CN114971535A - Method for predicting comprehensive bearing capacity of forest ecological system resources based on range standardization and entropy weight method - Google Patents

Method for predicting comprehensive bearing capacity of forest ecological system resources based on range standardization and entropy weight method Download PDF

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CN114971535A
CN114971535A CN202210502902.XA CN202210502902A CN114971535A CN 114971535 A CN114971535 A CN 114971535A CN 202210502902 A CN202210502902 A CN 202210502902A CN 114971535 A CN114971535 A CN 114971535A
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bearing capacity
forest
index
ecological system
value
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蒋一凡
李佳泽
江珺荻
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Hohai University HHU
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Abstract

The invention discloses a method for predicting comprehensive bearing capacity of forest ecological system resources based on range standardization and an entropy weight method, which comprises the following steps: collecting evaluation indexes capable of reflecting comprehensive bearing capacity of the forest ecological system; processing the evaluation index data by adopting a range standardization method; determining the weight of each evaluation index by an entropy weight method; calculating to obtain the bearing degree of each evaluation index; and calculating to obtain a numerical value capable of reflecting the comprehensive bearing capacity of the ecological system resources of the forest according to the weight and the bearing capacity of each evaluation index, and obtaining the comprehensive bearing capacity grade of the forest ecological system resources by looking up a table through the numerical value.

Description

Method for predicting comprehensive bearing capacity of forest ecological system resources based on range standardization and entropy weight method
Technical Field
The invention relates to the technical field of forest management, in particular to a method for predicting comprehensive bearing capacity of forest ecological system resources based on range standardization and an entropy weight method.
Background
The forest is the main body of a land ecosystem, the bearing capacity of the forest is the key of resource, environment and social and economic development, and is an important index for measuring the sustainable development capability of the regional society. A plurality of scholars construct forest ecological bearing capacity evaluation indexes from the aspects of different spatial latitudes, time differences and the like in China,
for example, Jiangjie and the like, a forest ecological bearing capacity evaluation index system is constructed, taking Guangdong province as an example [ J ] forestry and environmental science, 2021,37(04), 146-. The research result has important theoretical and practical significance for the evaluation of regional forest sustainability, the formulation of forest development strategy planning and the research of sustainable forest theory.
However, the above prior art has the disadvantages of single research index and lack of data support for evaluation results, so that the obtained bearing capacity result may have subjective factors, which affects the scientificity of the result.
Disclosure of Invention
The invention aims to solve the problems that the existing forest bearing capacity calculation method is single in research index and lack of data support in evaluation results, comprehensively considers forest ecological influence factors, divides the forest ecological influence factors into ecological support force, economic support force and social support force, selects a plurality of sub-indexes for data support, establishes a forest ecological system resource comprehensive bearing capacity index system, and comprehensively evaluates the resource bearing capacity of the forest from three different aspects.
The invention adopts the following technical scheme:
the method for predicting the comprehensive bearing capacity of the forest ecological system resources based on range standardization and the entropy weight method comprises the following steps:
step 1, collecting evaluation indexes capable of reflecting comprehensive bearing capacity of a forest ecological system;
step 2, processing the evaluation index data by adopting a range standardization method;
step 3, determining the weight of each evaluation index through an entropy weight method;
step 4, calculating to obtain the bearing capacity of each evaluation index;
and 5, calculating to obtain a numerical value capable of reflecting the comprehensive bearing capacity of the ecological system resources of the forest according to the weight and the bearing capacity of each evaluation index, wherein the numerical value is as follows:
Figure BDA0003635192570000021
wherein G is i For the bearing capacity of the ith index, n indicates that each index has n sample values, W i And E is a numerical value reflecting the comprehensive bearing capacity of the ecological system resources of the forest.
Further, the corresponding relationship between the E value calculated in step 5 and the comprehensive bearing capacity of the forest ecosystem resources is shown in the following table 1:
TABLE 1
Figure BDA0003635192570000022
The principle of the trisection method is utilized, namely, in the problems of hierarchical division, component analysis and the like, extreme values are half of intermediate values. Here we derive it as the extreme range is half of the middle range, i.e. the level range of crisis state, rich state is half of the level range of early warning state, equilibrium state. Meanwhile, the division is beneficial to analyzing the bearing capacity of more forest ecological system resources and reducing extreme states, and is beneficial to adopting corresponding appropriate forest management measures.
Preferably, the evaluation indexes in the step 1 comprise carbon seal stock, biodiversity, forest product annual average income, forest project income, environment quality index, soil pollution degree, annual average logging amount, forest protection fee, real estate development proportion and surrounding population density.
The carbon seal stock, the biodiversity, the soil pollution degree and the annual average cut amount represent the ecological supporting force of a forest ecological system, the carbon content, the animal and plant amount, the soil fertility and the raw wood amount of the forest are respectively considered by the four indexes, and the four indexes all belong to the basic detection amount of the ecological circle, so the four indexes can be used as the index amounts of the ecological supporting force. The annual average income of forest products, the income of forest project and the forest protection fee represent the economic supporting force of a forest ecosystem, and index values are all related to the economic income of forests and can be used as index values of the economic supporting force. The peripheral population density, the environmental quality index and the real estate development ratio represent the social support force of a forest ecosystem, and the three indexes have influence on the social benefit, the popularity, the social environment and the like of the forest, so that the three indexes can be used as index quantities of the social support force.
With ecological supporting force, economic supporting force and social supporting force as primary indexes and carbon seal stock, biodiversity, forest product annual average income, forestry project income, environmental quality index, soil pollution degree, annual average felling amount, forest protection fee, real estate development proportion, peripheral population density and the like as secondary indexes, establishing a forest ecological system resource comprehensive bearing capacity evaluation index system as shown in the following table 2:
TABLE 2
Figure BDA0003635192570000031
Preferably, in step 2, all the evaluation indexes are processed by distinguishing positive indexes from negative indexes, and after the positive indexes are normalized, the positive indexes are:
Figure BDA0003635192570000041
after negative indicators were normalized:
Figure BDA0003635192570000042
wherein, C ij For the jth sample value of the ith index,
Figure BDA0003635192570000043
is the minimum value of the samples in the index of the i-th item,
Figure BDA0003635192570000044
is the maximum value of the sample in the i index, Z ij Normalized data for the jth sample of the ith index.
In the scheme of the invention, the carbon seal stock, the biodiversity, the annual average income of forest products, the income of forestry projects and the environmental quality index are positive indexes, and the soil pollution degree, the annual average felling amount, the forest protection fee, the surrounding population density and the real estate development proportion are negative indexes.
Preferably, the forest ecosystem index value is not linearly related to the influence of the forest ecosystem index value on the comprehensive bearing capacity. When the index value is low, the influence of the index value on the comprehensive bearing capacity is small; when the index value is in the middle level, the influence on the comprehensive bearing capacity is increased along with the increase of the index value; when the index value is larger, the influence on the comprehensive bearing capacity is gradually reduced and tends to be saturated. Therefore, an optimized raised half gamma type distribution index formula is adopted in the step 4, and the relation between each evaluation index value and the influence of the evaluation index value on the comprehensive bearing capacity of the forest ecological system resources is reflected:
Figure BDA0003635192570000045
Figure BDA0003635192570000046
wherein G is i Bearing capacity, x, of the i-th index i And the average sample value of the resource bearing capacity index value after the standardization processing is carried out.
The invention has the beneficial effects that:
the method brings a plurality of factors influencing the forest into research, researches the resource bearing capacity of the forest ecological system by a plurality of indexes and factors, and compared with the prior art, the method overcomes the defects that the evaluation result lacks data support and has too much subjectivity, so the scientificity of the research method is improved, and the research result is more accurate.
Detailed Description
In this embodiment, the example of selecting the shao-pin forest in north of the great khan mountain of our country is to study the comprehensive bearing capacity of the forest ecosystem resources, which specifically includes:
step 1, establishing an evaluation index system of comprehensive bearing capacity of forest ecological systems of Xingan larch forests in the north of great Xingan mountains, selecting ecological bearing capacity, economic bearing capacity and social bearing capacity as first-level indexes for simplifying index classification and determining prediction results, and selecting related second-level indexes as shown in the following table 3.
TABLE 3
Figure BDA0003635192570000051
The data collected in the forest of Xingan larch forest in the north of great Xingan mountain in the last decade are shown in the following table 4:
TABLE 4
Figure BDA0003635192570000052
Figure BDA0003635192570000061
Step 2, processing the index data by adopting a range standardization method:
the forward indicators, after normalization, are:
Figure BDA0003635192570000062
wherein, C ij For the jth sample value of the ith index,
Figure BDA0003635192570000063
is the minimum value of the samples in the i-th index,
Figure BDA0003635192570000064
is the maximum value of the sample in the i index, Z ij Normalized data for the jth sample of the ith index.
In the embodiment, the carbon seal stock, the biodiversity, the annual average income of forest products, the income of forestry projects and the environmental quality index are positive indexes.
Taking the carbon seal stock as an example, the maximum value in the index data is 18.569, and the minimum value is 13.232, namely:
Figure BDA0003635192570000069
the 10 th sample value of the 1 st index is the maximum value of the sample;
Figure BDA00036351925700000610
meaning that the 3 rd sample value of the 1 st index is its minimum sample value. Then the first sample value of the 1 st index is normalized to:
Figure BDA0003635192570000065
and in this way, all the forward index standardization calculation methods are calculated in the same way.
After negative indicators were normalized:
Figure BDA0003635192570000066
in this embodiment, soil pollution, annual average cut amount, forest protection fee, real estate development ratio, and surrounding population density are negative indicators.
Taking the soil pollution degree as an example, the maximum value in the index data is 7.8092, and the minimum value is 6.4949, namely:
Figure BDA0003635192570000067
so the data normalized by the first and second sample values is:
Figure BDA0003635192570000068
Figure BDA0003635192570000071
and in this way, all negative direction index standardization calculation methods are calculated in the same way.
In this very poor normalization method, the data after all the index data are normalized are shown in the following table 5: (all retain four decimal places)
TABLE 5
Figure BDA0003635192570000072
And 3, determining the index weight by an entropy weight method, wherein the specific algorithm is as follows:
Figure BDA0003635192570000073
Figure BDA0003635192570000074
Figure BDA0003635192570000075
Figure BDA0003635192570000076
wherein, P ij The j sample representing the i index accounts for the weight of the index when Z ij When it is 0, calculate E i This value is not taken into account. m represents m indexes, n represents n sample values of each index, and k is a standard coefficient of the sample values. W i Weight of the i-th index, E i To assist in intermediate variables.
In this system, there are 10 indexes, i.e., m is 10, and each index has 12 sample values, i.e., n is 12, so:
Figure BDA0003635192570000081
for specific gravity P ij Taking the first index carbon seal stock as an example:
Figure BDA0003635192570000082
the indexes, specific gravities of the obtained carbon seal storage are calculated in sequence and are shown in the following table 6:
TABLE 6
Figure BDA0003635192570000083
And calculating to obtain:
Figure BDA0003635192570000084
by the same token, obtain E 1 -E 10 The values of (A) are shown in Table 7 below:
TABLE 7
Figure BDA0003635192570000085
Figure BDA0003635192570000091
Thus calculating
Figure BDA0003635192570000092
Calculating to obtain W 1 -W 10 The values are shown in table 8 below:
TABLE 8
W 1 0.026979459
W 2 0.076705432
W 3 0.081161164
W 4 0.100454485
W 5 0.103618055
W 6 0.116138662
W 7 0.09853852
W 8 0.124069866
W 9 0.128748385
W 10 0.143585973
And 4, because each index value is not in a linear relationship with the influence of the index value on the comprehensive bearing capacity of the forest ecological system resources, the optimized raised half inverted L-shaped distribution index formula is adopted to more accurately reflect the relationship between the index value and the influence of the index value on the comprehensive bearing capacity of the forest ecological system resources.
Figure BDA0003635192570000093
Figure BDA0003635192570000094
Wherein, G i Bearing capacity, x, of the i-th index i The average sample value of the grain resource bearing capacity index value after the standardization treatment is obtained.
Taking the first index carbon seal stock as an example:
Figure BDA0003635192570000101
Figure BDA0003635192570000102
in the same way, G can be calculated sequentially 1 -G 10 As shown in table 9 below:
TABLE 9
G 1 0.276612543
G 2 0.034262493
G 3 0.349223475
G 4 0.357566523
G 5 0.293624338
G 6 0.241876372
G 7 0.289189919
G 8 0.366397661
G 9 0.28201645
G 10 0.314472891
The comprehensive bearing capacity of the forest ecosystem resources is as follows:
Figure BDA0003635192570000103
accordingly, the comprehensive bearing capacity of the ecological system resources of the Xingan larch forest in the north of great Khingan mountains is still in an early warning state, namely the pressure which can be contained by the environmental resources under the existing conditions is in a larger level, the level of the bearing capacity of the resources is lower, and a larger lifting space is provided. Greater Khingan mountains has become one of the largest bases for wood production nationwide since the development. Due to the long-term excessive harvesting, the excessive utilization of forest resources, the natural disasters of forest fires and the like, the forest tree canvases great forest resource crisis and economic risks. According to the data recorded in the Chinese statistic yearbook, the data before 1988 and the statistic result in 2021, the forest land surface is greatly reduced, namely, the ever 639 million hectares are changed into 538 million hectares, and the reduction is about 15.8 percent; due to the special geographical situation and natural situation, the growth cycle is slow in alpine regions, the environmental capacity is small, and the balance of the ecological system is maintained in a fragile state, so that the comprehensive bearing capacity of the forest ecological system resources is naturally at a disadvantage.
The result obtained by prediction according to the invention is consistent with the reality, so a forest manager can make corresponding protective measures and policy systems according to the result, and an effort is made for improving the comprehensive bearing capacity of forest ecological system resources.

Claims (5)

1. The method for predicting the comprehensive bearing capacity of the forest ecological system resources based on range standardization and an entropy weight method is characterized by comprising the following steps:
step 1, collecting evaluation indexes capable of reflecting comprehensive bearing capacity of a forest ecological system;
step 2, processing the evaluation index data by adopting a range standardization method;
step 3, determining the weight of each evaluation index through an entropy weight method;
step 4, calculating the bearing degree of each evaluation index;
and 5, calculating to obtain a numerical value capable of reflecting the comprehensive bearing capacity of the ecological system resource of the forest according to the weight and the bearing capacity of each evaluation index, wherein the numerical value is as follows:
Figure FDA0003635192560000011
wherein G is i For the bearing degree of the ith index, n represents that each index has n sample values, W i And E is a numerical value reflecting the comprehensive bearing capacity of the forest ecological system resources.
2. The method for predicting the comprehensive bearing capacity of the forest ecological system resources based on the range standardization and the entropy weight method as claimed in claim 1, wherein the corresponding relationship between the E value calculated in the step 5 and the comprehensive bearing capacity of the forest ecological system resources is as shown in the following table 1:
TABLE 1
Figure FDA0003635192560000012
Figure FDA0003635192560000021
3. The method for predicting the comprehensive bearing capacity of the forest ecosystem resources based on the range standardization and the entropy weight method as claimed in claim 1, wherein the evaluation indexes in the step 1 comprise carbon seal stock, biodiversity, annual average income of forest products, income of forest projects, environmental quality index, soil pollution degree, annual average felling amount, forest protection fee, real estate development ratio and surrounding population density.
4. The method for predicting the comprehensive bearing capacity of the forest ecosystem resources based on the range standardization and the entropy weight method as claimed in claim 3, wherein the positive indicators and the negative indicators are distinguished for all the evaluation indicators in the step 2 for processing, and the positive indicators are normalized as follows:
Figure FDA0003635192560000022
after negative indicators were normalized:
Figure FDA0003635192560000023
wherein, C ij For the jth sample value of the ith index,
Figure FDA0003635192560000024
is the minimum value of the samples in the index of the i-th item,
Figure FDA0003635192560000025
is the sample maximum value in the i-th index,Z ij normalized data for the jth sample of the ith index.
5. The method for predicting the comprehensive bearing capacity of the forest ecological system resources based on the range standardization and the entropy weight method as claimed in claim 4, wherein an optimized raised half gamma type distribution index formula is adopted in the step 4 to reflect the relationship between each evaluation index value and the influence of the evaluation index value on the comprehensive bearing capacity of the forest ecological system resources:
Figure FDA0003635192560000031
Figure FDA0003635192560000032
wherein G is i Bearing capacity, x, of the i-th index i The average sample value of the grain resource bearing capacity index value after the standardization treatment is obtained.
CN202210502902.XA 2022-05-09 2022-05-09 Method for predicting comprehensive bearing capacity of forest ecological system resources based on range standardization and entropy weight method Pending CN114971535A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116307829A (en) * 2023-01-17 2023-06-23 福建实达集团股份有限公司 Method and device for evaluating influence of infectious diseases on social bearing capacity based on information entropy

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116307829A (en) * 2023-01-17 2023-06-23 福建实达集团股份有限公司 Method and device for evaluating influence of infectious diseases on social bearing capacity based on information entropy
CN116307829B (en) * 2023-01-17 2024-03-29 福建实达集团股份有限公司 Method and device for evaluating influence of infectious diseases on social bearing capacity based on information entropy

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