CN104298828B - Method for simulating influence of urban green space patterns on thermal environments - Google Patents
Method for simulating influence of urban green space patterns on thermal environments Download PDFInfo
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Abstract
The invention discloses a method for simulating influence of urban green space patterns on thermal environments. The method comprises, firstly, extracting the surface feature distribution information of a researched region on remote sensing images through the object-oriented segmentation technology, filtering Lidar point cloud data to obtain three-dimensional information of buildings and vegetations, integrating the surface feature distribution information and the three-dimensional information of the buildings and vegetations to structure a high-precision three-dimensional model; secondly, inverting surface temperature, atmosphere moisture content and air temperature through the remote sensing technology to estimate surface average temperature, and querying meteorological data to obtain the soil moisture of the region, and taking the surface average temperature, the soil moisture, the atmosphere moisture content and the air temperature as the initial conditions of a CFD (computational fluid dynamics) model. By means of the high-precision three-dimensional model and by accurately simulating the initial conditions in real time, the method for simulating the influence of the urban green space patterns on the thermal environments improves the simulating precision and the application reliability of the CFD model; meanwhile, the CFD model enhances the prediction capacity of the remote sensing technology.
Description
Technical field
The present invention relates to the analogy method that a kind of urban green space general layout affects on thermal environment, and in particular to one kind is based on remote sensing
The analogy method that the urban green space of technology and CFD model affects on thermal environment.
Background technology
" heat island " phenomenon be a certain some areas (mainly urban area) temperature it is projecting area phenomenon, heat island
Intensity can represent with the temperature difference of two representative measuring points, somewhere temperature and suburb meteorology measuring point temperature generally in city
The difference of degree.In recent decades, developed rapidly due to urbanization, the change of underlying surface structure and traffic heat extraction and building row
The impact of the factors such as heat, urban Heat Environment runs down, and " heat island " phenomenon and its negative effect day by day show especially.For one residential
For, due to by site coverage, construction material, architectural composition, ratio of green space, waterscape facilities and air-conditioning, vehicle row in planning and designing
The impact of the factors such as heat, the release of resident living energy, cell outside air temperature is also possible to " heat island " phenomenon occur." heat island " is existing
As the appearance in summer, the probability that can not only make people's Heat stroke becomes big, while also having promoted the formation of photochemical fog, having added
Heavily contaminated, and increase the consumption (such as building air conditioning power consumption) of the various energy, the Working Life for giving people is negatively affected.
Urban afforestation is one of the main method for improving urban area thermal environment.Afforestation vegetation is rising in a large number by its blade
Moisture and consume the radiant heat in city, and pass through branch and leaf formed dense shade stop the sun direct radiation heat and from road
The decreasing temperature and increasing humidity benefit for reflecting heat and generation of face, metope and adjacent objects, the heat island and effect of dry island to alleviating city, is reduced
Resident due to the generation of various diseases caused by arid and hot environment, bring improve resident health level, improve life comfort level and
The benefit of quality of life, in one residential under this specific environmental condition, with special and significance.
Hydrodynamics (CFD) and remote sensing technique are two extremely important handss for carrying out region thermal environment simulation and analysis
Section.CFD technologies are to carry out numerical experiment, computer mould using various mathematical methods to fit analysis and research, are regional temperature field mould
Intend providing theoretical basiss.In the research of urban Heat Environment, mould is carried out to the air current composition in certain space with CFD model
Intend, by setting up mathematics physics model, according to the reasonable boundary conditions and parameter that provide, can flow what is formed to regional air
Temperature field, velocity field carry out analogue simulation, intuitively show its design result, and according to design result to its feasibility and reasonability
It is analyzed research.Remote sensing technology can carry out large area earth's surface temperature measuring, reduce local environment artificial disturbance, visual rationing
Research heat island feature.And according to atural object different-waveband radiation value difference, using thermal infrared sensor to urban surface temperature
Degree carries out coverage count, can be by being calculated atural object heat spatial distribution.
But CFD model and remote sensing technology have certain limitation.For CFD simulations are calculated, the mould in region
It is a very important link that type is set up, if the accuracy that regional model is set up does not reach certain requirement, is calculated
Resultant error can be very big, no longer has Practical significance for analogue simulation.Therefore, setting up on area three-dimensional model,
Ensure there are enough precision.And remote sensing image is instantaneous image, earth's surface a certain moment specific temperature field can only be obtained, gained
As a result without continuity and predictability.And the temperature results that are finally inversed by of remote sensing image are the result of earth's surface, can not obtain away from
Temperature field result at ground on certain altitude, and air themperature is in the simulation of urban Heat Environment and environmental degree of comfort evaluation
In it is often critically important.
The content of the invention
The present invention is for CFD model and the deficiency of remote sensing technology, there is provided a kind of urban green space general layout affects on thermal environment
Analogy method, by CFD model increase remote sensing technology predictive ability, using remote sensing technology improve CFD model precision.
To reach above-mentioned purpose, the technical solution adopted in the present invention is：
The analogy method that a kind of urban green space general layout affects on thermal environment, comprises the following steps,
Step one, obtains one and studies area's remote sensing image, using remote sensing image Retrieval of Atmospheric Water Vapor content, temperature and earth's surface temperature
Degree；
Step 2, inquires about the soil moisture that meteorological data obtains the research area；
Step 3, by surface temperature earth's surface mean temperature is estimated；
Step 4, obtains the Lidar cloud datas in the research area, using the Lidar point clouds based on the gradient and region growing
Filtering algorithm is filtered process to the Lidar cloud datas for obtaining；
Step 5, to the Lidar cloud datas after Filtering Processing, is built using the extraction for being based on principal component analysiss and connectedness
The method for building thing information extracts building information；
Step 6, using TerraSolid softwares by the Lidar point cloud numbers after the remote sensing image in the research area, Filtering Processing
According to this and building information carries out geometrical registration, the three-dimensional information of the research area building and vegetation is obtained；
Step 7, using Remote sensing image classification of the ENVI softwares to the research area；
Step 8, sorted remote sensing image, earth's surface mean temperature, soil moisture, Water Vapor Content and temperature are led
Enter ENVI-met, according to the three-dimensional information of the building and vegetation for obtaining, set up the threedimensional model in the research area；
Step 9, imports the threedimensional model built up CFD software and is simulated calculating, and contrasts with Monitoring Data on the spot,
The boundary condition of CFD model is corrected；
Step 10, using the Characteristics of The Distribution of Temperature of the CFD model simulation different greenlands Distribution Pattern for having corrected.
In step one, the formula of Retrieval of Atmospheric Water Vapor content is as follows,
Wherein, w is Water Vapor Content；α and β is constant, and α=0.02, β=0.651 are taken respectively；Ref2 and ref19 point
It is not the ground surface reflectance of MODIS the 2nd and 19 wave bands；
Ref2=scale2 (band2-offset2)
Ref19=scale19 (band19-offset19)
Wherein, band2 and band19 are respectively the DN values of the 2nd and 19 wave bands；Scale2, offset2, scale19 and
Offset19 is MODIS scaling constants.
Using the gradient is based on the Lidar cloud datas for obtaining are filtered with the Lidar point cloud filtering algorithms of region growing
The step of ripple is processed is as follows,
A1) cancelling noise point；
A2) ground seed point is chosen；
A3 virtual grid) is set up；
A4) criterion is increased；
A5) criterion is terminated；
A6) centered on the grid that the i+1 ground seed point concentrated by ground seed point is located, repeat step A4 with
A5；
A7) all ground seed points that ground seed point is concentrated are traveled through；
A8) count each point in Lidar cloud datas and be judged to topocentric probability.
The step of using building information is extracted based on the method for principal component analysiss and the extraction building information of connectedness
It is as follows,
B1 the non-ground points after Filtering Processing) are found and converges interior any one point PiRadius for δ neighborhood point, if neighborhood
The number of point is less than 8, then the point is classified as into non-building object point, if the number of neighborhood point is more than 8, continues to go to B2；
B2) to point PiNeighborhood point carry out principal component analysiss, obtain the corresponding eigenvalue λ of its covariance matrix1、λ2And λ3；
B3) gauging surface variation characteristic value FsvWith horizontal measures characteristic value Fh, and respectively with setting show change threshold
TsvWith horizontal metric threshold ThRelatively, if point meets following condition simultaneously
Abuild1={ Pi|Pi∈ A, Fsv(Pi)<TsvAnd | Ni|≥8}
Abuild2={ Pi|Pi∈ A, Fh(Pi)<Th}
Then the point is judged to build object point, wherein Abuild1And Abuild2Represent that the point is building, A is Lidar point cloud numbers
Converge according to the non-ground points after Filtering Processing, NiRepresent neighborhood point number, Fsv(Pi) represent point PiSurface variation characteristic value, Fh
(Pi) represent point PiHorizontal measures characteristic value；
B4 each non-ground points) is traveled through, repeat step B1 to B3 obtains initial building point set；
B5) Delaunay triangulation network is built to initial building object point, the length of side of each triangle is calculated, if the side of triangle
The long dot density more than 2 times, deletes the length of side of triangle；
B6) different divisional planes are divided according to connective, calculates the area of each divisional plane, and threshold value T is less than to areas
Divisional plane delete, obtain last building object point.
Greenery patches, building, road and the different types of classification thematic map of water body are obtained after the classification of remote-sensing images.
In step 10, distribution of green areas general layout has 5 classes, respectively spot distribution greenery patches, annular spread greenery patches, ribbon distribution
Greenery patches, radial distribution greenery patches and wedge shape distribution greenery patches..
The beneficial effect that the present invention is reached：The present invention is extracted initially with object-oriented cutting techniques from remote sensing image
The atural object distributed intelligence in research area, Filtering Processing Lidar cloud data obtains the three-dimensional information of building and vegetation, fusion two
Person, builds high-precision three-dimensional model；Secondly, using remote sensing hormone inverting surface temperature, Water Vapor Content and temperature, estimation ground
Table mean temperature, inquires about the soil moisture that meteorological data obtains the region, by earth's surface mean temperature, soil moisture, atmosphere vapour
, used as the initial condition of CFD model, high accuracy three-dimensional model and in real time accurate simulation initial condition are improved for content and temperature
The precision of CFD model simulation and the credibility of application；Simultaneously CFD model increases the predictive ability of remote sensing technology.
Description of the drawings
Fig. 1 is the flow chart of the present invention.
Fig. 2 is the model in spot distribution greenery patches.
Fig. 3 is the model in annular spread greenery patches.
Fig. 4 is the model that ribbon is distributed greenery patches.
Fig. 5 is the model in radial distribution greenery patches
Fig. 6 is the model that wedge shape is distributed greenery patches
Fig. 7 highly locates temperature field simulation figure for spot distribution greenery patches 0.2m.
Fig. 8 highly locates temperature field simulation figure for spot distribution greenery patches 2m.
Fig. 9 highly locates temperature field simulation figure for spot distribution greenery patches 10m.
Figure 10 highly locates temperature field simulation figure for annular spread greenery patches 0.2m.
Figure 11 highly locates temperature field simulation figure for annular spread greenery patches 2m.
Figure 12 highly locates temperature field simulation figure for annular spread greenery patches 10m.
Figure 13 is that ribbon distribution greenery patches 0.2m highly locates temperature field simulation figure.
Figure 14 is that ribbon distribution greenery patches 2m highly locates temperature field simulation figure.
Figure 15 is that ribbon distribution greenery patches 10m highly locates temperature field simulation figure.
Figure 16 highly locates temperature field simulation figure for radial distribution greenery patches 0.2m.
Figure 17 highly locates temperature field simulation figure for radial distribution greenery patches 2m.
Figure 18 highly locates temperature field simulation figure for radial distribution greenery patches 10m.
Figure 19 is that wedge shape distribution greenery patches 0.2m highly locates temperature field simulation figure.
Figure 20 is that wedge shape distribution greenery patches 2m highly locates temperature field simulation figure.
Figure 21 is that wedge shape distribution greenery patches 10m highly locates temperature field simulation figure.
Figure 22 highly locates temperature field statistic histogram for spot distribution greenery patches 0.2m.
Figure 23 highly locates temperature field statistic histogram for spot distribution greenery patches 2m.
Figure 24 highly locates temperature field statistic histogram for spot distribution greenery patches 10m.
Figure 25 highly locates temperature field statistic histogram for annular spread greenery patches 0.2m.
Figure 26 highly locates temperature field statistic histogram for annular spread greenery patches 2m.
Figure 27 highly locates temperature field statistic histogram for annular spread greenery patches 10m.
Figure 28 is that ribbon distribution greenery patches 0.2m highly locates temperature field statistic histogram.
Figure 29 is that ribbon distribution greenery patches 2m highly locates temperature field statistic histogram.
Figure 30 is that ribbon distribution greenery patches 10m highly locates temperature field statistic histogram.
Figure 31 highly locates temperature field statistic histogram for radial distribution greenery patches 0.2m.
Figure 32 highly locates temperature field statistic histogram for radial distribution greenery patches 2m.
Figure 33 highly locates temperature field statistic histogram for radial distribution greenery patches 10m.
Figure 34 is that wedge shape distribution greenery patches 0.2m highly locates temperature field statistic histogram.
Figure 35 is that wedge shape distribution greenery patches 2m highly locates temperature field statistic histogram.
Figure 36 is that wedge shape distribution greenery patches 10m highly locates temperature field statistic histogram.
Specific embodiment
Below in conjunction with the accompanying drawings the invention will be further described.Following examples are only used for clearly illustrating the present invention
Technical scheme, and can not be limited the scope of the invention with this.
As shown in figure 1, the analogy method that a kind of urban green space affects on thermal environment, comprises the following steps：
Step one, obtains one and studies area's remote sensing image, using remote sensing image Retrieval of Atmospheric Water Vapor content, temperature and earth's surface temperature
Degree.
The formula of Retrieval of Atmospheric Water Vapor content is as follows,
Wherein, w is Water Vapor Content；α and β is constant, and α=0.02, β=0.651 are taken respectively；Ref2 and ref19 point
It is not the ground surface reflectance of MODIS the 2nd and 19 wave bands；
Ref2=scale2 (band2-offset2)
Ref19=scale19 (band19-offset19)
Wherein, band2 and band19 are respectively the DN values of the 2nd and 19 wave bands；Scale2, offset2, scale19 and
Offset19 is MODIS scaling constants.
Step 2, inquires about the soil moisture that meteorological data obtains the research area.
Step 3, estimates that earth's surface mean temperature (if uniform selection is done in research area, asks its average by surface temperature
Value).
Step 4, obtains the Lidar cloud datas in the research area, using the Lidar point clouds based on the gradient and region growing
Filtering algorithm is filtered process to the Lidar cloud datas for obtaining.
Using the gradient is based on the Lidar cloud datas for obtaining are filtered with the Lidar point cloud filtering algorithms of region growing
The step of ripple is processed is as follows,
A1) cancelling noise point；
A2) ground seed point is chosen；
A3 virtual grid) is set up；
A4) criterion is increased；
A5) criterion is terminated；
A6) centered on the grid that the i+1 ground seed point concentrated by ground seed point is located, repeat step A4 with
A5；
A7) all ground seed points that ground seed point is concentrated are traveled through；
A8) count each point in Lidar cloud datas and be judged to topocentric probability.
Step 5, to the Lidar cloud datas after Filtering Processing, is built using the extraction for being based on principal component analysiss and connectedness
The method for building thing information extracts building information.
The step of using building information is extracted based on the method for principal component analysiss and the extraction building information of connectedness
It is as follows,
B1 the non-ground points after Filtering Processing) are found and converges interior any one point PiRadius for δ neighborhood point, if neighborhood
The number of point is less than 8, then the point is classified as into non-building object point, if the number of neighborhood point is more than 8, continues to go to B2；
B2) to point PiNeighborhood point carry out principal component analysiss, obtain the corresponding eigenvalue λ of its covariance matrix1、λ2And λ3；
B3) gauging surface variation characteristic value FsvWith horizontal measures characteristic value Fh, and respectively with setting show change threshold
TsvWith horizontal metric threshold ThRelatively, if point meets following condition simultaneously
Abuild1={ Pi|Pi∈ A, Fsv(Pi)<TsvAnd | Ni|≥8}
Abuild2={ Pi|Pi∈ A, Fh(Pi)<Th}
Then the point is judged to build object point, wherein Abuild1And Abuild2Represent that the point is building, A is Lidar point cloud numbers
Converge according to the non-ground points after Filtering Processing, NiRepresent neighborhood point number, Fsv(Pi) represent point PiSurface variation characteristic value, Fh
(Pi) represent point PiHorizontal measures characteristic value；
B4 each non-ground points) is traveled through, repeat step B1 to B3 obtains initial building point set；
B5) Delaunay triangulation network is built to initial building object point, the length of side of each triangle is calculated, if the side of triangle
The long dot density more than 2 times, deletes the length of side of triangle；
B6) different divisional planes are divided according to connective, calculates the area of each divisional plane, and threshold value T is less than to areas
Divisional plane delete, obtain last building object point.
Step 6, using TerraSolid softwares by the Lidar point cloud numbers after the remote sensing image in the research area, Filtering Processing
According to this and building information carries out geometrical registration, the three-dimensional information of the research area building and vegetation is obtained.
Step 7, using Remote sensing image classification of the ENVI softwares to the research area, obtains greenery patches, building, road
And the different types of classification thematic map of water body.
Step 8, sorted remote sensing image, earth's surface mean temperature, soil moisture, Water Vapor Content and temperature are led
Enter ENVI-met, according to the three-dimensional information of the building and vegetation for obtaining, set up the threedimensional model in the research area.
Step 9, imports the threedimensional model built up CFD software and is simulated calculating, and contrasts with Monitoring Data on the spot,
The boundary condition of CFD model is corrected.
Step 10, using the Characteristics of The Distribution of Temperature of the CFD model simulation different greenlands Distribution Pattern for having corrected.
In order to further illustrate above-mentioned method, following experiment has been done
Experiment address is Nanjing, and research area's size is 200m × 200m, and ground surface type is normal soil, and simulated time is
High noon 12 on June 23rd, 2013:00.
According to existing common greenery patches shape, distribution of green areas general layout is fallen into 5 types, respectively spot distribution greenery patches, annular
Distribution greenery patches, ribbon distribution greenery patches, radial distribution greenery patches and wedge shape distribution greenery patches, and respectively it sets up CFD model, such as
Fig. 2,3,4,5 and 6.
According to above-mentioned method at spot distribution greenery patches modal distance earth's surface 0.2m, 2m remove and 10m at temperature field
It is simulated, simulation drawing is respectively Fig. 7, Fig. 8 and Fig. 9.
According to above-mentioned method at annular spread greenery patches modal distance earth's surface 0.2m, 2m remove and 10m at temperature field
It is simulated, simulation drawing is respectively Figure 10, Figure 11 and Figure 12.
According to above-mentioned method to ribbon be distributed greenery patches modal distance earth's surface 0.2m place, 2m except and 10m at temperature
Field is simulated, and simulation drawing is respectively Figure 13, Figure 14 and Figure 15.
According to above-mentioned method at radial distribution greenery patches modal distance earth's surface 0.2m, 2m remove and 10m at temperature
Field is simulated, and simulation drawing is respectively Figure 16, Figure 17 and Figure 18.
According to above-mentioned method to wedge shape distribution greenery patches modal distance earth's surface 0.2m at, the temperature field at 2m and at 10m
It is simulated, simulation drawing is respectively Figure 19, Figure 20 and Figure 21.
For the ease of statistical analysiss, the gross area in the range of different temperatures is contrasted, try to achieve in studied area not equality of temperature
Area shared by degree scope, and the statistic histogram made.The statistic histogram in spot distribution greenery patches be Figure 22, Figure 23 and Figure 24,
0.2m, 2m and 10m are corresponded to respectively；The statistic histogram in annular spread greenery patches is Figure 25, Figure 26 and Figure 27；Ribbon is distributed greenery patches
Statistic histogram be Figure 28, Figure 29 and Figure 30；The statistic histogram in radial distribution greenery patches is Figure 31, Figure 32 and Figure 33；Wedge
The statistic histogram in shape distribution greenery patches is Figure 34, Figure 35 and Figure 36.
By experimental data it can be seen that：
(1) it is best in the cooling-down effect of adjacent ground surface point-like layout, and the cooling-down effect of other several Distribution Pattern is differed
Less；According to statistic histogram, count its occupied area percentage ratio and calculate the samming in whole research area, it can be found that：Low temperature
Area, there are following rule, point-like general layout compared with low-temperature space area proportion>Wedge shape general layout>Radial general layout>Ribbon general layout>
Ring-type general layout；Therefore more than a small range of many residential quarters using the distribution of point-like general layout；Soil is not only saved in this distribution
Resource and space resources, and good cooling-down effect can be played to earth's surface.
(2) temperature field at 2 meters, the cooling-down effect of point-like general layout is but not so good as other several Distribution Pattern, now wedge shape
The cooling-down effect of structure and radial structure is better than that other are several, and the cooling-down effect of wherein wedge structure becomes apparent from；Here is high
Several Distribution Pattern are followed successively by the improvement ability of thermal environment on degree：Wedge shape general layout>Radial general layout>Ribbon general layout>Ring
Shape general layout>Point-like general layout.
(3) analog result at 10 meters shows that the impact to thermal environment of different general layouts there occurs change again.This time point
The cooling-down effect of shape general layout becomes worse compared with other several general layouts, and the cooling-down effect of ribbon general layout is obviously improved, and wedge shape
The cooling-down effect of general layout and radial general layout is more or less the same, and several Distribution Pattern are followed successively by the improvement ability of thermal environment：Band
Shape general layout>Wedge shape general layout>Radial general layout>Ring-type general layout>Point-like general layout.
(4) different green-space patterns is variant to the improvement of ambient thermal conditions；Large area ribbon is set up in suburb
Greenbelt is best to the improvement of ambient thermal conditions, and banded spatial distribution also takes full advantage of space resources；
The Distribution Pattern of point-like is preferably selected in down town area or residential quarters, there is good local cooling's effect；In city and suburb
The band in combination in area may be selected wedge shape or radial general layout.
In sum, the present invention can analyze the impact side with predicted city greenery patches and its change to local miniclimate around
Formula and feature, remote sensing technology can improve simulation precision of the CFD model to urban Heat Environment, and CFD model can increase remote sensing skill
Predictive ability of the art to urban Heat Environment, expands application depth and range of the remote sensing in urban Heat Environment.
The above is only the preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art
For member, on the premise of without departing from the technology of the present invention principle, some improvement and deformation can also be made, these improve and deform
Also should be regarded as protection scope of the present invention.
Claims (6)
1. the analogy method that a kind of urban green space general layout affects on thermal environment, it is characterised in that：Comprise the following steps,
Step one, obtains one and studies area's remote sensing image, using remote sensing image Retrieval of Atmospheric Water Vapor content, temperature and surface temperature；
Step 2, inquires about the soil moisture that meteorological data obtains the research area；
Step 3, by surface temperature earth's surface mean temperature is estimated；
Step 4, obtains the Lidar cloud datas in the research area, is filtered using the Lidar points cloud based on the gradient and region growing
Algorithm is filtered process to the Lidar cloud datas for obtaining；
Step 5, to the Lidar cloud datas after Filtering Processing, using the extraction building based on principal component analysiss and connectedness
The method of information extracts building information；
Step 6, using TerraSolid softwares by the Lidar cloud datas after the remote sensing image in the research area, Filtering Processing with
And building information carries out geometrical registration, the three-dimensional information of the research area building and vegetation is obtained；
Step 7, using Remote sensing image classification of the ENVI softwares to the research area；
Step 8, sorted remote sensing image, earth's surface mean temperature, soil moisture, Water Vapor Content and temperature are imported
ENVI-met, according to the three-dimensional information of the building and vegetation for obtaining, sets up the threedimensional model in the research area；
Step 9, imports the threedimensional model built up CFD software and is simulated calculating, and contrasts with Monitoring Data on the spot, to CFD
The boundary condition of model is corrected；
Step 10, using the Characteristics of The Distribution of Temperature of the CFD model simulation different greenlands Distribution Pattern for having corrected.
2. the analogy method that a kind of urban green space general layout according to claim 1 affects on thermal environment, it is characterised in that：Step
In rapid one, the formula of Retrieval of Atmospheric Water Vapor content is as follows,
Wherein, w is Water Vapor Content；α and β is constant, and α=0.02, β=0.651 are taken respectively；Ref2 and ref19 are respectively
The ground surface reflectance of MODIS the 2nd and 19 wave bands；
Ref2=scale2 (band2-offset2)
Ref19=scale19 (band19-offset19)
Wherein, band2 and band19 are respectively the DN values of the 2nd and 19 wave bands；Scale2, offset2, scale19 and
Offset19 is MODIS scaling constants.
3. the analogy method that a kind of urban green space general layout according to claim 1 affects on thermal environment, it is characterised in that：Adopt
With the step for being filtered process to the Lidar cloud datas for obtaining based on the gradient and the Lidar point cloud filtering algorithms of region growing
It is rapid as follows,
A1) cancelling noise point；
A2) ground seed point is chosen；
A3 virtual grid) is set up；
A4) criterion is increased；
A5) criterion is terminated；
Centered on the grid at the i+1 ground seed point place A6) concentrated by ground seed point, repeat step A4 and A5；
A7) all ground seed points that ground seed point is concentrated are traveled through；
A8) count each point in Lidar cloud datas and be judged to topocentric probability.
4. the analogy method that a kind of urban green space general layout according to claim 1 affects on thermal environment, it is characterised in that：Adopt
The step of extracting building information with the method for the extraction building information for being based on principal component analysiss and connectedness is as follows,
B1 the non-ground points after Filtering Processing) are found and converges interior any one point PiRadius for δ neighborhood point, if neighborhood point
Number is less than 8, then the point is classified as into non-building object point, if the number of neighborhood point is more than 8, continues to go to B2；
B2) to point PiNeighborhood point carry out principal component analysiss, obtain the corresponding eigenvalue λ of its covariance matrix1、λ2And λ3；
B3) gauging surface variation characteristic value FsvWith horizontal measures characteristic value Fh, and respectively with setting show change threshold TsvWith
Horizontal metric threshold ThRelatively, if point meets following condition simultaneously
Abuild1={ Pi|Pi∈ A, Fsv(Pi)<TsvAnd | Ni|≥8}
Abuild2={ Pi|Pi∈ A, Fh(Pi)<Th}
Then the point is judged to build object point, wherein Abuild1And Abuild2Represent that the point is building, A is the filter of Lidar cloud datas
Non-ground points after ripple process converge, NiRepresent neighborhood point number, Fsv(Pi) represent point PiSurface variation characteristic value, Fh(Pi)
Represent point PiHorizontal measures characteristic value；
B4 each non-ground points) is traveled through, repeat step B1 to B3 obtains initial building point set；
B5) Delaunay triangulation network is built to initial building object point, the length of side of each triangle is calculated, if the length of side of triangle is big
In 2 times of dot density, the length of side of triangle is deleted；
B6) different divisional planes are divided according to connective, calculates the area of each divisional plane, and threshold value T is less than to areasPoint
Face is deleted, and obtains last building object point.
5. the analogy method that a kind of urban green space general layout according to claim 1 affects on thermal environment, it is characterised in that：Institute
State and obtain after classification of remote-sensing images greenery patches, building, road and the different types of classification thematic map of water body.
6. the analogy method that a kind of urban green space general layout according to claim 1 affects on thermal environment, it is characterised in that：Step
In rapid ten, distribution of green areas general layout has 5 classes, respectively spot distribution greenery patches, annular spread greenery patches, ribbon distribution greenery patches, radiation
Shape is distributed greenery patches and wedge shape distribution greenery patches.
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