CN105868923A - Resource environmental bearing capacity evaluation method based on multi-factor coupling model - Google Patents
Resource environmental bearing capacity evaluation method based on multi-factor coupling model Download PDFInfo
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Abstract
The invention provides a resource environmental bearing capacity evaluation method based on a multi-factor coupling model. The method comprises the steps that a multi-layer resource environment bearing capacity evaluation system is built; according to each operation index preset in the multi-layer resource environment bearing capacity evaluation system, an evaluation value corresponding to the operation index is worked out; according to the evaluation value corresponding to each operation index, the operation index is scored according to preset rules; according to a scored value corresponding to each operation index, on the basis of the scored value of the operation index and a weight value corresponding to the index to which the operation index belongs, a scored value of the index to which the operation index belongs to is determined, and therefore a scored value of the resource environmental bearing capacity of a preset region is determined. According to the resource environmental bearing capacity evaluation method, the resource environmental bearing capacity evaluation accuracy can be improved.
Description
Technical field
The present invention relates to resource environment analysis field, particularly relate to a kind of money based on many key elements coupling model
Source Environment Carrying Capacity Assessment method.
Background technology
Resosurces environment loading capacity refers within certain period and certain regional extent, is maintaining region resource
Structure meet sustainable development needs regional environment function still have maintenance its stable state effect capability under conditions of,
Region resource environmental system can bear the ability of the various socio-economic activity of the mankind.At present, to region
When resosurces environment loading capacity is evaluated, exists and evaluate the problem that accuracy is relatively low.
Summary of the invention
The present invention provides a kind of resosurces environment loading capacity evaluation methodology based on many key elements coupling model, to solve
The problem that accuracy that certainly existing resource Environment Carrying Capacity Assessment mode exists is relatively low.
First aspect according to embodiments of the present invention, it is provided that a kind of resource ring based on many key elements coupling model
Border Bearing Capacity Evaluation method, including:
Set up multilamellar resosurces environment loading capacity appraisement system；
For each operation index preset in described multilamellar resosurces environment loading capacity appraisement system, calculate
The assessed value that this operation index is corresponding；
According to the assessed value that each operation index is corresponding, according to default rule respectively to each operation index
Mark；
For the score value that each operation index is corresponding, score value and upper genus thereof according to this operation index are each
The weighted value that layer index is corresponding, determines the score value belonging to each layer index on it, so that it is determined that described preset areas
The score value of the resosurces environment loading capacity in territory.
In the optional implementation of one, described method also includes:
Geographical national conditions spatial data based on predeterminable area, carries out stress and strain model to described predeterminable area；
The money reflected with each operation index is extracted respectively from the remote sensing information of described predeterminable area
The grid that source environmental key-element is corresponding.
In the optional implementation of another kind, described in calculate the assessed value bag that this operation index is corresponding
Include: calculate the area ratio of all grids corresponding to this operation index and described predeterminable area；
The described assessed value corresponding according to each operation index, operates each respectively according to default rule
Index carries out scoring and includes: all grids corresponding for each operation index and the face of described predeterminable area
Long-pending ratio, marks to each operation index respectively according to default rule.
In the optional implementation of another kind, extract respectively in the remote sensing information from described predeterminable area
After going out the grid that the resource environment key element reflected with each operation index is corresponding, described method also includes:
For each grid that each operation index is corresponding, count the corresponding resource environment of reflection in this grid
The pixel area of key element and the ratio of this grid area, and according to this ratio, according to described default rule
The operation index that this grid is corresponding is marked；
For the score value of each grid respective operations index, according to the score value of this operation index and on
Belong to the weighted value that each layer index is corresponding, determine the score value belonging to each layer index on it, so that it is determined that this grid
The score value of resosurces environment loading capacity.
In the optional implementation of another kind, in the assessed value corresponding according to each operation index, according to
Before each operation index is marked by rule respectively that preset, described method includes: operate each
Assessed value corresponding to index is normalized, so that the assessed value of each operation index has unified amount
Guiding principle.
In the optional implementation of another kind, described method also includes:
Applying rules correlational analysis method and SPSS analyze the resource bearing in described resosurces environment loading capacity
Power and these two groups of aggregate variables in multivariate normal distributions of environmental carrying capacity between overall relevancy carry out
Multi-variate statistical analysis.
The invention has the beneficial effects as follows:
1, the present invention is by setting up multilamellar resosurces environment loading capacity appraisement system, and is calculating the bottom
The assessed value of each operation index after determine the score value of each operation index according to assessed value, according to respectively
The score value of individual operation index and weighted value corresponding to upper genus each layer index thereof, determine that belonging to each layer on it refers to
Target score value, such that it is able to more accurately determine the resosurces environment loading capacity score value of predeterminable area；
2, the present invention passes through geographical national conditions spatial data based on predeterminable area, and predeterminable area is carried out grid
Divide, and corresponding extracting resource environment key element reflected with each operation index from remote sensing information
After grid, for each operation index, by the face of all grids corresponding for this operation index Yu predeterminable area
Long-pending ratio, as the assessed value of this operation index, thus can improve accuracy in computation and the speed of assessed value；
3, the present invention is by determining resosurces environment loading capacity for grid, can reflect resource ring intuitively
The space distribution situation of border bearing capacity, such that it is able to embody the continuous of resosurces environment loading capacity spatial distribution
Property and gradually changeable；
4, the present invention is by using Canonical Correlation Analysis (i.e. rule correlational analysis method) and SPSS to divide
Resources Carrying Capacity in resosurces environment loading capacity and environmental carrying capacity these two groups are combined by analysis in multivariate normal distributions
Close variable between overall relevancy carry out multi-variate statistical analysis, can intuitively reflect Resources and environment system
Bearing capacity characterizes the corresponding relation of index with function, identifies the crucial shadow of restriction regional carrying capacity of resources and environments
Ring the factor.
Accompanying drawing explanation
Fig. 1 is a reality of present invention resosurces environment loading capacity based on many key elements coupling model evaluation methodology
Execute example flow chart.
Detailed description of the invention
For the technical scheme making those skilled in the art be more fully understood that in the embodiment of the present invention, and make
The above-mentioned purpose of the embodiment of the present invention, feature and advantage can become apparent from understandable, the most right
In the embodiment of the present invention, technical scheme is described in further detail.
See Fig. 1, for present invention resosurces environment loading capacity based on many key elements coupling model evaluation methodology
One embodiment flow chart.The method may comprise steps of:
Step S101, set up multilamellar resosurces environment loading capacity appraisement system.
In the present embodiment, when setting up multilamellar resosurces environment loading capacity appraisement system, can be according to following table
Multilamellar resosurces environment loading capacity appraisement system is divided into three layers by 1: destination layer, rule layer and indicator layer,
Wherein indicator layer can include first class index, two-level index and operation index.
Step S102, in described multilamellar resosurces environment loading capacity appraisement system preset each operation refer to
Mark, calculates the assessed value that this operation index is corresponding.
In the present embodiment, for each operation index in multilamellar resosurces environment loading capacity appraisement system, obtaining
(such as operation index " cover by arable land to take each operation index reflected resource environment key element in this predeterminable area
Lid rate " reflect is this resource environment key element of ploughing in predeterminable area) accurate data, can be according to
Following methods calculates the assessed value that each operation index is corresponding:
1) for the operation index of two-level index " land resource index " subordinate:
Arable land coverage rate=Agd* (paddy field area+nonirrigated farmland area)/region gross area, in formula, Agd is cultivated
The normalization coefficient of ground coverage rate；
Field coverage rate=AYd* (orchard area+tea place area+mulberry field area+rubber plantation area+face, nursery
Long-pending+other field areas)/region the gross area, in formula, AYd is the normalization coefficient of field coverage rate；
2) for the operation index of two-level index " forest resourceies index " subordinate:
Woodland rent rate=Afor* (high forest ground area+shrub land area+Qiao's filling mixed forest ground area+
Bamboo grove ground area+opening area+young plantation's ground area+open shrublands area)/region the gross area, formula
In, Afor is the normalization coefficient of woodland rent rate；
Grass cover rate=Agra* (high coverage grassland area+middle coverage grassland area+low cover degree grass
Ground area+grassplot+fix the sand to fill grass+bank protection filling grass+other artificial pastures)/region the gross area, in formula, Agra
It it is the normalization coefficient of Grass cover rate；
3) for the operation index of two-level index " water resource index " subordinate:
Waters coverage rate=Awet* (the rivers and canals area+area of lake+pool, storehouse area)/region gross area, in formula,
Awet is the normalization coefficient of waters coverage rate (Wetland Area area ratio)；
4) for the operation index of two-level index " mineral resources index " subordinate:
Mining area coverage rate=Amq* (mining area area)/region gross area, in formula, Amq is mining area coverage rate
Normalization coefficient；
Ore deposit dot coverage=Amd* (counting in ore deposit)/region gross area, in formula, Amd is ore deposit dot coverage
Normalization coefficient；
5) for the operation index of two-level index " tourist resources index " subordinate:
Conservation of scenic spots district coverage rate=Atq* (scenic reserve area)/region gross area, in formula,
Atq is the normalization coefficient of scenic reserve coverage rate；
Scenic spot tourist attractions coverage rate=Atd* (treasure tour sight spot the number)/region gross area, in formula,
Atd is the normalization coefficient of scenic spot tourist attractions coverage rate；
6) for the operation index of two-level index " construction land quota " subordinate:
Construction land coverage rate=Ajd* (building construction land area+path area+structures land area+
Artificial heap picks up area)/region the gross area, in formula, Ajd is the normalization coefficient of construction land coverage rate；
7) for the operation index of two-level index " network of rivers index " subordinate:
The abundant degree of water in evaluation region, utilizes unit are river total length, water surface area in region
Represent with water resources quantity.When network of rivers dnesity index is more than 100, then take 100.Network of rivers density=Ariv*
(river length+irrigation canals and ditches length)/region area, in formula, Ariv river length normalization coefficient；
8) for the operation index of two-level index " road network index " subordinate:
Road traffic density in evaluation region, utilizes the gross area shared by road to represent divided by the region gross area.
When roading density index is more than 100, then take 100.Roading density=road total length/region area,
In formula, Ard link length normalization coefficient；
9) for the operation index of two-level index " medical and health organization's index " subordinate:
According to relevant regulations, urban district, district service radius is at 10km-15km；The service of health clinics in towns and townships half
Footpath 3km-5km；The service radius 1km-1.5km of at village level medical center.Medical and health organization in district is divided into
Town commune hospital and 2 grades, at village level medical center are respectively calculated.
Town commune hospital coverage: by distance town commune hospital 3000,4000,5000m distance calculate neighbouring
District, produces the proximity polygon of the multiple equivalent distance that spacing is 1000 meters, totally 3 weight, overlay region
Territory eliminates and merges, and calculates the area of proximity, utilizes anti-distance weighting differential technique to carry out unit and beat
Point；At village level medical center coverage: calculate proximity by 1000,1500,2000 meters of middle school of distance distance,
Producing the proximity polygon of the multiple equivalent distance that spacing is 500 meters, totally 3 weight, overlapping region is entered
Row eliminates and merges, and calculates the area of proximity, utilizes anti-distance weighting differential technique to carry out unit marking.
10) for the slave operation index of two-level index " educational institution's index ":
Specifying according to " city regular primary and secondary schools school schoolhouse construction criteria ", primary school's service radius is 500 meters；
Middle school's service radius is 1000 meters, and the educational institution in district is divided into primary school, middle school and vocational school 3
Level is respectively calculated.
Primary school's coverage: calculate proximity, between generation by 500,1000,1500 meters of primary school of distance distance
Away from the proximity polygon for the multiple equivalent distance of 500 meters, totally 3 weight, overlapping region eliminates
And fusion, calculate the area of proximity, utilize anti-distance weighting differential technique to carry out unit marking.
Middle school's coverage: calculate proximity by 1000,2000,3000 meters of middle school of distance distance, produce
Spacing is the proximity polygon of the multiple equivalent distance of 1000 meters, and totally 3 weight, overlapping region disappears
Remove and merge, calculating the area of proximity, utilize anti-distance weighting differential technique to carry out unit marking.Utilize
Anti-distance weighting differential technique carries out unit marking.
Vocational school's coverage: distance vocational school 2000,4000,6000 meters distance calculates proximity,
Producing the proximity polygon of the multiple equivalent distance that spacing is 2000 meters, totally 3 weight, overlapping region is entered
Row eliminates and merges, and utilizes anti-distance weighting differential technique to carry out unit marking.
11) for the operation index of two-level index " water environment index " subordinate:
Section number/section sum the * 100% of probability of meeting water quality standard=reach III class water quality
12) for the operation index of two-level index " atmospheric environment index " subordinate:
Air quality compliance rate=air quality natural law up to standard/whole year monitors total natural law * 100%, and evaluation region is empty
Gas requisite quality natural law accounts for the annual ratio monitoring total natural law, and evaluation criterion performs " GB3095-2012 ring
Border air quality standard ".
13) for the operation index of two-level index " geological environment " subordinate:
Ground calamity easily send out subregion coverage=Ageo* (calamity easily send out differentiation cloth area)/region the gross area, formula
In, Ageo is the normalization coefficient that ground calamity easily sends out subregion coverage；
Ground calamity preventing and treating subregion coverage=Agef* (calamity preventing and treating distinguish the cloth area)/region gross area, formula
In, Agef is the normalization coefficient of ground calamity preventing and treating subregion coverage；
14) for the operation index of two-level index " acoustic environment index " subordinate:
Regional traffic main line noise average refers to regional traffic main line each section monitoring result, by its road
The meansigma methods of the equivalent sound level of segment length weighting, evaluation criterion performs " standard for acoustic environmental quality
(GB3096-2008) ", two indices concentrated expression urban sound environmental quality condition, regional environmental noise
Meansigma methods refers to the equivalent sound level arithmetic mean of instantaneous value of environment noise grid monitoring in region.
15) for the slave operation index of two-level index " soil environment index ":
Soil erosion area ratio=Asoil* (Soil erosion, stony desertification the area)/region gross area, in formula,
Asoil is the normalization coefficient of Soil erosion area ratio；
16) for the operation index of two-level index " Engel's coefficient index " subordinate:
Engel's coefficient=Aeg* region Engel's index, in formula, Aeg is the normalization system of Engel's coefficient
Number；
17) for the operation index of two-level index " Greening Indicators " subordinate:
Afforestation coverage rate=Agre* (afforestation forest ground area+green lawn area)/region gross area, in formula,
Agre is the normalization coefficient of afforestation coverage rate；
18) for the operation index of two-level index " unit regional GDP " subordinate:
Unit regional GDP=Agdp* regional GDP/region gross area, in formula, Agdp is single
The normalization coefficient of position regional GDP；
19) for the operation index of two-level index " population density index " subordinate:
Population density=Apeo* total number of persons/region gross area, in formula, Apeo is the normalization of population density
Coefficient；
20) for the operation index of two-level index " production of unit regional industry " subordinate:
Unit regional industry production=Agip* total industrial output value/region gross area, in formula, Agip is single
The normalization coefficient that position regional industry produces.It is to be noted that the above-mentioned zone gross area is predeterminable area
The gross area.
It addition, when calculating the assessed value of each operation index in multilamellar resosurces environment loading capacity appraisement system,
It is also based on the geographical national conditions spatial data of predeterminable area, sets up spatial model and calculate.Now,
The geographical national conditions spatial data of predeterminable area can be primarily based on, predeterminable area is carried out stress and strain model, so
After from the remote sensing information of described predeterminable area, extract the resource ring reflected with each operation index respectively
The grid that border key element is corresponding.Owing to remote sensing information position in gatherer process there may exist deviation, therefore
Before extracting corresponding grid from remote sensing information, need first remote sensing information to be carried out position correction.
The resource environment key element reflected with operation index is (such as arable land, field coverage rate, grass as vegetation
Ground, woodland rent rate etc.) as a example by, when extracting grid corresponding to certain vegetation, can be first by
This kind of vegetation of remote sensing information is normalized by the modeling tool under Modeler, and utilizes
Signature Editor under Classification, selects the vegetation training center of predetermined number, checks
The vegetation index threshold value (such as maximum, minima, average and variance) of training center, and according to this vegetation
Index threshold determines default comparison threshold value, and this compares threshold value can be by positive and negative three times of average value standard deviation structures
The scope become.Hereafter recycling modeling tool carries out threshold process to the remote sensing information after normalization:
If the pixel value of this kind of vegetation is more than the comparison threshold value preset in grid, show vegetation, if less than, show
Background, then preserves output, thus extracts the grid corresponding with this kind of vegetation from remote sensing information.
Furthermore, the resource environment key element reflected with operation index is as water source (such as waters coverage rate etc.)
As a example by, when the grid that this waters coverage rate of extraction is corresponding, can check first with feature edit device
The characteristic threshold value of waters training center in remote sensing information, such as maximum, minima, average, variance；
Then utilize Conditional function, centered by average, with the multiple of variance as amplitude of variation, build
Vertical water area extraction algorithm, runs and image output, thus can extract from remote sensing information and waters pair
The grid answered, wherein this water area extraction algorithmic language is as follows:
EITHER 1IF ($ n1_peizhun (1)≤91.383AND $ n1_peizhun (1) >=64.371
AND $ n1_peizhun (2)≤76.27AND $ n1_peizhun (2) >=39.4AND
$ n1_peizhun (3)≤87.27AND $ n1_peizhun (3) >=20.358AND
$ n1_peizhun (4)≤41.46AND $ n1_peizhun (4) >=15.45AND
$ n1_peizhun (5)≤20.501AND $ n1_peizhun (5) >=13.619
AND $ n1_peizhun (6)≤18.426AND $ n1_peizhun (6) >=11.178) OR 0
OTHERWISE。
After extracting the grid that the resource environment key element reflected with each operation index is corresponding, permissible
Utilize cuclear density analysis, anti-distance weighting interpolation, raster symbol-base, the proximity calculating of multiple equivalent distance etc.
The method of spatial analysis, carries out SPATIAL CALCULATION to each operation index, such as each operation index,
Calculate the area ratio of all grids corresponding to this operation index and predeterminable area, so that it is determined that each operation
The assessed value that index is corresponding.
Step S103, according to assessed value corresponding to each operation index, according to default rule respectively to respectively
Individual operation index is marked.
In the present embodiment, after calculating the assessed value that each operation index is corresponding, can be first to respectively
The assessed value that individual operation index is corresponding is normalized, so that the assessed value of each operation index has
Unified dimension, then according to each operation index is marked by preset rules corresponding in table 2 below.
It is to be noted that 1, each two-level index be divided into a few class state, every class state by grade correspondence
Correspond respectively to different evaluation score values.2, scoring score value all genus grade class of each classification, respectively with
The percentage calculation of this grade of index weights；All genus numerical value classes, score by interpolation.3, all operations
The aggregate-value of index score value is the type and evaluates score value.
The present invention passes through geographical national conditions spatial data based on predeterminable area, predeterminable area is carried out grid and draws
Point, and from remote sensing information, extracting the net that resource environment key element reflected with each operation index is corresponding
After lattice, for each operation index, by the area of all grids corresponding for this operation index Yu predeterminable area
Ratio, as the assessed value of this operation index, thus can improve accuracy in computation and the speed of assessed value.
Step S104, for score value corresponding to each operation index, according to the score value of this operation index
And the weighted value that upper genus each layer index is corresponding, determine the score value belonging to each layer index on it, so that it is determined that
The score value of the resosurces environment loading capacity of described predeterminable area.
In the present embodiment, after calculating the score value of each operation index, in indicator layer each two
Level index, the score value of this two-level index is equal to the score value sum of its slave operation index, for index
Each first class index in Ceng, the score value of this first class index can be to refer to calculating its subordinate each two grades
After target score value and the weighted value sum of products thereof, the product of the weighted value of the sum of products and this first class index；
Natural resources carrying capacity:
Rn=[Rns*WRns+Rnf*WRnf+Rnw*WRnw+Rnm*WRnm+Rnt*WRnt] * WRn
In formula, Rns land resource, Rnf forest resourceies, Rnw water resource, Rnm mineral resources, Rnt
Tourist resources, W represents corresponding weighted value.
Social economy's Resources Carrying Capacity:
Rs=[Rsb*WRsb+Rsw*WRsw+Rsr*WRsr+Rsh*WRsh+Rse*WRse] * WRs
In formula, Rsb construction land coverage, Rsw network of rivers density, Rsr road mileage, Rsh medical treatment is defended
Life structure coverage, Rse educational institution coverage, W represents corresponding weighted value.
Natural environment bearing capacity:
En=[Enw*WEnw+Ena*WEna+Ent*WEnt+Env*WEnv+Ens*WEns] * WEn
In formula, Enw water environment, Ena atmospheric environment, Ent geological environment, Env acoustic environment, Ens soil
Earth environment, W represents corresponding weighted value.
Social economic environment bearing capacity:
Es=[Ese*WEse+Esg*WEsg+Esd*WEsd+Esp*WEsp+Esi*WEsi] * WEs
In formula, Rns Engel's coefficient, Esg coverage, Esd unit are regional GDP,
Esp population density, Esi unit regional industry produces, and W represents corresponding weighted value.
The score value of the Resources Carrying Capacity in rule layer can be to calculate its each first class index of subordinate
After the sum of products of score value and weighted value thereof, taking advantage of of the weighted value of this sum of products and this Resources Carrying Capacity
Long-pending, i.e. R=[Rn*WRn+Rs*WRs] * WR；Environmental carrying capacity can be calculate its subordinate each one
After the level score value of index and the sum of products of weighted value thereof, this sum of products and the power of this environmental carrying capacity
The product of weight values, i.e. can be expressed as E=[En*WEn+Es*WEs] * WE；In formula, Rn: natural resources
Bearing capacity；Rs: social economy's Resources Carrying Capacity；En natural environment bearing capacity；Es: social economic environment
Bearing capacity；W represents corresponding weighted value.
The score value of the resosurces environment loading capacity in destination layer can be to provide in calculating its subordinate rule layer
After source bearing capacity and environmental carrying capacity and the respective weighted value sum of products, i.e. RE=R*WR+E*WE, in formula,
RE: resosurces environment loading capacity；R: Resources Carrying Capacity；E: environmental carrying capacity；W represents corresponding weight
Value.Hereafter, according to table 3 below, the power of the resosurces environment loading capacity of predeterminable area can be evaluated:
| Classification | ＜ 20 | 21～40 | 41～60 | 61～80 | ＞ 80 |
| Evaluate | Weak carrying | Low carrying | Middle equivalent-load | Relatively high-mechanic | High-mechanic |
As seen from the above-described embodiment, the present invention by setting up multilamellar resosurces environment loading capacity appraisement system, and
Calculate the assessed value of each operation index of the bottom and determine each operation index according to assessed value
After score value, according to the weighted value that the score value of each operation index and upper genus each layer index thereof are corresponding, come
Determine the score value belonging to each layer index on it, such that it is able to more accurately determine the resource ring of predeterminable area
Border bearing capacity score value.
Refer to it addition, the present invention extracts in the remote sensing information from described predeterminable area respectively with each operation
After marking the grid that the resource environment key element reflected is corresponding, it is also possible to including:
For each grid that each operation index is corresponding, count the corresponding resource environment of reflection in this grid
The pixel area of key element and the ratio of this grid area, and according to this ratio, according to described default rule
The operation index that this grid is corresponding is marked.In the present embodiment, this rule preset can be by above
In table 2, " Index grading " and " rank scores " two column data is determined.
For the score value of each grid respective operations index, according to the score value of this operation index and on
Belong to the weighted value that each layer index is corresponding, determine the score value belonging to each layer index on it, so that it is determined that this grid
The score value of resosurces environment loading capacity.For the score value of each grid respective operations index, determine it
The score value of upper genus each layer index, with at predeterminable area for the score value of each operation index, determine it
The process of the score value of upper genus each layer index is identical, and the geographic range that simply operation index is corresponding is different, because of
And do not repeat them here.The present invention, can be the most anti-by determining resosurces environment loading capacity for grid
Mirror the space distribution situation of resosurces environment loading capacity, such that it is able to embody resosurces environment loading capacity space
The seriality of distribution and gradually changeable.
Furthermore, the present invention can also use Canonical Correlation Analysis (i.e. rule correlational analysis method) and SPSS
Analyze Resources Carrying Capacity in resosurces environment loading capacity and environmental carrying capacity these two groups in multivariate normal distributions
Aggregate variable between overall relevancy carry out multi-variate statistical analysis, thus can intuitively reflect resource
Environmental system bearing capacity characterizes the corresponding relation of index with function, identifies restriction regional carrying capacity of resources and environments
Key Influential Factors.
Those skilled in the art, after considering description and putting into practice invention disclosed herein, will readily occur to this
Other embodiment of invention.The application is intended to any modification, purposes or the adaptability of the present invention
Change, these modification, purposes or adaptations are followed the general principle of the present invention and include this
Bright undocumented common knowledge in the art or conventional techniques means.Description and embodiments only by
Being considered as exemplary, true scope and spirit of the invention are pointed out by claim below.
It should be appreciated that the invention is not limited in described above and illustrated in the accompanying drawings accurately
Structure, and various modifications and changes can carried out without departing from the scope.The scope of the present invention is only by institute
Attached claim limits.
Claims (6)
1. a resosurces environment loading capacity evaluation methodology based on many key elements coupling model, it is characterised in that
Including:
Set up multilamellar resosurces environment loading capacity appraisement system；
For each operation index preset in described multilamellar resosurces environment loading capacity appraisement system, calculate
The assessed value that this operation index is corresponding；
According to the assessed value that each operation index is corresponding, according to default rule respectively to each operation index
Mark；
For the score value that each operation index is corresponding, score value and upper genus thereof according to this operation index are each
The weighted value that layer index is corresponding, determines the score value belonging to each layer index on it, so that it is determined that described preset areas
The score value of the resosurces environment loading capacity in territory.
Method the most according to claim 1, it is characterised in that described method also includes:
Geographical national conditions spatial data based on predeterminable area, carries out stress and strain model to described predeterminable area；
The money reflected with each operation index is extracted respectively from the remote sensing information of described predeterminable area
The grid that source environmental key-element is corresponding.
Method the most according to claim 2, it is characterised in that
The described assessed value calculating this operation index corresponding includes: calculate the institute that this operation index is corresponding
There is the area ratio of grid and described predeterminable area；
The described assessed value corresponding according to each operation index, operates each respectively according to default rule
Index carries out scoring and includes: all grids corresponding for each operation index and the face of described predeterminable area
Long-pending ratio, marks to each operation index respectively according to default rule.
Method the most according to claim 2, it is characterised in that in the remote sensing from described predeterminable area
After information extracts the grid respectively corresponding with the resource environment key element that each operation index is reflected,
Described method also includes:
For each grid that each operation index is corresponding, count the corresponding resource environment of reflection in this grid
The pixel area of key element and the ratio of this grid area, and according to this ratio, according to described default rule
The operation index that this grid is corresponding is marked；
For the score value of each grid respective operations index, according to the score value of this operation index and on
Belong to the weighted value that each layer index is corresponding, determine the score value belonging to each layer index on it, so that it is determined that this grid
The score value of resosurces environment loading capacity.
Method the most according to claim 2, it is characterised in that corresponding according to each operation index
Assessed value, before respectively each operation index being marked according to default rule, described method bag
Include: the assessed value that each operation index is corresponding is normalized, so that the commenting of each operation index
Valuation has unified dimension.
Method the most according to claim 1, it is characterised in that described method also includes:
Applying rules correlational analysis method and SPSS analyze the resource bearing in described resosurces environment loading capacity
Power and these two groups of aggregate variables in multivariate normal distributions of environmental carrying capacity between overall relevancy carry out
Multi-variate statistical analysis.
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109426909A (en) * | 2017-08-30 | 2019-03-05 | 中国农业大学 | Arable land production capacity index acquisition methods and device based on random forest |
| CN110781267A (en) * | 2019-10-29 | 2020-02-11 | 江苏省基础地理信息中心 | Multi-scale space analysis and evaluation method and system based on geographical national conditions |
| CN110825754A (en) * | 2019-10-23 | 2020-02-21 | 北京蛙鸣华清环保科技有限公司 | Air quality spatial interpolation method, system, medium and device based on attributes |
| CN111241462A (en) * | 2020-01-20 | 2020-06-05 | 北京正和恒基滨水生态环境治理股份有限公司 | Bird habitat bearing capacity calculation method and device, storage medium and computer |
| CN111738629A (en) * | 2020-08-18 | 2020-10-02 | 中国科学院地理科学与资源研究所 | Method and device for measuring comprehensive bearing index of regional resource environment |
| CN112860822A (en) * | 2020-12-30 | 2021-05-28 | 中国测绘科学研究院 | Comprehensive analysis method for land resource bearing capacity based on geographical national situation view angle |
| CN114565157A (en) * | 2022-02-28 | 2022-05-31 | 中国科学院地理科学与资源研究所 | Urban structure multi-fractal feature identification method based on geographic mapping |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102314549A (en) * | 2011-07-12 | 2012-01-11 | 北京师范大学 | Environmental risk zoning-based decision support method for layout optimization adjustment |
| CN104794350A (en) * | 2015-04-23 | 2015-07-22 | 中国科学院地理科学与资源研究所 | System and method for evaluating comprehensive carrying capacity of region |
-
2016
- 2016-04-21 CN CN201610254750.0A patent/CN105868923A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102314549A (en) * | 2011-07-12 | 2012-01-11 | 北京师范大学 | Environmental risk zoning-based decision support method for layout optimization adjustment |
| CN104794350A (en) * | 2015-04-23 | 2015-07-22 | 中国科学院地理科学与资源研究所 | System and method for evaluating comprehensive carrying capacity of region |
Non-Patent Citations (1)
| Title |
|---|
| 王帆: "基于GIS技术的资源与环境承载力研究", 《中国优秀硕士学位论文全文数据库 工程科技Ⅰ辑》 * |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109426909A (en) * | 2017-08-30 | 2019-03-05 | 中国农业大学 | Arable land production capacity index acquisition methods and device based on random forest |
| CN109426909B (en) * | 2017-08-30 | 2021-04-13 | 中国农业大学 | Random forest based cultivated land productivity index obtaining method and device |
| CN110825754A (en) * | 2019-10-23 | 2020-02-21 | 北京蛙鸣华清环保科技有限公司 | Air quality spatial interpolation method, system, medium and device based on attributes |
| CN110825754B (en) * | 2019-10-23 | 2022-06-17 | 北京蛙鸣华清环保科技有限公司 | Air quality spatial interpolation method, system, medium and device based on attributes |
| CN110781267A (en) * | 2019-10-29 | 2020-02-11 | 江苏省基础地理信息中心 | Multi-scale space analysis and evaluation method and system based on geographical national conditions |
| CN111241462A (en) * | 2020-01-20 | 2020-06-05 | 北京正和恒基滨水生态环境治理股份有限公司 | Bird habitat bearing capacity calculation method and device, storage medium and computer |
| CN111241462B (en) * | 2020-01-20 | 2023-07-07 | 北京正和恒基滨水生态环境治理股份有限公司 | Bird habitat bearing capacity calculating method, device, storage medium and computer |
| CN111738629A (en) * | 2020-08-18 | 2020-10-02 | 中国科学院地理科学与资源研究所 | Method and device for measuring comprehensive bearing index of regional resource environment |
| CN112860822A (en) * | 2020-12-30 | 2021-05-28 | 中国测绘科学研究院 | Comprehensive analysis method for land resource bearing capacity based on geographical national situation view angle |
| CN112860822B (en) * | 2020-12-30 | 2024-02-09 | 中国测绘科学研究院 | Comprehensive analysis method for land resource bearing capacity based on geographical national condition view angle |
| CN114565157A (en) * | 2022-02-28 | 2022-05-31 | 中国科学院地理科学与资源研究所 | Urban structure multi-fractal feature identification method based on geographic mapping |
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Application publication date: 20160817 |