• <tr id="yyy80"></tr>
  • <sup id="yyy80"></sup>
  • <tfoot id="yyy80"><noscript id="yyy80"></noscript></tfoot>
  • 99热精品在线国产_美女午夜性视频免费_国产精品国产高清国产av_av欧美777_自拍偷自拍亚洲精品老妇_亚洲熟女精品中文字幕_www日本黄色视频网_国产精品野战在线观看 ?

    Combining spatial and economic criteria in tree-level harvest planning

    2020-07-16 07:20:08PetteriPackalenTimoPukkalaandAdriPascual
    Forest Ecosystems 2020年2期

    Petteri Packalen,Timo Pukkala and Adrián Pascual

    Abstract

    Keywords: Spatial optimization, Tree selection, Cellular automata, Remote sensing,Airborne laser scanning

    Background

    Forest inventories employing Airborne Laser Scanning(ALS) data have become common in many countries(Nilsson et al. 2017). The ALS-based forest inventory methods (Hyypp? et al. 2008) are typically categorized into two groups: the area-based approach (ABA) (Means et al. 2000; N?sset 2002; Magnussen et al. 2013) and individual tree detection (ITD) (Hyypp? et al. 2001;Koch et al. 2006; L?hivaara et al. 2014). So far, most operational forest inventories employing ALS data have been implemented with the ABA (Maltamo et al. 2014).In ABA, stand attribute models are fitted with sample plots using metrics calculated from ALS data and then these models are used to predict stand attributes of the whole inventory area, typically using square grid cells(e.g. 16 m×16 m) as inventory units.

    In ITD, the inventory unit is an individual tree. The first step is to detect and delineate the trees. Then ALS features, such as local maxima mimicking tree height,are extracted on a tree-by-tree basis and used to estimate tree-level attributes, such as tree height and diameter. Tree locations are also an intrinsic output of ITD.The disadvantage of ITD is that tree detection fails when tree crowns overlap, or if there are many small trees under the dominant tree layer (Falkowski et al. 2008;Lindberg et al. 2010). Failures in tree detection and errors in the prediction of tree attributes make ITD more sensitive to bias than ABA at the aggregated (e.g. forest stand) level (Vauhkonen 2010). However, the advantage of ITD is that it produces a more detailed description of forest: tree level attributes, including tree locations.

    Forest plans are typically composed of homogenous regions for which inventory data are available. Most commonly,this region or inventory unit is a forest stand.In the ALS era, stand-level data are usually derived from ABA predictions in grid cells. In spatial forest planning,the inventory unit has been a stand (?hman 2000),microstand (Pascual et al. 2019), hexagon (Packalen et al. 2011)or pixel/cell(Lu and Eriksson 2000).To date,the use of tree-level data in forest or harvest planning has received limited attention. For example, Martín-Fernández and García-Abril (2005) developed a tree-level optimization method following an approach based on close-to-nature forestry, and Bettinger and Tang (2015)maximized the tree-level species mingling value in order to intersperse tree species across a forest. Algorithms have also been proposed for tree cut selection using a distance-dependent growth model (e.g. Pukkala and Miina 1998). Vauhkonen and Pukkala (2016) selected trees based on their value growth rate, and Pukkala et al.(2015) optimized the tree selection rule in thinning treatments when the profitability of forest management is maximized. These studies accounted for the location of trees when deciding the order in which to cut the trees. However, the spatial distribution of harvested trees was not controlled in optimization although it may be of great importance from ecological and practical viewpoints (Heinonen et al. 2018). Wing et al. (2019) implemented a method for group-selection silviculture that accounts for spatial aggregation, and utilized manually corrected stem map data that was derived by ITD.

    Controlling the spatial distribution of individual harvested trees or other harvest units, such as micro-stands or stands, is possible both in mathematical programming(?hman 2002) and in heuristic planning methods(Heinonen et al. 2007). Heuristic methods are more flexible and may be easier to use in large and complicated spatial problems (Bettinger et al. 2002; Heinonen et al.2018). Two lines of heuristics have been developed for spatial forest planning problems: global and local methods (also called centralized and decentralized methods) (Heinonen and Pukkala 2007; Pukkala et al.2014). Common examples of centralized heuristics are simulated annealing, tabu search and genetic algorithms(Bettinger et al. 2002). Of the two categories of heuristics, decentralized methods may be faster and more feasible when the number of calculation units is very large,which is often the case in tree-level planning (Heinonen and Pukkala 2007).

    Examples of decentralized heuristics are cellular automata (CA) (Strange et al. 2001, 2002; Mathey et al.2007) and the spatial version of the reduced cost method(Hoganson and Rose 1984; Pukkala et al. 2008). The aim of decentralized heuristics is to maximize or minimize a local objective function, which in tree-level planning is a tree-level function. This function is modified with a part that takes into account the global objectives or constraints of the planning problem. In the reduced costs method, global objectives are dealt with the dual prices of global constraints, whereas the applications of CA employ a global priority function that is added to the local function.

    Cellular automata, which were used in the current study, are self-organizing algorithms based on the assumption that the interaction between cells decreases rapidly with increasing distance (Von Neumann 1966;Strange et al. 2001; Wolfram 2002). Although the name of the method refers to (square-shaped) cells, the method can also be used with other types of calculation units. Each unit takes one of a limited number of states,which can be management schedules, land uses or, as in the current study, the cut vs. uncut status of an individual tree. The purpose of CA is to find the optimal status for each cell,by considering the variables of the cell itself and the local neighborhood of the cell. The cell states evolve in discrete time steps according to a set of rules.Spatial relationships can be included in CA and other heuristic methods in various ways, for instance by considering only spatially adjacent calculation units(similar vs. different prescription), by also including the neighbors of adjacent units (Kurttila et al. 2002)or using distance as the criterion of neighborhood(Heinonen et al. 2018).

    The aim of the study is to present a tree selection method that removes economically mature trees from the stand while also considering the spatial distribution of the harvested trees. A user controls whether the pattern of harvested trees is clustered or dispersed. We present a solution as to how tree-level data can be used in the spatial formulation of the optimization model common in forest and harvesting planning. The proposed method was tested in Central Spain on a stone pine (Pinus pinea L.) forest area with two different spatial distributions of trees. For this purpose, we implemented a simple ITD inventory, although the focus is on the tree selection algorithm.

    Study area and materials

    The study area is the public forest MUP50 owned by the municipality of Portillo in the province of Valladolid(366658-371652 Easting, 4590001-4586476 Northing,711-862 m a.s.l.) located in Castilla y León (Central Spain). The study area of 1100 ha consists of pure stone pine stands at different stages of development. The area is part of the Northern Plateau (Calama et al. 2008)where the stone pine forests are managed using evenaged forestry with the aim of providing a constant flow of revenue (timber, firewood and nut production) and other ecosystem services, such as erosion control(Calama et al. 2011).

    We selected two sub-areas in a 78-year old stone pine forest. These two areas differed in average tree size and the spatial distribution of the trees (Fig.1). In Area #1(106.4 ha), the spatial distribution of the trees was more or less regular, i.e., tree spacing was somewhat constant.In Area #2 (47.9 ha), the trees grow more in groups and stand density and tree spacing was variable.

    Field sample plots

    Systematic sampling was used to establish a network of 35 circular sample plots in the area and used to model the relationship between tree height and diameter.Sample plots were collected in 2010. The size of each plot was 706.86 m2, i.e. circular plots with a radius of 15 m. The sample plots contained a total of 344 trees. The locations of the plot centers were determined by a submeter precision GNSS equipment (Garmin International Inc., Missouri, USA). On each plot, all trees with a diameter at breast height (DBH) over 7.5 cm were callipered and their heights were measured using a Vertex IV hypsometer (Hagl?f, Sweden). In addition, the distance and heading with respect to the plot center were determined with the distance meter and a Vertex IV compass (Hagl?f, Sweden). All the measured trees were stone pines. A summary of tree- and plot-level attributes is provided in Table 1.

    Airborne laser scanning data

    The ALS data were collected in 2010 using an ALS60 laser scanning system. The study area was scanned from an altitude of 2000 m above ground level with a scan angle of ±10 degrees. The average density of first echoes per square meter was 0.5. A digital terrain model (DTM)was constructed by first classifying echoes as ground and non-ground hits according to the approach described by Axelsson (2000). Then, a raster DTM of 1 m spatial resolution was interpolated from ground hits using Delaunay triangulation. Heights above ground level(AGL) were calculated by subtracting the DTM from the elevation of ALS echoes.

    Table 1 Minimum, mean,and maximum of tree and plot attributes

    Methods

    Individual tree detection and tree attribute prediction

    The canopy height model (CHM) of 1 m spatial resolution was interpolated by searching the highest ALS echo at AGL within each cell. If there were no ALS echoes within a cell, the value was interpolated from the neighboring cells. Individual trees were detected by searching treetops from the CHM. This was implemented with a local maximum filter (Hyypp? et al.2001). The CHM was slightly smoothed before searching local maxima to remove false positives (Koch et al.2006). Local maxima located <3 m (AGL) were removed in order to exclude local maxima on the ground and very small trees. Tree height was considered the same as the height (AGL) of the local maxima in the unsmoothed CHM. Tree detection worked quite well in the study area even though the ALS data had low echo density. The reason for this is that the crowns of individual stone pine trees do not usually touch each other.

    The trees measured in the field plots and ALSdetected trees were linked to each other if treetops were located within a 3-m Euclidean distance in threedimensional space (see details in Vauhkonen et al. 2011).Then a tree diameter model was fitted using successfully linked trees as follows:

    where DBH is the diameter at breast height in the fieldmeasured tree, HALSis the ALS detected tree height, and β0and β1are coefficients estimated from the data. We fitted the model with the least squares method using the nls function available in the R environment (R Development Core Team 2011). The estimated model coefficients (β0=5.3602, β1=2.2675) were statistically significant with both p-values less than 0.001.The coefficient of determination (R2) of the model was 0.66 and the RMSE was 5.26 cm. Finally, the model was used to predict DBH for all detected trees in the study area. The outputs of the ITD inventory were tree coordinates(XY), height and DBH for all detected trees.

    Growth and yield models

    The models presented in Calama and Montero (2006)were used to calculate the stem volume using predicted tree height and DBH for all trees. For each detected tree,the number of trees per hectare, basal area and dominant height (mean height of 100 largest trees per hectare)were computed using a buffer of 20 m around the tree.Site index was calculated using the existing model of Calama et al. (2003). The stand age in the study area was 78 years. The taper model (Calama and Montero 2006) was used to calculate the value of the stem. The following assortments, top diameters (dtop), minimum log lengths (hmin) and unit prices were assumed (Pasalodos-Tato et al. 2016): grade 1 (dtop≥40 cm, hmin2.4 m,30 €·m-3), grade 2 (dtop≥30 cm, hmin2.4 m, 18 €·m-3),grade 3(dtop≥20 cm, hmin2.4 m,13 €·m-3),grade 4 timber (dtop≥5 cm, hmin1.0 m, 5 €·m-3). The models from Calama and Montero (2004, 2005) were used to predict the 5-year increment in DBH and tree height, and the taper model was again used to compute the volume and the value of the trees 5 years later. Value increment was obtained as the difference of stem value at two time points. The relative value increment over a period of 5 years was calculated by dividing value increment by the value of the stem in the beginning of the 5-year period.See Pasalodos-Tato et al. (2016) for more details.

    A power diagram to create tree regions

    Tree-level forest data consist of detached tree regions(i.e. tree crowns) or points (i.e. stem locations), but not adjacent regions (e.g. grid cells or stands). Here we consider trees as point type objects. Then, we partition the space to trees with the assumption that a large tree occupies a larger area than a small tree. We refer to these partitions as tree regions. The partitioning is based on a power diagram, which is a type of weighted Voronoi diagram (Aurenhammer 1987). The power diagram is defined from a set of circles. Circle center is called a site.In this study, detected tree locations are sites and predicted DBH multiplied by 50 defines the radius of each circle. The power diagram consists of the points with the smallest power distance for a particular circle. In the case where all the circle radii are equal, the power diagram coincides with the Voronoi diagram. An example of the power diagram is given in Fig.2. Tree regions enable the use of adjacency relationships that take into account tree size, and the spatial optimization can be performed in a similar way as employed with traditional regions (e.g. grid cell or stand).

    Spatial optimization

    The cellular automaton developed for this study follows the ideas presented in Heinonen and Pukkala (2007) in which both local and global objectives are included in the priority function. In this study, the following priority function P was maximized for each tree:

    where VTreeis the volume of the subject tree (m3), VTotalis the volume of all trees within the optimization area,RelValInc is the relative value increment of the tree (%in 5 years), CC is the proportion of the cut-cut border(of the total border length with adjacent tree regions;Fig.3); CuC is the proportion of the cut-uncut border;w1, w2and w3are the weights of the “l(fā)ocal” tree-level objectives RelValInc, CC and CuC, respectively; p1, p2and p3are sub-priority functions of the local objective variables (Fig.4); w4is the initial weight of the “global”objective TotCut (total volume of cut trees) and p4is a priority function for the global objective. In this study,the proposed target volume to be harvested was 20% of the initial standing volume. The target harvest was 654.6 m3for Area #1 and 459.3 m3for Area #2.

    The priority function of Eq. 2 can be interpreted as a removal score for a tree. Low relative value increment(high economic maturity) and presence (aggregation problem) or absence (dispersion problem) of cut neighbors increases the probability of removal. Maximizing CC leads to the aggregation of cut trees and a large total area of cut trees, while the minimization of CuC contributes to compact aggregations on cut trees (Heinonen and Pukkala 2004).

    In the CA developed in this study, two options were inspected for every tree for several iterations:cutting the tree or letting it continue to grow. The option that maximizes the priority function was selected. All trees were inspected during each iteration in random order. After a certain number of iterations,the weighting of the global objective variable (w4) was progressively incremented using a certain step. Iterations with gradually increasing value for w4were repeated until the total volume of harvested trees was sufficiently close to the target value of harvested volume. In this study, the initial value of w4in the optimizations was always 0.01. Three iterations were conducted with the initial value, after which the value of w4was incremented by 0.01 after every additional iteration. Iterations were stopped when the difference between the achieved harvested volume and the target volume was less than 5% of the target removal.

    The CA described above is a simplified version of the automaton proposed by Strange et al. (2002) and Heinonen and Pukkala (2007). The first simplification is that the innovation probability is constant (one),implying that all trees are inspected at every iteration.The second simplification is that there are no mutations (mutation probability is constantly zero). Previous studies have shown that a high innovation probability and a low mutation probability work well in forest management problems (e.g., Heinonen and Pukkala 2007).

    The CA described above leads to aggregations of cut trees. If the aim is to disperse cut trees, CC needs to be minimized and CuC maximized.In that case, the priority functions for CC and CuC need to be replaced by a 1-0 and 0-1 sub-utility functions, respectively, as shown with dashed lines in Fig.4. The following weights were used to mimic the various silvicultural tree selection methods in optimization:

    · Non-spatial: RelValInc (w1=0.99), CC (w2=0.00),CuC (w3=0.00), TotCut(w4=0.01 initially)

    · Single tree: RelValInc (w1=0.84), CC (w2=0.05),CuC (w3=0.10), TotCut(w4=0.01 initially)

    · Tree group: RelValInc (w1=0.79), CC (w2=0.05),CuC (w3=0.15), TotCut(w4=0.01 initially)

    · Clearcut:RelValInc (w1=0.69), CC (w2=0.10),CuC (w3=0.20), TotCut(w4=0.01 initially)

    In the Single tree selection,the aim was to disperse trees,which was achieved by using the sub-priority functions for CC and CuC shown as dashed lines in Fig.4. In the Tree group and Clearcut selections, the aim was to aggregate trees, which was achieved by using the sub-priority functions for CC and CuC shown as continuous lines in Fig.4.

    Results

    Initial tree regions

    The number of tree regions in Area#1 and Area#2 was 13,438 and 4296, respectively. The tree regions, together with tree volume and relative value increment of the tree, are shown in Fig.5. Tree spacing and consequently the size of tree regions differed between areas.The size of tree regions was smaller in Area #1 (mean 73, range 6-279 m2) than in Area#2(mean 98,range 10-938 m2).In general,trees were much larger in Area #2 (mean 0.49 m3, max 1.70 m3per tree)than in Area#1(mean 0.26 m3,max 0.72 m3per tree).The relative value increment (% in 5 years) was almost equal in both areas but due to larger trees the value increment(€in 5 years)was substantially higher in Area#2.

    There was a clear east-west gradient in relative value increment in Area #2. This was partly due to small trees that rapidly increased their value but was also due to the higher site index (data not shown here) in the western part of Area #2. The relationship between tree volume and relative value increment was apparent: low tree volume indicated high relative value increment, which is logical due to the rapidly increasing proportion of valuable timber assortments in small trees.

    Effect of tree selection method on stem number, tree size and value increment

    In the absence of spatial objectives (Non-spatial), the proportion of cut trees (Table 2) and the relative value increment of those trees (Table 3) were less than in cases where spatial objective variables were included.

    Table 2 Effect of tree selection method on the number of trees and mean diameter at breast height (DBH)for cut and uncut trees

    Table 3 Effect of tree selection method on the relative value increment (%in 5 years)for cut, uncut and post-cut trees

    The difference between Non-spatial and Single tree selections was clearly evident: aiming for dispersed locations of cut trees increased the average relative value increment of cut trees by almost 70%. This means that the dispersion objective led to the removal of trees that were not among the most economically mature. In both areas, the inclusion of spatial objective variables slightly decreased the average DBH of the cut trees.

    In Tree group and Clearcut the purpose was to aggregate cut trees.The relative value increment of cut trees increased more (compared to Non-spatial) when more weighting was given to aggregating harvested trees,meaning that large cutting aggregations led to the removal of many economically productive trees. Logically, the difference in relative value increment between cut and uncut trees decreased with increasing importance of creating cutting aggregations. In Area #2, the relative value increment of cut trees was greatest with Clearcut, whereas the Single tree method in Area#1 clearly resulted in the greatest relative value increment of cut trees.This suggests that Clearcut in Area#2 and Single tree in Area#1 were most in conflict with the economic objective.

    The relative value increment was also computed after simulating the removal of the selected trees. The relative value increments were calculated with the assumption that the cut trees no longer existed in the stand.We call this the"post-cut" stage (Table 3). In general, the post-cut values were slightly greater than the uncut values. The difference in uncut and post-cut values was greatest in Single-tree selection and smallest in Clearcut selection, because Single tree selection decreased the competition of almost all trees,whereas the competition in Clearcut was decreased only for trees that were growing near the edges of the cut areas.

    Size and spatial distribution of harvest blocks

    The number of harvest blocks (continuous tree regions selected for cutting) was 4-5 times greater in Single tree than in the Non-spatial tree selection method (Table 4).Correspondingly, the mean size of the harvest blocks was about three times smaller in Single tree than in the Non-spatial selection method. Moving from the Nonspatial to the Tree group method increased the mean size of harvest blocks 8- or 9-fold. The Clearcut selection, which had a higher weighting on spatial objectives,clearly provided the largest harvest block size and the smallest number of harvest blocks.

    On average, the distance from a cut tree to the nearest cut tree was longest with the Single-tree selection method (Table 5). This proves that Single-tree selection performed as desired (the purpose was to disperse cut trees). In the Non-spatial selection, the mean distance between cut trees was shorter than with the Single-tree selection and the standard deviation of distances was greatest. This is a logical outcome because Non-spatial selection does not attempt to generate a particular spatial distribution of trees.In the Tree group and Clearcut selections, the distances of cut trees were shortest because the aim was to cluster cut trees. The mean distances from uncut trees to their nearest neighbors were also shortest in the Tree group and Clearcut where most of the area was not thinned at all. Compared to cut trees, however, the mean distances did not vary much between tree selection methods.

    Maps showing the spatial pattern of harvest blocks and the tree regions are displayed in Fig.6(Area #1) and Fig.7 (Area #2). Visual inspection verifies the conclusions drawn from Tables 4 and 5: (a) the Non-spatial selection does not show any particular spatial layout, (b)the Single-tree selection disperses trees to be cut, (c) the Tree group and (d) Clearcut selections cluster cut trees to various degrees, Clearcut more than Tree group. The spatial distribution of cut trees is slightly different in the two areas. In Area #1, where the spatial distribution is somewhat regular, the Single-tree method selected trees to be cut more evenly than in Area #2, where trees grow more in groups and stand density and tree spacing is variable.

    Table 4 Number of harvest blocks(i.e., continuous tree regions selected for cutting) and mean size(m2) of blocks

    Table 5 Mean distance between trees and their nearest neighbors.

    Discussion

    We presented a new approach for tree-level harvest planning that considers both the spatial distribution and the value increment of the trees. The problem is formulated as a multi-objective optimization problem, which is solved by a tailored CA algorithm. The idea is to bundle the tree selection method with tree-level inventory data obtained by means of ITD and ALS data. The ITD inventory is currently a feasible method for certain forest types in an operational setting.Therefore,there is a need to develop spatially explicit methods for tree-level harvest and forest planning.

    Spatial optimization in the forestry context is typically based on adjacency relationships of region type objects (Weintraub and Murray 2006). In practice, adjacency is often defined by computing cut-cut and cut-uncut border lengths of adjacent regions. Because tree-level data do not form adjacent regions, we partitioned the space to trees with the assumption that a large tree represents a larger area than a small tree.This means that both tree size and distance to its neighbors are included in the definition of adjacency:for large trees the length of the common border with adjacent trees is greater than for small trees. However, it is not apparent how strongly tree size should affect the size of the tree region. We used a power diagram to compose the tree regions, wherein the radius of a circle is the tuning parameter, the value of which depended on the tree size. We defined the radius of the circle to be 50×DBH. This value was selected arbitrarily, and future studies should examine the best approach to define its value more precisely.For example, the radius of a circle could be defined based on the growth potential of a tree.

    Tree selection was combined with the use of an individual tree-level growth model that takes into account the neighborhood of the target tree. If tree selection requires the use of growth models, such as the relative value increment used in this study, it makes sense to use distance-dependent tree-level growth models or regular tree-level growth models in a spatial manner. Otherwise,the growth model predicts similar growth for all spatial distributions of trees and does not properly react to cuttings in the neighborhood of a target tree.

    We controlled the spatial distribution of cut trees by modifying the weights and sub-priority functions of spatial objective variables CC and CuC. The weight of the global objective (total volume of cut trees) was fixed to a small initial value (0.01) in every case. The economic criteria (relative value increment) always received the remainder of the weights (1 - TotCut -CC - CuC). In the Non-spatial selection, the weights of CC and CuC were set to zero, thus the spatial aspect was ignored entirely (Figs. 5a and 6a). It provided a reference to other selections that took the spatial distribution of the trees into account. In the Single tree selection, CC was minimized and CuC was maximized. This clearly dispersed trees to be cut(Figs. 6b and 7b). In the Tree group selection, cutting aggregations were targeted with low weights for CC and CuC. This led to tree groups of different sizes(Figs. 6c and 7c). In the Clearcut selection, the spatial weights of CC and CuC were larger, which led to bigger tree groups resampling traditional clearcut areas(Figs. 6d and 7d). We deliberately used a rather simple priority function; there could be more objective variables and sub-priority functions. For instance, a constraint type sub-priority function could be used to force a certain size of tree groups.

    Modifying the sub-priority functions and the weights of the spatial objective variables (CC and CuC) offers a means to enable the CA to mimic different disturbance regimes (Kuuluvainen 2016; Kulakowski et al. 2017),while always aiming at economically profitable forestry.For example, dispersion of cut trees (Single tree) mimics the damage caused by some insects that kill individual weak trees, the Tree group selection produces a landscape similar to wind damage (Kulakowski et al. 2017)and the Clearcut selection might correspond to damage caused by forest fire. Therefore, varying the weightings and sub-priority functions of spatial objective variables makes it possible to produce forested landscapes resembling those that result from different natural disturbance regimes.

    In addition to disturbance regimes, it is also possible to mimic alternative silvicultural systems, ranging from continuous cover selection forestry (Single tree) via group selection (Wing et al. 2019) to evenaged forestry where clear-fellings are conducted in mature stands. The degree to which a certain disturbance regime or silvicultural system is pursued can be closely controlled. If low weights are given to the spatial objectives, the outcome of CA mainly depends on the heterogeneity of the forest. Large disturbances are created in forests where economically mature trees form large aggregations and tree groups are harvested when mature trees occur in groups. In this way, it is possible to mimic different disturbance regimes at minimal loss in profitability of timber production. A greater need to control the spatial aggregation of cut trees would increase economic losses.

    In this study, we present and evaluate the proposed tree selection method as a tool for tree-level harvest planning. However, the method can be used as a part of a multi-objective forest planning system, in which dynamic treatment units are composed from trees by means of spatial optimization. This means that the trees to be cut are selected in each planning period, and subsequent periods must take into consideration the silvicultural operations implemented in earlier periods. The use of the proposed tree selection method as a part of multi-objective forest planning needs to be examined in subsequent studies.

    Conclusions

    The proposed tree selection method considers the spatial distribution of harvested trees and economic goals. It can be used to simulate cuttings in different type of silvicultural systems and mimic various disturbance regimes. It is easy to control by adjusting the sub-priority functions and the weightings of the spatial objectives. The method is utilized here as a tool in tree-level harvest planning but it can also be used in longer term forest management planning.

    Abbreviations

    ALS: Airborne laser scanning; ABA: Area-based approach; ITD: Individual tree detection; CHM: Canopy height model; AGL: Above ground level;DTM: Digital terrain model; DBH: Diameter at breast height; RMSE: Root mean square error; HALS: Height of ALS detected tree; CR: Circle’s radius in power diagram; CA:Cellular automaton;RelValInc:Relative value increment of the tree; TotCut: Total volume of cut trees; CC:Proportion of cut-cut border; CuC: Proportion of cut-uncut border; VTree: Volume of the subject tree; VTotal: Volume of all trees

    Acknowledgements

    The authors would like to thank Mr. Francisco Rodríguez from the ‘For? Forest Technologies’ for providing the field plot data to this study.

    Authors’contributions

    All authors contributed to the design and implementation of analysis.Authors also wrote the manuscript together,and all authors read and approved the final manuscript.

    Funding

    This research was supported by the University of Eastern Finland Strategic Funding, School of Forest Sciences and the Strategic Research Council of the Academy of Finland for the FORBIO project (Decision Number 314224).Adrián Pascual was also partially funded by Portuguese National Funds through FCT -Funda??o para a Ciência e a Tecnologia, I.P. in the scope of Norma Transitória - DL57/2016/CP5151903067/CT4151900586, and the project MODFIRE-A multiple criteria approach to integrate wildfire behavior in forest management planning with the reference PCIF/MOS/0217/2017.

    Availability of data and materials

    The tree regions and associated tree attributes used in the study are available from the corresponding author on reasonable request.

    Ethics approval and consent to participate

    Not applicable.

    Consent for publication

    Not applicable.

    Competing interests

    The authors declare that they have no competing interests.

    Author details

    1School of Forest Sciences, University of Eastern Finland, PO Box 111, 80101 Joensuu, Finland.2Forest Research Center, School of Agriculture, University of Lisbon, Tapada da Ajuda, 1349-017 Lisboa, Portugal.

    Received: 11 September 2019 Accepted: 23 March 2020

    九色成人免费人妻av| 亚洲国产精品sss在线观看| 十八禁国产超污无遮挡网站| 免费看日本二区| 久久久久性生活片| 久久久久久久国产电影| 亚洲人成网站高清观看| 美女被艹到高潮喷水动态| 天堂av国产一区二区熟女人妻| 97超碰精品成人国产| 亚洲无线观看免费| 国产精品一区二区三区四区免费观看| 三级男女做爰猛烈吃奶摸视频| 三级国产精品欧美在线观看| 国产极品天堂在线| 色综合亚洲欧美另类图片| 男女边吃奶边做爰视频| 少妇丰满av| 亚洲成人中文字幕在线播放| 亚洲激情五月婷婷啪啪| 久久久精品94久久精品| 国产一区二区在线av高清观看| 国产一区亚洲一区在线观看| 亚洲伊人久久精品综合 | 在线免费观看的www视频| 久久亚洲精品不卡| 亚洲综合色惰| 亚洲熟妇中文字幕五十中出| 国国产精品蜜臀av免费| 欧美成人a在线观看| 99在线视频只有这里精品首页| 精品欧美国产一区二区三| 岛国在线免费视频观看| 国产亚洲最大av| 听说在线观看完整版免费高清| 综合色丁香网| 最后的刺客免费高清国语| 最近2019中文字幕mv第一页| 久久久欧美国产精品| 黄色日韩在线| 免费看光身美女| 国产淫语在线视频| 亚洲图色成人| 亚洲在线观看片| 18+在线观看网站| 色5月婷婷丁香| 午夜福利网站1000一区二区三区| 色综合亚洲欧美另类图片| 村上凉子中文字幕在线| 99视频精品全部免费 在线| 日本黄大片高清| 男的添女的下面高潮视频| 自拍偷自拍亚洲精品老妇| 黄片wwwwww| 国产精品一区二区三区四区久久| 国产精品熟女久久久久浪| 免费看av在线观看网站| av女优亚洲男人天堂| 国产精品久久久久久av不卡| 日韩欧美精品免费久久| 日韩亚洲欧美综合| 国产单亲对白刺激| 乱码一卡2卡4卡精品| 国产色爽女视频免费观看| 国产精品久久电影中文字幕| 亚洲一区高清亚洲精品| 嘟嘟电影网在线观看| 高清在线视频一区二区三区 | av天堂中文字幕网| 两个人的视频大全免费| 日本午夜av视频| 国产一区二区三区av在线| 国产女主播在线喷水免费视频网站 | 久久久国产成人精品二区| av线在线观看网站| 亚洲人成网站在线观看播放| 亚洲精品乱码久久久久久按摩| 99久久精品热视频| 色吧在线观看| 在线免费观看的www视频| 一本—道久久a久久精品蜜桃钙片 精品乱码久久久久久99久播 | 国产中年淑女户外野战色| 欧美另类亚洲清纯唯美| 国产免费又黄又爽又色| 欧美精品一区二区大全| 国内少妇人妻偷人精品xxx网站| 三级国产精品欧美在线观看| 一级黄色大片毛片| 3wmmmm亚洲av在线观看| 亚洲精品影视一区二区三区av| 欧美高清性xxxxhd video| 人人妻人人澡人人爽人人夜夜 | 国产成人精品婷婷| 国模一区二区三区四区视频| 一二三四中文在线观看免费高清| 亚洲aⅴ乱码一区二区在线播放| 亚洲精品影视一区二区三区av| 精品久久久久久久久久久久久| 亚洲色图av天堂| 久久这里只有精品中国| 精品久久久久久久末码| 日本一本二区三区精品| 亚洲真实伦在线观看| 亚洲精品色激情综合| 亚洲熟妇中文字幕五十中出| av在线天堂中文字幕| 青春草亚洲视频在线观看| 一个人观看的视频www高清免费观看| 毛片女人毛片| 亚洲图色成人| 日日摸夜夜添夜夜添av毛片| 午夜视频国产福利| 国产成人午夜福利电影在线观看| 人妻制服诱惑在线中文字幕| 亚洲精品日韩av片在线观看| 日韩欧美精品免费久久| 18+在线观看网站| av播播在线观看一区| 亚洲av一区综合| 国产v大片淫在线免费观看| 亚洲,欧美,日韩| 黄色一级大片看看| 亚洲,欧美,日韩| 国产黄色视频一区二区在线观看 | 变态另类丝袜制服| 国产精品久久久久久久久免| 国产av码专区亚洲av| 久久久久精品久久久久真实原创| 村上凉子中文字幕在线| 91av网一区二区| 日韩三级伦理在线观看| 春色校园在线视频观看| 免费观看精品视频网站| 久久国产乱子免费精品| 插阴视频在线观看视频| 日韩欧美在线乱码| 久久精品久久久久久噜噜老黄 | 麻豆久久精品国产亚洲av| 看非洲黑人一级黄片| 草草在线视频免费看| 亚洲av男天堂| 亚洲国产欧美在线一区| 国产淫片久久久久久久久| 亚洲国产日韩欧美精品在线观看| 草草在线视频免费看| 久久久色成人| 免费看光身美女| 国产成人91sexporn| 久久6这里有精品| 午夜爱爱视频在线播放| 老司机福利观看| 久久久精品大字幕| 免费观看在线日韩| 青春草亚洲视频在线观看| 女的被弄到高潮叫床怎么办| 国产女主播在线喷水免费视频网站 | 最近中文字幕高清免费大全6| 亚洲国产精品久久男人天堂| 中文资源天堂在线| 搞女人的毛片| 国产精品一区二区在线观看99 | 精品久久久久久电影网 | 男人舔奶头视频| 日韩成人av中文字幕在线观看| 久久久久久久久久久丰满| 人妻夜夜爽99麻豆av| 男女边吃奶边做爰视频| 18禁在线无遮挡免费观看视频| 久久久久网色| 三级国产精品片| 成人性生交大片免费视频hd| 晚上一个人看的免费电影| 在线a可以看的网站| 长腿黑丝高跟| 国产黄色视频一区二区在线观看 | 日本一二三区视频观看| 亚洲国产日韩欧美精品在线观看| 日本黄大片高清| 亚洲自拍偷在线| 亚洲av男天堂| 亚洲精品影视一区二区三区av| 又爽又黄a免费视频| 18+在线观看网站| 亚洲av熟女| 97热精品久久久久久| 视频中文字幕在线观看| 男女视频在线观看网站免费| 欧美成人一区二区免费高清观看| 能在线免费看毛片的网站| 日韩 亚洲 欧美在线| 亚洲美女搞黄在线观看| 久久久欧美国产精品| 波多野结衣高清无吗| 日日摸夜夜添夜夜爱| 国产淫语在线视频| 99久久精品热视频| 成人二区视频| 久久热精品热| 嫩草影院入口| 日韩欧美国产在线观看| 国产精品乱码一区二三区的特点| 成人亚洲欧美一区二区av| 亚洲自拍偷在线| 有码 亚洲区| 午夜精品国产一区二区电影 | 日韩一区二区三区影片| 精品国产一区二区三区久久久樱花 | 高清午夜精品一区二区三区| 麻豆久久精品国产亚洲av| 日日撸夜夜添| 久久精品国产亚洲网站| av天堂中文字幕网| 高清日韩中文字幕在线| 亚洲av二区三区四区| av在线老鸭窝| 亚洲中文字幕一区二区三区有码在线看| 全区人妻精品视频| 久久久久久久午夜电影| 麻豆乱淫一区二区| 国产激情偷乱视频一区二区| 精品人妻偷拍中文字幕| 精品久久国产蜜桃| 久久久久性生活片| 久久久久国产网址| 美女黄网站色视频| 亚洲人与动物交配视频| 日本熟妇午夜| 99热网站在线观看| 久久久色成人| 国产午夜精品论理片| 亚洲国产精品sss在线观看| 久久99热这里只频精品6学生 | 免费一级毛片在线播放高清视频| 日本五十路高清| 亚洲欧美日韩卡通动漫| 国产91av在线免费观看| 99久久九九国产精品国产免费| 亚洲精品久久久久久婷婷小说 | 又爽又黄无遮挡网站| 国产精品三级大全| 99久国产av精品国产电影| 精品久久久久久久人妻蜜臀av| 亚洲精品日韩在线中文字幕| 观看美女的网站| 免费黄网站久久成人精品| 日本黄大片高清| 久久久精品94久久精品| 青春草国产在线视频| 国产精品99久久久久久久久| ponron亚洲| 男女国产视频网站| 欧美丝袜亚洲另类| 免费看光身美女| 少妇被粗大猛烈的视频| 岛国在线免费视频观看| 色视频www国产| 国产精品野战在线观看| 自拍偷自拍亚洲精品老妇| 综合色av麻豆| a级毛色黄片| 欧美潮喷喷水| 亚洲精品亚洲一区二区| 91久久精品国产一区二区三区| 少妇人妻精品综合一区二区| 亚洲在线观看片| 成年女人看的毛片在线观看| 国产一区亚洲一区在线观看| 最近手机中文字幕大全| 联通29元200g的流量卡| 91久久精品电影网| 免费观看的影片在线观看| 99久久九九国产精品国产免费| 精品久久国产蜜桃| 大又大粗又爽又黄少妇毛片口| 男女下面进入的视频免费午夜| 久久精品夜色国产| 亚洲中文字幕一区二区三区有码在线看| 中文精品一卡2卡3卡4更新| 最后的刺客免费高清国语| 欧美成人午夜免费资源| 成人亚洲欧美一区二区av| 免费看美女性在线毛片视频| 亚洲丝袜综合中文字幕| 天堂√8在线中文| 亚洲美女搞黄在线观看| 久久精品久久久久久噜噜老黄 | 国产一区二区三区av在线| 乱系列少妇在线播放| 亚洲性久久影院| 日本午夜av视频| 久久精品久久精品一区二区三区| 麻豆成人午夜福利视频| 免费av观看视频| 亚州av有码| 久久久久久久国产电影| eeuss影院久久| 久久精品国产亚洲网站| 亚洲精品日韩在线中文字幕| 午夜激情福利司机影院| 亚洲精品乱码久久久久久按摩| 大香蕉97超碰在线| 亚洲第一区二区三区不卡| 国产亚洲午夜精品一区二区久久 | 亚洲精品亚洲一区二区| 日本猛色少妇xxxxx猛交久久| 久久精品国产亚洲av天美| 人人妻人人澡欧美一区二区| 国产成人aa在线观看| 亚洲欧美精品自产自拍| 午夜精品在线福利| 久久精品91蜜桃| 毛片一级片免费看久久久久| 成年版毛片免费区| 色哟哟·www| 亚洲一区高清亚洲精品| 人人妻人人看人人澡| 一区二区三区高清视频在线| 床上黄色一级片| 国产久久久一区二区三区| 日韩欧美精品v在线| 男人的好看免费观看在线视频| 夜夜爽夜夜爽视频| 国产成人91sexporn| 亚洲人成网站在线播| 91av网一区二区| 亚洲欧美中文字幕日韩二区| 乱码一卡2卡4卡精品| 亚洲av日韩在线播放| 国产av在哪里看| 成年女人永久免费观看视频| 久久精品国产99精品国产亚洲性色| 亚洲av日韩在线播放| 性色avwww在线观看| 亚洲av二区三区四区| 女人十人毛片免费观看3o分钟| 亚洲成av人片在线播放无| 精品久久久久久久久av| 久久精品熟女亚洲av麻豆精品 | 久久精品久久久久久噜噜老黄 | 一个人看视频在线观看www免费| 亚洲欧美精品专区久久| 日韩欧美精品免费久久| 九草在线视频观看| 国产成人a∨麻豆精品| 亚洲,欧美,日韩| av在线天堂中文字幕| 欧美高清成人免费视频www| 亚洲国产欧美人成| 精品久久久久久久人妻蜜臀av| 亚洲内射少妇av| 亚洲三级黄色毛片| 菩萨蛮人人尽说江南好唐韦庄 | 老司机福利观看| 欧美激情久久久久久爽电影| 国产私拍福利视频在线观看| 99久久人妻综合| 成人国产麻豆网| 欧美极品一区二区三区四区| 草草在线视频免费看| 免费黄网站久久成人精品| 久久精品国产自在天天线| 亚洲欧美精品综合久久99| 神马国产精品三级电影在线观看| av视频在线观看入口| 在线免费观看不下载黄p国产| 国产欧美另类精品又又久久亚洲欧美| 天美传媒精品一区二区| 青春草国产在线视频| 中国美白少妇内射xxxbb| 亚洲成人精品中文字幕电影| 美女xxoo啪啪120秒动态图| 中文字幕免费在线视频6| 网址你懂的国产日韩在线| 日韩一本色道免费dvd| 国产精品一及| 欧美xxxx黑人xx丫x性爽| 国产69精品久久久久777片| 日韩欧美在线乱码| 国产精品爽爽va在线观看网站| 看黄色毛片网站| 男女国产视频网站| 欧美日韩国产亚洲二区| 两个人视频免费观看高清| 精品少妇黑人巨大在线播放 | 人人妻人人看人人澡| 日韩强制内射视频| 熟妇人妻久久中文字幕3abv| 日本黄大片高清| 久久久精品94久久精品| 国产亚洲一区二区精品| 国语自产精品视频在线第100页| 国产 一区精品| 亚洲欧美清纯卡通| 欧美日韩国产亚洲二区| 深爱激情五月婷婷| 国内精品一区二区在线观看| 一个人观看的视频www高清免费观看| h日本视频在线播放| 天天躁日日操中文字幕| 精品国产一区二区三区久久久樱花 | 哪个播放器可以免费观看大片| 色播亚洲综合网| 国产色婷婷99| 国产真实伦视频高清在线观看| 三级毛片av免费| 汤姆久久久久久久影院中文字幕 | 直男gayav资源| 高清日韩中文字幕在线| 啦啦啦韩国在线观看视频| 久久人人爽人人爽人人片va| 国产伦精品一区二区三区四那| 亚洲国产欧美人成| 亚洲国产精品国产精品| 免费看a级黄色片| 国产高清有码在线观看视频| 亚洲国产精品久久男人天堂| 午夜福利网站1000一区二区三区| 国语对白做爰xxxⅹ性视频网站| 免费观看在线日韩| 好男人在线观看高清免费视频| 久久久久久九九精品二区国产| 水蜜桃什么品种好| 国产精品无大码| 非洲黑人性xxxx精品又粗又长| 黄片无遮挡物在线观看| 成人亚洲精品av一区二区| 菩萨蛮人人尽说江南好唐韦庄 | 亚洲av二区三区四区| 国产精品久久久久久精品电影| 日韩一区二区三区影片| 波野结衣二区三区在线| 嫩草影院入口| 三级国产精品欧美在线观看| 国产乱来视频区| 日本午夜av视频| 直男gayav资源| 免费不卡的大黄色大毛片视频在线观看 | 在线免费十八禁| 伦理电影大哥的女人| 亚洲一级一片aⅴ在线观看| 青春草国产在线视频| 国产精品一区二区性色av| 亚洲成人久久爱视频| 亚洲精品乱码久久久久久按摩| 欧美极品一区二区三区四区| 精品国产一区二区三区久久久樱花 | 亚洲成人久久爱视频| 国产亚洲5aaaaa淫片| 女人被狂操c到高潮| 黄片wwwwww| videos熟女内射| 国产黄a三级三级三级人| 观看免费一级毛片| 精品久久久久久电影网 | 日产精品乱码卡一卡2卡三| 插阴视频在线观看视频| 国产黄片美女视频| 一个人观看的视频www高清免费观看| 美女脱内裤让男人舔精品视频| 久久久国产成人免费| av在线观看视频网站免费| 欧美极品一区二区三区四区| 亚洲精品色激情综合| 精品久久久久久久人妻蜜臀av| 午夜亚洲福利在线播放| 国产高清不卡午夜福利| 国产精品美女特级片免费视频播放器| 亚洲欧美精品专区久久| 尤物成人国产欧美一区二区三区| 看非洲黑人一级黄片| 成人鲁丝片一二三区免费| 边亲边吃奶的免费视频| 性色avwww在线观看| 女人久久www免费人成看片 | 白带黄色成豆腐渣| 亚洲最大成人手机在线| 波多野结衣高清无吗| 中文字幕制服av| 久久久久久伊人网av| 国产精品野战在线观看| 狠狠狠狠99中文字幕| 国产精品久久久久久久电影| 欧美xxxx性猛交bbbb| 国产真实伦视频高清在线观看| 在线播放国产精品三级| 最近最新中文字幕免费大全7| 欧美成人午夜免费资源| 哪个播放器可以免费观看大片| 在线免费观看不下载黄p国产| 欧美三级亚洲精品| av国产久精品久网站免费入址| 欧美不卡视频在线免费观看| 18禁在线无遮挡免费观看视频| 永久免费av网站大全| 又爽又黄无遮挡网站| 菩萨蛮人人尽说江南好唐韦庄 | 亚洲综合色惰| 少妇的逼水好多| 丝袜美腿在线中文| 边亲边吃奶的免费视频| 99久国产av精品| 久久久久久久久久黄片| 成人综合一区亚洲| 亚洲18禁久久av| 尤物成人国产欧美一区二区三区| 日韩中字成人| 麻豆av噜噜一区二区三区| 2022亚洲国产成人精品| 人妻系列 视频| 欧美日韩精品成人综合77777| 男女那种视频在线观看| 少妇的逼好多水| 国产伦理片在线播放av一区| 亚洲五月天丁香| 成人毛片60女人毛片免费| 国产亚洲一区二区精品| 色综合亚洲欧美另类图片| 赤兔流量卡办理| 免费搜索国产男女视频| 在线观看av片永久免费下载| 日本黄色片子视频| 精品人妻视频免费看| 一级av片app| 欧美日韩在线观看h| 午夜激情福利司机影院| 久久久亚洲精品成人影院| 在线免费十八禁| 22中文网久久字幕| 人妻少妇偷人精品九色| 哪个播放器可以免费观看大片| 一级爰片在线观看| 免费大片18禁| 欧美日韩综合久久久久久| 在线免费十八禁| 26uuu在线亚洲综合色| 又黄又爽又刺激的免费视频.| 舔av片在线| www.av在线官网国产| 97热精品久久久久久| 插逼视频在线观看| 中文乱码字字幕精品一区二区三区 | 欧美最新免费一区二区三区| 少妇猛男粗大的猛烈进出视频 | 桃色一区二区三区在线观看| 亚洲成人中文字幕在线播放| 在线播放国产精品三级| 高清av免费在线| 免费一级毛片在线播放高清视频| 成人漫画全彩无遮挡| 日韩在线高清观看一区二区三区| 日本av手机在线免费观看| 伦理电影大哥的女人| 狠狠狠狠99中文字幕| 老司机影院毛片| 欧美一区二区国产精品久久精品| 亚洲人成网站在线观看播放| 久久久久久大精品| 亚洲,欧美,日韩| 久久午夜福利片| 边亲边吃奶的免费视频| 国产单亲对白刺激| 七月丁香在线播放| 免费观看精品视频网站| 欧美性感艳星| 夫妻性生交免费视频一级片| 男人和女人高潮做爰伦理| 久久99精品国语久久久| 国产精品久久电影中文字幕| 久久久久久伊人网av| 成人午夜精彩视频在线观看| 欧美性感艳星| 伦理电影大哥的女人| 18禁裸乳无遮挡免费网站照片| 成人毛片a级毛片在线播放| 亚洲人与动物交配视频| 亚洲真实伦在线观看| 欧美成人免费av一区二区三区| 最新中文字幕久久久久| 国产日韩欧美在线精品| 搡老妇女老女人老熟妇| 边亲边吃奶的免费视频| 色尼玛亚洲综合影院| 国产精品女同一区二区软件| 国产成人精品久久久久久| 国产69精品久久久久777片| 国产亚洲91精品色在线| 国语自产精品视频在线第100页| 麻豆乱淫一区二区| 五月玫瑰六月丁香| 国产白丝娇喘喷水9色精品| 少妇人妻精品综合一区二区| 亚洲一级一片aⅴ在线观看| 日韩,欧美,国产一区二区三区 | 成人三级黄色视频| 99久久精品一区二区三区| 亚洲五月天丁香| 村上凉子中文字幕在线| 啦啦啦韩国在线观看视频| 18禁在线无遮挡免费观看视频| 亚洲乱码一区二区免费版| 国产成人精品一,二区| 久久韩国三级中文字幕| 日本五十路高清| 国产色婷婷99| 18禁在线无遮挡免费观看视频| 黄片无遮挡物在线观看| 成人欧美大片| 午夜福利在线观看吧| 亚洲国产精品sss在线观看| a级一级毛片免费在线观看| 亚洲va在线va天堂va国产| 欧美bdsm另类| 午夜免费男女啪啪视频观看| 99久国产av精品国产电影| 国产精品av视频在线免费观看|