility classes was moved to the areas of more common adjacent fertility classes. The overestimate of predicted volume increment, calculated by the models of Pukkala et al. (2013), was 2.5% for southern Finland and 2.7% for northern Finland.
Remedies
The availability of cell level information makes it possible to calculate improved estimates for the areas of fertility classes, volume increment, and other variables. A method titled “virtual stands” was developed and tested in the current study. In this method, additional variables were calculated from the celllevel values and these additional variables were used to split the delineated stands into several stand records (virtual stands) that reflected withinstand variation in site productivity and growing stock variables. These additional variables were: proportions of different site fertility classes, standard deviation of stand basal area, standard deviation of mean diameter, and correlation coefficient between stand basal area and mean diameter.
The additional variables were used to create virtual stands as follows. First, the proportions of different fertility classes were used to divide the stand into several substands, their areas corresponding to the areas of fertility classes. Second, each substand was partitioned into five virtual stands that differed in basal area and mean diameter. It was assumed that the two growing stock variables were multinormally distributed. Basal area was deviated from its average value by one standard deviation and the mean diameter corresponding to the deviated basal area was calculated using the standard deviations and the correlation coefficient. Then, mean diameter was deviated in the same way and the stand basal area was calculated for the deviated mean diameters (Fig. 6). The mean tree heights corresponding to deviated mean diameters were calculated with a height model (Siipilehto 1999), which was scaled so that the model prediction was equal to average mean height of the cells when diameter was equal to average mean diameter of the cells.
Together with average values of mean diameter and stand basal area the deviated values resulted in five combinations of stand basal area and mean diameter. The proportions of these combinations were obtained from the probability density function of the multinormal distribution. The area of the substand was divided among the five virtual stands according to these proportions.
Table 3 shows that the use of virtual stands removed the biases from the proportions of different fertility classes. The bias of the growth prediction was also greatly reduced, especially in southern Finland. In northern Finland, the bias was reduced only about 60%. One reason for the remaining bias might be that stand basal area and mean tree diameter correlate with site fertility, which was ignored in the creation of virtual stands.
Discussion
ALS grid data are available from all private forests of Finland but this free data source is rarely used in forest planning now (2020). The reason is the lack of methodologies and experience to deal with datasets that differ from traditional data formats. The situation is similar also in other countries: more finegrained inventory data, compared to traditional standlevel inventories, are becoming available for forest planning with the increased use of ALS, but forest planners are not prepared to fully utilize the potentials of the new data formats.
The way in which ALS data were used in the current study is only one possibility among several alternatives. As an alternative approach, the ALS pulse data could be used more directly for stand delineation instead of using stand variables interpreted from pulse data for cells (Mustonen et al. 2008; Olofsson and Holmgren 2014). Variables that describe the pulse data at certain location (in a very small cell or polygon) can be aggregated into homogeneous areas, often called as micro segments (Pascual et al. 2018). Then, the stand variables are interpreted for these segments instead of grid cells. This approach would eliminate the problem of mixed cells, which are cases where different parts of the cell belong to different stands, i.e. the cell is located at stand boundary (Pascual et al. 2018).
The third approach is to interpret individual trees from the pulse data and use the trees in planning calculations (Wing et al. 2018), or calculate the stand variables required in panning from the predicted characteristics of individual trees (Hyyppä et al. 2008). All these alternative lines to use ALS data in forest planning require further research and new methodological development.
This study alleviated the problem of lacking experience in the use of ALS data by developing methodologies for employing ALSbased grid data in forest planning. A cellular automaton was used to aggregate grid cells into stands that correspond to traditional stand compartments. As an enhancement to previous research, also the forecasted cuttings were used as criteria in stand delineation. Another enhancement was a new method, based on virtual stands, to deal with withinstand variation in planning calculations.
When delineating stands from the grid data, much weight was given to the stand area criterion. The purpose was to have stands that are large enough to serve as treatment units in the implementation of the plan. Large stands make it unnecessary to use spatial optimization to aggregate treatments. Spatial optimization has been used for a long time in forest research (e.g., Borges and Hogansson 1999; Lu and Eriksson 2000; Bettinger et al. 2002; Jumppanen et al. 2003) but its use in practical forest planning in Finland is very limited. Therefore, the methods developed in this study for stand delineation are easily adaptable to the current forest management systems.
The other methodological development, namely the use of virtual stands, requires that a few more variables are imported and stored in the forest database (the new variables are standard deviation of basal area and mean diameter, correlation coefficient between basal area and mean diameter, and proportions of fertility classes). Another required modification is the generation of the virtual stands as the first step of calculation, which is not a difficult step.
Reasonably large stands and avoidance of small stands are usually wanted in forest planning because this eliminates the need to use spatial optimization in planning calculations. However, large stand size increases withinstand variation in stand variables and decreases the proportion of variance explained by the stand delineation (Mustonen et al. 2008). For example, in the study of Pukkala (2019a) the R2 of mean diameter was 0.814 (within stand standard deviation, s_{D}, 0.61 cm) when the average stand area was as small as 0.36 ha. The R2 decreased to 0.692 (s_{D}, 0.73 cm) when the average stand area was 1.27 ha. In the current study, the R2 of mean diameter was 0.682 (s_{D}, 0.43 cm) in the southern case study forest) with mean stand area of 3.51 ha, and 0.516 (s_{D}, 0.55 cm) with 4.36 ha mean stand area. Therefore, aiming at large stands in automated stand delineation increases the need to consider withinstand variation in planning calculations.
If withinstand variation is ignored, the use of large stands results in overestimated growth prediction and underestimated areas of rare site types (Pukkala 2019a). The same problem of course exists with the traditional, visually demarcated stands (Pukkala 1990). Sometimes, other errors may cancel the bias. For example, the used growth models may underestimate growth. However, using information on withinstand variation helps to remove one source of error from growth predictions. The other sources of error must be corrected using other methods.
Withinstand variation may also affect the optimal treatments. For example, Pukkala and Miina (2005) found that heterogeneous stands should be thinned at lower average basal area than homogeneous stands. Therefore, there might be a need to use the virtual stands also in the simulation of treatment alternatives for stands. This would improve the predictions of stand development, optimality of treatment prescriptions, and estimated timber removals and incomes.
The results of this study showed that automated stand delineation with a cellular automaton produces stand boundaries that closely follow the borders between mineral soil and peat land. The degree of explained variation is also quite high for fertility class, but a part of the stands might be mixtures of two or more fertility classes. Some very small stands remained in all delineations (10%–20% of the number of stands). This outcome is partly unavoidable since Finnish landscapes often have small isolated patches of productive forest within agricultural lands or as islands in lakes and nonproductive peatlands.
The results also showed that the stand delineation is not very sensitive to variables used in the cellular automaton. However, the optimal weights of the used variables suggest that stand basal area should be used, together with either mean height or mean diameter. For the avoidance of inoptimality losses due to nonoptimal prescriptions, mean diameter has been found to be even more important than stand basal area (Kangas et al. 2011). Of the cutting variables, the calculated probability of cutting seems to be the most important for good stand delineation. Thinning probability and thinning type variables are less important, most probably because these variables do not vary much. According to Pukkala (2018), the optimal cutting is almost always thinning, and the optimal type of thinning is almost always thinning from above. The thinning diameter (diameter at which thinning intensity is 50%) correlates strongly with mean diameter, and is therefore not necessary as a separate criterion for stand demarcation. Therefore, the use of the following variables can be recommended for automated stand delineation: soil type, fertility class, stand basal area, mean diameter, and probability of cutting. The calculated probability that immediate cutting is the optimal decision depends on mean diameter, stand basal area, fertility class and temperature sum, in addition to discount rate (Eq. 1). The variable can be interpreted to describe the interactions of these variables.
Parameter optimization is not necessary in the practical application of the cellular automaton. Based on the results reported in this article and a few additional CA runs, the following parameter values can be recommended: the weights of common border, stand area and similarity of stand variables (a_{1}, a_{2} and a_{3} in Eq. 5) may all be 0.333; the weights of soil type, fertility class, mean diameter, stand basal area and cutting probability (Eq. 8) should be 0.3, 0.2, 0.2, 0.2 and 0.1, respectively (the weights of the other variables should be zero); parameters b_{1}, b_{2}, c_{1} and c_{2} of Eqs. 6 and 7 should be − 25, 0.8, − 3 and 1, respectively; and the weight of the corner cell should be 0.3.
When the cellular automaton was optimized in this study, only two of the three criteria that were judged important for good stand delineation, namely small withinstand variation and large enough stand area, were included in the objective function that was maximized in particle swarm optimization. The third criterion, stand shape, was pursued by constraining some of the CA parameters within certain limits. For example, the maximum weight of corner cell was 0.3 and the minimum weight of common border (Eq. 1) was 0.4. The manner in which the common border affected the probability of joining a cell to a certain stand (Fig. 2) was also constrained. These constraints prevented the CA from creating irregular (e.g. very long and narrow) stands.
Future research may develop methods to include also stand shape in the objective function. One possibility would be to use the areaperimeter ratio or the form heterogeneity variables suggested by Baatz and Schäpe (2000). Other segmentation methods and spatial optimization should also be tested in automated stand delineation. In addition, different ways to deal with withinstand variation in forest planning should be developed and compared. Alternative ways to create virtual stands and use them in forest planning also deserves further research.
Conclusions
Stand delineation with a cellular automaton produces stand boundaries that closely follow the borders between mineral soil and peat land. The degree of explained variation is also quite high for fertility class and growing stock variables, but a part of the stands might be mixtures of two or more fertility classes. Stand delineation created by cellular automaton is not sensitive to the set of stand variables that are used in the delineation. However, use of stand basal area and mean diameter is recommendable, together with soil type and fertility class. Optimization of the parameters of cellular automaton is not necessary in the practical use of the method. Virtual stands, suggested in the current study, provide a means to reduce biases in results calculated for heterogeneous segments.
Availability of data and materials
The analysis is based on data publicly available at http://www.metsaan.fi/paikkatietoaineistot.
Change history
05 May 2020
In the original publication of this article Pukkala, 2020 the Fig. 6 is not correct, the correct figure is as below. The error in this Correction and low quality Fig. 4 and Fig. 5 have been updated in the original article and the caption of Fig. 5 was modified.
Abbreviations
 ALS:

Airborne laser scanning
 CA:

Cellular automaton
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Pukkala, T. Delineating forest stands from grid data. For. Ecosyst. 7, 13 (2020). https://doi.org/10.1186/s40663020002218
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Keywords
 Cellular automata
 Segmentation
 Stand demarcation
 Particle swarm optimization