• <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

    久久中文看片网| 国产成人一区二区三区免费视频网站| 欧美久久黑人一区二区| 天天一区二区日本电影三级| 韩国精品一区二区三区| 亚洲中文av在线| 成人手机av| 亚洲男人的天堂狠狠| 波多野结衣巨乳人妻| 久久久久久免费高清国产稀缺| 美女大奶头视频| 亚洲av电影在线进入| 亚洲精品久久国产高清桃花| 亚洲欧洲精品一区二区精品久久久| 欧美 亚洲 国产 日韩一| 大型黄色视频在线免费观看| 淫秽高清视频在线观看| 欧美日韩一级在线毛片| 午夜免费鲁丝| 悠悠久久av| 在线免费观看的www视频| 日本a在线网址| 免费在线观看亚洲国产| 女人爽到高潮嗷嗷叫在线视频| 日本免费一区二区三区高清不卡| 欧美性猛交黑人性爽| 国产欧美日韩一区二区精品| 91大片在线观看| 国产乱人伦免费视频| 国产精品久久久av美女十八| 亚洲一码二码三码区别大吗| 精品久久蜜臀av无| 国产精品1区2区在线观看.| 搡老岳熟女国产| 久久久久久国产a免费观看| 亚洲色图 男人天堂 中文字幕| 最新美女视频免费是黄的| 老熟妇乱子伦视频在线观看| 亚洲成人国产一区在线观看| 男人的好看免费观看在线视频 | 欧美一级a爱片免费观看看 | 亚洲精品美女久久av网站| 一本久久中文字幕| 夜夜躁狠狠躁天天躁| 亚洲三区欧美一区| 一区二区三区精品91| 色av中文字幕| 黑人操中国人逼视频| 久久久久久人人人人人| 国产精品二区激情视频| 99精品久久久久人妻精品| 欧美最黄视频在线播放免费| 伊人久久大香线蕉亚洲五| 久久精品影院6| 两性夫妻黄色片| 国产又黄又爽又无遮挡在线| 身体一侧抽搐| 国产精品日韩av在线免费观看| 在线观看免费午夜福利视频| 9191精品国产免费久久| 在线视频色国产色| 老鸭窝网址在线观看| 国产激情久久老熟女| 香蕉久久夜色| 巨乳人妻的诱惑在线观看| 国产伦人伦偷精品视频| 日韩欧美在线二视频| 欧美不卡视频在线免费观看 | 人成视频在线观看免费观看| 欧美日韩亚洲国产一区二区在线观看| 熟女电影av网| av有码第一页| 久久久久久亚洲精品国产蜜桃av| 韩国av一区二区三区四区| 人人妻人人澡欧美一区二区| 免费观看人在逋| 最新在线观看一区二区三区| 一本综合久久免费| av欧美777| 免费搜索国产男女视频| 久久午夜综合久久蜜桃| 老司机靠b影院| 波多野结衣高清作品| 欧美成狂野欧美在线观看| 最新美女视频免费是黄的| 久久精品91蜜桃| 深夜精品福利| 热99re8久久精品国产| 久久国产亚洲av麻豆专区| 999久久久国产精品视频| 色综合站精品国产| 国产v大片淫在线免费观看| 不卡av一区二区三区| 亚洲国产精品999在线| 性色av乱码一区二区三区2| 久久草成人影院| 日本熟妇午夜| 国产一卡二卡三卡精品| 可以在线观看的亚洲视频| 久久国产亚洲av麻豆专区| 久久久久亚洲av毛片大全| 黄色视频,在线免费观看| 亚洲成a人片在线一区二区| 中文资源天堂在线| 一区二区日韩欧美中文字幕| 一级作爱视频免费观看| 少妇熟女aⅴ在线视频| 久久精品夜夜夜夜夜久久蜜豆 | 欧美色视频一区免费| 精品国产乱子伦一区二区三区| 每晚都被弄得嗷嗷叫到高潮| 免费观看人在逋| 色av中文字幕| 成人av一区二区三区在线看| 午夜亚洲福利在线播放| xxx96com| av天堂在线播放| 91av网站免费观看| 日韩欧美国产在线观看| 少妇粗大呻吟视频| 天天添夜夜摸| 午夜成年电影在线免费观看| 麻豆久久精品国产亚洲av| 久久香蕉国产精品| 在线看三级毛片| 精品高清国产在线一区| 精品一区二区三区视频在线观看免费| 午夜福利高清视频| 国内揄拍国产精品人妻在线 | 国产成人欧美在线观看| 怎么达到女性高潮| 欧美 亚洲 国产 日韩一| 国产亚洲精品久久久久久毛片| 女人高潮潮喷娇喘18禁视频| 免费av毛片视频| 欧美日韩亚洲综合一区二区三区_| 国产成人影院久久av| av片东京热男人的天堂| 制服人妻中文乱码| 亚洲一区二区三区不卡视频| 国产视频一区二区在线看| 无遮挡黄片免费观看| 久久久国产精品麻豆| 嫁个100分男人电影在线观看| 亚洲国产精品久久男人天堂| 亚洲 国产 在线| 香蕉丝袜av| 欧洲精品卡2卡3卡4卡5卡区| ponron亚洲| 欧美性猛交╳xxx乱大交人| 91麻豆av在线| 丝袜在线中文字幕| 亚洲aⅴ乱码一区二区在线播放 | 国内少妇人妻偷人精品xxx网站 | 制服丝袜大香蕉在线| 亚洲男人的天堂狠狠| 动漫黄色视频在线观看| 男人舔奶头视频| 少妇被粗大的猛进出69影院| av在线天堂中文字幕| 欧美成人一区二区免费高清观看 | 亚洲熟妇中文字幕五十中出| 精品国产亚洲在线| 啦啦啦免费观看视频1| 久久久久久久午夜电影| 国产精品 国内视频| 亚洲成国产人片在线观看| 波多野结衣巨乳人妻| 男人舔女人的私密视频| av在线播放免费不卡| 两个人视频免费观看高清| 亚洲欧美精品综合久久99| 精品不卡国产一区二区三区| 国产精品九九99| 久久久国产成人精品二区| 国产精品一区二区精品视频观看| 午夜福利免费观看在线| 亚洲欧美激情综合另类| 国产亚洲精品综合一区在线观看 | 美女午夜性视频免费| 老熟妇仑乱视频hdxx| 国产亚洲av高清不卡| 麻豆久久精品国产亚洲av| www日本黄色视频网| 国产欧美日韩精品亚洲av| 啪啪无遮挡十八禁网站| 99热6这里只有精品| 人人澡人人妻人| 老司机在亚洲福利影院| 亚洲av成人不卡在线观看播放网| 欧美黑人欧美精品刺激| cao死你这个sao货| 桃色一区二区三区在线观看| 麻豆一二三区av精品| 国产视频一区二区在线看| 国内揄拍国产精品人妻在线 | 国产亚洲欧美精品永久| 777久久人妻少妇嫩草av网站| 国产又色又爽无遮挡免费看| 人人妻人人澡人人看| 天天一区二区日本电影三级| 亚洲无线在线观看| 国产精品一区二区免费欧美| 亚洲国产看品久久| 美女 人体艺术 gogo| 久久久久国产精品人妻aⅴ院| 亚洲久久久国产精品| 十分钟在线观看高清视频www| 熟女电影av网| cao死你这个sao货| av电影中文网址| 欧美激情 高清一区二区三区| 人人妻人人澡人人看| 亚洲精品一卡2卡三卡4卡5卡| 亚洲av日韩精品久久久久久密| 色av中文字幕| 最好的美女福利视频网| 男人操女人黄网站| 亚洲专区国产一区二区| 日本免费a在线| 国产人伦9x9x在线观看| 日韩 欧美 亚洲 中文字幕| 好男人在线观看高清免费视频 | 91国产中文字幕| 久久久久免费精品人妻一区二区 | 老司机福利观看| 国产一区二区三区视频了| 日韩欧美免费精品| 日日爽夜夜爽网站| 日本精品一区二区三区蜜桃| 亚洲av电影不卡..在线观看| 人人妻,人人澡人人爽秒播| 午夜福利在线观看吧| 一区二区三区国产精品乱码| www.999成人在线观看| 狂野欧美激情性xxxx| 精品国产超薄肉色丝袜足j| 搡老妇女老女人老熟妇| 9191精品国产免费久久| 国产三级黄色录像| 国产成人av激情在线播放| 美女大奶头视频| 别揉我奶头~嗯~啊~动态视频| 久久久久国产精品人妻aⅴ院| 国产精品九九99| 精品久久久久久久久久久久久 | 久99久视频精品免费| 婷婷亚洲欧美| 精品免费久久久久久久清纯| 亚洲午夜理论影院| 天堂√8在线中文| 一卡2卡三卡四卡精品乱码亚洲| 久久久久久九九精品二区国产 | 国产久久久一区二区三区| 欧美+亚洲+日韩+国产| 久久久久九九精品影院| 亚洲一区中文字幕在线| 久久中文字幕人妻熟女| 男男h啪啪无遮挡| 免费电影在线观看免费观看| 精品久久蜜臀av无| 村上凉子中文字幕在线| 免费在线观看成人毛片| 91字幕亚洲| 一边摸一边抽搐一进一小说| 国产乱人伦免费视频| 俄罗斯特黄特色一大片| 免费一级毛片在线播放高清视频| 岛国视频午夜一区免费看| 人妻久久中文字幕网| www.自偷自拍.com| 久9热在线精品视频| 老熟妇乱子伦视频在线观看| 久久精品国产亚洲av高清一级| 一级毛片高清免费大全| 亚洲精品色激情综合| 精品欧美一区二区三区在线| 精品久久久久久,| 免费在线观看黄色视频的| 一边摸一边做爽爽视频免费| 麻豆av在线久日| 欧美 亚洲 国产 日韩一| 欧美日韩中文字幕国产精品一区二区三区| 黄色a级毛片大全视频| 一区二区日韩欧美中文字幕| 亚洲avbb在线观看| 精品久久久久久,| 女警被强在线播放| 久久婷婷成人综合色麻豆| 一个人观看的视频www高清免费观看 | 他把我摸到了高潮在线观看| 欧美日韩亚洲国产一区二区在线观看| 国产成人系列免费观看| 国产精品亚洲av一区麻豆| 少妇粗大呻吟视频| 久久香蕉激情| 12—13女人毛片做爰片一| 日韩高清综合在线| 国产精品99久久99久久久不卡| 少妇裸体淫交视频免费看高清 | 欧美 亚洲 国产 日韩一| 19禁男女啪啪无遮挡网站| 国产aⅴ精品一区二区三区波| 999久久久国产精品视频| 丝袜美腿诱惑在线| 9191精品国产免费久久| 黑丝袜美女国产一区| 可以在线观看毛片的网站| 精品国产超薄肉色丝袜足j| 日本 欧美在线| 中文字幕精品免费在线观看视频| 制服丝袜大香蕉在线| 午夜日韩欧美国产| 成人亚洲精品一区在线观看| 精品第一国产精品| 88av欧美| 欧美久久黑人一区二区| 男女床上黄色一级片免费看| 国产又黄又爽又无遮挡在线| 日韩欧美三级三区| 精品久久久久久久久久免费视频| 日本精品一区二区三区蜜桃| 欧美一级a爱片免费观看看 | 亚洲人成电影免费在线| 日本免费一区二区三区高清不卡| 免费在线观看黄色视频的| 免费在线观看影片大全网站| 亚洲成av人片免费观看| 中文亚洲av片在线观看爽| 免费人成视频x8x8入口观看| 久久久久久久久中文| 国产三级在线视频| 日本免费a在线| 成人亚洲精品av一区二区| 91在线观看av| 99re在线观看精品视频| 亚洲五月婷婷丁香| 国产99久久九九免费精品| 国产野战对白在线观看| 精品久久久久久久末码| 日韩大尺度精品在线看网址| 亚洲欧美日韩无卡精品| 久久精品成人免费网站| 久久青草综合色| 午夜福利18| 俺也久久电影网| 亚洲中文日韩欧美视频| av欧美777| 精品熟女少妇八av免费久了| 日韩国内少妇激情av| 国产精品,欧美在线| 国内精品久久久久久久电影| 成年版毛片免费区| 国产精品九九99| 色尼玛亚洲综合影院| 久久精品人妻少妇| 在线观看午夜福利视频| 日本熟妇午夜| 亚洲黑人精品在线| 亚洲色图av天堂| 夜夜爽天天搞| 中文亚洲av片在线观看爽| 18禁裸乳无遮挡免费网站照片 | 搡老妇女老女人老熟妇| 曰老女人黄片| 久久久久国内视频| 夜夜看夜夜爽夜夜摸| 亚洲精品久久国产高清桃花| 国产午夜福利久久久久久| 亚洲成av人片免费观看| 国产精品综合久久久久久久免费| 精品无人区乱码1区二区| 日韩欧美国产一区二区入口| 欧美丝袜亚洲另类 | 午夜福利高清视频| 亚洲人成伊人成综合网2020| 久久久国产欧美日韩av| 免费看日本二区| 久久久国产欧美日韩av| 人妻久久中文字幕网| 午夜a级毛片| 成年女人毛片免费观看观看9| 丁香欧美五月| 亚洲国产精品久久男人天堂| 黄片小视频在线播放| 成人18禁高潮啪啪吃奶动态图| 精品国产美女av久久久久小说| 国产高清videossex| 国产av一区二区精品久久| 亚洲国产中文字幕在线视频| 国产免费av片在线观看野外av| 黄色丝袜av网址大全| 国产免费av片在线观看野外av| 亚洲欧洲精品一区二区精品久久久| 香蕉av资源在线| 亚洲欧洲精品一区二区精品久久久| 宅男免费午夜| 可以在线观看毛片的网站| 亚洲中文av在线| 精品国产国语对白av| 国产精品精品国产色婷婷| 国产伦一二天堂av在线观看| 亚洲国产精品成人综合色| 亚洲精品一卡2卡三卡4卡5卡| 中国美女看黄片| 日韩中文字幕欧美一区二区| 别揉我奶头~嗯~啊~动态视频| 91字幕亚洲| xxxwww97欧美| av在线播放免费不卡| 国内久久婷婷六月综合欲色啪| 在线永久观看黄色视频| 我的亚洲天堂| 国产一区二区三区在线臀色熟女| 中文字幕精品免费在线观看视频| 国产精品 国内视频| 欧美成人午夜精品| 99在线视频只有这里精品首页| 一区二区三区高清视频在线| 三级毛片av免费| 精品人妻1区二区| 精品久久蜜臀av无| 一进一出好大好爽视频| 欧美最黄视频在线播放免费| 一个人免费在线观看的高清视频| 亚洲最大成人中文| 这个男人来自地球电影免费观看| 少妇粗大呻吟视频| 国产亚洲欧美98| 欧美av亚洲av综合av国产av| svipshipincom国产片| 久久精品国产清高在天天线| 可以在线观看毛片的网站| or卡值多少钱| 欧美国产精品va在线观看不卡| 日本 欧美在线| 精品一区二区三区视频在线观看免费| 国产成人一区二区三区免费视频网站| 97人妻精品一区二区三区麻豆 | 村上凉子中文字幕在线| 亚洲国产精品久久男人天堂| 十八禁网站免费在线| 男男h啪啪无遮挡| 日韩欧美免费精品| 一级a爱视频在线免费观看| 99riav亚洲国产免费| 不卡一级毛片| 欧美成人一区二区免费高清观看 | 午夜精品在线福利| 亚洲三区欧美一区| 欧美黑人巨大hd| 露出奶头的视频| 男人的好看免费观看在线视频 | 妹子高潮喷水视频| 麻豆av在线久日| √禁漫天堂资源中文www| 看黄色毛片网站| 欧美一区二区精品小视频在线| 女警被强在线播放| 不卡一级毛片| 又大又爽又粗| 变态另类成人亚洲欧美熟女| 日本a在线网址| 日本一本二区三区精品| 久久狼人影院| 99久久综合精品五月天人人| 午夜影院日韩av| 青草久久国产| 亚洲熟女毛片儿| 国产精品久久视频播放| 欧美绝顶高潮抽搐喷水| 免费女性裸体啪啪无遮挡网站| 男男h啪啪无遮挡| 十八禁网站免费在线| 国内久久婷婷六月综合欲色啪| 亚洲国产精品999在线| 在线观看免费午夜福利视频| 熟女电影av网| 欧美黑人巨大hd| 又黄又爽又免费观看的视频| 国产精品自产拍在线观看55亚洲| 精品国产乱子伦一区二区三区| 精品国产美女av久久久久小说| 熟女少妇亚洲综合色aaa.| 亚洲国产精品999在线| 欧美色欧美亚洲另类二区| 亚洲全国av大片| 在线观看日韩欧美| 亚洲精品在线美女| 国产成人影院久久av| 亚洲一区中文字幕在线| 中文字幕另类日韩欧美亚洲嫩草| 欧美乱码精品一区二区三区| 亚洲精品美女久久av网站| 我的亚洲天堂| 亚洲熟妇中文字幕五十中出| 国产99久久九九免费精品| 19禁男女啪啪无遮挡网站| 欧美最黄视频在线播放免费| 日韩三级视频一区二区三区| 日日夜夜操网爽| 久久久国产欧美日韩av| 亚洲av日韩精品久久久久久密| 精品国产乱子伦一区二区三区| 国产黄a三级三级三级人| 中文在线观看免费www的网站 | 久久久久久人人人人人| 黄色片一级片一级黄色片| 日韩精品青青久久久久久| 亚洲成a人片在线一区二区| 午夜影院日韩av| 最新在线观看一区二区三区| 在线视频色国产色| 女人被狂操c到高潮| 久久久国产欧美日韩av| 99久久综合精品五月天人人| 日本 av在线| 欧美大码av| 18禁国产床啪视频网站| 一级毛片女人18水好多| 99在线人妻在线中文字幕| 一级毛片精品| av欧美777| 中文字幕最新亚洲高清| 国产蜜桃级精品一区二区三区| 1024视频免费在线观看| 免费在线观看影片大全网站| 99在线视频只有这里精品首页| 日本五十路高清| 色综合欧美亚洲国产小说| 久久狼人影院| 最近最新中文字幕大全电影3 | 给我免费播放毛片高清在线观看| 国产成人欧美| 国产精品 国内视频| 999久久久精品免费观看国产| 别揉我奶头~嗯~啊~动态视频| 久久精品人妻少妇| 国产97色在线日韩免费| 一区二区日韩欧美中文字幕| 国内精品久久久久精免费| 露出奶头的视频| 国产精品国产高清国产av| 两性夫妻黄色片| 女人高潮潮喷娇喘18禁视频| 欧美另类亚洲清纯唯美| 国产免费av片在线观看野外av| 日韩大尺度精品在线看网址| АⅤ资源中文在线天堂| 好看av亚洲va欧美ⅴa在| cao死你这个sao货| 国产一级毛片七仙女欲春2 | 91成人精品电影| 亚洲avbb在线观看| 18禁国产床啪视频网站| 99精品在免费线老司机午夜| 嫩草影院精品99| 日韩精品中文字幕看吧| 国产成人影院久久av| 男人舔女人的私密视频| 成年免费大片在线观看| 午夜a级毛片| 一级毛片女人18水好多| 亚洲av电影在线进入| 曰老女人黄片| 制服诱惑二区| 黄色 视频免费看| 国产成人一区二区三区免费视频网站| 午夜福利18| 国产片内射在线| 亚洲中文av在线| 欧美国产精品va在线观看不卡| 亚洲精品久久国产高清桃花| 好男人电影高清在线观看| av片东京热男人的天堂| 国产av一区二区精品久久| 日日夜夜操网爽| 男女之事视频高清在线观看| 国产精品免费视频内射| 国产熟女午夜一区二区三区| 亚洲真实伦在线观看| 老司机福利观看| 两个人免费观看高清视频| 久久午夜综合久久蜜桃| 久久久久久久午夜电影| 亚洲va日本ⅴa欧美va伊人久久| 高清毛片免费观看视频网站| 久久精品人妻少妇| 老司机福利观看| 91麻豆精品激情在线观看国产| 国产激情欧美一区二区| 亚洲av第一区精品v没综合| 亚洲熟女毛片儿| 两人在一起打扑克的视频| 午夜福利一区二区在线看| 精品国内亚洲2022精品成人| 中文字幕另类日韩欧美亚洲嫩草| 日韩精品中文字幕看吧| 成年人黄色毛片网站| 日本五十路高清| 国产欧美日韩一区二区精品| 无遮挡黄片免费观看| 日韩欧美国产在线观看| 亚洲中文av在线| 亚洲国产精品合色在线| 久久午夜亚洲精品久久| 97碰自拍视频| 十分钟在线观看高清视频www| 久久国产乱子伦精品免费另类| 国产成人啪精品午夜网站| 美女高潮喷水抽搐中文字幕| 9191精品国产免费久久| 亚洲中文av在线| 精品高清国产在线一区| 亚洲欧美一区二区三区黑人|