CN110472357A - 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 - Google Patents
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 Download PDFInfo
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Abstract
The present invention relates to the construction method of the remote sensing comprehensive ecological model RSIEI of the different effect of assessment Mining Development compact district earth's surface thermal environment point a kind of and applications.The construction method includes: to study area's surface temperature using conduct radiation equation method inverting；With photosynthetic vegetation coverage parameter and non-photosynthetic vegetation coverage parameter characterization surface vegetation biomass；Soil moisture content is characterized with soil moisture index parameters；With exposed soil and building index parameters characterization exposed soil and building dense degree；Integrated building RSIEI is carried out to four basic Ecological Parameters based on principal component analytical method.Its application includes: by statistical method, contribution of four Ecological Parameters of quantitative analysis to the different effect of Mining Concentrated District earth's surface thermal environment point.The present invention is applicable not only to the different effective matrix of thermal environment point of Mining Development dense city, and assesses equally possible using Administrative boundaries as the different effect of the earth's surface thermal environment in the small intensive small towns of scale Mining Development of boundary point.
Description
Technical field
The present invention relates to the ecological assessment model based on remote sensing technology, specifically a kind of assessment Mining Development compact district earth's surface
The construction method of the remote sensing comprehensive ecological model RSIEI of the different effect of thermal environment point and application.
Background technique
In recent decades, quick industrialization and urbanization has become a worldwide principal phenomena, although
Industrial land is seldom come the research for measuring tropical island effect as a kind of independent soil form, its overall tribute to urban heat island value
It offers also than relatively limited, but industrial land is often the accumulation regions of highest surface temperature, and the hot-zone of urban inner is substantially all
Concentrate on industrial area.
The city risen with the energy is located the production facilities such as a large amount of exploitation mining areas, industrial park, is promoting place warp
While Ji, culture etc. are fast-developing, some ecological environment problems are also brought.Such as in Mining Concentrated District, due to mineral products
The exploitation of resource causes and induces a large amount of environmental problem, and vegetation is destroyed, and land resource falls sharply, atmospheric environment and water ring
Border deteriorates, these variations reduce vegetation and evapotranspire, and increases absorption of the impermeable material to sun heat radiation, leads to Ground Heat ring
The appearance of border building-up effect.
Mining Concentrated District refers to for land quantity occupied by either bargh's quantity or bargh
It is all the region of Relatively centralized.In recent years, using the research of earth's surface analysis of Thermal Environment as improvement Mining Concentrated District ecology ring
The important component in border, receives more and more attention.Therefore, if Mining Concentrated District can that accurately and quickly be obtained
Surface temperature and earth's surface relevant parameter, quantitative assessment local area ecological thermal environment point different effect, it will be the intensive city of Mining Development
Industrial pattern, mineral exploration and exploitation, ecological protection and the sustainable development in city provide important directive significance.
Surface temperature (LST) is a kind of form of expression of Land surface energy budget, is widely used in the research of urban Heat Environment.
In the past, many researchers measured surface temperature using surface-based observing station or expensive accurate instrument, and utilized remote sensing heat
Infrared band detects surface temperature, is a kind of simple and cost-effective method.
The variation of surface temperature is mainly raw from different Land Use/land cover classifications, land surface in urban area
State situation is related.Many scholar's primary studies quantitative relationship of land surface biophysical parameters and earth's surface thermal environment.In the past
Earth's surface thermal environment mostly is assessed with single or multiple Ecological Parameters in research, is such as based on vegetation index or vegetation coverage
There are many research of relationship between (Vegetation Fractional Coverage, VFC) and surface temperature, and research conclusion is basic
Unanimously, i.e., negative correlation between vegetation and surface temperature, vegetation have the function of reducing surface temperature.There are also scholar's researchs
The relationship of region surface temperature and several surface biological physical parameters, the results showed that surface temperature and surface biological physical parameter
Between there are stronger relationships, wherein with normalization building index (NDBI), normalized differential vegetation index (NDVI) and normalize water
The research of index (NDWI) etc. is commonplace.But due to land surface thermal characteristics and the close phase of numerous biophysical properties
It closes, it is insufficient for only studying influence relationship of the single surface biological physical parameter to surface temperature.There is choosing in previous research
Remote sensing ecological models are constructed with four biophysical parameters, carry out the case of evaluation region eco-environmental quality, the model is by earth's surface
Temperature carries out the building of model as a parameter, is unable to reach comprehensive four biophysical parameters assessment area earth's surface thermal environments
Purpose.Therefore by selecting four that the are different from the type model indexs for not including surface temperature parameter, building remote sensing is comprehensive
Symphysis states model (RSIEI) studies a plurality of types of biophysics ingredients for the synthesis of the different effect of assessment area thermal environment point
Effect is necessary.The present invention is research area with Mining Concentrated District, facilitates us and more fully understands multiple types
Synthetic relationship between type biophysics ingredient and the different effect of Mining Concentrated District earth's surface thermal environment point, aid decision making person's system
Fixed effective Ground Heat environmental improvement policy.
Summary of the invention
The technical problem to be solved in the present invention is to provide a kind of based entirely on remotely-sensed data and technology, for assessing mining industry
Develop construction method and the application of the remote sensing comprehensive ecological model RSIEI of the different effect of compact district earth's surface thermal environment point.The model is not
It is only applicable to the different effective matrix of thermal environment point of Mining Development dense city, and is assessed using Administrative boundaries as the small scale of boundary
The different effect of the earth's surface thermal environment in the intensive small towns of Mining Development point is equally possible.
The technical solution used to solve the technical problems of the present invention is that:
A kind of building of the remote sensing comprehensive ecological model RSIEI of the different effect of assessment Mining Development compact district earth's surface thermal environment point
Method carries out as follows:
Step S1: the remote sensing image in research area is pre-processed；
Step S2: area's surface temperature LST is studied using the inverting of conduct radiation equation method；
Step S3: photosynthetic vegetation coverage fPV parameter and non-photosynthetic vegetation coverage fNPV parameter is selected to characterize earth's surface
Vegetation biomass；
Step S4: soil moisture index parameters 1-TVDI characterizes soil moisture content；
Step S5: exposed soil and building dense degree are characterized with exposed soil and building index parameters NDBI；
Step S6: integrated building remote sensing comprehensive ecological mould is carried out to four basic Ecological Parameters based on principal component analytical method
Type RSIEI.
A kind of remote sensing comprehensive ecological model RSIEI's of the different effect of assessment Mining Development compact district earth's surface thermal environment point answers
With, comprising:
(1) by statistical method, four Ecological Parameters of quantitative analysis are to Mining Concentrated District earth's surface thermal environment point
The contribution of different effect；
(2) by statistical method, the remote sensing comprehensive ecological model RSIEI of quantitative analysis building is close to assessment Mining Development
Collect the applicability of the different effect of regional earth's surface thermal environment point；
(3) by statistical method, the small scale Mining Development of remote sensing comprehensive ecological model RSIEI assessment for verifying foundation is close
Collect the applicability and feasibility in small towns.
The present invention by adopting the above technical scheme has the advantages that compared to the prior art
Contribution by each Ecological Parameter of regression analysis quantitative analysis to the different effect of research area's earth's surface thermal environment point
Degree, the results showed that four Ecological Parameters of selection and the different effect relation of Mining Concentrated District earth's surface thermal environment point are close,
FPV and (1-TVDI) and LST be in significant linear negative correlativity (P < 0.01), fNPV and NDBI on 0.01 horizontal (bilateral) with
LST in significant linear positive correlation (P < 0.01), can integrate four Ecological Parameters on 0.01 horizontal (bilateral) completely
Construct the different effect of remote sensing comprehensive ecological model evaluation Mining Concentrated District earth's surface thermal environment point；
The remote sensing comprehensive ecological model constructed using principal component analytical method, advantage is that the weight of each Ecological Parameter is not people
To set, but the contribution degree of first principal component (PCA1) is determined according to parameters, the side of this determining index weights
Method is more objective rationally；
The remote sensing comprehensive ecological model (RSIEI) of building and the spatial distribution of LST inversion result are it is found that study high temperature in area
Area is located in the stope of mining area, corresponding with the region of eco-environmental quality difference on RSIEI model inversion result image, low temperature position
It is corresponding with the good region of eco-environmental quality on RSIEI model inversion result image in forest land, illustrate that RSIEI has with LST
The characteristics of space inverse association, can effectively assess the different effect of Mining Concentrated District earth's surface thermal environment point using the model completely
It answers；
Verified, the RSIEI model established based on principal component analytical method is applicable not only to Mining Development dense city
The different effective matrix of thermal environment point, and assess using Administrative boundaries as the Ground Heat ring in the small intensive small towns of scale Mining Development of boundary
The different effect in border point is equally possible.
Further, preferred embodiment of the invention is as follows:
In the step S1, remote sensing image is pre-processed, comprising steps of
(1-1) carries out radiation calibration and FLAASH atmospheric correction to visible light wave range, is radiation by pixel grayvalue transition
Brightness value eliminates atmospheric effect；
(1-2) is to the 6th wave band of Landsat5TM image and two thermal infrareds of the 10th wave band of Landsat8TIRS image
Wave band individually carries out radiation calibration, saves as BSQ format for LST inverting；
(1-3) research on utilization area and the research small towns area Nei Ge vector boundary file carry out batch to pre-processed results and cut
To each research area image.
In the step S2, using conduct radiation equation method inverting surface temperature LST, comprising steps of
The expression formula for the thermal infrared radiation brightness value L λ that (2-1) satellite sensor receives:
L λ=[ε B (LST)+(1- ε) L ↓] τ+L ↑
ε is Land surface emissivity in formula；LST is earth's surface true temperature (K)；B (LST) is blackbody radiation brightness, unit
W/(m2*μm*sr)；τ is transmitance of the atmosphere in Thermal infrared bands；L ↑, L ↓ is respectively atmosphere uplink, downlink radiation brightness, single
Position W/ (m2* μm of * sr)；Atmospheric profile parameter is obtained by the website that NASA is provided；
(2-2) Land surface emissivity: ε=0.004*VFC+0.986
In the calculating of Land surface emissivity ε, using three sub-model inverting VFC of pixel, i.e. hypothesis mixed pixel is by PV, NPV
It is formed with BS three parts；
(2-3) assumes that earth's surface, atmosphere have lambert's volume property to heat radiation, then temperature is the black matrix of T in Thermal infrared bands
Radiance B (LST) are as follows: B (LST)=[L λ-L ↑-τ (1- ε) L ↓]/τ ε
The function of ground true temperature LST planck formula obtains in (2-4) above formula:
LST=K2/ln (K1/B (LST)+1)
For Landsat5TM Band6, K1=607.76W/ (m2* μm of * sr), K2=1260.56K；
For Landsat8TIRS Band10, K1=774.89W/ (m2* μm of * sr), K2=1321.08K.
In the step S3, photosynthetic vegetation coverage fPV parameter and non-photosynthetic vegetation coverage fNPV parameter is selected to carry out table
Expropriation of land table vegetation biomass, comprising steps of
(3-1) uses pixel three sub-model inverting fPV and fNPV, and wherein NPV information is extracted by withered fuel index DFI,
And it is verified in the applicability in the region；PV is represented with more mature normalized differential vegetation index NDVI；It establishes with NDVI and DFI
It is characterized the feature space of parameter, then extracts the characteristic value at the end PV and NPV；Formula is as follows:
(3-2) NDVI=(Band (NIR)-Band (R))/(Band (NIR)+Band (R))
(3-3) DFI=100* (1-Band (SWIR2)/Band (SWIR1)) * Band (R)/Band (NIR)
(3-4) using minimal noise partition method eliminate each wave band of multispectral image existing for influence of noise, using more at
Ripe Pure pixel index method extracts end member characteristic value, reduces error caused by artificial selection.
In the step S4, soil moisture index parameters 1-TVDI is selected to characterize soil moisture content, comprising steps of
(4-1) utilizes temperature vegetation drought index method inverting TVDI；
TVDI=(TS-TSmin)/(TSmax-TSmin)
In above formula, TS is the surface temperature of any pixel, and TSmin indicates the minimum surface temperature in the case of identical NDVI,
TSmax indicates highest surface temperature in the case of identical NDVI；
(4-2) TSmin=a1+b1*NDVI
(4-3) TSmax=a2+b2*NDVI
Above formula is referred to as wet side, dry Bian Fangcheng, and in formula, a1 and b1 are the coefficients of wet side equation, and a2 and b2 are dry Bian Fang
The coefficient of journey；According to the principle of TVDI, TVDI is bigger, closer from the dry side of feature space, then soil moisture is smaller；Conversely, then
Soil moisture is bigger；Numerical value to keep TVDI big represents soil moisture height, further subtracts calculated TVDI with 1, obtains soil
Earth humidity parameter.
In the step S5, exposed soil and building index parameters NDBI is selected to characterize exposed soil and building dense extent index,
Comprising steps of
(5-1) characterizes exposed soil using the NDBI index of the exposed earth's surface information including capable of enhancing research area's exposed soil and building
And architectural modulus；
Calculation formula:
(5-2) NDBI=(Band (MIR1)-Band (NIR))/(Band (MIR1)+Band (NIR))
Band (MIR1) and Band (NIR) respectively represent middle infrared band and near infrared band in formula.
In the step S6, it is comprehensive that integrated building remote sensing is carried out to four basic Ecological Parameters based on principal component analytical method
Ecological model RSIEI, comprising steps of
(6-1) is first to four basic Ecological Parameters: photosynthetic vegetation coverage fPV, non-photosynthetic vegetation coverage fNPV, soil
Earth humidity 1-TVDI, exposed soil and building concentration class NDBI inversion result standardization, make the codomain of result be fixed on [0,1] model
In enclosing, formula is as follows:
NI=(I-Imin)/(Imax-Imin)
Wherein I is the numerical values recited of the index；
Imax and Imin is respectively maximum value and minimum value of the index in image；
(6-2) carries out band combination to four basic Ecological Parameter inversion results Jing Guo standardization；
(6-3) carries out principal component analysis using result of the principal component analytical method PCA to four Ecological Parameter band combinations；
The first principal component of obtained principal component analysis result is saved out by (6-4), to make the picture that numerical value is big in result
Member represents the good region of eco-environmental quality, obtains initial remote sensing comprehensive ecological model with 1-PCA1；
(6-5) handles initial remote sensing comprehensive ecological model standardization, and codomain is fixed in [0,1] range, obtains final
Remote sensing comprehensive ecological model RSIEI.
The contribution by four Ecological Parameters of statistical method quantitative analysis to the different effect of Ground Heat environment point, packet
Include step:
(1.1) it is extracted in four basic Ecological Parameters and LST inversion result image respectively using Arcgis10.2 software
500 random points, for the coincidence of anti-stop, constraint distance is greater than 60 meters；
(1.2) using the data dependence analysis tool of SPSS22.0 software using LST as dependent variable, four Ecological Parameters
Regression analysis is carried out respectively as independent variable.
The remote sensing comprehensive ecological model RSIEI constructed by statistical method quantitative analysis, to assessment Ground Heat
The applicability of the different effect of environment point, comprising steps of
(2.1) 500 random points are extracted on RSIEI and LST inversion result image using Arcgis10.2 software, be anti-
Stop is overlapped, and constraint distance is greater than 60 meters；
(2.2) using the data dependence analysis tool of SPSS22.0 software using LST as dependent variable, RSIEI is used as certainly
Variable carries out regression analysis.
It is described by statistical method, the remote sensing comprehensive ecological model RSIEI for verifying foundation assesses small scale mining industry and opens
The applicability and feasibility for sending out small towns intensive, comprising steps of
(3.1) 8 intensive small towns of Mining Development are chosen in research area, is with Administrative boundaries using Arcgis10.2 software
Boundary extracts the random point in 8 Small-scale spaces in four Ecological Parameters and LST inversion result image, each parameter extraction
500 random points, for the coincidence of anti-stop, constraint distance is greater than 60 meters；Utilize the data dependence analysis work of SPSS22.0 software
For tool using LST as dependent variable, four Ecological Parameters carry out regression analysis respectively as independent variable；
(3.2) 8 intensive small towns of Mining Development are chosen in research area, is with Administrative boundaries using Arcgis10.2 software
Boundary extracts the random point in 8 Small-scale spaces on RSIEI and LST inversion result image, and each parameter extraction 500 random
Point, for the coincidence of anti-stop, constraint distance is greater than 60 meters；LST is made using the data dependence analysis tool of SPSS22.0 software
For dependent variable, four Ecological Parameters carry out regression analysis respectively as independent variable.
Detailed description of the invention
Fig. 1 is flow chart of the present invention；
Fig. 2 is the inversion process figure of remote sensing comprehensive ecological model RSIEI in the present invention；
When Fig. 3 is inverting fPV and fNPV in the present invention, area 2008-2018 NDVI-DFI feature space scatter plot is studied；
When Fig. 4 is inverting in the present invention (1-TVDI), area 2008-2018 dry and wet side fitted figure is studied；
Fig. 5 is that area normalization LST in 2018 and normalization RSIEI spatial distribution characteristic are studied in the present invention；
Fig. 6 is that area 2000-2008 normalization RSIEI and normalization LST regression analysis figure are studied in the present invention；
Fig. 7 is scale research area RSIEI 2018 small and normalization LST regression analysis figure in the present invention.
Specific embodiment
The technical solution of this explanation is described further with reference to the accompanying drawings and examples.
A kind of building of the remote sensing comprehensive ecological model RSIEI of the different effect of assessment Mining Development compact district earth's surface thermal environment point
Method carries out inverting first with four Ecological Parameters of the remote sensing technology to selection, carries out to four Ecological Parameter inversion results
Standardization, making its, respectively codomain is all fixed in [0,1] range.Then band group is carried out to four Ecological Parameter inversion results
It closes, then carries out principal component analysis using band combination result, first principal component is saved out.In order to represent big numerical value
Eco-environmental quality is good as a result, subtract first principal component with 1, and is standardized its result to get to building
Remote sensing comprehensive ecological model RSIEI.
Referring to Fig. 1, the specific steps are as follows:
Step S1: the remote sensing image in research area is pre-processed；
Step S2: conduct radiation equation method (Radiative Transfer Equation-RTE, also known as atmosphere school are utilized
Execute) inverting research area's surface temperature LST；
Step S3: photosynthetic vegetation coverage fPV parameter and non-photosynthetic vegetation coverage fNPV parameter is selected to characterize earth's surface
Vegetation biomass；
Step S4: soil moisture index parameters 1-TVDI characterizes soil moisture content；
Step S5: exposed soil and building dense degree are characterized with exposed soil and building index parameters NDBI；
Step S6: basic to four based on principal component analytical method (PCA, principal component analysis)
Ecological Parameter carries out integrated building remote sensing comprehensive ecological model (RSIEI).
In the present embodiment, the remote sensing image for studying area is pre-processed, and is comprised the concrete steps that:
(1-1) carries out radiation calibration and FLAASH atmospheric correction to visible light wave range, is radiation by pixel grayvalue transition
Brightness value eliminates atmospheric effect.
(1-2) is to the 6th wave band of Landsat5TM image and the 10th wave band (spoke of wave band 11 of Landsat8TIRS image
It is larger to penetrate calibration deviation, therefore here with the 10th wave band inverting LST) two Thermal infrared bands individually carry out radiation calibration, it protects
BSQ format is saved as LST inverting.
(1-3) research on utilization area and the research small towns area Nei Ge vector boundary file carry out batch to pre-processed results and cut
To each research area image.
In the present embodiment, using conduct radiation equation method (Radiative Transfer Equation-RTE, also known as greatly
Gas correction method) inverting research area's surface temperature LST, it comprises the concrete steps that:
The expression formula (radiation transfer equation) for the thermal infrared radiation brightness value L λ that (2-1) satellite sensor receives:
L λ=[ε B (LST)+(1- ε) L ↓] τ+L ↑
ε is Land surface emissivity in formula；LST is earth's surface true temperature (K)；B (LST) is blackbody radiation brightness, unit
W/(m2*μm*sr)；τ is transmitance of the atmosphere in Thermal infrared bands.L ↑, L ↓ is respectively atmosphere uplink, downlink radiation brightness, single
Position W/ (m2* μm of * sr).Atmospheric profile parameter is defeated in the website (http://atmcorr.gsfc.nasa.gov/) that NASA is provided
Enter video imaging time, center longitude and other parameters acquisition of information.
Table 1 is the atmospheric profile parameter of 3 phase images.
| Atmospheric profile parameter | 2008-9-12 | 2013-9-26 | 2018-9-8 |
| Atmosphere is in Thermal infrared bands transmitance τ | 0.80 | 0.93 | 0.83 |
| Atmosphere uplink radiation brightness L ↑ (unit: W/ (m2* μm of * sr)) | 1.51 | 0.50 | 1.27 |
| Downward atmospheric long-wave radiation brightness L ↓ (unit: W/ (m2* μm of * sr)) | 2.51 | 0.88 | 2.14 |
(2-2) calculates Land surface emissivity: ε=0.004*VFC+0.986
In the calculating of Land surface emissivity ε, the previous NDVI threshold method studying multi-purpose Sobrino and proposing, i.e. simplified plant
Coating cover degree (VFC) computation model inverting VFC.When the NDVI value of model calculating vegetation and exposed soil, need to take certain confidence
Degree obtains minimum and maximum NDVI value, this process has biggish subjectivity, has certain influence to inversion result precision.
For this purpose, this research uses three sub-model inverting VFC of pixel, i.e. hypothesis mixed pixel is made of PV, NPV and BS three parts.
(2-3) assumes that earth's surface, atmosphere have lambert's volume property to heat radiation, then temperature is the black matrix of T in Thermal infrared bands
Radiance B (LST) calculation formula are as follows: B (LST)=[L λ-L ↑-τ (1- ε) L ↓]/τ ε
Ground true temperature LST can be obtained with the function of planck formula in (2-4) above formula:
LST=K2/ln (K1/B (LST)+1)
For Landsat5TM Band6, K1=607.76W/ (m2* μm of * sr), K2=1260.56K；
For Landsat8TIRS Band10, K1=774.89W/ (m2* μm of * sr), K2=1321.08K.
In the present embodiment, photosynthetic vegetation coverage fPV parameter and non-photosynthetic vegetation coverage fNPV parameter is selected to characterize
Surface vegetation biomass, comprises the concrete steps that:
(3-1) is using pixel three sub-model the inverting fPV and fNPV by propositions such as Guerschman.Wherein NPV information by
Withered fuel index DFI is extracted, and verifies it in the applicability in the region.PV is with more mature normalized differential vegetation index NDVI
It represents.The feature space of the parameter characterized by NDVI and DFI is established, the characteristic value at the end PV and NPV, such as Fig. 3 are then extracted.Formula
It is as follows:
(3-2) NDVI=(Band (NIR)-Band (R))/(Band (NIR)+Band (R))
(3-3) DFI=100* (1-Band (SWIR2)/Band (SWIR1)) * Band (R)/Band (NIR)
(3-4) uses minimal noise partition method since there are serious noises for each wave band of multispectral image first
(Minimum Noise Fraction, MNF) eliminates its influence, to 3 phase image datas using minimal noise convert (MNF) method into
Row dimensionality reduction generates the random test vector largely inside data acquisition system.Then more mature Pure pixel index is utilized
Method (Pixel Purity Index method, PPI) extracts end member characteristic value, projects to 6 therein main wave bands,
The number of iterations is set as 2000, threshold coefficient 3, the pixel by PPI index greater than 3 is considered as pure end member, takes NDVI-DFI special
Levy characteristic value of the pure end member average value on each vertex of space scatter plot triangle as corresponding end member.
In the present embodiment, selects soil moisture index parameters 1-TVDI to characterize soil moisture content, comprises the concrete steps that:
The temperature vegetation drought index method inverting TVDI that (4-1) is proposed first with Sandholt et al..
TVDI=(TS-TSmin)/(TSmax-TSmin)
In above formula, TS is the surface temperature of any pixel, and TSmin indicates the minimum surface temperature in the case of identical NDVI,
TSmax indicates highest surface temperature in the case of identical NDVI.
(4-2) TSmin=a1+b1*NDVI
(4-3) TSmax=a2+b2*NDVI
Above formula is referred to as wet side, dry Bian Fangcheng.In formula, a1 and b1 are the coefficients of wet side equation, and a2 and b2 are dry Bian Fang
The coefficient of journey.According to the principle of TVDI, TVDI is bigger, closer from the dry side of feature space, then soil moisture is smaller；Conversely, then
Soil moisture is bigger.
It is utilized respectively NDVI and LST inversion result, the fitting of Ts-NDVI feature space dry and wet side is carried out, obtains three phase images
Dry and wet side fitted figure and fit equation, such as Fig. 4.In order to which the numerical value for keeping TVDI big represents soil moisture height, further subtracted with 1
Calculated TVDI obtains soil moisture parameter of the invention.
In the present embodiment, exposed soil and building index NDBI is selected to characterize exposed soil and building dense extent index, it is specific to walk
Suddenly it is:
(5-1) characterizes exposed soil using the NDBI index of the exposed earth's surface information including capable of enhancing research area's exposed soil and building
And architectural modulus.
Calculation formula:
(5-2) NDBI=(Band (MIR1)-Band (NIR))/(Band (MIR1)+Band (NIR))
Band (MIR1) and Band (NIR) respectively represent middle infrared band and near infrared band in image in formula.
In the present embodiment, based on principal component analytical method (PCA, principal component analysis) to four
Basic Ecological Parameter carries out integrated building remote sensing comprehensive ecological model (RSIEI) and comprises the concrete steps that such as Fig. 2:
(6-1) is first to four basic Ecological Parameter (photosynthetic vegetation coverage fPV, non-photosynthetic vegetation coverage fNPV, soil
Earth humidity 1-TVDI, exposed soil and building concentration class NDBI) inversion result standardization, so that the codomain of result is fixed on [0,1]
In range, formula is as follows:
NI=(I-Imin)/(Imax-Imin)
Wherein I is the numerical values recited of the index；
Imax and Imin is respectively maximum value and minimum value of the index in image.
(6-2) carries out band combination to four basic Ecological Parameter inversion results Jing Guo standardization；
(6-3) carries out principal component analysis using result of the principal component analytical method PCA to four Ecological Parameter band combinations；
The first principal component of obtained principal component analysis result is saved out by (6-4), to make the picture that numerical value is big in result
Member represents the good region of eco-environmental quality, obtains initial remote sensing comprehensive ecological model with (1-PCA1)；
(6-5) handles initial remote sensing comprehensive ecological model standardization, and codomain is fixed in [0,1] range, obtains final
Remote sensing comprehensive ecological model RSIEI.
The remote sensing comprehensive ecological model of the different effect of assessment Mining Development compact district earth's surface thermal environment point described in the present embodiment
The application of RSIEI, comprising:
(1) by statistical method, four Ecological Parameters of quantitative analysis are to Mining Concentrated District earth's surface thermal environment point
The contribution of different effect；
(2) by statistical method, the remote sensing comprehensive ecological model RSIEI of quantitative analysis building is close to assessment Mining Development
Collect the applicability of the different effect of regional earth's surface thermal environment point；
(3) by statistical method, the small scale Mining Development of remote sensing comprehensive ecological model RSIEI assessment for verifying foundation is close
Collect the applicability and feasibility in small towns.
In order to verify the applicability of above-mentioned model evaluation Mining Concentrated District earth's surface thermal environment point different effect and feasible
Property, respectively extract research four, area Ecological Parameter and remote sensing comprehensive ecological model and Surface Temperature Retrieval result image on
Machine point analyzes the relationship and remote sensing of four Ecological Parameters and surface temperature using the regression analysis in statistics respectively
Relationship between comprehensive ecological model and surface temperature.
In order to verify the model in the applicability and feasibility of Small-scale space, administration cell ruler in research area is extracted respectively
It spends in range, four Ecological Parameters and remote sensing comprehensive ecological model and surface temperature in 8 intensive small towns of Mining Development are anti-
The random point on result image is drilled, using the regression analysis in statistics, analyzes four ecologies in Small-scale space respectively
Relationship between parameter and the relationship and remote sensing comprehensive ecological model and surface temperature of surface temperature.
Referring to Fig. 1, the above-mentioned specific application method of model is described below:
(1) by four, statistical method Quantitative Study area Ecological Parameter respectively to the different effect of Ground Heat environment point
Contribution, comprises the concrete steps that:
(1.1) it is extracted in four basic Ecological Parameters and LST inversion result image respectively using Arcgis10.2 software
500 random points, for the coincidence of anti-stop, constraint distance is greater than 60 meters；
(1.2) using the data dependence analysis tool of SPSS22.0 software using LST as dependent variable, four Ecological Parameters
Regression analysis is carried out respectively as independent variable.It the results are shown in Table 2.
(1.3) Regression Analysis Result shows that four Ecological Parameters and the different effect relation of earth's surface thermal environment point are close.From 3
From the point of view of four Ecological Parameters in time and the Regression residues equation of LST, fPV and LST are in significant line on 0.01 horizontal (bilateral)
Property negative correlativing relation (P < 0.01), illustrates that the increase of photosynthetic vegetation coverage can be such that surface temperature declines, and photosynthetic vegetation is covered
Cover degree is every to increase by 10%, and can make surface temperature accordingly reduces by 10.95~18.47 DEG C；FNPV and LST is on 0.01 horizontal (bilateral)
In significant linear positive correlation (P < 0.01), non-photosynthetic vegetation coverage is every to increase by 10%, can accordingly increase surface temperature
5.70~9.22 DEG C；(1-TVDI) and LST are in significant linear negative correlativity (P < 0.01), explanation on 0.01 horizontal (bilateral)
The increase of soil moisture can be such that surface temperature declines, and soil moisture is every to increase by 10%, can make surface temperature accordingly decline 6.23~
11.31℃；NDBI and LST in significant linear positive correlation (P < 0.01), illustrates exposed soil and builds on 0.01 horizontal (bilateral)
The increase for building area can aggravate the rising of surface temperature, exposed soil and the every increase by 10% of construction area, surface temperature can be made accordingly to rise
It is 4.30~6.37 DEG C high.
(2) by the remote sensing comprehensive ecological model (RSIEI) of statistical method quantitative analysis building to assessment Ground Heat ring
The applicability of the different effect in border point, comprises the concrete steps that:
(2.1) 500 random points are extracted on RSIEI and LST inversion result image using Arcgis10.2 software, be anti-
Stop is overlapped, and constraint distance is greater than 60 meters；
(2.2) using the data dependence analysis tool of SPSS22.0 software using LST as dependent variable, RSIEI is used as certainly
Variable carries out regression analysis.Regression Analysis Result is as shown in Figure 6.
(2.3) correlativity for being further comparative analysis RSIEI and LST, by the normalization RSIEI in research area in 2018
It is learnt (such as Fig. 5) with normalization LST spatial distribution image, research area has the different effect of apparent thermal environment point, peony within the border
The high-temperature region of Regional Representative is distributed mainly on mining area stope and cities and towns village settlement place, and low in light red and white area representative
Warm area is distributed mainly on forest land, meadow, field and water and pours arable land region；Bottle green Regional Representative ecological environment is good in Fig. 5 (b)
Region appears in forest land, meadow, field and water mostly and pours arable land, and light green color and white represent the region of ecological environment difference, main
It is distributed in mining area stope and cities and towns village settlement place.As known to both above spatial distribution characteristic, the totality of RSIEI is normalized
The overall space distribution of spatial distribution and normalization LST have exactly the opposite distribution characteristics.Vegetation has Ground Heat effect
Relaxation effect, and Mining Land and cities and towns village settlement place have biggish contribution function for Ground Heat building-up effect.Fig. 5 (a)
In high-temperature region correspond to the region of ecological environment difference in Fig. 5 (b), and the low-temperature space in Fig. 5 (a) correspond to it is ecological in Fig. 5 (b)
The good region of environment, the two have the characteristics that inverse space correlation.For studying in area using small towns administrative unit as 8 mines on boundary
Industry develops intensive small towns, and the two also has similar distribution characteristics.
(3) by statistical method, the remote sensing comprehensive ecological model (RSIEI) for verifying foundation assesses small scale Mining Development
The applicability and feasibility in intensive small towns, comprise the concrete steps that:
(3.1) 8 intensive small towns of Mining Development are chosen in research area, is with Administrative boundaries using Arcgis10.2 software
Boundary extracts the random point in 8 Small-scale spaces in four Ecological Parameters and LST inversion result image, each parameter extraction
500 random points, for the coincidence of anti-stop, constraint distance is greater than 60 meters.Utilize the data dependence analysis work of SPSS22.0 software
For tool using LST as dependent variable, four Ecological Parameters carry out regression analysis respectively as independent variable.
Four Ecological Parameters in (3.2) 2018 years intensive small towns of 8 small scale Mining Development and the regression result table of LST
Bright, four Ecological Parameters and the different effect relation of earth's surface thermal environment point of small towns administration cell scale are close, and fPV and LST are 0.01
It is in significant linear negative correlativity (P < 0.01) on horizontal (bilateral), illustrates that the increase of photosynthetic vegetation coverage can make surface temperature
Decline, and the every increase by 10% of photosynthetic vegetation coverage, can make surface temperature accordingly reduces by 8.51~14.94 DEG C；FNPV and LST
In significant linear positive correlation (P < 0.01) on 0.01 horizontal (bilateral), non-photosynthetic vegetation coverage is every to increase by 10%, meeting
Surface temperature is set accordingly to rise 4.01~12.08 DEG C；(1-TVDI) is in significant linear negative phase on 0.01 horizontal (bilateral) with LST
Pass relationship (P < 0.01) illustrates that the increase of soil moisture can be such that surface temperature declines, and soil moisture is every to increase by 10%, can make earth's surface
Temperature accordingly declines 6.73~9.71 DEG C；NDBI and LST on 0.01 horizontal (bilateral) in significant linear positive correlation (P <
0.01) rising of surface temperature can be aggravated by, illustrating the increase of exposed soil and construction area, exposed soil and construction area is every increases by 10%,
Surface temperature can be made accordingly to increase 3.36~6.03 DEG C.
(3.3) the intensive small towns of 8 small scale Mining Development is chosen in research area, using Arcgis10.2 software with administration
Boundary is the random point in boundary 8 Small-scale spaces of extraction on RSIEI and LST inversion result image, each parameter extraction 500
A random point, for the coincidence of anti-stop, constraint distance is greater than 60 meters.It will using the data dependence analysis tool of SPSS22.0 software
LST carries out regression analysis respectively as independent variable as dependent variable, four Ecological Parameters.As shown in Figure 7.
The regression result of the RSIEI and LST in 8 intensive small towns of Mining Development in 2018 show that RSIEI and small towns are administrative single
The different effect relation of the earth's surface thermal environment of first scale point is close.From the point of view of the Regression residues equation of RSIEI and LST, the RSIEI of Fig. 7 a
It is in significant linear negative correlativity (P < 0.01) on 0.01 horizontal (bilateral) with LST, the value of RSIEI is every to rise 10%, earth's surface
Temperature accordingly declines 6.50 DEG C；The RSIEI and LST of Fig. 7 b on 0.01 horizontal (bilateral) in significant linear negative correlativity (P <
0.01), the value of RSIEI is every rises 10%, and surface temperature accordingly declines 4.81 DEG C；The RSIEI and LST of Fig. 7 c is in 0.01 level
It is in significant linear negative correlativity (P < 0.01) on (bilateral), the value of RSIEI is every to rise 10%, and surface temperature accordingly declines 4.58
℃；The RSIEI and LST of Fig. 7 d is in significant linear negative correlativity (P < 0.01) on 0.01 horizontal (bilateral), and the value of RSIEI is every
Rise 10%, surface temperature accordingly declines 4.91 DEG C；The RSIEI and LST of Fig. 7 e is on 0.01 horizontal (bilateral) in significant linear
Negative correlativing relation (P < 0.01), the value of RSIEI is every to rise 10%, and surface temperature accordingly declines 3.57 DEG C；The RSIEI of Fig. 7 f with
LST is in significant linear negative correlativity (P < 0.01) on 0.01 horizontal (bilateral), and the value of RSIEI is every to rise 10%, earth's surface temperature
Corresponding 2.81 DEG C of the decline of degree；The RSIEI and LST of Fig. 7 g on 0.01 horizontal (bilateral) in significant linear negative correlativity (P <
0.01), the value of RSIEI is every rises 10%, and surface temperature accordingly declines 2.09 DEG C；The RSIEI and LST of Fig. 7 h is in 0.01 level
It is in significant linear negative correlativity (P < 0.01) on (bilateral), the value of RSIEI is every to rise 10%, and surface temperature accordingly declines 1.25
℃。
Conclusions show that the remote sensing collective model by the integrated creation of four Ecological Parameters assesses Mining Development dense city,
And assessment is effectively simultaneously by the different effect of the earth's surface thermal environment in the small intensive small towns of scale Mining Development of boundary point of Administrative boundaries
It is feasible.
Above-described embodiment only illustrates that spirit of the invention.Those skilled in the art can
To make various modifications or additions to the described embodiments or be substituted in a similar manner, but without departing from
Spirit or beyond the scope defined by the appended claims of the invention.
Claims (11)
1. the building side of the remote sensing comprehensive ecological model RSIEI of the different effect of assessment Mining Development compact district earth's surface thermal environment point a kind of
Method, which is characterized in that carry out as follows:
Step S1: the remote sensing image in research area is pre-processed；
Step S2: area's surface temperature LST is studied using the inverting of conduct radiation equation method；
Step S3: photosynthetic vegetation coverage fPV parameter and non-photosynthetic vegetation coverage fNPV parameter is selected to characterize surface vegetation
Biomass；
Step S4: soil moisture index parameters 1-TVDI characterizes soil moisture content；
Step S5: exposed soil and building dense degree are characterized with exposed soil and building index parameters NDBI；
Step S6: integrated building remote sensing comprehensive ecological model is carried out to four basic Ecological Parameters based on principal component analytical method
RSIEI。
2. the remote sensing comprehensive ecological mould of the different effect of assessment Mining Development compact district earth's surface thermal environment point according to claim 1
The construction method of type RSIEI, which is characterized in that in the step S1, remote sensing image is pre-processed, comprising steps of
(1-1) carries out radiation calibration and FLAASH atmospheric correction to visible light wave range, is radiance by pixel grayvalue transition
Value eliminates atmospheric effect；
(1-2) is to the 6th wave band of Landsat5 TM image and two thermal infrared waves of the 10th wave band of Landsat8 TIRS image
Section individually carries out radiation calibration, saves as BSQ format for LST inverting；
(1-3) research on utilization area and the research small towns area Nei Ge vector boundary file carry out batch cutting to pre-processed results and obtain respectively
A research area image.
3. the remote sensing comprehensive ecological mould of the different effect of assessment Mining Development compact district earth's surface thermal environment point according to claim 1
The construction method of type RSIEI, which is characterized in that in the step S2, using conduct radiation equation method inverting surface temperature LST,
Comprising steps of
The expression formula for the thermal infrared radiation brightness value L λ that (2-1) satellite sensor receives:
L λ=[ε B (LST)+(1- ε) L ↓] τ+L ↑
ε is Land surface emissivity in formula；LST is earth's surface true temperature (K)；B (LST) is blackbody radiation brightness, unit W/
(m2*μm*sr)；τ is transmitance of the atmosphere in Thermal infrared bands；L ↑, L ↓ is respectively atmosphere uplink, downlink radiation brightness, unit
W/(m2*μm*sr)；Atmospheric profile parameter is obtained by the website that NASA is provided；
(2-2) Land surface emissivity: ε=0.004*VFC+0.986
In the calculating of Land surface emissivity ε, using three sub-model inverting VFC of pixel, i.e. hypothesis mixed pixel is by PV, NPV and BS
Three parts composition；
(2-3) assumes spoke of the black matrix in Thermal infrared bands that earth's surface, atmosphere have lambert's volume property to heat radiation, then temperature is T
Penetrate brightness B (LST) are as follows: B (LST)=[L λ-L ↑-τ (1- ε) L ↓]/τ ε
The function of ground true temperature LST planck formula obtains in (2-4) above formula:
LST=K2/ln (K1/B (LST)+1)
For Landsat5 TM Band6, K1=607.76W/ (m2* μm of * sr), K2=1260.56K；
For Landsat8 TIRS Band10, K1=774.89W/ (m2* μm of * sr), K2=1321.08K.
4. the remote sensing comprehensive ecological mould of the different effect of assessment Mining Development compact district earth's surface thermal environment point according to claim 1
The construction method of type RSIEI, which is characterized in that in the step S3, select photosynthetic vegetation coverage fPV parameter and non-photosynthetic plant
Cover degree fNPV parameter is coated to characterize surface vegetation biomass, comprising steps of
(3-1) uses pixel three sub-model inverting fPV and fNPV, and wherein NPV information is extracted by withered fuel index DFI, and is tested
It is demonstrate,proved in the applicability in the region；PV is represented with more mature normalized differential vegetation index NDVI；Establishing with NDVI and DFI is spy
The feature space of parameter is levied, the characteristic value at the end PV and NPV is then extracted；Formula is as follows:
(3-2) NDVI=(Band (NIR)-Band (R))/(Band (NIR)+Band (R))
(3-3) DFI=100* (1-Band (SWIR2)/Band (SWIR1)) * Band (R)/Band (NIR)
(3-4) eliminates influence of noise existing for each wave band of multispectral image using minimal noise partition method, using more mature
Pure pixel index method extracts end member characteristic value, reduces error caused by artificial selection.
5. the remote sensing comprehensive ecological mould of the different effect of assessment Mining Development compact district earth's surface thermal environment point according to claim 1
The construction method of type RSIEI, which is characterized in that in the step S4, soil moisture index parameters 1-TVDI is selected to characterize soil
Earth water content, comprising steps of
(4-1) utilizes temperature vegetation drought index method inverting TVDI；
TVDI=(TS-TSmin)/(TSmax-TSmin)
In above formula, TS is the surface temperature of any pixel, and TSmin indicates the minimum surface temperature in the case of identical NDVI, TSmax
Indicate highest surface temperature in the case of identical NDVI；
(4-2) TSmin=a1+b1*NDVI
(4-3) TSmax=a2+b2*NDVI
Above formula is referred to as wet side, dry Bian Fangcheng, and in formula, a1 and b1 are the coefficients of wet side equation, and a2 and b2 are dry side equations
Coefficient；According to the principle of TVDI, TVDI is bigger, closer from the dry side of feature space, then soil moisture is smaller；Conversely, then soil
Humidity is bigger；Numerical value to keep TVDI big represents soil moisture height, further subtracts calculated TVDI with 1, and it is wet to obtain soil
Spend parameter.
6. the remote sensing comprehensive ecological mould of the different effect of assessment Mining Development compact district earth's surface thermal environment point according to claim 1
The construction method of type RSIEI, which is characterized in that in the step S5, select exposed soil and building index parameters NDBI naked to characterize
Soil and building dense extent index, comprising steps of
(5-1) characterizes exposed soil using the NDBI index of the exposed earth's surface information including capable of enhancing research area's exposed soil and building and builds
Build parameter；
Calculation formula:
(5-2) NDBI=(Band (MIR1)-Band (NIR))/(Band (MIR1)+Band (NIR))
Band (MIR1) and Band (NIR) respectively represent middle infrared band and near infrared band in formula.
7. the remote sensing comprehensive ecological mould of the different effect of assessment Mining Development compact district earth's surface thermal environment point according to claim 1
The construction method of type RSIEI, which is characterized in that in the step S6, based on principal component analytical method to four basic ecology ginsengs
Number carries out integrated building remote sensing comprehensive ecological model RSIEI, comprising steps of
(6-1) is first to four basic Ecological Parameters: photosynthetic vegetation coverage fPV, non-photosynthetic vegetation coverage fNPV, soil are wet
1-TVDI, exposed soil and building concentration class NDBI inversion result standardization are spent, the codomain of result is made to be fixed on [0,1] range
Interior, formula is as follows:
NI=(I-Imin)/(Imax-Imin)
Wherein I is the numerical values recited of the index；
Imax and Imin is respectively maximum value and minimum value of the index in image；
(6-2) carries out band combination to four basic Ecological Parameter inversion results Jing Guo standardization；
(6-3) carries out principal component analysis using result of the principal component analytical method PCA to four Ecological Parameter band combinations；
The first principal component of obtained principal component analysis result is saved out by (6-4), to make the pixel generation that numerical value is big in result
The good region of table eco-environmental quality obtains initial remote sensing comprehensive ecological model with 1-PCA1；
(6-5) handles initial remote sensing comprehensive ecological model standardization, and codomain is fixed in [0,1] range, obtains final distant
Feel comprehensive ecological model RSIEI.
8. a kind of remote sensing comprehensive ecological of the different effect of assessment Mining Development compact district earth's surface thermal environment point as described in claim 1
The application of model RSIEI characterized by comprising
(1) by statistical method, four Ecological Parameters of quantitative analysis are to the different effect of Mining Concentrated District earth's surface thermal environment point
The contribution answered；
(2) by statistical method, the remote sensing comprehensive ecological model RSIEI of quantitative analysis building to assessment Mining Development densely
The applicability of the different effect of area's earth's surface thermal environment point；
(3) by statistical method, the remote sensing comprehensive ecological model RSIEI for verifying foundation assesses the small intensive township of scale Mining Development
The applicability and feasibility in town.
9. the remote sensing comprehensive ecological mould of the different effect of assessment Mining Development compact district earth's surface thermal environment point according to claim 8
The application of type RSIEI, which is characterized in that by four Ecological Parameters of statistical method quantitative analysis to the different effect of Ground Heat environment point
The contribution answered, comprising steps of
(1.1) 500 are extracted in four basic Ecological Parameters and LST inversion result image respectively using Arcgis10.2 software
Random point, for the coincidence of anti-stop, constraint distance is greater than 60 meters；
(1.2) using the data dependence analysis tool of SPSS22.0 software using LST as dependent variable, four Ecological Parameter difference
Regression analysis is carried out as independent variable.
10. the remote sensing comprehensive ecological of the different effect of assessment Mining Development compact district earth's surface thermal environment point according to claim 8
The application of model RSIEI, which is characterized in that by statistical method quantitative analysis construct remote sensing comprehensive ecological model RSIEI,
To the applicability of the different effect of assessment earth's surface thermal environment point, comprising steps of
(2.1) 500 random points are extracted on RSIEI and LST inversion result image using Arcgis10.2 software, for anti-stop
It is overlapped, constraint distance is greater than 60 meters；
(2.2) using the data dependence analysis tool of SPSS22.0 software using LST as dependent variable, RSIEI is as independent variable
Carry out regression analysis.
11. the remote sensing comprehensive ecological of the different effect of assessment Mining Development compact district earth's surface thermal environment point according to claim 8
The application of model RSIEI, which is characterized in that by statistical method, verify the remote sensing comprehensive ecological model RSIEI assessment of foundation
The applicability and feasibility in the small intensive small towns of scale Mining Development, comprising steps of
(3.1) 8 intensive small towns of Mining Development are chosen in research area, using Arcgis10.2 software using Administrative boundaries as boundary
Extract the random point in 8 Small-scale spaces in four Ecological Parameters and LST inversion result image, each parameter extraction 500
Random point, for the coincidence of anti-stop, constraint distance is greater than 60 meters；It will using the data dependence analysis tool of SPSS22.0 software
LST carries out regression analysis respectively as independent variable as dependent variable, four Ecological Parameters；
(3.2) 8 intensive small towns of Mining Development are chosen in research area, using Arcgis10.2 software using Administrative boundaries as boundary
Extract the random point in 8 Small-scale spaces on RSIEI and LST inversion result image, 500 random points of each parameter extraction,
For the coincidence of anti-stop, constraint distance is greater than 60 meters；Using the data dependence analysis tool of SPSS22.0 software using LST as because
Variable, four Ecological Parameters carry out regression analysis respectively as independent variable.
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| CN113128134B (en) * | 2021-06-17 | 2021-09-14 | 中国矿业大学(北京) | Mining area ecological environment evolution driving factor weight quantitative analysis method |
| CN114861423A (en) * | 2022-04-22 | 2022-08-05 | 西北核技术研究所 | Method for simulating dynamic response numerical value of underground medium under action of thermal radiation |
| CN115791866A (en) * | 2023-01-13 | 2023-03-14 | 太原理工大学 | Soil moisture content real-time monitoring system and method based on thermal infrared remote sensing data |
| CN115791866B (en) * | 2023-01-13 | 2023-04-21 | 太原理工大学 | Soil moisture content real-time monitoring system and method based on thermal infrared remote sensing data |
| CN116778345A (en) * | 2023-08-18 | 2023-09-19 | 中国科学院、水利部成都山地灾害与环境研究所 | Mountain forest fire trace vegetation coverage inversion method and device and electronic equipment |
| CN116778345B (en) * | 2023-08-18 | 2023-11-14 | 中国科学院、水利部成都山地灾害与环境研究所 | Mountain forest fire trace vegetation coverage inversion method and device and electronic equipment |
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