CN110208878A - Green Roof weather monitoring and tropical island effect impact evaluation method - Google Patents
Green Roof weather monitoring and tropical island effect impact evaluation method Download PDFInfo
- Publication number
- CN110208878A CN110208878A CN201910515202.2A CN201910515202A CN110208878A CN 110208878 A CN110208878 A CN 110208878A CN 201910515202 A CN201910515202 A CN 201910515202A CN 110208878 A CN110208878 A CN 110208878A
- Authority
- CN
- China
- Prior art keywords
- roof
- temperature
- greening
- value
- city
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01W—METEOROLOGY
- G01W1/00—Meteorology
- G01W1/02—Instruments for indicating weather conditions by measuring two or more variables, e.g. humidity, pressure, temperature, cloud cover or wind speed
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01W—METEOROLOGY
- G01W1/00—Meteorology
- G01W1/18—Testing or calibrating meteorological apparatus
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2411—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/243—Classification techniques relating to the number of classes
- G06F18/24323—Tree-organised classifiers
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A30/00—Adapting or protecting infrastructure or their operation
- Y02A30/60—Planning or developing urban green infrastructure
Landscapes
- Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Environmental & Geological Engineering (AREA)
- Life Sciences & Earth Sciences (AREA)
- Theoretical Computer Science (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Biology (AREA)
- Ecology (AREA)
- Artificial Intelligence (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Biodiversity & Conservation Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Environmental Sciences (AREA)
- Evolutionary Computation (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Atmospheric Sciences (AREA)
- Investigating Or Analyzing Materials Using Thermal Means (AREA)
- Radiation Pyrometers (AREA)
Abstract
The present invention provides Green Roof weather monitorings and tropical island effect impact evaluation method, building roof includes non-greening roof and greening roof, non- greening roof and greening roof surface temperature are acquired respectively, temperature gap with building roof meteorology automatic monitor station is as dependent variable, observation time is as independent variable, supporting vector machine model is inputted, roof is obtained respectively and does not afforest the influence efficiency Model with greening roof surface to temperature；Surface Temperature Retrieval is carried out to city satellite remote sensing date and Heat Island calculates, and urban surface hygrogram is corrected by the contributive rate that meteorology automatic monitor station point temperature each in city and supporting vector machine model are calculated, it is final to realize that Urban Roof afforests the assessment alleviated to heat island in conjunction with Decision-Tree Method.The present invention provides quantitative analysis and decision support for Urban Roof greening, is suitble to widely popularize.
Description
Technical field
The present invention relates to the appraisal procedures that weather monitoring and tropical island effect influence, and especially relate to Green Roof weather monitoring and heat
Island effects appraisal procedure.
Background technique
Urban heat island is a kind of city public hazards, can bring the loss that can not be underestimated to human health and social economy.One side
Face shows as influencing industrial output value and generates heavy losses, and water consumption power consumption increases severely, and jeopardize people's lives and health etc.；Separately
On the one hand it can then accelerate city soil erosion, cause pavement cracking and occupied and surface subsidence etc..Since nineteen ninety, to city
The research in city's " heat island " is existing very much.The method for monitoring urban heat land effect is also relatively more, has his own strong points, generally speaking, mainly
Have two classes: first is that surveying the intensity and Four seasons change that temperature studies urban heat land effect by automatic Weather Station, the advantages of this method is
Observation precision is high, the disadvantage is that coverage is not wide, is difficult to study the specific distribution situation of heat island.Another kind is distant using satellite
Sense technology, from the influence to heat island of spatial-temporal distribution characteristic and all kinds of underlying surfaces for macroscopically disclosing urban heat island, this method
Advantage and disadvantage are just opposite with first method.Therefore, it is necessary to seek a kind of all standing advantage that can play remote sensing technology and
Reach the heat island appraisal procedure of automatic Weather Station measured precision.Currently, due to lacking core technology, it both can not be to the mode of roof greening
Carry out science research, also can not the effect to roof greening effectively assessed.
Summary of the invention
In view of the above technical problems, the present invention passes through temperature data acquisition, supporting vector machine model, analysis automatic meteorological prison
The actual measurement temperature record of survey station, eventually by remote sensing technology, is simulated analysis result to entire to correct satellite remote sensing date
City provides quantitative analysis and decision support for roof greening.Technical solution provided by the invention is as follows:
Green Roof weather monitoring and tropical island effect impact evaluation method, comprising the following steps:
(1) building roof includes non-greening roof and greening roof, obtains do not afforest room described in same observation time respectively
Top surface temperature value, the greening roof surface temperature value and the building roof meteorology automatic monitor station temperature value；
Respectively will the non-greening roof area surface temperature value and the greening roof area surface temperature value with it is described
The temperature gap of building roof meteorology automatic monitor station temperature value is set as dependent variable, and the observation time is set as independent variable, input
Supporting vector machine model obtains contributive rate model of the building roof surface to temperature, shadow of the building roof surface to temperature
Ringing rate model includes influence efficiency of influence efficiency Model and greening roof surface of the non-greening roof surface to temperature to temperature
Model；
(2) the city satellite remote sensing date that will acquire corresponding same observation time respectively obtains city by Surface Temperature Retrieval
City's surface temperature figure, obtain in the city of corresponding same observation time N number of meteorological automatic monitor station actual measurement temperature record with it is described
Building roof surface carries out temperature adjustmemt calculating to the contributive rate model of temperature, obtains city satellite remote sensing date temperature adjustmemt
Value；
(3) the city satellite remote sensing date obtains urban surface hygrogram by Surface Temperature Retrieval, by the city
Surface temperature figure carries out Heat Island calculating, obtains urban heat island strength distribution map；
(4) the city satellite remote sensing date is calculated by decision tree classification, obtains city underlying surface distribution map；
(5) roof for needing to afforest is chosen in the city underlying surface distribution map that step (4) obtain as simulation room
Region is pushed up, obtains the corresponding city of the simulation roof area from step (2) the city satellite remote sensing date temperature corrected value
Satellite remote sensing date correction value substitutes into step (3) described urban heat island strength distribution map and carries out heat island simulation calculating, obtains institute
Urban heat island change modeling figure after stating simulation roof area greening.
Further, platinum resistance temperature sensor, the platinum electricity is respectively set in the non-greening roof and the greening roof
It hinders temperature sensor and is used for the non-greening roof surface temperature of real-time detection and greening roof surface temperature.
Further, the calculation method of the supporting vector machine model is as follows:
In formula, f (x) indicates the dependent variable of step (1), αiTo need the supporting vector optimized, αi *For optimal support to
Amount, l indicate the number of dependent variable sample data, k (xi, x) and it is kernel function, for the data sample of low-dimensional to be mapped to higher-dimension
Feature space in, b is amount of bias；Kernel function k (the x that the support vector machines is usedi, x) and it is radial basis function, expression
Formula are as follows:
Wherein, x indicates detection time, xiFor the central value of detection time sample, σ is the width parameter of radial basis function.
Further, the calculation method of the corresponding city satellite remote sensing date correction value of the simulation roof area is as follows:
According to the transit time of city satellite remote sensing date, the simulation roof area meteorological automatic monitor station nearby is obtained
Observed temperature value efficiency Model and institute are influenced with the non-greening roof surface of identical period in step (1) on temperature respectively
Stating greening roof surface influences efficiency Model addition to temperature, obtains non-greening roof landscape characteristics estimated value and greening respectively
Roof landscape characteristics estimated value；
According to the transit time of corresponding city satellite remote sensing date, obtaining step is distinguished from urban surface hygrogram
(5) N number of non-greening roof underlying surface surface temperature value and N number of greening roof underlying surface earth's surface temperature near the simulation roof area
Angle value finds out non-greening roof underlying surface surface temperature average value and greening roof underlying surface surface temperature average value respectively；
Respectively will the non-greening roof landscape characteristics estimated value and the greening roof landscape characteristics estimated value with
It is poor that the non-greening roof underlying surface surface temperature average value and the greening roof underlying surface surface temperature average value make
It calculates, obtains the corresponding city satellite remote sensing date correction value of the simulation roof area.
Further, the Surface Temperature Retrieval calculation is mono window algorithm or Split-window algorithm.
Further, the Heat Island calculation formula are as follows:
In formula, IiIndicate i-th of pixel on urban surface temperature pattern, corresponding Heat Island, TiFor urban surface temperature
Degree schemes surface temperature corresponding to upper i-th of pixel, TnFor temperature reference points rural on urban surface hygrogram.
Further, the decision tree classification calculation method is as follows:
Vegetation identification uses normalized differential vegetation index (NDVI) method:
Wherein, RNIRFor the reflectivity of remote sensing near infrared band, RREDFor the reflectivity of remote sensing red spectral band；As NDVI >
When 0.12, type of ground objects is vegetation；
Identifying water boy uses normalized difference water body index MNDWI (Modified NDWI) method:
Wherein, RGREENFor the reflectivity of remote sensing green light band, RMIRFor the reflectivity of infrared band in remote sensing, work as MNDWI
When > 0.12, type of ground objects is water body；
Building roof identification is using normalization building index (NDBI) method:
Wherein, RMIRFor the reflectivity of infrared band in remote sensing, RNIRFor the reflectivity of remote sensing near infrared band, as NDBI >
When 0.19, type of ground objects is building roof.
Further, the heat island simulation method is as follows:
According to city satellite remote sensing date temperature corrected value described in step (2) to step (3) urban heat island strength
Non- greening roof surface temperature value and greening roof surface temperature value are modified in distribution map, obtain revised urban heat island
Intensity distribution；
Influence efficiency Model and the greening roof surface pair by non-greening roof surface described in step (1) to temperature
The influence efficiency Model of temperature carries out making poor calculating, obtains the temperature difference estimated value on non-greening roof surface and greening roof surface；
Step (5) the simulation roof area is found out in the revised urban heat island strength distribution map；Described in acquisition
It simulates the surface temperature value of roof area and is carried out with the temperature difference estimated value on the non-greening roof surface and greening roof surface
Make difference to calculate, the urban heat island change modeling figure after obtaining the simulation roof area greening.
Further, the simulation roof area is building roof region or entire city roof area or entire city
Roof area.
Further, the non-greening roof is cement roof or concrete roof, and the greening roof is to be equipped with plant
The roof of quilt.
Advantages of the present invention
1, the present invention combines traditional heat island monitoring means, satellite remote sensing technology and artificial intelligence to calculate, and passes through temperature data
Acquisition, supporting vector machine model analyze the actual measurement temperature record at Automatic meteorology monitoring station, to correct city satellite remote sensing number
According to eventually by remote sensing technology, analysis result simulation to entire city, heat island alleviation is commented in realization Urban Roof greening
Estimate, provides quantitative analysis and decision support for roof greening；
2, the present invention is that one kind can play remote sensing technology all standing advantage and reach meteorological automatic monitor station actual measurement essence
The heat island appraisal procedure of degree solves the problems, such as that meteorological automatic monitor station city coverage rate is low in conventional method, also solves distant
The sense technology problem low to Heat Island precision, is suitble to widely popularize.
Detailed description of the invention
Fig. 1 is the flow diagram of Green Roof weather monitoring and tropical island effect impact evaluation method of the invention.
Fig. 2 is the block diagram of decision tree classification of the invention.
Specific embodiment
Below in conjunction with drawings and examples, the present invention is further described in detail, it should be understood that described herein
Specific examples are only used to explain the present invention, is not intended to limit the present invention.
Green Roof weather monitoring and tropical island effect impact evaluation method, comprising the following steps:
(1) building roof includes non-greening roof and greening roof, and platinum is arranged in non-greening roof and greening roof respectively
Resistance temperature sensor, platinum resistance temperature sensor are used for real-time detection vegetation roof surface temperature and cement roof surface temperature
Degree；The real-time temperature values of detection are transferred to collector by cable by platinum resistance temperature sensor, and collector is by the real-time of acquisition
Temperature value is sent to center management server by wire communication mode or wireless communication mode；Wireless communication mode is WIFI
Communication modes or mobile communication mode；
Platinum resistance temperature sensor is made of induction part, stainless steel pillar, insulation pillar and signal cable.According to platinum electricity
The characteristic that the resistance value of resistance varies with temperature carrys out measuring temperature.
When platinum resistance temperature sensor detects temperature, platinum resistance temperature sensor is mounted in thermometer screen using bracket,
The central part of sensing element is 1.5m apart from roof ground level；When detecting cement roof surface temperature, platinum resistance temperature is passed
Sensor levels are tightly attached on roofing by sensor using bracket, and probe is answered in half embedment roofing, and half is basseted,
The part for being embedded to roofing should be closely connected with roofing, does not interspace, and part of basseting keeps dry and hard；Detection is equipped with the roof of vegetation
When surface temperature, platinum resistance temperature sensor is mounted in vegetation using support level, platinum resistance temperature sensor distance soil
The distance of earth should be the half of vegetation.
The resistance value calculation formula of platinum resistance temperature sensor when the building roof surface temperature is t are as follows:
Rt=R0(1+αt+βt2)
In formula, R0Indicate the resistance value of platinum resistance temperature sensor when temperature is 0 DEG C, α and β are coefficient, by desk-top essence
Close two channel standards platinum resistance temperature table measures its value, and t indicates surface temperature when detecting the building roof, RtIndicate institute
State the resistance value of platinum resistance temperature sensor when building roof surface temperature is t.
Center management server obtains the non-greening roof surface temperature value of same observation time, greening roof surface temperature respectively
Meteorological automatic monitor station temperature value near angle value and building roof；
It respectively will be near non-greening roof area surface temperature value and greening roof area surface temperature value and building roof
The temperature gap of meteorological automatic monitor station temperature value is set as dependent variable, and observation time is set as independent variable, inputs support vector machines mould
Type obtains building roof surface to the contributive rate model of temperature, and building roof surface includes not green to the contributive rate model of temperature
Change influence efficiency Model and greening roof surface influence efficiency Model to temperature of the roof surface to temperature；
The calculation method of supporting vector machine model are as follows:
Firstly, fitting function f (x) form are as follows:
In formula,It is the Nonlinear Mapping from the input space to high-dimensional feature space, b is amount of bias；
Seek excellent regression hyperplane, make:
Wherein, ‖ w ‖2The complexity of representative function f (x)；C is the penalty constant of setting, is punished for controlling error sample
The degree penalized i.e. adjusting parameter realizes the compromise between error sample number and model complexity；
For empiric risk；The insensitive loss function of ε is introduced again:
|y-f(x)|ε=max 0, | and y-f (x) |-ε } 3.
3. formula indicates the error term for not punishing deviation less than ε, take empiric risk are as follows:
Then the regression problem of support vector machines is equivalent to solve a quadratic programming QpProblem；Optimization problem are as follows:
Need optimization problem are as follows:
For slack variable, using the principle of duality, method of Lagrange multipliers and nuclear technology, the antithesis of above-mentioned optimization problem
Form are as follows:
It, 6. can be in the hope of parameter alpha by solution formula using quadratic programming methodi、Utilize the KKT item of functional analysis
Part in the hope of parameter b, can find out the analytical expression of the estimation function f (x) of fitting sample set, last regression support vector machine
Output are as follows:
Formula 7. in, f (x) is the dependent variable of step (1), indicates non-greening roof surface temperature value and building roof gas nearby
As the temperature gap of automatic monitor station temperature value；L indicates that non-greening roof surface temperature value and building roof are meteorological automatic nearby
The number of the temperature gap sample data of monitoring station temperature value；αiTo need the supporting vector optimized, αi *For optimal support to
Amount；
Similarly, 7. referring to formula, function f (x) indicates greening roof surface temperature value and building roof meteorological automatic prison nearby
The temperature gap of survey station temperature value；L indicates greening roof surface temperature value and building roof meteorological automatic monitor station temperature nearby
The number of the temperature gap sample data of value；
It can be seen that coefficient of correspondenceSample (xi,yi) it is all supporting vector.Kernel function k (xi, x) and it is to meet
Any symmetric function of Mercer condition, common kernel function is radial basis function:
Wherein, x indicates detection time, xiFor the central value of detection time sample, σ is the width parameter of radial basis function.
(2) center management server will acquire corresponding same observation time city satellite remote sensing date respectively and pass through earth's surface temperature
Degree inverting obtains urban surface hygrogram, obtains N number of meteorological automatic monitor station actual measurement temperature in corresponding same observation time city
Data and building roof surface carry out temperature adjustmemt calculating to the contributive rate model of temperature, obtain city satellite remote sensing date temperature
Correction value；
(3) city satellite remote sensing date obtains urban surface hygrogram by Surface Temperature Retrieval, by urban surface temperature
Figure carries out Heat Island calculating, obtains urban heat island strength distribution map；
To city satellite remote sensing date progress Surface Temperature Retrieval, in the mono window algorithm that Qin Zhihao is proposed, bright temperature is converted to
The formula of surface temperature are as follows:
Ts={ a (1-C-D)+[b (C+D)+c] T6-DTa}/C
In formula, a=-67.355351, b=0.5414, c=0.458606 are empirical, TsFor the bright temperature of earth's surface, other
Parameter is intermediate variable, expression formula are as follows:
Ta=16.011+0.92621T0
C=t6e6
D=(1-t6)[1+t6(1-e6)]
In formula, TaFor Atmospheric mean temperature, T0For surface layer temperature, averaged by weather station data
It acquires. t6For transmissivity, e6For emissivity, L is radiance value, T6For bright temperature, K1For 607.76W/ (cm2Sr μm),
K2For 1260.56K, the two values are the empirical value determined in experiment.
In the Split-window algorithm that Qin Zhihao is proposed, bright temperature is converted to the formula of surface temperature are as follows:
Ts=A0+A1T31-A2T32
In formula: TsFor surface temperature, T31And T32It is Aqua and Terra satellite MODIS sensor the 31st and the 32nd wave respectively
The brightness temperature of section, is calculated by planck formula:
Wherein, L31And L32For the radiance value of MODIS sensor the 31st and the 32nd wave band.Pass through MODIS sensor pair
Wave band DN value radiation calibration is answered to obtain:
L31=8.4002 × 10-4×(DN31-1577.33972168)
L32=7.2970 × 10-4×(DN32-1658.22131348)
DN31And DN32Directly obtained from MODIS data.
A0、A1And A2It is the parameter of Split-window algorithm, is defined respectively as:
A0=[D32(1-C31-D31)/(D32C31-D31C32)]a31-[D31(1-C32-D32)/(D32C31-D31C32)]a32
A1=1+D31/(D32C31-D31C32)+[D32(1-C31-D31)/(D32C31-D31C32)]b31
A2=D31/(D32C31-D31C32)+[D31(1-C32-D32)/(D32C31-D31C32)]b32
In formula, a31, b31, a32And b32It is constant, value are as follows:
a31=-64.603
b31=0.440817
a32=-68.72575
b32=0.473463
Above-mentioned formula intermediate parameters C31、C32、D31And D32Calculation method is as follows:
Ci=εiτi
Di=[1- τi][1+(1-εi)τi]
In formula, i refers to MODIS the 31st and 32 wave bands, respectively i=31 or i=32, εiFor Land surface emissivity, τiFor
Atmospheric transmittance.The estimation equation of atmospheric transmittance is as shown in table 1:
Table 1:
Wherein, w is Water Vapor Content, is obtained by MODIS the 2nd and the 19th wave band inverting:
In formula, α and β are constant, α=0.02, β=0.6321, R19And R2The respectively reflection of MODIS the 19th and the 2nd wave
Rate can be got from satellite remote sensing date.
Land surface emissivity (εi) calculation formula it is as follows:
εi=PvRvεiv+(1-Pv)Rsεis+dε
In formula, εivAnd εisIt is the emissivity of vegetation and exposed soil in i wave band respectively, takes ε respectively31v=0.98672, ε32v
=0.98990, ε31s=0.96767, ε32s=0.97790.PvIt is the vegetation coverage of pixel, calculation method are as follows:
Pv=[(NDVI-NDVIS)/(NDVIV-NDVIS)]2
Wherein, PvFor vegetation coverage, NDVI is the normalized differential vegetation index value of each pixel, NDVIVAnd NDVISRespectively
For the NDVI value of dense vegetative coverage and exposed soil pixel, NDVI is usually takenV=0.65, NDVIS=0.05.RvAnd RsIt is to plant respectively
The radiation ratio of quilt and exposed soil, calculation method are as follows:
Rv=0.92762+0.07033Pv
Rs=0.99782+0.08362Pv
D ε is interaction correction item, and evaluation method is as shown in table 2:
Table 2:
| Dense planting is capped condition | Interact item estimation equation |
| Pv=0 or Pv=1 | ε=0 d |
| 0 < Pv< 0.5 | D ε=0.003796Pv |
| 0.5 < Pv< 1 | ε=0.003796 d (1-Pv) |
| Pv=0.5 | ε=0.001898 d |
After Surface Temperature Retrieval, you can get it urban surface temperature pattern, the urban surface temperature pattern is based on
Calculate Heat Island.
The calculation formula of the Heat Island are as follows:
In formula, IiFor Heat Island corresponding to i-th of pixel on image, TiFor the corresponding surface temperature of point, work as analysis
In city when the temperature difference, TnFor temperature reference points in city, when analyzing the town and country temperature difference, TnFor town and country temperature reference points.
According to the classification method that the color China of leaf proposes, Heat Island is divided into 4 grades (as shown in table 3).
3 Heat Island grade classification of table
Urban surface hygrogram obtains urban heat island strength distribution map after Heat Island calculates.
(4) city satellite remote sensing date is calculated by decision tree classification, obtains city underlying surface distribution map；
The decision tree classification calculation method:
Vegetation identification uses normalized differential vegetation index (NDVI) method:
Wherein, RMIRFor the reflectivity of remote sensing near infrared band, RREDFor the reflectivity of remote sensing red spectral band；As NDVI >
When 0.12, type of ground objects is vegetation；
Improvement normalized difference water body index MNDWI (Modified NDWI) method that identifying water boy uses the Xu Han autumn to propose:
Wherein, RGREENFor the reflectivity of remote sensing green light band, RMIRFor the reflectivity of infrared band in remote sensing, work as MNDWI
When > 0.12, type of ground objects is water body；
Building roof identification is using normalization building index (NDBI) method:
Wherein, RMIRFor the reflectivity of infrared band in remote sensing, RMIRFor the reflectivity of remote sensing near infrared band, as NDBI >
When 0.19, type of ground objects is building roof.
City underlying surface distribution map is carried out to city satellite remote sensing date using decision tree analysis method and divides into vegetation, water
Body, other urban lands, exposed soil and building roof, as shown in Figure 2.
(5) roof for needing to afforest is chosen in the city underlying surface distribution map that step (4) obtain as simulation room
Region is pushed up, obtains the corresponding city satellite of the simulation roof area from step (2) city satellite remote sensing date temperature corrected value
Remotely-sensed data correction value substitutes into step (3) urban heat island strength distribution map and carries out heat island simulation calculating, obtains simulation roof section
Urban heat island change modeling figure after the greening of domain.
The calculation method for simulating the corresponding city satellite remote sensing date correction value of roof area is as follows:
According to the transit time of city satellite remote sensing date, the simulation roof area meteorological automatic monitor station nearby is obtained
Observed temperature value efficiency Model and institute are influenced with the non-greening roof surface of identical period in step (1) on temperature respectively
Stating greening roof surface influences efficiency Model addition to temperature, obtains non-greening roof landscape characteristics estimated value and greening respectively
Roof landscape characteristics estimated value；
According to the transit time of corresponding city satellite remote sensing date, obtaining step is distinguished from urban surface hygrogram
(5) N number of non-greening roof underlying surface surface temperature value and N number of greening roof underlying surface earth's surface temperature near the simulation roof area
Angle value finds out non-greening roof underlying surface surface temperature average value and greening roof underlying surface surface temperature average value respectively；
Respectively will the non-greening roof landscape characteristics estimated value and the greening roof landscape characteristics estimated value with
It is poor that the non-greening roof underlying surface surface temperature average value and the greening roof underlying surface surface temperature average value make
It calculates, obtains the corresponding city satellite remote sensing date correction value of the simulation roof area.
The heat island simulation calculates step are as follows: according to city satellite remote sensing date temperature corrected value pair described in step (2)
Non- greening roof surface temperature value and greening roof surface temperature value carry out in step (3) the urban heat island strength distribution map
Amendment, obtains revised urban heat island strength distribution map；
Influence efficiency Model and the greening roof surface pair by non-greening roof surface described in step (1) to temperature
The influence efficiency Model of temperature carries out making poor calculating, obtains the temperature difference estimated value on non-greening roof surface and greening roof surface；
Step (5) the simulation roof area is found out in the revised urban heat island strength distribution map；Described in acquisition
It simulates the surface temperature value of roof area and is carried out with the temperature difference estimated value on the non-greening roof surface and greening roof surface
Make difference to calculate, the urban heat island change modeling figure after obtaining the simulation roof area greening.
Using non-greening roof surface as cement roof surface, greening roof surface is laid with Manila thatched cottage top surface and is lifted
Example explanation: when observation time is 14:00, the temperature of meteorological automatic monitor station is 31.5 DEG C near building roof, then such as 4 institute of table
Show:
Table 4:
Classification in 3 months and fitting are carried out to supporting vector machine model, finally obtain cement roof surface respectively to temperature shadow
Ringing efficiency Model and Manila thatched cottage top surface influences efficiency Model to temperature.
By the transit time of satellite remote sensing date, 5, certain city city gas is read respectively from certain weather bureau's database
As the observed temperature value of automatic monitor station and the longitude and latitude of meteorological automatic monitor station.Respectively by 5 city meteorology automatic monitor stations
Temperature value and same time cement roof surface on temperature influence efficiency Model and and Manila thatched cottage top surface to temperature
It influences efficiency Model to be added, obtains cement roof surface landscape characteristics estimated value and Manila thatched cottage top surface underlying surface respectively
Temperature estimated value.
In conjunction with certain city underlying surface distribution map and certain urban surface hygrogram, with obtaining cement roof surface underlying surface respectively
Table temperature value and Manila thatched cottage top surface underlying surface surface temperature value, find out cement roof surface underlying surface surface temperature respectively
Average value and Manila thatched cottage top surface underlying surface surface temperature average value.
Respectively by cement roof surface landscape characteristics estimated value and Manila thatched cottage top surface landscape characteristics estimated value
It is carried out with cement roof surface underlying surface surface temperature average value and Manila thatched cottage top surface underlying surface surface temperature average value
Make difference to calculate, obtains the city satellite remote sensing date correction value in certain city.
According to the city satellite remote sensing date correction value in certain city respectively in the urban heat island strength distribution map in certain city
Cement roof surface temperature value and Manila thatched cottage top surface temperature value be modified, obtain the city in revised certain city
Heat Island distribution map.
Influence efficiency Model and Manila grass by the same transit time of remotely-sensed data, by cement roof surface to temperature
Influence efficiency Model of the roof surface to temperature make poor calculating, obtains cement roof surface and Manila thatched cottage top surface
Temperature difference estimated value.
Mapping is padded in conjunction with the Urban Underground in certain city, the surface temperature value on certain Urban Roof surface is obtained, by certain city
The surface temperature value and cement roof surface of city's roof surface and the temperature difference estimated value of Manila thatched cottage top surface carry out making poor meter
It calculates, the surface temperature figure after obtaining the greening of certain Urban Roof.
Surface temperature figure after the greening of certain Urban Roof is carried out heat island simulation to calculate, finally obtains the greening of certain Urban Roof
Urban heat island distribution map afterwards.
By comparing the urban heat island strength distribution map and the city after the greening of certain Urban Roof that certain Urban Roof is not afforested
Heat Island distribution map, the emphasis that can analyze certain city implement the scheme of roof greening, obtain certain according to embodiments of the present invention
Urban heat island strength distribution map after Urban Roof greening implements the roof greening in certain city, the average heat island in certain city after greening
1.1 DEG C of strength reduction, which can provide decision support for urban planning.
Claims (10)
1. Green Roof weather monitoring and tropical island effect impact evaluation method, which comprises the following steps:
(1) building roof includes non-greening roof and greening roof, obtains non-greening roof table described in same observation time respectively
Face temperature value, the greening roof surface temperature value and the building roof meteorology automatic monitor station temperature value；
It respectively will the non-greening roof area surface temperature value and the greening roof area surface temperature value and the building
The temperature gap of roof meteorology automatic monitor station temperature value is set as dependent variable, and the observation time is set as independent variable, and input is supported
Vector machine model obtains contributive rate model of the building roof surface to temperature, contributive rate of the building roof surface to temperature
Model includes influence efficiency Model of influence efficiency Model and greening roof surface of the non-greening roof surface to temperature to temperature；
(2) it will acquire the city satellite remote sensing date of corresponding same observation time respectively by Surface Temperature Retrieval with obtaining city
Table hygrogram obtains N number of meteorological automatic monitor station actual measurement temperature record and the building in the city of corresponding same observation time
Roof surface carries out temperature adjustmemt calculating to the contributive rate model of temperature, obtains city satellite remote sensing date temperature corrected value；
(3) the city satellite remote sensing date obtains urban surface hygrogram by Surface Temperature Retrieval, by the urban surface
Hygrogram carries out Heat Island calculating, obtains urban heat island strength distribution map；
(4) the city satellite remote sensing date is calculated by decision tree classification, obtains city underlying surface distribution map；
(5) roof for needing to afforest is chosen in the city underlying surface distribution map that step (4) obtain as simulation roof section
Domain obtains the corresponding city satellite of the simulation roof area from step (2) the city satellite remote sensing date temperature corrected value
Remotely-sensed data correction value substitutes into step (3) described urban heat island strength distribution map and carries out heat island simulation calculating, obtains the mould
Urban heat island change modeling figure after quasi- roof area greening.
2. Green Roof weather monitoring according to claim 1 and tropical island effect impact evaluation method, which is characterized in that institute
It states non-greening roof and platinum resistance temperature sensor is respectively set in the greening roof, the platinum resistance temperature sensor is for real
When the non-greening roof surface temperature of detection and greening roof surface temperature.
3. Green Roof weather monitoring according to claim 1 and tropical island effect impact evaluation method, which is characterized in that institute
The calculation method for stating supporting vector machine model is as follows:
In formula, f (x) indicates the dependent variable of step (1), αiTo need the supporting vector optimized, αi *For optimal supporting vector, l
Indicate the number of dependent variable sample data, k (xi, x) and it is kernel function, for the data sample of low-dimensional being mapped to the feature of higher-dimension
In space, b is amount of bias；Kernel function k (the x that the support vector machines is usedi, x) and it is radial basis function, expression formula are as follows:
Wherein, x indicates detection time, xiFor the central value of detection time sample, σ is the width parameter of radial basis function.
4. Green Roof weather monitoring according to claim 1 and tropical island effect impact evaluation method, which is characterized in that institute
The calculation method for stating the corresponding city satellite remote sensing date correction value of simulation roof area is as follows:
According to the transit time of city satellite remote sensing date, obtaining step (5) the simulation roof area is meteorological nearby to supervise automatically
The observed temperature value of survey station influences efficiency Model with the non-greening roof surface of identical period in step (1) to temperature respectively
With the greening roof surface on temperature influence efficiency Model is added, obtain respectively non-greening roof landscape characteristics estimated value with
Greening roof landscape characteristics estimated value；
According to the transit time to Yingcheng City satellite remote sensing date, obtaining step (5) are described respectively from urban surface hygrogram
N number of non-greening roof underlying surface surface temperature value and N number of greening roof underlying surface surface temperature value near roof area are simulated, point
Non- greening roof underlying surface surface temperature average value and greening roof underlying surface surface temperature average value are not found out；
Respectively will the non-greening roof landscape characteristics estimated value and the greening roof landscape characteristics estimated value with it is described
Non- greening roof underlying surface surface temperature average value and the greening roof underlying surface surface temperature average value carry out making poor calculating,
Obtain the corresponding city satellite remote sensing date correction value of the simulation roof area.
5. Green Roof weather monitoring according to claim 1 and tropical island effect impact evaluation method, which is characterized in that institute
Stating Surface Temperature Retrieval calculation is mono window algorithm or Split-window algorithm.
6. Green Roof weather monitoring according to claim 1 and tropical island effect impact evaluation method, which is characterized in that institute
State Heat Island calculation formula are as follows:
In formula, IiIndicate i-th of pixel on urban surface temperature pattern, corresponding Heat Island, TiFor urban surface hygrogram
Surface temperature corresponding to upper i-th of pixel, TnFor temperature reference points rural on urban surface hygrogram.
7. Green Roof weather monitoring according to claim 1 and tropical island effect impact evaluation method, which is characterized in that institute
It is as follows to state decision tree classification calculation method:
Vegetation identification uses normalized differential vegetation index (NDVI) method:
Wherein, RNIRFor the reflectivity of remote sensing near infrared band, RREDFor the reflectivity of remote sensing red spectral band；As NDVI > 0.12
When, type of ground objects is vegetation；
Identifying water boy uses normalized difference water body index MNDWI (Modified NDWI) method:
Wherein, RGREENFor the reflectivity of remote sensing green light band, RMIRFor the reflectivity of infrared band in remote sensing, as MNDWI > 0.12
When, type of ground objects is water body；
Building roof identification is using normalization building index (NDBI) method:
Wherein, RMIRFor the reflectivity of infrared band in remote sensing, RNIRFor the reflectivity of remote sensing near infrared band, as NDBI > 0.19
When, type of ground objects is building roof.
8. Green Roof weather monitoring according to claim 1 and tropical island effect impact evaluation method, which is characterized in that institute
It is as follows to state heat island simulation method:
Step (3) urban heat island strength is distributed according to city satellite remote sensing date temperature corrected value described in step (2)
Non- greening roof surface temperature value and greening roof surface temperature value are modified in figure, obtain revised urban heat island strength
Distribution map；
Influence efficiency Model and the greening roof surface by non-greening roof surface described in step (1) to temperature is to temperature
Influence efficiency Model make poor calculating, obtain the temperature difference estimated value on non-greening roof surface and greening roof surface；
Step (5) the simulation roof area is found out in the revised urban heat island strength distribution map；Obtain the simulation
The surface temperature value of roof area and with the temperature difference estimated value on the non-greening roof surface and greening roof surface carry out make it is poor
It calculates, the urban heat island change modeling figure after obtaining the simulation roof area greening.
9. according to claim 1, Green Roof weather monitoring and tropical island effect impact evaluation side described in 4,8 any one
Method, which is characterized in that the simulation roof area is building roof region or entire city roof area or entire city room
Push up region.
10. according to claim 1, Green Roof weather monitoring and tropical island effect impact evaluation described in 2,4,8 any one
Method, which is characterized in that the non-greening roof is cement roof or concrete roof, and the greening roof is to be equipped with vegetation
Roof.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910515202.2A CN110208878B (en) | 2019-06-14 | 2019-06-14 | Green roof meteorological monitoring and heat island effect influence assessment method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910515202.2A CN110208878B (en) | 2019-06-14 | 2019-06-14 | Green roof meteorological monitoring and heat island effect influence assessment method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110208878A true CN110208878A (en) | 2019-09-06 |
| CN110208878B CN110208878B (en) | 2020-01-31 |
Family
ID=67792712
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910515202.2A Active CN110208878B (en) | 2019-06-14 | 2019-06-14 | Green roof meteorological monitoring and heat island effect influence assessment method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110208878B (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113360989A (en) * | 2021-06-23 | 2021-09-07 | 中科青城(天津)科技有限公司 | City green roof planning method |
| CN113404226A (en) * | 2021-06-23 | 2021-09-17 | 中科青城(天津)科技有限公司 | Urban green roof construction scale decision-making method oriented to maximization of cooling effect |
| CN113887780A (en) * | 2021-08-26 | 2022-01-04 | 国家卫星气象中心(国家空间天气监测预警中心) | Method, device and equipment for estimating earth surface temperature by satellite remote sensing |
| CN114974453A (en) * | 2022-05-30 | 2022-08-30 | 中南大学 | High-coverage reconstruction method for concentration of atmospheric carbon dioxide column |
| CN115482312A (en) * | 2022-10-13 | 2022-12-16 | 重庆市地理信息和遥感应用中心 | Ground surface temperature space simulation correction method based on DEM and urban heat island |
| CN116578662A (en) * | 2023-05-23 | 2023-08-11 | 安徽建筑大学 | Urban heat island effect monitoring system and method |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2006083559A (en) * | 2004-09-15 | 2006-03-30 | Hiroshi Misawa | Heat island phenomenon eliminating device |
| CN101866385A (en) * | 2010-06-24 | 2010-10-20 | 浙江大学 | Target parcel ground surface temperature simulation and optimization method |
| CN104537222A (en) * | 2014-12-18 | 2015-04-22 | 中国科学院东北地理与农业生态研究所 | Remote-sensing-based method for estimating influences of area vegetation cover on earth surface air temperature |
| CN105607157A (en) * | 2016-02-03 | 2016-05-25 | 东南大学 | City near-surface layer thermal environment multi-point instant sampling measurement method |
| CN105930633A (en) * | 2016-04-05 | 2016-09-07 | 中国科学院遥感与数字地球研究所 | Method for forecasting urban heat island effect |
| CN108020511A (en) * | 2016-11-01 | 2018-05-11 | 中国科学院遥感与数字地球研究所 | A kind of shallow macrophytic lake water quality parameter remote-sensing monitoring method and device |
| CN108168710A (en) * | 2017-12-28 | 2018-06-15 | 福建农林大学 | A kind of city tropical island effect appraisal procedure based on remote sensing technology |
| CN108776360A (en) * | 2018-06-13 | 2018-11-09 | 华南农业大学 | A kind of method of urban heat island strength Monitoring on Dynamic Change |
| CN109375287A (en) * | 2018-11-09 | 2019-02-22 | 国网江苏省电力有限公司无锡供电分公司 | Comprehensive meteorologic factor effective temperature modification method relevant to air conditioner load |
-
2019
- 2019-06-14 CN CN201910515202.2A patent/CN110208878B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2006083559A (en) * | 2004-09-15 | 2006-03-30 | Hiroshi Misawa | Heat island phenomenon eliminating device |
| CN101866385A (en) * | 2010-06-24 | 2010-10-20 | 浙江大学 | Target parcel ground surface temperature simulation and optimization method |
| CN104537222A (en) * | 2014-12-18 | 2015-04-22 | 中国科学院东北地理与农业生态研究所 | Remote-sensing-based method for estimating influences of area vegetation cover on earth surface air temperature |
| CN105607157A (en) * | 2016-02-03 | 2016-05-25 | 东南大学 | City near-surface layer thermal environment multi-point instant sampling measurement method |
| CN105930633A (en) * | 2016-04-05 | 2016-09-07 | 中国科学院遥感与数字地球研究所 | Method for forecasting urban heat island effect |
| CN108020511A (en) * | 2016-11-01 | 2018-05-11 | 中国科学院遥感与数字地球研究所 | A kind of shallow macrophytic lake water quality parameter remote-sensing monitoring method and device |
| CN108168710A (en) * | 2017-12-28 | 2018-06-15 | 福建农林大学 | A kind of city tropical island effect appraisal procedure based on remote sensing technology |
| CN108776360A (en) * | 2018-06-13 | 2018-11-09 | 华南农业大学 | A kind of method of urban heat island strength Monitoring on Dynamic Change |
| CN109375287A (en) * | 2018-11-09 | 2019-02-22 | 国网江苏省电力有限公司无锡供电分公司 | Comprehensive meteorologic factor effective temperature modification method relevant to air conditioner load |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113360989A (en) * | 2021-06-23 | 2021-09-07 | 中科青城(天津)科技有限公司 | City green roof planning method |
| CN113404226A (en) * | 2021-06-23 | 2021-09-17 | 中科青城(天津)科技有限公司 | Urban green roof construction scale decision-making method oriented to maximization of cooling effect |
| CN113887780A (en) * | 2021-08-26 | 2022-01-04 | 国家卫星气象中心(国家空间天气监测预警中心) | Method, device and equipment for estimating earth surface temperature by satellite remote sensing |
| CN113887780B (en) * | 2021-08-26 | 2023-11-24 | 国家卫星气象中心(国家空间天气监测预警中心) | Satellite remote sensing earth surface temperature estimation method, device and equipment |
| CN114974453A (en) * | 2022-05-30 | 2022-08-30 | 中南大学 | High-coverage reconstruction method for concentration of atmospheric carbon dioxide column |
| CN114974453B (en) * | 2022-05-30 | 2023-08-29 | 中南大学 | High-coverage reconstruction method for atmospheric carbon dioxide column concentration |
| CN115482312A (en) * | 2022-10-13 | 2022-12-16 | 重庆市地理信息和遥感应用中心 | Ground surface temperature space simulation correction method based on DEM and urban heat island |
| CN116578662A (en) * | 2023-05-23 | 2023-08-11 | 安徽建筑大学 | Urban heat island effect monitoring system and method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110208878B (en) | 2020-01-31 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110208878A (en) | Green Roof weather monitoring and tropical island effect impact evaluation method | |
| Han et al. | Crop evapotranspiration prediction by considering dynamic change of crop coefficient and the precipitation effect in back-propagation neural network model | |
| Sánchez et al. | Monitoring daily evapotranspiration at a regional scale from Landsat-TM and ETM+ data: Application to the Basilicata region | |
| Mallick et al. | Land surface emissivity retrieval based on moisture index from LANDSAT TM satellite data over heterogeneous surfaces of Delhi city | |
| CN105608293B (en) | Merge Biomass retrieval method and system on the woodland of spectrum and textural characteristics | |
| Mo et al. | Simulating temporal and spatial variation of evapotranspiration over the Lushi basin | |
| CN109581372A (en) | A kind of Remote Sensing Monitoring of Ecological Environment method | |
| CN107024439A (en) | A kind of paddy rice different growing chlorophyll content EO-1 hyperion estimating and measuring method | |
| CN109508824A (en) | A kind of detection of crop growth situation and yield predictor method | |
| CN109871637B (en) | Near-ground air temperature estimation method under cloud-sky condition | |
| CN104166782A (en) | Method for remote sensing and estimating woodland soil organic carbon | |
| Zhang et al. | A study of tree crown characteristics and their cooling effects in a subtropical city of Australia | |
| CN106525731B (en) | Canopy leaf nitrogen detecting vertical distribution method and device based on remote sensing and agricultural knowledge | |
| Zhang et al. | Tree crowns and their associated summertime microclimatic adjustment and thermal comfort improvement in urban parks in a subtropical city of China | |
| Zhao et al. | Effect of vertical distribution of crop structure and biochemical parameters of winter wheat on canopy reflectance characteristics and spectral indices | |
| CN108592888B (en) | Residential area extraction method | |
| CN101866385B (en) | Target parcel ground surface temperature simulation and optimization method | |
| CN110472357A (en) | Assess construction method and the application of the remote sensing comprehensive ecological model RSIEI of the different effect of Mining Development compact district earth's surface thermal environment point | |
| CN110119717A (en) | A kind of Crop classification method based on multi-temporal NDVI and LST | |
| Kuang et al. | Examining the impacts of urbanization on surface radiation using Landsat imagery | |
| CN113553907A (en) | Forest ecological environment condition evaluation method based on remote sensing technology | |
| CN113887024B (en) | Surface soil moisture inversion method based on normalized temperature construction and drought index | |
| CN113987778B (en) | Water and soil loss analog value space-time weighted correction method based on field site | |
| CN103918514B (en) | A kind of urban green space Form and pattern optimization method based on environmental effect field model | |
| CN111192315A (en) | Actual irrigation area extraction method based on multi-source information |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |