A method for updating stand factors based on two-period forest data

Abstract

The application discloses a method for updating stand factors based on two-period forest data and belongs to the technical field of data processing. The method comprises the following steps: acquiring base-period forest survey data and current-period forest survey data, converting base-period map blocks in the base-period forest survey data into base-period stand types according to a preset mapping rule, and converting current-period map blocks in the current-period forest survey data into current-period stand types according to the preset mapping rule; performing spatial overlay analysis on the base-period forest survey data and the current-period forest survey data, and classifying the current-period map blocks into first-type map blocks or second-type map blocks according to an overlay result; selecting corresponding growth updating models and updating time length parameters according to a classification result and the current-period stand types of the current-period map blocks; and calculating and obtaining updated values of target stand factors by using the selected growth updating models and the updating time length parameters. The application classifies and matches models by distinguishing stand change types through spatial overlay, and significantly improves the updating precision of stand factors.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method for updating stand factors based on two periods of forest data. Background Technology

[0002] In forest resource monitoring, it is necessary to regularly update stand factors such as average diameter at breast height (DBH), average tree height, volume per hectare, and number of trees per hectare in arbor forests. Current updating methods are mainly divided into two categories:

[0003] The first type relies on constructing a single growth model based on remote sensing imagery or sample plot data for estimation. This method does not distinguish the growth characteristics of various forest stand types classified by origin, tree species, and age group. In addition, the representativeness of sample plot data is limited, resulting in large estimation errors. Furthermore, it does not take into account the spatial distribution and growth continuity of forest stands.

[0004] The second method involves directly replacing and updating data from the previous and subsequent surveys, where the current forest survey data (data at the current point in time) covers the base period forest survey data (historical data at the current point in time). This method does not perform spatial overlay analysis on the two map patches and cannot distinguish between normal forest growth and abnormal changes such as logging and disasters. In map patches where the land type has not changed but the forest stand type has changed, the updated results deviate significantly from the actual situation.

[0005] Therefore, it is particularly necessary to provide a method for updating stand factors based on two periods of forest data to solve the problems of low update accuracy and inability to distinguish stand change types in existing technologies. Summary of the Invention

[0006] The purpose of this invention is to provide a method for updating stand factors based on two periods of forest data, which addresses the problems mentioned above. This method can solve the problems of low update accuracy and inability to distinguish stand change types in existing technologies.

[0007] The technical solution adopted in this invention is as follows:

[0008] A method for updating stand factors based on two periods of forest data includes the following steps:

[0009] S1. Data preprocessing: Obtain base period forest survey data and current period forest survey data, convert base period patches in the base period forest survey data into base period stand types according to preset mapping rules, and convert current period patches in the current period forest survey data into current period stand types according to preset mapping rules;

[0010] S2. Spatial analysis: Spatial overlay analysis is performed between the base period forest survey data and the current period forest survey data. Based on the overlay results, the current period's map patches are classified into type I map patches or type II map patches.

[0011] S3. Model matching: Based on the classification results and the current stand type of the current map patch, select the corresponding growth and update model and update duration parameters;

[0012] S4. Factor Update: Using the selected growth update model and update duration parameter, calculate the updated values ​​of the target stand factors.

[0013] Furthermore, in step S1:

[0014] The step of converting base period map patches in the base period forest survey data into base period stand types according to preset mapping rules specifically includes: converting each base period map patch in the base period forest survey data into stand type origin, stand type dominant tree species group, and stand type age group respectively according to base period origin mapping rules, base period dominant tree species mapping rules, and base period age group mapping rules; and combining the converted stand type origin, stand type dominant tree species group, and stand type age group to obtain the base period stand type.

[0015] The step of converting the current period map patches in the current period forest survey data into the current period forest stand type according to the preset mapping rules specifically includes: converting each current period map patch in the current period forest survey data into the forest stand type origin, forest stand type dominant tree species group and forest stand type age group respectively according to the current period origin mapping rule, the current period dominant tree species mapping rule and the current period age group mapping rule, and combining the converted forest stand type origin, forest stand type dominant tree species group and forest stand type age group to obtain the current period forest stand type.

[0016] Furthermore, both the base period forest stand type and the current period forest stand type are composed of three factors: forest stand type origin, dominant tree species group of forest stand type, and age group of forest stand type.

[0017] The combination method of the base period forest stand type is: base period forest stand type = forest stand type origin + forest stand type dominant tree species group + forest stand type age group;

[0018] The combination of stand types in this period is as follows: Stand type in this period = Stand type origin + Stand type dominant tree species group + Stand type age group.

[0019] Furthermore, in step S2, the step of spatially overlaying the base period forest survey data with the current period forest survey data and classifying the current period's map patches into first-type map patches or second-type map patches based on the overlay results specifically includes: spatially overlaying the base period forest survey data with the current period forest survey data to generate fine patches formed by the mutual cutting of the map patch boundaries of the two periods; calculating the area proportion of each fine patch in the current period's map patches; and classifying the entire current period's map patches into first-type map patches or second-type map patches based on the area proportion and whether the base period forest stand type corresponding to the fine patch is the same as the current period's forest stand type; wherein, first-type map patches are normal growth map patches, and second-type map patches are abnormal change map patches.

[0020] Furthermore, the criteria for classifying the entire current period's map patch as either a first-type or second-type map patch are as follows: Among all the small patches that overlap with the base period forest survey data, select the small patch with the largest area proportion. If the forest stand type corresponding to this small patch is the same as the base period forest stand type, and the area proportion of this small patch is greater than or equal to a preset threshold, then the entire current period's map patch is classified as a first-type map patch; if the condition is not met, then the entire current period's map patch is classified as a second-type map patch.

[0021] Furthermore, in step S3, the step of selecting the corresponding growth and renewal model and the renewal duration parameter based on the classification results and the current stand type of the current map patch specifically includes: for the current map patch classified as the first type, selecting the base period forest survey data as the renewal base, and setting the renewal duration parameter to the number of years from the base period forest survey data point to the expected data point; for the current map patch classified as the second type, selecting the current period forest survey data as the renewal base, and setting the renewal duration parameter to the number of years from the current period forest survey data point to the expected data point; then, based on the current stand type of the current map patch, selecting the corresponding hectare volume growth model and mean diameter at breast height change model from the preset model library.

[0022] Furthermore, in step S4, the step of calculating the update value of the target stand factor using the selected growth renewal model and update duration parameter specifically includes: first updating the average diameter at breast height (DBH) and hectare volume; then calculating the average tree height using the tree height-DBH regression model based on the updated average DBH; then calculating the single tree volume using the binary volume formula based on the updated average DBH and average tree height; and finally dividing the hectare volume by the single tree volume to obtain the number of trees per hectare.

[0023] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0024] This invention solves the problem of neglecting the differences in forest stand types and spatial variation in existing technologies by unifying the classification, spatial analysis, classification matching model, and factor calculation, and significantly improves the overall accuracy of forest stand factor updates. Attached Figure Description

[0025] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0027] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0028] like Figure 1 As shown, this invention discloses a method for updating stand factors based on two periods of forest data, comprising the following steps:

[0029] S1. Data preprocessing: Obtain base period forest survey data and current period forest survey data, convert base period patches in the base period forest survey data into base period stand types according to preset mapping rules, and convert current period patches in the current period forest survey data into current period stand types according to preset mapping rules;

[0030] S2. Spatial analysis: Spatial overlay analysis is performed between the base period forest survey data and the current period forest survey data. Based on the overlay results, the current period's map patches are classified into type I map patches or type II map patches.

[0031] S3. Model matching: Based on the classification results and the current stand type of the current map patch, select the corresponding growth and update model and update duration parameters;

[0032] S4. Factor Update: Using the selected growth update model and update duration parameter, calculate the updated values ​​of the target stand factors.

[0033] This method first performs S1 data preprocessing: acquiring base period forest survey data (historical time point data) and current period forest survey data (current time point data), converting base period patches to base period stand types according to preset mapping rules, and converting current period patches to current period stand types (e.g., "natural Yunnan pine juvenile forest") according to preset mapping rules. This conversion eliminates classification inconsistencies between the two periods due to differences in survey standards, tree species codes, etc. Next, S2 spatial analysis is performed: spatially overlaying the two periods' data, classifying current period patches into either type I patches (normal growth) or type II patches (abnormal changes) based on spatial location relationships. Then, S3 model matching is performed: based on the above classification results and the current period stand type of the patches, a corresponding growth update model (e.g., hectare volume index model) is selected from the model library, and the update duration parameter (e.g., 8 years or 3 years) is determined. Finally, S4 factor update is performed: combining the model and parameters to calculate the updated stand factor values.

[0034] By unifying the entire chain of classification, spatial analysis, classification matching model, and factor calculation, the problems of ignoring differences in forest stand types and spatial variation in existing technologies have been solved, significantly improving the overall accuracy of forest stand factor updates.

[0035] Furthermore, in step S1:

[0036] The step of converting base period map patches in the base period forest survey data into base period stand types according to preset mapping rules specifically includes: converting each base period map patch in the base period forest survey data into stand type origin, stand type dominant tree species group, and stand type age group respectively according to base period origin mapping rules, base period dominant tree species mapping rules, and base period age group mapping rules; and combining the converted stand type origin, stand type dominant tree species group, and stand type age group to obtain the base period stand type.

[0037] The step of converting the current period map patches in the current period forest survey data into the current period forest stand type according to the preset mapping rules specifically includes: converting each current period map patch in the current period forest survey data into the forest stand type origin, forest stand type dominant tree species group and forest stand type age group respectively according to the current period origin mapping rule, the current period dominant tree species mapping rule and the current period age group mapping rule, and combining the converted forest stand type origin, forest stand type dominant tree species group and forest stand type age group to obtain the current period forest stand type.

[0038] In step S1, each base period map patch in the base period forest survey data is converted into stand type origin, stand type dominant tree species group, and stand type age group according to the base period origin mapping rules (e.g., natural forest → "natural", plantation or artificially promoted forest → "artificial"), base period dominant tree species mapping rules (e.g., Yunnan pine → "Yunnan pine", oak → "oak", etc.), and base period age group mapping rules (e.g., young forest → "young forest", over-mature forest → "mature forest"), and then spliced ​​to obtain the base period stand type. Similarly, the current period map patches are converted into the current period stand type according to the current period mapping rules (e.g., origin source 10 → "natural", 20 → "artificial", etc.).

[0039] Standardized mapping enables comparison of data from two periods under the same forest stand type classification system, avoiding misjudgment of forest stand type due to differences in original data coding or classification standards, and providing a reliable data foundation for subsequent spatial overlay analysis.

[0040] Furthermore, both the base period forest stand type and the current period forest stand type are composed of three factors: forest stand type origin, dominant tree species group of forest stand type, and age group of forest stand type.

[0041] The combination method of the base period forest stand type is: base period forest stand type = forest stand type origin + forest stand type dominant tree species group + forest stand type age group;

[0042] The combination of stand types in this period is as follows: Stand type in this period = Stand type origin + Stand type dominant tree species group + Stand type age group.

[0043] These three factors describe the forest's reproductive method (natural or artificial), main tree species (such as Yunnan pine, oak, etc.), and developmental stage (juvenile, middle-aged, near-mature, mature). The combination of these three factors can uniquely identify the growth characteristics of a forest stand.

[0044] This combination method ensures that forest stand types not only have ecological significance but also facilitate rapid computer retrieval and matching of corresponding growth models. The model stores parameters according to forest stand type, allowing direct location based on the concatenated string, avoiding complex multi-condition queries.

[0045] Furthermore, in step S2, the step of spatially overlaying the base period forest survey data with the current period forest survey data and classifying the current period's map patches into first-type map patches or second-type map patches based on the overlay results specifically includes: spatially overlaying the base period forest survey data with the current period forest survey data to generate fine patches formed by the mutual cutting of the map patch boundaries of the two periods; calculating the area proportion of each fine patch in the current period's map patches; and classifying the entire current period's map patches into first-type map patches or second-type map patches based on the area proportion and whether the base period forest stand type corresponding to the fine patch is the same as the current period's forest stand type; wherein, first-type map patches are normal growth map patches, and second-type map patches are abnormal change map patches.

[0046] In step S2, the base period forest survey data and the current period forest survey data are spatially overlaid in GIS software to generate fine patches formed by the mutual cutting of the boundaries between the two periods' patches. For example, if a base period patch partially overlaps with a current period patch, three fine patches will be generated: base period unique, overlapping, and current period unique. Then, the area proportion of each fine patch in the current period's patch is calculated (e.g., the percentage of overlapping fine patch area to the total area of ​​the current period's patch). Based on the area proportion and whether the base period stand type corresponding to the fine patch is the same as the current period's stand type, the entire current period's patch is classified into either type I (normal growth) or type II (abnormal change). Normal growth means that the stand type within the patch remains unchanged and is mainly natural growth; abnormal change includes situations such as logging, fire, pests, and diseases that lead to changes in stand type or structural abrupt changes.

[0047] This invention quantifies the similarities and differences between two phases of map patches in terms of spatial distribution, enabling an objective judgment of the nature of forest stand changes and providing a scientific basis for selecting different renewal strategies (base selection, duration setting).

[0048] Furthermore, the criteria for classifying the entire current period's map patch into either the first type or the second type are as follows: Among all the small patches that overlap with the base period forest survey data, select the small patch with the largest area proportion. If the forest stand type corresponding to this small patch is the same as the base period forest stand type, and the area proportion of this small patch is greater than or equal to a preset threshold, then the entire current period's map patch is classified as the first type; if the condition is not met, then the entire current period's map patch is classified as the second type, where the preset threshold is 30%.

[0049] This decision-making rule avoids the complex calculations of voting or averaging all small patches, instead focusing on the largest overlapping small patches, making it simple and efficient. At the same time, the introduction of a threshold avoids interference from small, heterogeneous patches (such as jagged boundaries or sporadic land cover variations), ensuring the stability of the classification.

[0050] Furthermore, in step S3, the step of selecting the corresponding growth and renewal model and the renewal duration parameter based on the classification results and the current stand type of the current map patch specifically includes: for the current map patch classified as the first type, selecting the base period forest survey data as the renewal base, and setting the renewal duration parameter to the number of years from the base period forest survey data point to the expected data point; for the current map patch classified as the second type, selecting the current period forest survey data as the renewal base, and setting the renewal duration parameter to the number of years from the current period forest survey data point to the expected data point; then, based on the current stand type of the current map patch, selecting the corresponding hectare volume growth model and mean diameter at breast height change model from the preset model library.

[0051] In step S3, for the first type of patch (normal growth), the update duration parameter is set to the full number of years from the base period forest survey data point to the expected data point, using the stand factor values ​​(e.g., hectare volume) in the base period forest survey data as the base. (e.g., 8 years from the base period of 2016 to the expected period of 2024). For the second type of patch (abnormal change), the update duration parameter is set to the shorter number of years from the current period forest survey data point to the expected data point, using the factor values ​​in the current period forest survey data as the base. (e.g., 3 years from the current period of 2021 to the expected period of 2024). Then, based on the current period stand type of the patch (e.g., "natural Yunnan pine juvenile forest"), the corresponding hectare volume growth model (e.g., exponential model) and mean diameter at breast height (DBH) change model (e.g., rational function model) are selected from the preset model library.

[0052] Normal growth patches follow historical trajectories and grow over a long period, conforming to the laws of natural succession; abnormal change patches are extrapolated from the most recent survey data in a short period of time, avoiding the introduction of erroneous historical states into the model, thereby improving the update accuracy of various types of patches.

[0053] Furthermore, the expression for the hectare volume growth model is as follows:

[0054]

[0055] in, This represents the base period hectares of stock volume used as the basis for the update. The calculated expected hectares of stock are represented by p, the growth rate is represented by n, and the update duration parameter is represented by n.

[0056] The exponential model can well fit the continuous growth process of forest stock under undisturbed conditions, and is especially suitable for the rapid growth stage from young to near-mature forests. The model structure is simple, and the parameter p can be obtained by fitting data from fixed sample plots, which facilitates its widespread application.

[0057] Furthermore, the expression for the average chest diameter variation model is as follows:

[0058]

[0059] in, This represents the base period mean thoracic diameter, which serves as the basis for the update. denoted as the calculated expected average diameter at breast height, c represents the growth rate, and n represents the update duration parameter.

[0060] This model can describe the nonlinear increase in diameter at breast height (DBH) over time with a gradually slowing growth rate. Compared with the linear model, this model fits the upward curve of DBH growth better, especially in the young to middle-aged forest stage, avoiding the over-extrapolation that may occur with the linear model.

[0061] Furthermore, in step S4, the step of calculating the update value of the target stand factor using the selected growth renewal model and the update duration parameter specifically includes: first updating the average diameter at breast height (DBH) and hectare volume; then calculating the average tree height using the updated average DBH through a tree height-DBH regression model; then calculating the single tree volume using the updated average DBH and average tree height through a binary volume formula, and finally dividing the hectare volume by the single tree volume to obtain the number of trees per hectare.

[0062] This sequence follows the biological logic between stand factors: diameter at breast height (DBH) is the basic growth rate, tree height can be estimated from DBH, the volume per tree is calculated from DBH and tree height using a binary volume formula, and the number of trees per hectare is obtained by the ratio of hectare volume to per tree volume. This ensures that the updated factors are coordinated with each other and avoids contradictions such as "large DBH paired with small tree height".

[0063] Furthermore, the general expression for the tree height-diameter-at-breast-width regression model is:

[0064]

[0065] Where d represents the updated average diameter at breast height, h represents the calculated updated average tree height, and a, b, and k are regression coefficients.

[0066] This model captures the allometric growth relationship between diameter at breast height (DBH) and tree height using a power function (i.e., rapid height growth in saplings and slower growth in mature trees). The model is flexible; by adjusting coefficients and exponents, it can effectively fit tree height curves for different tree species and stand types. Using this model, tree height can be calculated simply by knowing the DBH, avoiding the need to measure tree height individually for each map patch.

[0067] Furthermore, a specific expression for the tree height-diameter-at-breast-thickness regression model for young plantations of eucalyptus trees is as follows:

[0068]

[0069] Where d represents the updated average diameter at breast height (DBH), and h represents the calculated updated average tree height. The constants -26.0038, 19.5104, and the power coefficient -0.37381 in the formula are model parameters obtained by fitting a large amount of measured sample data from artificial eucalyptus sapling forest areas through nonlinear regression analysis.

[0070] Furthermore, the step of calculating the number of trees per hectare specifically includes: using the updated average diameter at breast height and average tree height, calculating the volume of a single tree using the binary volume formula, and then dividing the volume per hectare by the volume of a single tree to obtain the number of trees per hectare.

[0071] The general expression for the binary volume formula is:

[0072]

[0073] Where V represents the volume of a single tree, d represents the updated average diameter at breast height (DBH), h represents the updated average tree height, and a, b, and c are model parameters obtained by regression fitting of measured data.

[0074] The expression for dividing the volume per hectare by the volume per tree to obtain the number of trees per hectare is:

[0075] N=M / V

[0076] Where N represents the updated number of trees per hectare, M represents the updated volume per hectare, and V represents the volume per tree calculated using the binary volume formula.

[0077] Furthermore, for middle-aged natural Yunnan pine forests, a specific expression for the binary volume formula of the single-tree volume V is as follows:

[0078]

[0079] Where d represents the updated average diameter at breast height (DBH), and h represents the updated average tree height. The constants 0.000058290117, the power coefficients 1.9796344 and 0.90715155 in the formula are model parameters obtained by fitting a large amount of analytical tree measurement data (including DBH, tree height, volume, etc.) from natural Yunnan pine middle-aged forest areas through nonlinear regression analysis.

[0080] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for updating stand factors based on two periods of forest data, characterized in that, Includes the following steps: S1. Data preprocessing: Obtain base period forest survey data and current period forest survey data, convert base period patches in the base period forest survey data into base period stand types according to preset mapping rules, and convert current period patches in the current period forest survey data into current period stand types according to preset mapping rules; S2. Spatial analysis: Spatial overlay analysis is performed between the base period forest survey data and the current period forest survey data. Based on the overlay results, the current period's map patches are classified into type I map patches or type II map patches. S3. Model matching: Based on the classification results and the current stand type of the current map patch, select the corresponding growth and update model and update duration parameters; S4. Factor Update: Using the selected growth update model and update duration parameter, calculate the updated values ​​of the target stand factors.

2. The method according to claim 1, characterized in that, In step S1: The step of converting base period map patches in the base period forest survey data into base period stand types according to preset mapping rules specifically includes: converting each base period map patch in the base period forest survey data into stand type origin, stand type dominant tree species group, and stand type age group respectively according to base period origin mapping rules, base period dominant tree species mapping rules, and base period age group mapping rules; and combining the converted stand type origin, stand type dominant tree species group, and stand type age group to obtain the base period stand type. The step of converting the current period map patches in the current period forest survey data into the current period forest stand type according to the preset mapping rules specifically includes: converting each current period map patch in the current period forest survey data into the forest stand type origin, forest stand type dominant tree species group and forest stand type age group respectively according to the current period origin mapping rule, the current period dominant tree species mapping rule and the current period age group mapping rule, and combining the converted forest stand type origin, forest stand type dominant tree species group and forest stand type age group to obtain the current period forest stand type.

3. The method according to claim 2, characterized in that, Both the base period forest stand type and the current period forest stand type are composed of three factors: forest stand type origin, forest stand type dominant tree species group, and forest stand type age group. The combination method of the base period forest stand type is: base period forest stand type = forest stand type origin + forest stand type dominant tree species group + forest stand type age group; The combination of stand types in this period is as follows: Stand type in this period = Stand type origin + Stand type dominant tree species group + Stand type age group.

4. The method according to claim 1, characterized in that, In step S2, the step of spatially overlaying the base period forest survey data with the current period forest survey data and classifying the current period's map patches into first-type map patches or second-type map patches based on the overlay results specifically includes: spatially overlaying the base period forest survey data with the current period forest survey data to generate fine patches formed by the mutual cutting of the map patch boundaries of the two periods; calculating the area proportion of each fine patch in the current period's map patches; and classifying the entire current period's map patches into first-type map patches or second-type map patches based on the area proportion and whether the base period forest stand type corresponding to the fine patch is the same as the current period's forest stand type; wherein, first-type map patches are normal growth map patches, and second-type map patches are abnormal change map patches.

5. The method according to claim 4, characterized in that, The criteria for classifying the entire current period map patch as either a first-type map patch or a second-type map patch are as follows: Among all the small patches that overlap with the base period forest survey data, select the small patch with the largest area proportion. If the forest stand type corresponding to the small patch is the same as the forest stand type in the base period, and the area proportion of the small patch is greater than or equal to a preset threshold, then the entire current period map patch is classified as a first-type map patch. If the conditions are not met, the entire current period's map features will be classified as type II map features.

6. The method according to claim 1, characterized in that, In step S3, the step of selecting the corresponding growth and renewal model and the renewal duration parameter based on the classification results and the current stand type of the current map patch specifically includes: for the current map patch classified as the first type, selecting the base period forest survey data as the renewal base, and setting the renewal duration parameter to the number of years from the base period forest survey data point to the expected data point; for the current map patch classified as the second type, selecting the current period forest survey data as the renewal base, and setting the renewal duration parameter to the number of years from the current period forest survey data point to the expected data point; then, based on the current stand type of the current map patch, selecting the corresponding hectare volume growth model and mean diameter at breast height change model from the preset model library.

7. The method according to claim 1, characterized in that, In step S4, the step of calculating the update value of the target stand factor using the selected growth renewal model and update duration parameter specifically includes: first updating the average diameter at breast height (DBH) and hectare volume; then calculating the average tree height using the tree height-DBH regression model based on the updated average DBH; then calculating the single tree volume using the binary volume formula based on the updated average DBH and average tree height; and finally dividing the hectare volume by the single tree volume to obtain the number of trees per hectare.

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