CN105930633A - Method for forecasting urban heat island effect - Google Patents
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Abstract
The invention discloses a method for forecasting urban heat island effect. The method includes the steps of performing temperature inversion for remote sensing image data using a single window algorithm to obtain urban surface temperature data; grading the urban surface temperature data according to mean parameters and standard deviation parameters; conducting statistics of heat island area data of different grades; and calculating an initial state matrix S(0) based on the heat island region data of different grades at the first moment, calculating a transition matrix P based on the heat island region data of different grades at the second moment and the initial state matrix S(0), and calculating the evolution state data of heat island regions in the subsequent required time based on the initial state matrix S(0) and the transition matrix P. The invention constructs the state transition matrix and the initial state matrix by utilizing the temperature inversion grading results, and forecasts the next evolution trend of the heat island based on Markov chain transition probabilities. The invention is high in forecasting accuracy and has a great practical significance.
Description
Technical field
The present invention relates to a kind of Remote Sensing Data Processing method, particularly relate to the prediction of a kind of urban heat land effect
Method.
Background technology
Tropical island effect refers to when urban development to certain scale, due to the change of city underlying surface character, air
Pollute and artificial waste heat discharge etc. makes city temperature apparently higher than suburb, forms showing of similar high temperature isolated island
As.Underlying surface refers to the earth surface directly contacted with air lower floor, it include landform, geology, soil,
River and vegetation etc., be one of key factor affecting weather.Have from literature record from tropical island effect,
Just there are many scientists that many big and small cities, the whole world are observed, find according to statistics, to 2012
Till Nian, having in the world in the city of more than 1000 different scales and there is tropical island effect, scope is throughout south
Different latitude position, the Northern Hemisphere, different altitude height areas.Nowadays, tropical island effect has become as one
City public hazards, healthy to social economy, urban ecology and resident works the mischief.
In order to deeply probe into the Forming Mechanism of tropical island effect, alleviate tropical island effect adverse effect,
Lot of domestic and foreign scholar has carried out the research for urban heat island, and urban heat land effect has become current city
City's weather and one of highly important research contents in environmental studies.According to basic data source difference, city
The research method of city's thermal environment effect can be divided into four classes: meteorological data method, observation method of layouting, numerical value
Simulation method and remote sensing method.Owing to remotely-sensed data is for the abundant expression forms of urban thermal environment effect, can
Respond well depending on changing, beneficially the dynamic monitoring of city thermal field and Inner construction analysis, became city in recent years
The main stream approach of city's thermal environment effect study.At present, remote sensing technology research heat island, three kinds of sides can be divided into again
Method:
One, monitoring method based on vegetation index.
Vegetation index can react the density state of the ground vegetation extracted, and is widely used in qualitative and fixed
In the research evaluating vegetation coverage and vigor of amount, city's heat island is had by substantial amounts of research display surface vegetation
There is mitigation.Additionally one of shortcoming of vegetation index be requirement height above sea level within the specific limits, if high
Path difference reaches more than 500m such as hilly country, and result will be far from each other, its two be to ground cover
Type has certain requirement, vegetation is not enough in the winter time, be difficult to extract in the case of, result of study will lack
Weary accuracy.
Its two, the radiation temperature value method of inversion.
The bright temperature of satellite that thermal infrared remote sensing detects, by remotely-sensed data inverting surface temperature and temperature three
There is substantial connection, utilize bright temperature or surface temperature that urban Heat Environment is analyzed research and there is its conjunction
Rationality.Owing to the method difference for the treatment of temperature can be divided into again: monitoring method based on bright temperature and based on ground temperature
Monitoring method.The former advantage is take into account the factor impact of radiating surface and air etc., and shortcoming is
Calculating process is complicated, and X factor impact is too many.In view of the hardware condition of present stage satellite itself, base
Inverting in surface temperature there be difficulties involved when, currently mainly by trying to achieve atmospheric parameter, emissivity
Inverting surface temperature is carried out etc. parameter.
Its three, detection method based on " thermal landscape ".
Utilize the viewpoint of landscape esthetics, under the support of GIS and remote sensing technology, for thermal environment spatial framework with
Process sets up a set of research method and evaluation index.The method by multiformity, separating degree, dominance, point
The many factors such as dimension, shape index composition evaluation index, the advantage of the method is can be quickly and easily
Obtain the key elements such as the land use pattern in spatial dimension, ground temperature, NDVI；Shortcoming is exactly Time Continuous
Deficiency in property.
In the above four kinds of method.The advantage of conventional method is the description to urban heat land effect and announcement
Simple and brief, but it is substantially the research of on point scale or pure concept, Points replacing surfaces, is difficult to true
With effectively extend to and on, the feature such as layout, internal structure thus for the flat of research urban heat island
There is the biggest difficulty." urban heat island " that although RS obtains and temperature " urban heat island " traditionally
There is marked difference, " urban heat island " that obtain such as RS is the strongest, spatial variations rate is maximum,
Antithesis, but the two distribution on space-time is similar to this " urban heat island " characterized with temperature, and
And RS can carry out surface temperature mensuration timely, objective, in large area, describe urban heat island time
Space division cloth has the advantage that conventional method is incomparable.Especially with the quickening of remote sensing quantification paces,
Increasing Remote Sensing Parameters can be obtained by remotely-sensed data inverting, this more remote sensing technology wide
A good basis has been established in general application.
Urban heat land effect is monitored, analysis of law, extraction factor of influence simultaneously, use a certain
The research that tropical island effect is predicted by kind forecast model is also little, calculates heat further based on forecast model
Island effect tend to be steady state time distribution characteristics the most less.And study urban heat land effect, not only need
Deeply to probe into current tropical island effect distribution characteristics and inherent law, predict tropical island effect based on current state
Next step regularity of distribution is to improving urban environment and city decision-making is the most significant.
Summary of the invention
For drawbacks described above, the invention provides the Forecasting Methodology of a kind of urban heat land effect.
The Forecasting Methodology of the urban heat land effect that the present invention provides, including:
Step 1, uses mono window algorithm to carry out temperature retrieval remote sensing image data, it is thus achieved that urban surface temperature
Degrees of data；
Step 2, carries out classification to urban ground temperature data according to Mean Parameters and standard deviation criteria；
Step 3, adds up the heat island area data of each rank, described heat island area data include position,
Area, account for total area ratio；
Step 4, is calculated original state square according to the heat island area data of each rank in the first moment
Battle array S (0), according to heat island area data and the described original state matrix S of each rank in the second moment
(0) it is calculated transfer matrix P, counts according to described original state matrix S (0) and transfer matrix P
Calculate the described heat island region evolving state data in the follow-up required moment.
Above-mentioned Forecasting Methodology can also have the following characteristics that
In step 1, temperature retrieval specifically includes:
It is calculated radiation brightness according to remotely-sensed data, is calculated ground brightness according to described radiation brightness
Temperature value；
Calculate normalized differential vegetation index, calculate vegetation coverage according to described normalized differential vegetation index, according to
Described vegetation coverage calculates earth's surface emissivity；
It is calculated described surface temperature number according to described ground brightness temperature value and described earth's surface emissivity
According to.
Above-mentioned Forecasting Methodology can also have the following characteristics that
Described step 2 carries out classification according to below table:
Above-mentioned Forecasting Methodology can also have the following characteristics that
Described transfer matrix P meets following condition: the value of each element is positive number, and each row vector
All elements sum be 1.
Above-mentioned Forecasting Methodology can also have the following characteristics that
The temperature data in the second moment according to described heat island region and described original state in described step 4
Matrix S (0) is calculated transfer matrix P and includes:
Described second moment is k1 with the discrete time difference in described first moment, according to described heat island region
The temperature data in the second moment is calculated state matrix S (k1) corresponding to the second moment, according to S (k1)=S
(0)Pk1It is calculated P；
Original state matrix S described in described step 4 (0) and transfer matrix P calculate described heat island region
Evolving state in the follow-up required moment includes:
The described required moment is k2 with the discrete time difference in described first moment, is calculated institute according to following formula
Take and carve corresponding state matrix S (k2): S (k2)=S (0) Pk2。
Above-mentioned Forecasting Methodology can also have the following characteristics that
Described method also includes: use iterative method pre-according to described original state matrix and described transfer matrix
Survey the heat island region of each rank evolving state under plateau.
Above-mentioned Forecasting Methodology can also have the following characteristics that
Described use iterative method predicts heat island region according to described original state matrix and described transfer matrix
Evolving state under plateau includes:
The difference calculating the transposed matrix P ' of described transfer matrix P and the E of unit matrix obtains matrix A, root
It is calculated matrix X according to AX=0 and is the matrix of heat island region evolving state under plateau.
Above-mentioned Forecasting Methodology can also have the following characteristics that
Described it be calculated matrix X according to AX=0 and include: iteration precision is set, arranges at the beginning of matrix X
Initial value, calculates the value of the X after iteration according to following formula, and after carrying out successive ignition calculating, obtain changes
The error of the respective element of the X before the value of each element of the X after Dai and iteration is respectively less than described iteration
During precision, using the X that finally gives as calculated matrix X；
Wherein, the line number of matrix A and columns are the value that n, i and j are between 0 to n, aijFor matrix
The element of the i-th row jth row in A,For the element value before iteration,For the element value after iteration.
Above-mentioned Forecasting Methodology can also have the following characteristics that
When the initial value of matrix X is set, it is set to random value, or below the meeting of matrix X is set
Condition: the value of each element is all higher than 0 and less than 1, and the element sum often gone is 1.
The present invention utilizes temperature retrieval classification results to construct state-transition matrix and original state matrix, root
Walk transition probability according to Markov Chain K, next step evolving trend of heat island is predicted, meanwhile, according to
The thought of the steady chain of Markov, the problem using the iterative algorithm infinite solution of solving matrix equation, and then solve
The certainly calculating of the steady chain of Markov.The prediction accuracy of the present invention is high, has bigger Practical significance.
Accompanying drawing explanation
Fig. 1 is the flow chart of the Forecasting Methodology of urban heat land effect in the present invention.
Specific embodiment
For making the purpose of the embodiment of the present invention, technical scheme and advantage clearer, below in conjunction with this
Accompanying drawing in bright embodiment, is clearly and completely described the technical scheme in the embodiment of the present invention,
Obviously, described embodiment is a part of embodiment of the present invention rather than whole embodiments.Based on
Embodiment in the present invention, those of ordinary skill in the art are obtained under not making creative work premise
The every other embodiment obtained, broadly falls into the scope of protection of the invention.It should be noted that do not conflicting
In the case of, the embodiment in the application and the feature in embodiment can mutual combination in any.
Fig. 1 is the flow chart of the Forecasting Methodology of urban heat land effect in the present invention.The method includes:
Step 1, uses mono window algorithm to carry out temperature retrieval remote sensing image data, it is thus achieved that urban surface temperature
Degrees of data；
Step 2, carries out classification to urban ground temperature data according to Mean Parameters and standard deviation criteria；
Step 3, adds up the heat island area data of each rank, heat island area data include position, area,
Account for total area ratio；
Step 4, is calculated original state square according to the heat island area data of each rank in the first moment
Battle array S (0), according to heat island area data and the original state matrix S of each rank in the second moment
(0) it is calculated transfer matrix P, calculates heat according to original state matrix S (0) and transfer matrix P
Region, island is in the evolving state data in follow-up required moment.
The following detailed description of this method:
In step 1, temperature retrieval specifically includes:
Step 101: be calculated radiation brightness according to remotely-sensed data, is calculated ground according to radiation brightness
Face brightness temperature value.
Concrete, according to Lλ=(Gain*DN)+offset following formula is calculated amount of radiation temperature Lλ, wherein, Gain
Representing gain coefficient, DN represents the size of each pixel, and offset represents deviation ratio.
Bright temperature T in ground is calculated according to following formula.
T is bright temperature (unit: DEG C), K1、K2For revising coefficient.
Step 102: calculate normalized differential vegetation index, calculates vegetation coverage according to normalized differential vegetation index,
Earth's surface emissivity is calculated according to vegetation coverage；
Normalized differential vegetation index NDVI is calculated according to following formula
V in formulaNIR、VRIt is respectively near infrared band, the pixel value of red spectral band.
According to following formula calculating vegetation coverage:
Wherein VNDVIRepresent NDVI value, i.e. normalized differential vegetation index, VNDVI_minAnd VNDVI_maxRepresent respectively
The maximum of NDVI and minima, can respectively be taken as 0.5 and 0.2.
Step 103: be calculated surface temperature data according to ground brightness temperature value and earth's surface emissivity.
Surface temperature Ts is calculated according to following formula
Wherein, λ represents the centre wavelength of Thermal infrared bands, ρ=h c/ σ 1.438 × 10‐2M K, wherein
Planck's constant h=6.626 × 10-34J s, Boltzmann constant σ=1.38 × 10-23J/K, the light velocity
C=2.998 × 108m/s。
Wherein, ε is earth's surface emissivity.
ε=εsoil, work as VNDVI＜ 0.2
ε=εveg, work as VNDVI＞ 0.5
ε=εsoilPV+ ε=εVEG(1-PV), as 0.2≤VNDVI≤0.5
Wherein, ε in formulasoilFor soil emissivity, εvegFor vegetation emissivity, PVFor vegetation coverage.
The empirical value that can also directly use Nichol to propose in 1994 in this method, i.e. has vegetation
Ground surface ε=0.95, unvegetated ground surface ε=0.92.
Described step 2 carries out classification according to below table:
Wherein, μ is Mean Parameters, and std is standard deviation criteria, and Ts is temperature parameter.
Transfer matrix P meets following condition: the value of each element is positive number, and the institute of each row vector
Having element sum is 1.
The temperature data in the second moment according to heat island region and original state matrix S (0) meter in step 4
Calculation obtains transfer matrix P and includes: the second moment was k1 with the discrete time difference in the first moment, according to heat island
The temperature data in second moment in region is calculated state matrix S (k1) corresponding to the second moment, according to
S (k1)=S (0) Pk1It is calculated P；
In step 4, original state matrix S (0) and transfer matrix P calculating heat island region is taken follow-up
The evolving state carved includes: the required moment is k2 with the discrete time difference in the first moment, calculates according to following formula
Obtain state matrix S (k2): S (k2)=S (0) P corresponding to required momentk2。
Method also includes: use iterative method to predict each rank according to original state matrix and transfer matrix
Heat island region evolving state under plateau.
Iterative method is used to predict that heat island region is under plateau according to original state matrix and transfer matrix
Evolving state include: the difference of the E calculating transposed matrix P ' and the unit matrix of transfer matrix P obtains square
Battle array A, is calculated matrix X according to AX=0 and is heat island region evolving state under plateau
Matrix.
It is calculated matrix X according to AX=0 to include: arrange iteration precision, the initial value of matrix X is set,
The value of the X after iteration is calculated, after carrying out successive ignition calculating, after the iteration obtained according to following formula
When the error of the respective element of the X before the value of each element of X and iteration is respectively less than iteration precision, will be
The X obtained eventually is as calculated matrix X；
Wherein, the line number of matrix A and columns are the value that n, i and j are between 0 to n, aijFor matrix
The element of the i-th row jth row in A,For the element value before iteration,For the element value after iteration.
When the initial value of matrix X is set, it is set to random value, or below the meeting of matrix X is set
Condition: the value of each element is all higher than 0 and less than 1, and the element sum often gone is 1.
Specific embodiment
Obtain the history remotely-sensed data of city A, use mono window algorithm to carry out temperature retrieval, it is thus achieved that city
Surface temperature data, carry out classification to urban ground temperature data according to Mean Parameters and standard deviation criteria,
It is divided into low-temperature space, secondary middle warm area, middle warm area, secondary high-temperature region, high-temperature region, extra-high warm area；Add up each
The heat island area data of rank, heat island area data includes position, area, accounts for total area ratio.According to
The heat island area data of each rank in October, 2009 is calculated original state matrix S (0), root
It is calculated current matrix S (K) according to the heat island area data of each rank in October, 2014.Root
Relation S (K)=S (0) P according to S (0) and S (K)K, obtain transfer matrix P.According to S (2K)
=S (0) P2KRelation be calculated the data in October, 2019.Calculate the transposition square of transfer matrix P
The difference of battle array P ' and the E of unit matrix obtains matrix A, is calculated matrix X according to AX=0 and is city
The matrix data of A evolving state under plateau.
The present invention utilizes temperature retrieval classification results to construct state-transition matrix and original state matrix, root
Walk transition probability according to Markov Chain K, next step evolving trend of heat island is predicted, meanwhile, according to
The thought of the steady chain of Markov, the problem using the iterative algorithm infinite solution of solving matrix equation, and then solve
The certainly calculating of the steady chain of Markov.The prediction accuracy of the present invention is high, has bigger Practical significance.
Descriptions above can combine enforcement individually or in every way, and these modification
Mode is all within protection scope of the present invention.
It should be noted that in this article, term " include ", " comprising " or its any other variant meaning
Containing comprising of nonexcludability, so that include that the article of a series of key element or equipment not only include
Those key elements, but also include other key elements being not expressly set out, or also include for this article
Or the key element that equipment is intrinsic.In the case of there is no more restriction, statement " including ... " limit
Key element, it is not excluded that in the article including key element or equipment, there is also other identical element.
Above example is only in order to illustrate technical scheme and unrestricted, reference only to preferably implementing
The present invention has been described in detail by example.It will be understood by those within the art that, can be to this
Bright technical scheme is modified or equivalent, without deviating from spirit and the model of technical solution of the present invention
Enclose, all should contain in the middle of scope of the presently claimed invention.
Claims (9)
1. the Forecasting Methodology of a urban heat land effect, it is characterised in that including:
Step 1, uses mono window algorithm to carry out temperature retrieval remote sensing image data, it is thus achieved that urban surface temperature
Degrees of data；
Step 2, carries out classification to urban ground temperature data according to Mean Parameters and standard deviation criteria；
Step 3, adds up the heat island area data of each rank, described heat island area data include position,
Area, account for total area ratio；
Step 4, is calculated original state square according to the heat island area data of each rank in the first moment
Battle array S (0), according to heat island area data and the described original state matrix S of each rank in the second moment
(0) it is calculated transfer matrix P, counts according to described original state matrix S (0) and transfer matrix P
Calculate the described heat island region evolving state data in the follow-up required moment.
2. the Forecasting Methodology of urban heat land effect as claimed in claim 1, it is characterised in that
In step 1, temperature retrieval specifically includes:
It is calculated radiation brightness according to remotely-sensed data, is calculated ground brightness according to described radiation brightness
Temperature value；
Calculate normalized differential vegetation index, calculate vegetation coverage according to described normalized differential vegetation index, according to
Described vegetation coverage calculates earth's surface emissivity；
It is calculated described surface temperature number according to described ground brightness temperature value and described earth's surface emissivity
According to.
3. the Forecasting Methodology of urban heat land effect as claimed in claim 1, it is characterised in that
Described step 2 carries out classification according to below table:
Wherein, μ is Mean Parameters, and std is standard deviation criteria, and Ts is temperature parameter.
4. the Forecasting Methodology of urban heat land effect as claimed in claim 1, it is characterised in that
Described transfer matrix P meets following condition: the value of each element is positive number, and each row vector
All elements sum be 1.
5. the Forecasting Methodology of urban heat land effect as claimed in claim 1, it is characterised in that
The temperature data in the second moment according to described heat island region and described original state in described step 4
Matrix S (0) is calculated transfer matrix P and includes:
Described second moment is k1 with the discrete time difference in described first moment, according to described heat island region
The temperature data in the second moment is calculated state matrix S (k1) corresponding to the second moment, according to S (k1)=S
(0)Pk1It is calculated P；
Original state matrix S described in described step 4 (0) and transfer matrix P calculate described heat island region
Evolving state in the follow-up required moment includes:
The described required moment is k2 with the discrete time difference in described first moment, is calculated institute according to following formula
Take and carve corresponding state matrix S (k2): S (k2)=S (0) Pk2。
6. the Forecasting Methodology of urban heat land effect as claimed in claim 1, it is characterised in that
Described method also includes: use iterative method pre-according to described original state matrix and described transfer matrix
Survey the heat island region of each rank evolving state under plateau.
7. the Forecasting Methodology of urban heat land effect as claimed in claim 6, it is characterised in that
Described use iterative method predicts heat island region according to described original state matrix and described transfer matrix
Evolving state under plateau includes:
The difference calculating the transposed matrix P ' of described transfer matrix P and the E of unit matrix obtains matrix A, root
It is calculated matrix X according to AX=0 and is the matrix of heat island region evolving state under plateau.
8. the Forecasting Methodology of urban heat land effect as claimed in claim 7, it is characterised in that
Described it be calculated matrix X according to AX=0 and include: iteration precision is set, arranges at the beginning of matrix X
Initial value, calculates the value of the X after iteration according to following formula, and after carrying out successive ignition calculating, obtain changes
The error of the respective element of the X before the value of each element of the X after Dai and iteration is respectively less than described iteration
During precision, using the X that finally gives as calculated matrix X；
Wherein, the line number of matrix A and columns are the value that n, i and j are between 0 to n, aijFor matrix
The element of the i-th row jth row in A,For the element value before iteration,For the element value after iteration.
9. the Forecasting Methodology of urban heat land effect as claimed in claim 8, it is characterised in that
When the initial value of matrix X is set, it is set to random value, or below the meeting of matrix X is set
Condition: the value of each element is all higher than 0 and less than 1, and the element sum often gone is 1.
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Cited By (5)
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| CN109211791A (en) * | 2018-08-10 | 2019-01-15 | 北京观微科技有限公司 | Crop condition monitoring method and system |
| CN109612587A (en) * | 2018-12-18 | 2019-04-12 | 广州大学 | A kind of urban Heat Environment cause diagnosis method and system |
| CN110208878A (en) * | 2019-06-14 | 2019-09-06 | 广西海佩智能科技有限公司 | Green Roof weather monitoring and tropical island effect impact evaluation method |
| CN112200349A (en) * | 2020-09-16 | 2021-01-08 | 平衡机器科技(深圳)有限公司 | Remote sensing image heat island effect prediction method based on single window algorithm and PredRNN |
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