CN111241712B - Prediction method of conifer specific leaf area - Google Patents
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- 241000218631 Coniferophyta Species 0.000 title claims abstract description 58
- 238000000034 method Methods 0.000 title claims abstract description 46
- 240000007263 Pinus koraiensis Species 0.000 claims abstract description 26
- 235000011615 Pinus koraiensis Nutrition 0.000 claims abstract description 26
- 210000000481 breast Anatomy 0.000 claims abstract description 26
- 238000000611 regression analysis Methods 0.000 claims abstract description 7
- 238000012795 verification Methods 0.000 claims description 10
- 210000000038 chest Anatomy 0.000 claims description 5
- 238000005070 sampling Methods 0.000 claims description 4
- 238000001035 drying Methods 0.000 claims description 3
- 241000196324 Embryophyta Species 0.000 abstract description 8
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 8
- 238000010276 construction Methods 0.000 description 2
- 241000894007 species Species 0.000 description 2
- 238000010200 validation analysis Methods 0.000 description 2
- 235000005205 Pinus Nutrition 0.000 description 1
- 241000218602 Pinus <genus> Species 0.000 description 1
- 235000008585 Pinus thunbergii Nutrition 0.000 description 1
- 241000218686 Pinus thunbergii Species 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 230000010485 coping Effects 0.000 description 1
- 230000001066 destructive effect Effects 0.000 description 1
- 230000000243 photosynthetic effect Effects 0.000 description 1
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Abstract
A prediction method of conifer specific leaf area belongs to the field of forest ecology. The invention utilizes regression analysis to construct an empirical model of the specific area and the breast diameter of conifer seeds, wherein the empirical model is SLA = -8.983ln (DBH) +124.11, R 2 =0.73, where SLA is the conifer specific leaf area, DBH (diameter after height) is the diameter at breast height of conifer species, R 2 Is the ratio of the regression sum of squares to the sum of squares of the total deviations. The accuracy of predicting SLA by the empirical model is 83-97%, the average prediction accuracy is 92%, and the empirical model has strong applicability in different areas of Korean pine distribution. The invention provides a method for inverting SLA by obtaining the breast diameter of the plant which is easy to measure, and provides technical support for rapidly and accurately measuring SLA of evergreen coniferous leaves with different diameters in different areas by measuring the breast diameter.
Description
Technical Field
The invention belongs to the field of forest ecology; in particular to a prediction method of conifer specific leaf area.
Background
Korean pine (Pinus koraiensis) is a population establishing species of broad leaf Korean pine forest, which is a regional epipolar vegetation in the eastern mountain area of northeast China, and is important for establishing an implementation scheme for recovering degraded secondary forest to epipolar vegetation.
Specific Leaf Area (SLA) is one of the most important leaf functional traits, which directly determines the photosynthetic capacity of plants and is also an important index reflecting the ecological strategy of plants. Therefore, the rapid and accurate determination of the SLA of the plants can be of great significance for analyzing the resource utilization efficiency of the vegetation and the coping strategies for the climate change. At present, SLA is mainly determined by a destructive sampling method, namely after sample leaves are collected, the leaf area and the leaf dry weight of the sample leaves are determined, and then the SLA is obtained by the ratio of the leaf area to the leaf dry weight. While this method is accurate, it is time consuming, laborious and disruptive; meanwhile, due to the technical limitation of collecting sample leaves, the SLA of tall plants is very difficult to determine by the conventional method; and SLA has obvious difference along with the change of the plant diameter grade.
Disclosure of Invention
The invention aims to provide a method for rapidly and accurately predicting SLA by obtaining the breast diameter of a plant which is easy to measure.
The invention is realized by the following technical scheme:
a prediction method of conifer specific leaf area comprises constructing empirical model of conifer specific leaf area and breast diameter by regression analysis, wherein the empirical model is SLA = -8.983ln (DBH) +124.11, R 2 =0.73, where SLA is the conifer specific leaf area, DBH (diameter at Breast height) is the diameter at breast height of conifer species, R 2 Is the ratio of the regression sum of squares to the sum of squares of the total deviations.
According to the prediction method of the conifer species leaf comparison area, data of the empirical model are collected in 128 '20 ' east longitude and 53' north latitude and 47 '50 ' north latitude of a national natural protection area of the level of cold water in Heilongjiang, 1-2 sample trees are randomly selected at intervals of 1cm by taking Korean pine in the last ten days of 9 months as a collection object, sample leaves are collected for each sample tree, and chest diameter data are recorded to obtain the leaf comparison area data.
The invention relates to a prediction method of conifer species specific leaf area, which is characterized in that for each sample tree, korean pine sample leaves are collected in the south of a crown of a forest, wherein the Korean pine sample leaves comprise current-year and perennial sample leaves, and at least 100 sample leaves are collected for each sample tree.
The invention discloses a prediction method of conifer specific leaf area, wherein the chest diameter size range of Korean pine is 0.3cm-100cm.
According to the inventionThe method for predicting the specific leaf area of conifer species comprises the steps of calculating specific leaf area data, randomly dividing sample leaves of each sample tree into 3 repeated sample groups, measuring the number n of needles and the average needle length l of each sample leaf group, measuring the volume v of each sample leaf group by using a drainage method, and then according to a leaf area formula
Measuring the leaf area of the sample leaf; and then drying the sample leaves at 65 ℃ to constant weight to obtain dry leaf weight, wherein the ratio of the leaf area to the dry leaf weight is SLA, and the average value of 3 repeated sample groups of SLA of each sample tree is the SLA value of the sample tree.
According to the prediction method of the conifer species specific leaf area, all data are divided into 2 groups by adopting a random sampling method according to the SLA value of the sample tree, the empirical model building group and the empirical model prediction precision verification group are provided, the empirical model building group data account for 75% of the total data, and the empirical model prediction precision verification group data account for 25% of the total data.
The prediction method of the specific leaf area of the conifer species is based on empirical model construction group data, regression analysis is carried out, and an empirical model of the specific leaf area and the breast diameter of the conifer species is established.
The prediction method of the conifer species specific leaf area calculates the average absolute error MAE (MAE) and the prediction precision FC (required absolute error, FC) based on the empirical model prediction precision verification group:
in the formula y i And
measured SLA values and SLA values predicted based on empirical models are respectively obtained, and n is a sample size.
According to the prediction method of the conifer specific area, the empirical model of the conifer specific area and the breast diameter is suitable for conifer species with 126 degrees 27-129 degrees 53 'of east longitude and 41 degrees 41-49 degrees 40' of north latitude.
The method for predicting the specific leaf area of the needle tree species is applicable to a Guilin Changbai mountain country-level natural protection area of 41-degree 41' -42-degree 51N and 127-degree 42' -128 ' 16E, a Black Longjiang Mukung Tokuai Yew country-level natural protection area of 44-degree 20' -44-degree 30N and 129-degree 40-129-53E, a Black Longjiang Tokuai Yew country-level natural protection area of 48-degree 02' -48-degree 12N and 128-degree 59' -129-15E, and a Black Longjiang Fenggai country-level natural protection area of 49-degree 25' -49-degree 40N and 126-degree 27' -127 ' 02-02E.
According to the prediction method of conifer specific leaf area, SLA of Korean pine in different areas is 74.7-97.8cm 2 The average absolute error of SLA predicted by the empirical model is 8cm based on data of an empirical model prediction precision verification group in a cold water area 2 (iv) g; the maximum prediction precision is 99.9%, and the average prediction precision is 92% (table 2), which shows that the empirical model can accurately predict the SLA of the Korean pine with different diameter grades in the cold water area. In other Korean pine distribution areas, the accuracy of SLA prediction of the empirical model of the invention is 83-97%, and the average prediction accuracy is 92%, thus the empirical model of the invention has strong applicability in different areas of Korean pine distribution in China.
The invention provides a method for inverting SLA by obtaining the breast diameter of the plant which is easy to measure, and provides technical support for rapidly and accurately measuring SLA of evergreen coniferous leaves with different diameters in different areas by measuring the breast diameter.
Drawings
Fig. 1 is an empirical model curve between the specific area and the breast diameter.
Detailed Description
The first specific implementation way is as follows:
a prediction method of conifer specific leaf area is constructed by regression analysisEmpirical model of the specific area and breast diameter of conifer species, SLA = -8.983ln (DBH) +124.11,R 2 =0.73, wherein SLA is conifer specific leaf area, DBH is conifer breast diameter, R 2 Is the ratio of the regression sum of squares to the sum of squares of the total deviations.
In the method for predicting conifer species leaf area according to the embodiment, the empirical model data is collected from 128 '20 ' east longitude and 53' north latitude and 47 '50 ' north latitude in the natural protection area of the national level of cool water in Heilongjiang, 1 to 2 sample trees are randomly selected at intervals of 1cm, and sample leaves are collected and chest diameter data are recorded for each sample tree to obtain leaf area data.
In the method for predicting the specific leaf area of conifer species according to the embodiment, pinus koraiensis-like leaves are collected in the south of the canopy of each sample tree, wherein the sample leaves include current-year and perennial sample leaves, and at least 100 sample leaves are collected for each sample tree.
In the method for predicting the specific leaf area of conifer species according to the embodiment, the breast diameter size of the Korean pine ranges from 0.3cm to 100cm.
In the method for predicting the specific leaf area of conifer species according to the embodiment, the specific leaf area data is calculated by randomly dividing the sample leaves of each sample tree into 3 repeated sample groups, measuring the number of needles n and the average needle length l of each group of sample leaves, measuring the volume v of each group of sample leaves by using a drainage method, and then calculating the specific leaf area according to a leaf area formula
Measuring the leaf area of the sample leaves; and then drying the sample leaves at 65 ℃ to constant weight to obtain dry leaf weight, wherein the ratio of the leaf area to the dry leaf weight is SLA, and the average value of 3 repeated sample groups of SLA of each sample tree is the SLA value of the sample tree.
In the prediction method for the conifer species specific leaf area according to the embodiment, the obtained SLA values of the sample tree adopt a random sampling method, all data are divided into 2 groups, and the data are an empirical model building group and an empirical model prediction precision verification group, wherein the empirical model building group data account for 75% of total data, and the empirical model prediction precision verification group data account for 25% of the total data.
In the method for predicting the specific leaf area of conifer species according to the present embodiment, 170 sample trees are selected in total.
In the prediction method of the conifer specific leaf area according to the embodiment, the empirical model is constructed by performing regression analysis on the basis of the empirical model construction group data, and establishing an empirical model of the conifer specific leaf area and the breast diameter.
In the prediction method of the conifer species specific leaf area according to the embodiment, the average absolute error MAE and the prediction accuracy FC are calculated based on the empirical model prediction accuracy validation group:
in the formula y i And
the measured SLA value and the SLA value predicted based on the empirical model are respectively, and n is the sample size.
Fig. 1 is an empirical model curve between a specific area and a breast diameter of a conifer species according to the present embodiment.
In the prediction method of conifer species specific leaf area according to the embodiment, table 1 shows specific leaf area data of a pinus koraiensis distribution area, and table 2 shows average absolute error and prediction accuracy of SLAs of pinus koraiensis predicted by an empirical model. As can be seen from Table 1, the average SLA of Korean pine in cold water areas was 97.8cm 2 (ii) in terms of/g. As can be seen from Table 2, based on the data of the empirical model prediction accuracy verification group in the cold water region, the average absolute error of the SLA predicted by the empirical model is 8cm 2 (ii)/g; the maximum prediction precision is 99.9%, and the average prediction precision is 92%, which shows that the empirical model of the embodiment can accurately predict SLAs of Korean pine of different diameter grades in cold water areas.
TABLE 1 leaf area of the distribution area of Liangshui Pinus thunbergii (SLA)
TABLE 2 mean absolute error and prediction accuracy of cool water Korean pine SLA prediction by empirical model
The second embodiment is as follows:
according to a method for predicting the specific leaf area of a conifer species, according to a first embodiment, the empirical model of the specific leaf area and the breast diameter of the conifer species is applied to conifer species with 126 ° to 129 ° 53 'east longitude and 41 ° to 49 ° north latitude and 40' east longitude.
According to the method for predicting the specific leaf area of the conifer species, empirical models of the specific leaf area and the breast diameter of the conifer are applied to a Jilin Changbai mountain national natural protection area of 41 degrees 41' to 42 degrees 51' N and 127 degrees 42' to 128 degrees 16' E, a Black Longjiang Douglas yew national natural protection area of 44 degrees 20' to 44 degrees 30' N and 129 degrees 40' to 129 degrees 53' E, a Black Longjiang Tokyo Yew national natural protection area of 48 degrees 02' to 48 degrees 12' N and 128 degrees 59' to 129 degrees 15' E, a Black Longjiang Fenghun mountain national natural protection area of 49 degrees 25' to 49 degrees 40' N and 126 degrees 27' to 127 ' 02' E.
In the prediction method of the conifer species specific leaf area according to the embodiment, the average absolute error MAE and the prediction accuracy FC are calculated based on the empirical model prediction accuracy validation group:
in the formula y i And
measured SLA values and SLA values predicted based on empirical models are respectively obtained, and n is a sample size.
In the prediction method for the conifer species specific leaf area according to the embodiment, 3 sample trees are randomly selected in 4 Korean pine distribution areas in different areas, the DBH range of the sample trees is 45-70cm, the DBH and SLA of each sample tree are measured, the average absolute error MAE and the prediction accuracy FC are calculated based on an empirical model prediction accuracy verification group, and the applicability of empirical models for constructing the conifer species specific leaf area and the breast diameter in other areas by using regression analysis is tested.
In the prediction method of conifer species specific leaf area according to the embodiment, specific leaf area data of 4 Korean pine distribution areas in different areas are shown in table 3, prediction accuracy of 4 SLAs in the areas predicted by an empirical model is shown in table 4, and average absolute error of 4 SLAs in the areas predicted by the empirical model is shown in table 5:
TABLE 3 specific leaf area of 4 Korean pine distribution areas of different regions (SLA)
TABLE 4 prediction accuracy FC (%) of 4 other regional SLAs predicted by empirical model
TABLE 5 empirical model predicts mean absolute error MAE (cm) for 4 other regions SLA 2 /g)
In the method for predicting the specific leaf area of conifer species according to this embodiment, the specific leaf area range value of the Korean pine distribution region of 4 different regions is 74.7-90.9cm 2 In the other Korean pine distribution,/g, as can be seen from tables 4 and 5In this embodiment, the prediction accuracy of the empirical model for predicting SLA is 83-97%, the average prediction accuracy is 92%, and the average absolute error of different regions is 2.6-12.4cm 2 (ii) in terms of/g. It can be seen that the empirical model in the embodiment has strong applicability in different areas of the distribution of Korean pine. The embodiment can provide technical support for rapidly and accurately measuring SLA of evergreen coniferous leaves with different diameters in different areas.
Claims (4)
1. A prediction method of conifer species specific leaf area is characterized in that: an empirical model of the specific area and the breast diameter of the conifer is constructed by using regression analysis, and the empirical model is SLA = -8.983ln (DBH) +124.11, R 2 =0.73, wherein SLA is conifer specific leaf area, DBH is conifer breast diameter, R 2 Is the ratio of the regression sum of squares to the total sum of squared deviations;
the data of the empirical model are collected from 128 ' 53' east longitude, 47 ' 10' north latitude 50 ' of natural protection district of national level of Liangshui of Heilongjiang, 1-2 sample trees are randomly selected at intervals of 1cm by taking Korean pine in the last ten days of 9 months as a collection object, sample leaves are collected for each sample tree, and chest diameter data are recorded to obtain specific leaf area data;
collecting Korean pine-like leaves in the south of the canopy of each sample tree, wherein the Korean pine-like leaves comprise current-year-old and perennial sample leaves, and each sample tree collects at least 100 sample leaves;
the chest diameter of the Korean pine ranges from 0.3cm to 100cm;
calculating specific leaf area data, randomly dividing the leaves of each tree into 3 repeated sample groups, measuring the number n and average length l of needles of each group of leaves, measuring the volume v of each group of leaves by using a drainage method, and calculating the leaf area according to a leaf area formula
Measuring the leaf area of the sample leaves; then drying the sample leaves at 65 ℃ to constant weight to obtain dry leaf weight, wherein the ratio of the leaf area to the dry leaf weight is SLA, and the average value of 3 repeated sample groups SLA of each sample tree is the SLA value of the sample tree;
dividing all data into 2 groups by adopting a random sampling method according to the SLA value of the sample tree, wherein the empirical model building group data account for 75% of total data, and the empirical model prediction precision verification group data account for 25% of the total data;
and constructing group data based on the empirical model, performing regression analysis, and establishing an empirical model of the conifer specific surface area and the breast diameter.
2. The method for predicting the specific leaf area of a conifer according to claim 1, wherein: calculating an average absolute error MAE and a prediction precision FC based on an empirical model prediction precision verification group:
in the formula y i And
the measured SLA value and the SLA value predicted based on the empirical model are respectively, and n is the sample size.
3. The method for predicting the specific leaf area of a conifer according to claim 2, wherein: the empirical model of the specific area and the breast diameter of the conifer species is suitable for conifer species with 126 degrees 27-129 degrees 53 'of east longitude and 41 degrees 41-49 degrees 40' of north latitude.
4. The method as claimed in claim 3, wherein the prediction of the specific leaf area of conifer species is performed by: the empirical model of the needle-leaf tree specific area and the breast diameter is suitable for a Jilin Changbai mountain national natural protection area of 41-42-51 ' N and 127-42-128-16 ' E, a Black Longjiang Douglas yew national natural protection area of 44-20-44-30 ' N and 129-40-129-53 ' E, a Black Longjiang Tokyo China natural protection area of 48-02 ' 12' N and 128-59-129-15 ' E, and a Black Longjiang Shangu national natural protection area of 49-25 ' 49-40 ' N and 126-27-127 ' 02' E.
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