Classification of multilayered forest development ...

Forestry

An International Journal of Forest Research

Forestry 2016; 89, 392–401, doi:10.1093/forestry/cpw010 Advance Access publication 22 February 2016

Classification of multilayered forest development classes from low-density national airborne lidar datasets Rube´n Valbuena*, Matti Maltamo and Petteri Packalen School of Forest Sciences, University of Eastern Finland, PO Box 111, Joensuu 80100, Finland *Corresponding author. Tel: +358 50 379 9743; E-mail: [email protected]

Compared with traditional inventory methods based on field plot sampling, airborne laser scanning (ALS) has the potential to assess forest structural properties with greater detail over space and time and at lower cost. Many national ALS survey programmes covering entire countries for topographic mapping are currently in progress and some have provided data that are in the public domain. Although the point density of these datasets is relatively low, there is an interest in developing methods that employ these types of data for categorizing different approaches to forest management. Using Finnish national ALS data with a point density of 0.91 pulses per square metre, we carried out a classification of 252 000 ha of boreal forests into silvicultural development classes (DC) used in practical forest management. Taking into account all eight DCs, the overall accuracy was 74.1 per cent and k ¼ 0.70. We conclude that the dataset is adequate for discriminating multilayered forests from even-aged ones. This result was compared with a method based on mathematical rules, which succeeded in discriminating multilayered stands with regeneration of shade-intolerant species without the need of field data for training. However, the low point density may hamper the detection of shade-tolerant understories in mature high forests with closed canopies. We, therefore, recommend the use of this supervised classification in the presence of shade-tolerant species. Keywords: airborne laser scanning, forest structure, stand development, supervised classification, support vector machine

Introduction Europe is currently undergoing a shift in forest management strategies from plantation monocultures towards systems that more closely resemble natural forests and, therefore, provide a wider range of ecosystem services (Nabuurs et al., 2007; Schu¨tz et al., 2012). Biodiversity is enhanced by increasing the complexity of forest stands (Oliver et al., 1998), which can be achieved by using a range of silvicultural management systems across landscapes (Redon et al., 2014). Remote sensing can assist in reliably evaluating this heterogeneity from fine scale to landscape level (Marvin et al., 2014; Vihervaara et al., 2015), and therefore, remote sensing could play a major role in monitoring biodiversity. Remote sensing can provide objective means for assessing and monitoring the effects of management practices on forest structure. In particular, airborne laser scanning (ALS) poses an opportunity for retrieving information on the structural properties of forests and evaluating their changes (Lefsky et al., 1999; Zimble et al., 2003; Hall et al., 2005; Maltamo et al., 2005; Kellner and Asner, 2009; Jaskierniak et al., 2011; Valbuena et al., 2013a; Waser et al., 2013). ALS allows assessment of these properties at greater detail over space and time, which enables the practical implementation of more complex forest management systems (Burger, 2009; Packale´n et al., 2011). Stand development classifications are employed for assisting decision making in forest management systems. Although the

exact definition of forest development classes (DCs) may differ slightly among countries, according to the forest ecosystems involved and the specific needs of their management practice, they are all based on the following chronosequence: stand initiation, stem exclusion, understorey reinitiation and old growth (ancient woodland) (Oliver and Larson, 1996). Forest disturbance produces complex structures, often leading to multilayered DCs (Franklin et al., 2002). In Finland, stand-wise rotation forestry systems based on clearcut and planting have dominated forest management planning over the second half of the twentieth century (Siiskonen, 2007). During the last decades, several pieces of scientific evidence have found that alternative silvicultural systems to clear-cutting can be effective at balancing timber production with the increasing social and environmental demands on forests in Finland (Laiho et al., 2011; Pukkala et al., 2011). These have led to amendments to the Finnish Forest Act (FFA) 1093/1996, which permit the inclusion of uneven-aged silvicultural practices (MMM – Ministry of Agriculture and Forestry, Finland, 2014). Under the new regulations, forest managers will in practice be allowed to apply regeneration fellings on small areas within a forest stand (MMM, 2014: 5a§) achieving natural regeneration in patches. As a result, within-stand heterogeneity will increase substantially, and traditional standwise inventory may not be sufficient for forest monitoring. The implementation of silvicultural systems involving patchy multilayered

# Institute of Chartered Foresters, 2016. All rights reserved. For Permissions, please e-mail: [email protected].

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Received 21 August 2015

Lidar Classification of multilayered forest development classes

opportunity to develop methods that can be directly replicated by any interested stakeholder. Although the point density of national datasets is relatively low, they are also demonstrably useful for forest inventory (Villikka et al., 2012; Gonza´lez-Ferreiro et al., 2014) and ecological applications (Vihervaara et al., 2015). There is an interest in developing objective methods for assisting forest management using this low-density ALS data, which would facilitate the practical implementation of the new forest law. The main purpose of this research was to develop methods for automated detection of multilayered DCs using NLS laser scanning datasets. A supervised classification of the remote sensing data into DCs was carried out, and its results evaluated with regards to the capacity of ALS to discriminate multilayered forests. Results were also compared with those obtained by a rule-based method, which made no use of field training plots (Valbuena et al., 2015). The relative benefits and opportunities of using either method were assessed.

Methods Study area and ALS data The research was conducted in a pilot study area in the region of North Karelia. The area surrounds the town of Joensuu and extends to Outokumpu in the west and Pyha¨selka¨ in the south (Figure 1). The total extent of the pilot area is 252 000 ha, 200 000 ha of which are covered by forest, the remaining (20 per cent) being lakes, urban and agricultural land. Laser data were acquired by Blom Kartta Oy (Finland) during May 2012 (deciduous leaf-off season and absence of snow cover) with an ALS60 system from Leica Geosystems (Switzerland). Flying at a height of 2300 m above ground rendered an average density of 0.91 points m22. These datasets can be downloaded free of charge from the webpage of National Land Survey of Finland (NLS, 2013).

DCs used in forest management Silvicultural DCs are used in Finland to classify forest stands and assist in decision making for forest management planning. There are three types of DCs that are multilayered, and therefore, the principal target of this research is as follows: † Seed-tree stands are stands where a few mature parent trees have been left after harvesting, and provide seed for the next generation of seedlings. In boreal forests, the seed-tree system is used for natural regeneration of pine and birch, which are shade-intolerant species, and therefore, mature seed trees (parent) have a very low density (30–50 stems per hectare), but they are present (in contrast to a clear-cut). In the case of pine, the seed trees must be placed on dry or nutrient-poor sites, so that the absence of grass allows for seed germination and seedling growth in a non-competitive environment. † Shelterwood stands are formed using the shelterwood system. In boreal forests, the system is used for natural regeneration of spruce, which is shade tolerant and therefore, the overstorey has a much higher density (100–300 stems per hectare, or even higher) than in Seed-tree stands. The trees forming the shelterwood overstorey may be even denser and more mature than in the Mature DC (see below). It may also be a late-seral stage of maturity in boreal birch and pine-dominated overstoreys, which usually develop spruce in the understorey from natural regeneration. Therefore, while the overstorey contains mature birch and pine, serving as shelter, the understorey will be dominated by spruce. † Multi-storied stands are stands in which the understorey has been established, and the next management treatment for it will be the removal of the parent tree overstorey. The overstorey is relatively young and serves

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forest structures poses two main challenges for practical management. Firstly, the optimization method – i.e. maximization of economic, ecological or multi-criteria targets – is more complex to develop when trees of different ages share the same forest area and compete with one another (Gove et al., 1995; O’Hara and Gersonde, 2004; Pukkala et al., 2010). Secondly, it may require a system for inventorying the whole forest area (rather than surveying sample plots discretely) and monitoring it with higher frequency, since within-stand complexity requires the means for detecting forest areas in need of specific treatments. ALS can provide the means for fine-scale detection of specific structural properties of the forests, such as areas with a need for specific treatments (Pippuri et al., 2012; Korhonen et al., 2013) or ingrowth (Valbuena et al., 2013a). This is especially the case in large and continuous forest areas with difficult access. ALS remote sensing provides the opportunity for developing monitoring systems capable to deal with this increasing complexity, since they can supply detailed information on smaller scales (Packale´n et al., 2011). Recent advances in remote sensing provide new opportunities to monitor forest structure over large spatial scales, and increase our knowledge on ecosystem dynamics. Before the advent of remote sensing, producing a spatially continuous inventory of forest structural complexity was considered an impossible undertaking. Thanks to ALS remote sensing, such comprehensive surveys are nowadays a reality, as ALS allows observation of structural differences across broad areas of forests (Lefsky et al., 1999; Zimble et al., 2003; Marvin et al., 2014). Therefore, ALS can provide reliable methods to compare forest areas and evaluate structural changes in the canopy due to small-scale disturbance (d’Oliveira et al., 2012). For this reason, forest managers foresee that the possibilities of ALS for large-scale mapping will be valuable for assisting integrated multi-purposive management of forested environments (Burger, 2009). ALS can provide valuable information on properties related to the shape of tree diameter distributions (Maltamo et al., 2007), structural diversity (Ozdemir and Donoghue, 2013; Waser et al., 2013) or tree size inequality (Valbuena et al., 2013b). For this reason, ALS remote sensing is a suitable technology for monitoring forest management and logging, assuring its compliance with certification standards or legal restrictions related to forest management and logging (d’Oliveira et al., 2012; Valbuena et al., 2016a). Measurements of canopy structure from ALS can also be used to increase our knowledge on ecosystem functioning. They have been used as a proxy to assess wildlife distribution (Palminteri et al., 2012; Vierling et al., 2013; Melin et al., 2014; Vihervaara et al., 2015), plant diversity (Gould, 2000; Simonson et al., 2012), tree competition (Pedersen et al., 2012), patterns in spatial locations of trees (Packale´n et al., 2013) or forest growth and disturbance dynamics (Kellner and Asner, 2009). In recent years, many countries throughout the world have invested in nationwide ALS survey programmes (National ALS datasets are currently being surveyed throughout the world including – besides Finland – USA (USIEI), UK (ARSF), Spain (PNOA), Denmark (Kortforsyningen), the Netherlands (AHN2), Poland (ISOK), Switzerland (DOM), Sweden (Lantmateriet) and also others like Germany, France, Italy, Norway, Austria, etc.), primarily to obtain high-resolution terrain maps (Ahokas et al., 2005). In Finland, the national programme is being carried out by collaboration of the Finnish Forest Centre (Suomen Metsa¨keskus, SMK) and the National Land Survey of Finland (NLS, 2013). With a plan to cover the whole country by 2019, these data are openly accessible to the public, giving an

Forestry

as a shelter for an understorey of saplings and seedlings. The Multistoried DC differs from the Shelterwood DC in the degree of maturity of the shelter layer, and also that the Multi-storied DC regenerates deciduous species, while Shelterwood DC regenerates spruce. These are to be discriminated from those silvicultural DCs that are considered even-sized forest structures, which are as follows: † Young thinning stands. Stands belonging to the Young thinning DC are composed mainly of poles with a diameter at breast height (DBH) of 8–16 cm, which usually implies that the canopy is 7–9 m high on average. Pre-commercial thinning is applied at this stage, to concentrate future growth on the best trees, therefore increasing evenness among tree sizes. † Advanced thinning stands. In stands belonging to the Advanced thinning DC, trees have already reached a DBH of .16 cm. Commercial thinning is usually applied, which also often reduces tree size inequality in that forest. † Mature stands. Stands belonging to the Mature DC have a quadratic mean diameter reaching 18–25 cm. This would be the latest development stage if a clear-cutting system is followed, or otherwise it could further develop into the Seed-tree DC (for pine) or the Shelterwood or Multistoried DCs (for spruce or birch) when natural regeneration is chosen as an alternative to planting. The above-mentioned DCs must also be discriminated from non-forest areas and forest DCs that belong to the stand reinitiation phase at the start of a rotation in a silvicultural management system based on clear-cut and planting: † Seedling stands are stands in which the planted seedlings have an average height lower than 1.3 m. If the stand was established by

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natural regeneration, there will probably be mature seed trees, and therefore, the stands would belong to the Seed-tree DC. † Sapling stands are stands in which the height of the trees are greater than 1.3 m, and the DBH is ,8 cm. If the stand originated from natural regeneration, there will probably be an overstorey, and therefore, the stands would belong to the Multi-storied DC.

Field data Three different field datasets (D1, D2 and D3) were employed at different stages of this study: † D1: plots for multilayered DCs (Seed-tree: 29 plots, Shelterwood: 31 plots and Multi-storied: 29 plots); † D2: plots for even-sized DCs (Young thinning: 113 plots, Advanced thinning: 151 plots and Mature: 80 plots); † D3: plots containing seedlings and samplings only (Seedling: 36 plots and Sapling: 212 plots). Dataset D1 consisted of 89 plots that were acquired by the University of Eastern Finland during August 2013 for the purposes of this study. Plot locations were determined randomly over areas that were likely to belong to Seed-tree, Shelterwood or Multi-storied DCs, based on SMK’s stand-wise information from previous forest management plans. Final DC determination was, however, corroborated in the field by the measuring crew, using expert forester knowledge and the DC descriptions summarized above. On the other hand, datasets D2 and D3 amounted for 344 and 248 plots, respectively, which were also measured during summer 2013. They were provided by SMK from their operational ALS-assisted forest inventory carried out at this same area. We randomly sub-sampled these datasets D2 and D3, in

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Figure 1 Study area. Inset shows location within Finland of enhanced area. The codes are those used by NLS for the specific blocks used in this research (NLS, 2013). Colour figures available online.

Lidar Classification of multilayered forest development classes

order to balance sample size among all the DCs. The field data protocol for multilayered DCs (dataset D1, see below) was adapted from the ordinary practice followed by SMK, which modifies plot size according to tree development (Figure 2). For even-sized DCs (i.e. Young thinning, Advanced thinning and Mature), the field data were acquired over circular plots of 9 m radius (dataset D2). For the dataset D3, plot radius was reduced to 5.64 m in Sapling stands, whereas seedlings were simply counted by species using a 2.82-m-long stick from four distributed positions (Figure 2). For acquiring dataset D1, SMK’s original system was adapted to the case of multilayered DCs. Instead of choosing either of the plot types in Figure 2, all ‘subplots’ were measured from one plot centre simultaneously. Whether or not a tree was measured was, therefore, determined as a function of its size and distance to plot centre. Every tree with a DBH of .8 cm was measured within a radius of 9.77 m from the plot centre, while the smaller seedlings that have not yet grown to 1.3 m in height were recorded within a 5.64 m radius. We recorded species and DBH for every tree measured under the combined DBH/distance to plot centre criteria, determining the distances on-the-go with a Vertex ultrasound transponder (Haglo¨f UAB, Sweden). The number of seedlings were also determined for multilayered DCs following the same procedure described for Seedling DC stands. Following Valbuena et al. (2013b), all subplots were merged into one by direct repetition of the data acquired in the inner subplots. In order to allow this direct procedure, the radius of the overstorey subplot was slightly increased to 9.77 m so that the outer area became an integer multiplier of the inner areas. Moreover, due to the exceptionally low density of the overstorey in Seed-tree stands, plot size was extended to a buffer area in which seed or retention trees with a DBH of .16 cm were measured as well (which typically contained 0 –3 trees in practice). The radius of this area was determined to be 19.54 m, also allowing integer multiplication of areas for inner subplots. All plot centres were located by random sampling and stacked out by high-grade global navigation satellite system positioning (Valbuena et al., 2010).

ALS data processing The digital terrain model (DTM) with 2 m resolution corresponding to that same ALS data was also provided by the NLS. Heights above ground for individual ALS returns were calculated by subtracting the DTM underneath. We considered that, as seedlings and saplings were included in field mensuration, their influence in laser pulse interception had to be accounted for in ALS metric computation. Consequently, a very small height threshold of 0.1 m was applied, with the intention to mask out the influence of the ground. Software FUSION (version 3.1, USDA Forest Service) was used to compute a large array of ALS predictors (Table 1; McGaughey, 2012). For training the supervised classification, these metrics were computed at plot positions, from the height distribution of all the ALS returns belonging

to the area that corresponded to the outer overstorey subplot (Figure 2). Those same metrics were also computed over a 16 ×16 m regular grid covering the entire study area.

Supervised classification and accuracy assessment We conducted a supervised classification of all the possible DCs (see above), using the ALS metrics as predictor variables, using R statistical environment version 3.1.0 (R Development Core Team, 2014). As detailed in Valbuena et al. (2016b), support vector machine (SVM) classification was selected after considering many alternatives for classifying the dataset (quadratic discriminant analysis, naive Bayesian classifier, minimum volume ellipsoid estimator, artificial neural networks, random forest and nearest neighbour). The use of SVM for classification of ALS data has recently become popular (Dalponte et al., 2008; Garcı´a et al., 2011), probably due to its ability to deal with multi-dimensional and noisy datasets. In SVM, a hyperplane separating classes is defined by structural risk minimization, with the cost function being defined as a combination of: (1) maximizing the distance between the hyperplane and the training samples and (2) minimizing the misclassification error. These hyperplanes are computed based on class posterior probabilities and quadratic optimization. We computed a SVM C-classification (e.g. Garcı´a et al., 2011) using the svm function in package e1071 (Meyer et al. 2014a). Residuals were computed in a leave-one-out cross-validation fashion, using the capability included in the svm function (i.e. attribute cross equalled to total sample size). To avoid over-fitting to the sample and collinearity effects among predictors, the initial large set of ALS metrics (Table 1; McGaughey, 2012) was reduced to the most relevant metrics regarding the final classification of the target DCs. Selection of the feature space was carried out by computing this same classification for all plausible combinations of five (or less) predictors. The limit of five predictors was set after observing over-fitting effects for larger numbers of predictors, as revealed when comparing the accuracies obtained with and without cross-validation (Valbuena et al., 2013b). Final predictor combination was selected under the criteria of maximizing accuracy (see next paragraph). Table 2 includes the header of a table ranking all the combinations, and therefore, they are most beneficial options for ALS metric selection. The accuracy of cross-validated classifications was assessed with the help of confusion matrices, and their statistical significance was evaluated with a x2 test using the ‘gmodels’ package (Warnes, 2013). For this study, we carried out two levels of accuracy assessment. First, the method was evaluated for its capacity to discriminate all the DCs (see DC descriptions above), as they were used in the training stage. In the second level, DCs were aggregated into groups at the validation stage, to evaluate the capacity of the methods for the actual purpose of this research on discriminating multilayered forests from other DCs. Thus, individual DCs were grouped

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Figure 2 Field plot design for multilayered forest.

Forestry

Table 1 List of metrics used as predictors. Statistics computed from the distribution of airborne laser scanning return heights (McGaughey, 2012) Description

Min Max Mean P50 Mode SD CV Skew Kurt AAD MAD.Median MAD.Mode L1, L2, L3, L4 L.CV L.Skew L.Kurt P10, P20 . . . P80, P90 Count Count.f Cover Cover.mean Cover.mode Cover.f Cover.f.mean Cover.f.mode Count/f Cover.mean/f Cover.mode/f

Minimum Maximum Mean Median (i.e. 50th percentile) Mode Standard deviation Coefficient of variation Skewness Kurtosis Average absolute deviation Median of the absolute deviations from the overall median Median of the absolute deviations from the overall mode L-moments L-coefficient of variation L-moment skewness L-moment kurtosis Percentile values (1st, 5th, 10th, 20th, 25th, 30th, 40th, 50th, 60th, 70th, 75th, 80th, 90th, 95th, 99th percentiles) Number of returns Number of first returns Percentage of all returns above 1 m Percentage of all returns above the mean height Percentage of all returns above the mode height Percentage of first returns above 1 m Percentage of first returns above the mean height Percentage of first returns above the mode height Number of returns above one metre/total first returns×100 Number of returns above the mean height/total first returns×100 Number of returns above the mode height/total first returns×100

evaluated by the final overall accuracy and Cohen’s (1960) kappa coefficient (k), using the ‘vcd’ package (Meyer et al., 2014b). As detailed above, overall accuracy and k were also used in feature selection (Table 2).

Table 2 Summary of best subset variable selection results Predictor

Ranked variable combinations 1st

2nd

3rd

4th

5th

6th

x x x x x

x x x x

x x x x

x x x x

x x x

x x x

The rule-based method Min MAD.Median L4 L.CV AAD P70 P60 SD CV k Overall accuracy (%)

x x x x

0.703 74.1

0.699 73.6

0.694 73.2

0.694 73.2

x 0.694 73.2

x x 0.689 72.8

As an alternative to the SVM classification, a method carrying out a classification of multilayered forests according to statistics describing the distribution of the ALS return heights was also tested for the same studyarea. We refer to this as the rule-based method (Valbuena et al., 2015). In contrast to inductive machine learning methods like SVM, the rule-based method employed deductive mathematical rules for describing distributions. The logic behind this method was described by Valbuena et al. (2013a), who discriminated shelterwood regeneration areas using values of the L-coefficient of variation (i.e. Gini coefficient) and asymmetryas thresholds. It is worth noting that, contrary to the supervised classification, the rule-based method made no use of field data support at any stage other than the assessment of its accuracy. The study of Valbuena et al. (2015) gives more details about the rule-based method.

Top six variable combinations.

into: multilayered (Seed-tree, Shelterwood and Multi-storied) and evensized DCs (Seedling, Sapling, Young thinning, Advanced thinning and Mature). Bias was assessed as the discrepancy between producer and user accuracies for each group of DCs. The degree of misclassification was

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Results The SVM classification was statistically significant (x2¼ 897.5, df ¼ 49, P-value ,0.01), and therefore, it can be considered that the ALS metrics have potential for discriminating among the DCs used in

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Code

Lidar Classification of multilayered forest development classes

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Figure 3 Examples of resulting maps stratified by supervised classification. Upper: canopy height model with 2 m resolution (reference). Middle: all DCs (Table 3). Lower: in yellow, multilayered DCs aggregated. Compare against rule-based method in Valbuena et al. (2015). Colour figures available online.

forest management practices in Finland. Figure 3 shows examples of the resulting maps. Taking into account all the silvicultural classes separately, the supervised classification reached an overall accuracy of 74.1 per cent, obtaining a coefficient of agreement k ¼ 0.70 (Table 3). Estimation was also fairly unbiased for most classes, as it can be assessed by comparing the proportions in the predicted classes (row totals) against the class proportion in the training sample (column totals). There was, however, some degree of overestimation for Seedling and Shelterwood stands, while Multi-storied stands were under-estimated.

Most confusion occurred among DCs that were not multilayered, thus being irrelevant for the objectives of this research. For instance, many Sapling DC plots were incorrectly predicted as being Seedling, and even-sized DCs (Young thinning, Advanced thinning and Mature) were not correctly discriminated from each other. However, there were also errors pertaining to multilayered forests, which were more pertinent to analyse. The most important errors were for Shelterwood forests, which were largely over-estimated from misclassified Advanced thinning or Mature DC plots. Likewise, there was some degree of confusion between Multi-storied and

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Table 3 Confusion matrix of classification obtained by SVM, including all the silvicultural DCs (codes are explained in the Introduction) Observed Seedling

Sapling

Seedling Sapling Young thinning Advanced thinning Mature Shelterwood Multi-storied Seed-tree

29 1

6 24

Omission errors

Young

24 4 2

3.3%

20.0%

20.0%

Advanced

1 25 1 3

16.7%

Seed-tree DC plots as well. Additionally, many Seed-tree areas were simply misclassified as Seedling or Sapling stands. The approach was successful with regards to the actual objectives of the research, as most multilayered plots were correctly classified as such (Figure 3). Overall, 82.0 per cent of multilayered DCs (D1) were correctly discriminated from the even-sized areas. Plots in even-sized DCs (D2) were classified with a higher accuracy of 94.4 per cent, whereas 88.3 per cent of Seedling and Sapling stands (D3) were correctly identified. When aggregating the classified plots into even-sized and multilayered DCs, the overall accuracy was 88.3 per cent with a k ¼ 0.75.

Discussion The results showed that silvicultural DCs may be reliably identified by supervised classification of ALS metrics, depending on the accuracy requirements needed for each purpose. Despite its low density, ALS data from the national laser surveying programme (NLS, 2013) may be consistently employed for discriminating the DCs that are used in common silvicultural practices, therefore being able to assist practical forest management up to nationwide scales (Burger, 2009; Marvin et al., 2014). They can also be employed to develop more complex management systems (Packale´n et al., 2011; Valbuena et al., 2013a; Redon et al., 2014), and assist in monitoring compliance of silvicultural practices with the new legal provisions of the FFA 1093/1996 (MMM, 2014; Valbuena et al., 2016a). NLS (2013) data were shown to be sufficient for discriminating multilayered DCs from even-sized DCs. As ALS acquisition parameters are consistent throughout the dataset (Ahokas et al., 2005), there is potential for upscaling of the results across Finland. Similar national ALS datasets in other countries may also have the capacity to classify forest DCs. Since many of these datasets are publicly available, the methods presented in this article can be directly replicated, or adapted to different DC definitions relevant for forest management practice in other countries. However, whether reliable accuracies can also be reached in forest types other than boreal is a matter for further research. This application of low-density ALS data to detect multilayered DCs is an addition to the many uses already found for these data (Villikka et al., 2012; Gonza´lez-Ferreiro et al., 2014; Vihervaara et al., 2015). It expands the usability of these public datasets

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Mature

Shelter.

Multi-storied

Seed-tree

1 2

3 2

6 21 2 1

4 12 10

1 1 1 21

32.3%

58.6%

27.6%

1 2 21 5 2

30.0%

Commission errors 25.6% 17.2% 7.7% 19.4% 27.6% 41.7% 29.4% 34.4% 239

beyond their applications for precisely mapping topography and measuring growing stock (Ahokas et al., 2005). We foresee that many other applications will rise in the coming years as well, thanks to the open availability and extensive coverage of these ALS data. There are many research opportunities that can be pursued. There are still many tasks on the researchers’ agenda in order to be able to use ALS and other remote sensing sources to assist the determination of allowable cut for maintaining the sustainability of forest management (Burger, 2009; Vauhkonen et al., 2014). There are many other key attributes to be assessed for assisting forest management systems, such as diameter distributions (Maltamo et al., 2007), timber assortments (Korhonen et al., 2008; Kankare et al., 2014), regeneration (Bollandsa˚s et al., 2008; Valbuena et al., 2013a), stocking above a given tree size (Gove, 2004; Valbuena et al., 2014), competition (Pedersen et al., 2012) or various biodiversity assets (Palminteri et al., 2012; Ozdemir and Donoghue, 2013; Melin et al., 2014), for which the capacity of these low-density ALS datasets for providing reliable estimations still has to be evaluated. Nonetheless, Valbuena et al. (2015) also observed that the low density may have been insufficient for detecting ingrowth of shade-tolerant tree species in the understorey. ALS data with a higher point density may be better at discriminating among these specific DCs, e.g. between Shelterwood forest and Mature DCs (Table 1). We postulate that already thinned Advanced or Mature DCs stands may allow larger proportions of ALS pulses to be back-scattered from tree trunks, and therefore to be mistaken by Shelterwood stands with understorey recruitment. The relation of covariance that relative density may have in this discrimination may, therefore, be further researched in the future. Additionally, many Seed-tree stands were misclassified as being Seedling or Sapling stands. The cause for this confusion may be the low-density nature of the dataset, since individual seed trees may not backscatter any of the ALS pulses as the swath was surveyed. There was also a large number of Two-storied stands misclassified as Seed-tree stands. This did not affect the ultimate objective of detecting multilayered forests (D1). Also, most probably they are representing regeneration of shade-intolerant species, since Valbuena et al. (2015) found that such classification of Multistoried areas differed largely from those presenting shade-tolerant regeneration, in terms of the signal back-scattered from the ALS pulse (Lefsky et al., 1999).

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Predicted

Lidar Classification of multilayered forest development classes

The maps obtained from SVM classification of all DCs showed satisfactory results that would, however, perhaps require segmentation procedures for their further analysis at stand level, as they show a speckled salt-and-pepper effect (stands showing a mixture of differently-classified pixels), which is quite typical in pixel-level remote sensing estimation (Figure 3). Nevertheless, the pixel-level classification has a value in itself, as mature forests showing a high degree of tree size inequality were well detected, and the highest values of tree size inequality were observed in the presence of seed and retention trees, as well as stand edges, which were easily observed in the resulting maps.

Conclusions The most relevant conclusions reached in this research were as follows: † The NLS low-density ALS survey, which is publicly available, was demonstrably sufficient for discriminating multilayered DCs from even-aged ones. † Seed-tree stands and, in general, forest areas where light availability is not a resource limitation may be detected directly by simple rules, without the need of field data. † Multilayered shelterwood mature stands, on the other hand, benefited from employing a supervised SVM classification, as the low point-density nature of the NLS dataset may hamper the detection of shade-tolerant understories in mature high forests with closed canopies.

Acknowledgements Special thanks to Juho Heikkila¨ and Jussi Lappalainen (SMK), Heli Laaksonen (NLS) and Aki Suvanto (Blom Kartta Oy) for their support at different stages of this study. We are also grateful for the efforts of the editors and anonymous reviewers throughout the reviewing process, which have significantly contributed to the quality of the final article. Tim Green (European Forest Institute) revised the English language and style of the final version.

Conflict of interest statement None declared.

Funding This research was funded by Suomen Metsa¨keskus (SMK, Finnish Forest centre).

References Ahokas, E., Yu, X., Oksanen, J., Hyyppa¨, J., Kaartinen, H. and Hyyppa¨, H. 2005 Optimization of the scanning angle for countrywide laser scanning. In ISPRS Workshop ALS III/3 III/4V/3, September 12 –14, Enschede, Netherlands. pp. 115–119. Asner, G.P., Clark, J.K., Mascaro, J., Galindo Garcia, G.A., Chadwick, K.D., Navarrete Encinales, D.A. et al. 2012 High-resolution mapping of forest carbon stocks in the Colombian Amazon. Biogeosciences 9, 2683– 2696.

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Using this type of supervised (i.e. with support from field training plots) classification approach, it is possible to tailor the target classes to the specific purpose of a given research topic (e.g. Simonson et al., 2012). The method can, therefore, be replicated elsewhere by tailoring the definition of DCs to other silvicultural systems (Gove et al., 1995; O’Hara and Gersonde, 2004; Schu¨tz et al., 2012), and training with local field plots. The field protocol applied in this study can be employed to increase the understanding of the relations between the ALS metrics and the characteristics of the diameter distributions. This can be used to discriminate the DCs specific to different management systems (e.g. Valbuena et al., 2013a). This supervised classification can be compared against an alternative approach that applied direct mathematical thresholds, which we refer to as rule-based classification (Valbuena et al., 2015). The SVM model was calibrated to the training data, and therefore, its accuracy was expected to be better than rule-based classification. The 82.0 per cent of correctly classified multilayered DCs obtained by supervised method was substantially better than the 63.8 per cent obtained by the rule-based method (Valbuena et al., 2015). To allow direct comparison of the supervised classification against the rule-based approach, classes in Table 3 were also be aggregated into two groups: even-sized (Seedling, Sapling, Young thinning, Advanced thinning and Mature) and multilayered (Seed-tree, Shelterwood and Multi-storied). The overall accuracy obtained (88.3 per cent overall, and k ¼ 0.75) was comparable with that obtained by the rule-based classification (87.0 per cent overall, and k ¼ 0.53). Since the rule-based classification could allow the training stage to be omitted, it could suffice for certain purposes like detecting regeneration of shade-intolerant species. However, the supervised classification presented in this article was more successful in detecting understorey development in Shelterwood forests (Figure 3), perhaps because this DC was the most challenging to identify. We, therefore, recommend this approach in forests containing shadetolerant species (Waser et al., 2013). With respect to the role of ALS metrics (Table 1) in the determination of forest DCs, the most important metrics selected (Table 2) reveal a difference with typical ALS-assisted predictions of traditional stand attributes – dominant height, basal area, density, etc. – for which ALS metrics describing the mean or maximum of ALS heights are quite common (e.g. Maltamo et al., 2005; Korhonen et al., 2008; Asner et al., 2012; Villikka et al., 2012; Gonza´lez-Ferreiro et al., 2014; Kankare et al. 2014). Instead, there is a predomination of metrics describing the dispersion (L.CV, MAD.Median, AAD, SD and CV) or shape (L4) of the ALS return height distribution, along with some providing information about understorey development (Min, P60 and P70). There is an absence of metrics related to vegetation cover (Cover, Cover.mean, etc.; see Table 1), which are usually selected for describing characteristics related to gap fraction and stand density (e.g. d’Oliveira et al., 2012; McGaughey, 2012; Palminteri et al., 2012; Pippuri et al., 2012; Packale´n et al., 2013; Vihervaara et al., 2015). We also wish to emphasize that the presence of the L-coefficient of variation in metric selection (see Table 2 and rule-based method in Figure 3) has been a convergent result across methods and study areas (Valbuena et al., 2013a, b, 2014), and therefore, it seems to play a key role in describing forest areas with a high degree of tree size inequality (Valbuena et al., 2015).

Forestry

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