CN111398182A - Quantitative evaluation method for space-time differential effect of earth surface thermal environment based on pixel three-component model - Google Patents
Quantitative evaluation method for space-time differential effect of earth surface thermal environment based on pixel three-component model Download PDFInfo
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Abstract
The invention discloses a quantitative evaluation method for a space-time differential effect of a surface thermal environment based on a pixel trisection model, which is used for estimating vegetation coverage of a mining development compact area, extracting an end member characteristic value by constructing an NDVI-DFI characteristic space, decomposing a mixed pixel on a medium-resolution remote sensing image into photosynthetic/non-photosynthetic vegetation and bare soil 3 parts, estimating the part which is consistent with the actual situation, improving the capability of acquiring vegetation information by remote sensing, quantitatively evaluating the space-time differential characteristic relation of the photosynthetic/non-photosynthetic vegetation and the bare soil on the surface thermal environment of the area, and providing a quantitative reference basis for revealing the influence mechanism of the photosynthetic/non-photosynthetic vegetation and bare soil on the space-time heterogeneity of the surface thermal environment in the mining development compact area.
Description
Technical Field
The invention relates to a quantitative evaluation method for a space-time differential effect of a surface thermal environment, in particular to a quantitative evaluation method for the space-time differential effect of the surface thermal environment of a mining development compact area based on a pixel three-component model.
Background
Surface temperature (L ST) and vegetation coverage (VFC) are important index factors of ecological environment change, and research on space-time change and correlation of the surface temperature and the vegetation coverage has important significance for evaluating regional ecological environment construction and improving regional ecological environment quality.
The vegetation cover has influence on both surface energy exchange and surface heat flow, and is an important influence factor of surface temperature. Analyzing the influence of vegetation on the thermal environment of the regional ground surface based on various vegetation indexes, such as Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), Normalized Difference Mountain Vegetation Index (NDMVI), or vegetation coverage has become a research hotspot, and it is concluded that vegetation cover is substantially consistent, i.e., vegetation cover and ground surface temperature are in a negative correlation, and vegetation has an effect of reducing the ground surface temperature.
Vegetation coverage, which is generally defined as the percentage of the vertical projection area of vegetation (leaves, stems, branches) on the ground to the total area of a statistical area, is one of the main indicators for measuring the vegetation condition on the earth surface, is also an important indicator for the environmental change of the regional ecosystem, and occupies a significant position in the atmospheric space, soil space, water space and biosphere. Vegetation coverage is closely related to vegetation transpiration, which is an important component of energy balance and moisture balance and is a process in water heat flux transmission of a soil-vegetation-atmosphere system, and a lot of researches show that the correlation between the vegetation coverage and the earth surface temperature is higher than that between the vegetation index and the earth surface temperature by calculating the correlation coefficient between the earth surface temperature and the vegetation index and the vegetation coverage, so that the vegetation coverage is suggested to be used for researching the cooling effect of the vegetation on the earth surface.
Much of the past has focused on exploiting the relationship between Photosynthetic Vegetation (PV) and the terrestrial thermal environment by inverting regional vegetation coverage using a binary pixel model (DPM) that considers only 2 components of PV and Bare Soil (BS). Natural vegetation includes not only green and healthy photosynthetic vegetation but also a large amount of non-photosynthetic vegetation (NPV). Litters of vegetation in nature, branches and stems of dried vegetation, and dried crop stubbles left after harvesting crops and the like are included in the category of non-photosynthetic vegetation. Research shows that the non-photosynthetic vegetation can influence the hydrothermal transmission condition between the atmosphere and the soil, and further influence the surface thermal environment differentiation effect. Therefore, in some areas (especially, arid and semi-arid mining development dense areas disturbed by mining, grasslands, desertification areas and the like), a mixed pixel is only decomposed into 2 components of bare soil and photosynthetic vegetation, and the analysis of the relationship between the components and the space-time differential effect of the surface thermal environment is not reasonable.
For mining development intensive areas, the quantity of mining enterprises and the quantity of occupied land of the mining enterprises are relatively concentrated. The implementation of the mining activity directly peels off a large amount of stope surface soil in the mining area, and the large-area exposed earth surface is consistent with the spectral characteristics of the bare soil on the remote sensing image. Because improper excavation, cover occupation and land occupation caused by mining development directly change the thermal characteristics of the underlying surface of the area, the changes reduce vegetation evapotranspiration, increase the absorption of solar heat radiation by impervious materials, lead to the generation of a large amount of dry branches and dahurian patrinia leaves, and a large amount of crop stubbles left after the farmland crops are harvested can also cause certain influence on the ground surface thermal environment differential effect.
The quantitative evaluation of the regional earth surface thermal environment is a main means for deeply disclosing the formation mechanism of the earth surface thermal environment and is also an important basis for dealing with the accumulation effect of the earth surface thermal environment and formulating a mitigation strategy. In the current research on the surface thermal environment differential effect of the area, non-photosynthetic vegetation is used as a component, a mixed pixel of a medium-resolution multispectral remote sensing image is decomposed into 3 components of photosynthetic/non-photosynthetic vegetation and bare soil based on a pixel tri-component model to invert the vegetation coverage, and the evolution research on the analysis of the surface thermal environment spatial-temporal differential effect of the mixed pixel and the surface thermal environment of a mining development compact area is rarely reported.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a quantitative evaluation method for the space-time differential effect of the earth surface thermal environment based on a pixel three-component model, which is used for inverting the photosynthetic vegetation coverage (fractional coverage of PV, f) based on an NDVI-DFI pixel three-component modelPV) Fractional cover of NPV, fNPV) And fractional coverage of the bare soil (BS, f)BS) And quantitatively evaluating the relationship between the space-time differential characteristics of the regional surface thermal environment and the photosynthetic vegetation, the non-photosynthetic vegetation and the bare soil, wherein the method provides a quantitative reference basis for revealing the influence mechanism of the space-time heterogeneity of the surface thermal environment of the mining development compact district caused by the photosynthetic vegetation, the non-photosynthetic vegetation and the bare soil.
In order to solve the problems, the technical scheme adopted by the invention is as follows:
a quantitative evaluation method for a space-time differential effect of a surface thermal environment based on a pixel trisection model comprises the following steps:
s1, preprocessing the remote sensing images with the resolution in L andsat acquired at different times in a research area;
s2: carrying out land utilization classification on land features in a research area;
s3: carrying out precision verification on the land utilization classification result;
s4, inverting the surface temperature of the research area (L ST) by using a Radial Transfer Equation (RTE);
s5: performing precision verification on the surface temperature inversion result;
s6: fractional cover of PV, f based on NDVI-DFI pixel three-component model inversion photosynthetic vegetation coveragePV) Fractional cover of NPV, fNPV) And bare soil coverage (fractional coverage of BS, f)BS)；
S7: performing precision verification on the photosynthetic vegetation coverage, the non-photosynthetic vegetation coverage and the bare soil coverage obtained in the step S6;
s8: calculating the mean value of the ground surface temperature during the research period according to the multi-period ground surface temperature obtained in the step S4, and calculating the variation index of the thermal field of the mean value of the ground surface temperature during the research period by using a variation index formula of the thermal field, wherein the variation index formula of the thermal field is as follows:
HI＝(T-Tmean)/Tmean
in the formula, HI is a thermal field variation index, T is the surface temperature (DEG C), and Tmean is the surface temperature mean value (DEG C);
dividing the calculation result of the thermal field variation index of the ground surface temperature mean value in the research period into 5 grades, and comparing and analyzing the calculation result with the vegetation coverage mean value spatial distribution in the research period; HI is less than-0.03 and is a low-temperature zone, -HI more than or equal to 0.03 and less than 0.03 is a secondary low-temperature zone, HI more than or equal to 0.03 and less than 0.08 is a medium-temperature zone, HI more than or equal to 0.08 and less than 0.13 is a secondary high-temperature zone, and HI more than or equal to 0.13 is a high-temperature zone;
s9: obtaining vegetation coverage and earth surface temperature change image maps in 3 time periods of a research area by using a red-blue-difference image method (remote sensing change detection technology), and analyzing the time-space differential effect of the earth surface thermal environment of the research area along with the change of the vegetation coverage;
s10: analyzing the correlation between the surface temperature change and the change of the photosynthetic vegetation coverage, the non-photosynthetic vegetation coverage and the bare soil coverage during the research period, and performing correlation analysis on the surface temperature difference result and the vegetation coverage difference result during the research period by using a correlation analysis method;
s11: and (2) quantitatively analyzing a response rule of a space-time differential effect on the plant coverage degree and space-time change in the ground surface thermal environment of the mining development compact area, and respectively randomly extracting 500 sample points on mean images of the photosynthetic vegetation coverage degree, the non-photosynthetic vegetation coverage degree and the bare soil coverage degree in the research period by utilizing a random point creating function of Arcgis10.2 software so as to prevent the point coincidence constraint distance from being more than 60 meters. And (3) performing regression analysis by using a data correlation analysis tool of SPSS22.0 software and taking the ground surface temperature mean value during the research period as a dependent variable and respectively taking the photosynthetic vegetation coverage, non-photosynthetic vegetation coverage and bare soil coverage mean values as independent variables.
The further technical scheme is that in step S1, the preprocessing of the L andsat medium-resolution remote sensing images acquired at different times in the study area is performed according to the following steps:
s1-1, performing radiometric calibration and F L AASH atmospheric correction on visible light wave bands of resolution remote sensing images in L andsat acquired at different times, converting pixel gray values into radiance values, eliminating atmospheric influence and using the radiance values for inversion of vegetation coverage;
s1-2, performing radiometric calibration on the thermal infrared band of the remote sensing image with the resolution in L andsat acquired at different times for inversion of the earth surface temperature;
s1-3: and cutting the preprocessing result by using the vector boundary file of the research area to obtain an image of the research area.
Further technical solution is that, in step S2, the land use classification of the land features in the research area is specifically:
the land features in the research area are divided into 6 categories of forest land, cultivated land, industrial and mining land, residential land, bare land and water area by using a Random Forest (RF) algorithm based on Grid Search (GS) parameter optimization.
Further, according to a further technical scheme, in the step S3, the accuracy verification is performed on the land use classification result according to the following steps:
s3-1: acquiring a GF-1 image with the spatial resolution of 8m and the same period as the last period of remote sensing image for preprocessing; the pre-processing includes radiometric scaling, orthorectification, mosaicing, and cropping.
S3-2: and (4) carrying out precision verification on the classification result by utilizing the preprocessed GF-1 image, and calculating the overall classification precision and the kappa coefficient.
A further technical solution is that, in the step S4, the inverting the surface temperature by using the radiation transport equation specifically includes:
radiation transmission equation of thermal infrared radiation brightness value received by the satellite sensor:
Lλ＝[B(T)+(1-)Ld]τ+Lu
formula (III) LλRadiance value W/(m) obtained after radiometric calibration of thermal infrared band2·μmSr); the surface emissivity is used; b (T) is the black body heat radiation brightness W/(m)2·μm·sr)；LuAnd LdRespectively the atmospheric up and down radiation brightness W/(m)2Mum sr), tau is the transmittance of the atmosphere in the thermal infrared band, and atmospheric profile parameters tau, L can be obtained by inputting the longitude and latitude of the image center, the imaging time and other parameter information in the website provided by NASA (http:// atmcorruAnd Ld；
Surface emissivity formula:
＝0.004Pv+0.986
Pvfor vegetation coverage of each pixel, a pixel trisection model is used for inverting PvSubstituting the result into a formula to obtain; assuming that the atmosphere and the earth surface have Lambertian properties for thermal radiation, the radiation brightness B (T) of a black body with a temperature T in the thermal infrared band is
B(T)＝[Lλ-Lu-τ(1-)Ld]/τ
Inverting the ground real temperature T according to the Planck formula
T＝K2/ln(K1/B(T)+1)
For the 6 th band, K, of L andsat5TM image1＝607.76W/(m2·μm·sr)，K21260.56K, K for the 10 th band of L andsat8 TIRS images1＝774.89W/(m2·μm·sr)，K2＝1321.08K。
In a further technical solution, in step S5, the accuracy of the surface temperature inversion result is verified according to the following steps:
firstly, acquiring air temperature data of a meteorological station in a research area on the same day as each period of image, then comparing and analyzing the air temperature data with the surface temperature inversion value obtained in the step S4, if the surface temperature inversion value is consistent with the actually measured temperature trend of the meteorological station, indicating that the surface temperature inversion value is consistent with the actual temperature and the inversion precision is higher, otherwise, indicating that the inversion precision is low and the result is unreliable.
Further, in step S6, the method comprises inverting the photosynthetic vegetation coverage (fractional cover of PV, f)PV)、Fractional cover of NPV, fNPV) And fractional coverage of the bare soil (BS, f)BS)：
S6-1: establishing a feature space with NDVI and DFI as feature parameters;
NDVI is calculated as:
NDVI＝(Rnir-Rred)/(Rnir+Rred)
in the formula RredAnd RnirSpectral reflectances of a red band and a near-infrared band, respectively;
DFI calculation formula:
DFI＝100×(1-Rswir2/Rswir1)×Rred/Rnir
in the formula Rswir1、Rswir2、RredAnd RnirSpectral reflectances of a short wave infrared 1 band, a short wave infrared 2 band, a red band and a near infrared band, respectively;
s6-2: because each wave band of the multispectral image has serious noise, a minimum noise separation and transformation (MNF Rotation) tool is adopted to reduce the dimension of image data at each stage, the first 6 wave bands of the image at each stage are selected, the iteration frequency is set to be 2000, and the threshold coefficient is set to be 3;
s6-3: utilizing the MNF Rotation calculation result to generate a pure pixel index PPI; taking the pixel with PPI larger than 5 and close to each vertex of the feature space scatter diagram as a pure end member, and taking the average index value of each vertex pure end member as the characteristic value of the corresponding end member;
s6-4: and respectively substituting the 3 end member characteristic values into a pixel trisection model tool of an ENVI5.3 remote sensing image processing software platform, and performing inversion to obtain a vegetation coverage RGB space distribution diagram of the research area.
In step S7, a fractional cover of PV, f is obtained by performing inversion on the NDVI-DFI pixel three-component model by an actual measurement methodPV) And fractional vegetation coverage (of NPV, f)NPV) And fractional coverage of the bare soil (BS, f)BS) And carrying out precision verification.
Adopt the produced beneficial effect of above-mentioned technical scheme to lie in: the method disclosed by the invention fully utilizes the characteristics of easiness in operation, low cost and high space-time resolution of remote sensing data, fully utilizes the advantages that a vegetation coverage algorithm based on pixel three-component model inversion can estimate not only green photosynthetic vegetation covered at high density, but also non-green photosynthetic vegetation and bare soil coverage, and improves the accuracy and reliability of area scale vegetation coverage calculation, and provides a method for quantitatively evaluating the space-time differential effect of the ground surface thermal environment of a mining development compact area based on a pixel three-component model by combining the vegetation types in a research area, so that the vegetation coverage is improved to serve as the accuracy, reliability and credibility for measuring the main indexes of regional ground surface thermal environment change, and the method has strong universality and adaptability and is easy to popularize and apply.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a plot of land use classifications and locations for a research area;
FIG. 3 is a comparison graph of the inversion result of the earth surface temperature of the 5-phase image and the actually measured air temperature data of the weather station in the same-phase research area;
FIG. 4 is a characteristic space diagram of NDVI-DFI in 2018 of the study area;
FIG. 5 is an RGB spatial distribution diagram of the 2018 inversion result of vegetation coverage;
FIG. 6 shows a typical mine field f of 2018PV、fNPVAnd fBSA linear fit plot of the inverted values and the measured values;
FIG. 7 is a distribution diagram of the earth's surface temperature mean temperature grade based on the thermal field variation index in 2018-;
FIG. 8 is a mean spatial distribution diagram of vegetation coverage in the year 2000-2018;
FIG. 9 is a graph of the vegetation coverage and the change of the surface temperature in 3 periods (2008, 2018, and 2018).
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
A method for quantitatively evaluating the spatiotemporal differential effect of the surface thermal environment of a mining development compact area based on a pixel trisection model established by a remote sensing technology comprises the steps of firstly preprocessing a resolution remote sensing image in a selected research area 2000-year L andsat in the 5 th period of 2018 by using the remote sensing technology, carrying out radiometric calibration and atmospheric correction on a visible light waveband, converting a pixel gray value into a radiance value, eliminating atmospheric influence, carrying out land utilization classification and vegetation coverage inversion, independently carrying out radiometric calibration on a thermal infrared waveband for surface temperature inversion, carrying out vegetation coverage and surface temperature inversion on the resolution remote sensing image in the L andsat the 5 th period of 2018 after the research area is preprocessed based on an NDVI-DFI pixel trisection model and a radiometric transmission method respectively, carrying out precision verification on the vegetation coverage and surface temperature inversion results respectively by using an actual measurement method and meteorological station data, and finally quantitatively analyzing the response rule of the spatiotemporal differential disturbance effect of the surface thermal environment of the mining development compact area by means of a hierarchical statistics method, a red-blue difference image method, a correlation analysis method and a meteorological station analysis method respectively.
Referring to fig. 1, the specific steps are as follows:
s1, preprocessing the remote sensing image with the resolution (the resolution is 30m) in the 5-stage L andsat of the research area 2000-2018.
The resolution remote sensing image data in the 5-stage research area L ardsat are obtained and are shown in table 1.
TABLE 1 remote sensing image data
The remote sensing image with the resolution in the 5 th period L andsat of 2000-year 2018 is preprocessed according to the following steps.
S1-1, performing radiometric calibration and F L AASH atmospheric correction on the visible light wave band of the resolution remote sensing image in L andsat 5-stage of 2018, converting the pixel gray value into a radiance value, eliminating atmospheric influence and using the radiance value for vegetation coverage inversion.
S1-2, performing radiometric calibration on the thermal infrared band (the 6 th band) of the L andsat5TM image in the 3 rd stage of 9/6 th year, 8/14 th year, and 9/12 th year in 2008 on the L andsat8 TIRS image (the radiometric calibration deviation of the band 11 is large, so that the surface temperature is inverted by using the 10 th band) independently for surface temperature inversion.
S1-3: and cutting the preprocessing result by utilizing the vector boundary file of the mining development compact area to obtain the image of the research area.
S2: and carrying out land utilization classification on land features in the research area.
The land features in the research area are divided into 6 categories of forest land, cultivated land, industrial and mining land, residential land, bare land and water area by using a Random Forest (RF) algorithm based on Grid Search (GS) parameter optimization.
S3: and carrying out precision verification on the land utilization classification result.
S3-1: acquiring a GF-1 image with the spatial resolution of 8m and the same period as the last period of remote sensing image for preprocessing; the pre-processing includes radiometric scaling, orthorectification, mosaicing, and cropping.
S3-2: and (4) carrying out precision verification on the classification result by utilizing the preprocessed GF-1 image, and calculating the overall classification precision and the kappa coefficient.
S4 inversion of the surface temperature of the study area (L ST) using the Radial Transfer Equation (RTE).
Radiation transmission equation of thermal infrared radiation brightness value received by the satellite sensor:
Lλ＝[B(T)+(1-)Ld]τ+Lu
formula (III) LλRadiance value W/(m) obtained after radiometric calibration of thermal infrared band2μ m · sr); the surface emissivity is used; b (T) is the black body heat radiation brightness W/(m)2·μm·sr)；LuAnd LdRespectively atmospheric up and down radiation brightnessW/(m2The atmospheric profile parameters tau, L can be obtained by inputting the longitude and latitude of the image center, the imaging time and other parameter information in the website (http:// atmcorr. gsfc. NASA. gov /) provided by NASAuAnd Ld. Table 2 shows the atmospheric profile parameters of the phase 5 images.
TABLE 2 atmospheric Profile parameters of phase 5 images of the study area
Surface emissivity formula:
＝0.004Pv+0.986
Pvfor vegetation coverage of each pixel, a pixel trisection model is used for inverting PvAnd substituting the result into a formula to obtain the product.
Assuming that the atmosphere and the earth surface have Lambertian properties for thermal radiation, the radiation brightness B (T) of a black body with a temperature T in the thermal infrared band is
B(T)＝[Lλ-Lu-τ(1-)Ld]/τ
Inverting the ground real temperature T according to the Planck formula
T＝K2/ln(K1/B(T)+1)
For the 6 th band, K, of L andsat5TM image1＝607.76W/(m2·μm·sr)，K21260.56K, K for the 10 th band of L andsat8 TIRS images1＝774.89W/(m2·μm·sr)，K2＝1321.08K。
S5: and carrying out precision verification on the surface temperature inversion result.
Firstly, acquiring air temperature data of a weather station in a research area on the same day as the phase 5 image, then comparing and analyzing the air temperature data with the surface temperature inversion value obtained in the step S4, if the surface temperature inversion value is consistent with the actually measured temperature trend of the weather station, indicating that the surface temperature inversion value is consistent with the actual temperature and the inversion precision is higher, otherwise, indicating that the inversion precision is low and the result is unreliable.
FIG. 3 shows that the inversion result of the earth surface temperature of the 5-phase image is compared with the actually measured air temperature data of the weather station in the same-phase research area, the variation trend of the earth surface temperature data inverted by the 5-phase image is basically consistent with that of the same-phase air temperature data, the inversion result is in accordance with the actual situation, the precision is high, and the method can be used for the next study on the temporal-spatial heterogeneity of the earth surface thermal environment.
S6: fractional cover of PV, f based on NDVI-DFI pixel three-component model inversion photosynthetic vegetation coveragePV) Fractional cover of NPV, fNPV) And bare soil coverage (fractional coverage of BS, f)BS)；
S6-1: establishing a feature space with NDVI and DFI as feature parameters; the NDVI-DFI feature space in the research area 2018 is shown in FIG. 4.
NDVI is calculated as:
NDVI＝(Rnir-Rred)/(Rnir+Rred)
in the formula RredAnd RnirThe spectral reflectivities of the red band and the near infrared band, respectively.
DFI calculation formula:
DFI＝100×(1-Rswir2/Rswir1)×Rred/Rnir
in the formula Rswir1、Rswir2、RredAnd RnirRespectively, short wave infrared 1 band, short wave infrared 2 band, red band and near infrared band.
S6-2: because each wave band of the multispectral image has serious noise, the dimension of 5-stage image data is reduced by adopting a minimum noise fraction (MNF Rotation) tool, the first 6 wave bands of each-stage image are selected, the iteration frequency is set to be 2000, and the threshold coefficient is set to be 3.
S6-3: utilizing the MNF Rotation calculation result to generate a pure pixel index PPI; and taking the pixel with PPI larger than 5 and close to each vertex of the feature space scatter diagram as a pure end member, and taking the average index value of each vertex pure end member as the characteristic value of the corresponding end member.
S6-4: and respectively substituting the 3 end member characteristic values into a pixel trisection model tool of an ENVI5.3 remote sensing image processing software platform, and performing inversion to obtain a vegetation coverage RGB space distribution diagram of the research area. Taking 2018 as an example, the RGB spatial distribution of the inversion result of vegetation coverage is shown in fig. 5.
S7: and D, performing precision verification on the photosynthetic vegetation coverage, the non-photosynthetic vegetation coverage and the bare soil coverage obtained in the step S6.
In this embodiment, a fractional cover of PV, f is obtained by performing an inversion on a three-component model based on NDVI-DFI pixels by an actual measurement methodPV) Fractional cover of NPV, fNPV) And fractional coverage of the bare soil (BS, f)BS) And carrying out precision verification.
Selecting a typical mining area in a research area, dispatching a plurality of actually measured personnel with a certain geographical theoretical basis and field working experience in the same period or near period of images in each period, selecting a plurality of grids corresponding to remote sensing image pixels in the mining area in the field, and respectively recording longitude and latitude coordinates of central points of a plurality of grid units, if the actually measured personnel 5 with the field working experience in 9.9.2018 dispatch the certain geographical theoretical basis and select 10 grids of 30m × 30m in the mining area, respectively recording the longitude and latitude coordinates of the central points of the 10 grid units, wherein the vegetation coverage around the central points of the grids has certain difference and contains 0-100% of vegetation coverage range as much as possible, and each grid is observed by 5 observers standing at the central points of the grid units in turn respectively for visual observation and respectively recording the observation results.
Extracting corresponding position pixel f of the sampling point on the spot according to the principle of consistent longitude and latitudePV、fNPVAnd fBSAnd (3) performing linear fitting on the measured value and the inverted value. Typical mining area in research area 2018 fPV、fNPVAnd fBSThe inversion values and the measured values are linearly fitted as in fig. 6.
S8: calculating the mean value of the earth surface temperature in the research area between 2000 and 2018 years according to the earth surface temperature in the multi-period obtained in the step S4, and calculating the mean value of the earth surface temperature in the research area between 2000 and 2018 years by using a thermal field variation index formula, wherein the thermal field variation index formula comprises the following steps:
HI＝(T-Tmean)/Tmean
in the formula, HI is a thermal field variation index, T is the surface temperature (DEG C), and Tmean is the surface temperature mean (DEG C).
The calculation result of the thermal field variation index of the mean surface temperature value in the year of 2000 plus 2018 is divided into 5 grades, HI is less than-0.03 and is a low-temperature region, -HI is more than or equal to 0.03 and less than 0.03 and is a secondary low-temperature region, HI is more than or equal to 0.03 and less than 0.08 and is a medium-temperature region, HI is more than or equal to 0.08 and less than 0.13 and is a secondary high-temperature region, and HI is more than or equal to 0.13. As shown in fig. 7. Compared with the mean spatial distribution of vegetation coverage (fig. 8) between 2000 and 2018, the regions with higher photosynthetic vegetation coverage, accompanied regions of photosynthetic vegetation and non-photosynthetic vegetation components, regions with higher non-photosynthetic vegetation distribution, accompanied regions of non-photosynthetic vegetation and bare soil components, and regions with higher bare soil distribution in the study area during 19 years have a large-area overlapping phenomenon with the low-temperature region, the sub-low-temperature region, the middle-temperature region, the sub-high-temperature region, and the high-temperature region in the ground surface temperature mean thermal field variation index temperature grade distribution map (fig. 7), respectively, which shows that the spatial distribution of vegetation coverage influences the spatial distribution of the ground surface temperature to a certain extent. The temperature of the ground surface of the area covered by the photosynthetic vegetation is lowest, the temperature of the ground surface of the area where the photosynthetic vegetation and non-photosynthetic vegetation are associated is second lowest, the temperature of the ground surface of the area covered by the non-photosynthetic vegetation is intermediate, the temperature of the ground surface of the area where the non-photosynthetic vegetation and bare soil are associated is second highest, and the temperature of the ground surface of the area covered by the bare soil is highest. Statistics shows that the low-temperature area and the secondary low-temperature area in the research area are large, the high-temperature area and the secondary high-temperature area are small, the bare soil area in the high-temperature area with the smallest occupation ratio is the highest, the high-temperature aggregation effect is very obvious, and the surface temperature average value reaches 36.68 ℃ at most in 19 years.
S9: a red-blue-difference image method (remote sensing change detection technology) is utilized to obtain vegetation coverage and earth surface temperature change image maps in 3 time periods (2008, 2008 2018 and 2018), and as shown in FIG. 9, the time-space difference effect of the earth surface thermal environment of the research area changing along with the vegetation coverage is analyzed.
The red areas represent areas of elevated ground temperature or vegetation coverage, the blue areas represent areas of reduced ground temperature or vegetation coverage, and the white areas represent areas of constant coverage. In 2000-2008, a large amount of green land is occupied due to the increase of mining land and urban industrial field, the value of the ground surface temperature of a research area is gradually increased in the years due to the gradual increase of the areas of bare soil and non-photosynthetic vegetation, and 99% of the area of a difference image of the ground surface temperature is red in 2000-2008. In 2008-2018, the ecological environment quality is obviously improved in deep treatment, so that the photosynthetic vegetation is gradually increased in the years, the values of the ground surface temperature are correspondingly reduced due to the gradual reduction of the non-photosynthetic vegetation and the bare soil, and 27% of the area in a difference image of the ground surface temperature in 2008-2018 is displayed as blue. In 2000-2008, the damage of the earth surface vegetation is serious, although the ecological restoration work is emphasized in 2008-2018, the effect is displayed slowly, so that the earth surface temperature in 2018 is still higher than 2000, and 99% of the area in the earth surface temperature difference image in 2000-2018 is red.
From the difference map, it can be seen that the surface temperature is greatly affected by the vegetation coverage. In the difference map of 3 periods of vegetation coverage, fPVArea reduced region and fNPVAnd fBSThe area of increased area corresponds to the area of increased temperature in the 3 period difference chart of the surface temperature; f. ofPVArea increase and fNPVAnd fBSThe area of reduced area corresponds to the area of reduced temperature in the difference plot for 3 periods of surface temperature. It can be seen that fPVIs inversely related to the surface temperature, fNPVAnd fBSPositively correlated with surface temperature, i.e. fPVWill cause the surface temperature to decrease, and fNPVAnd fBSThis increase in surface temperature will result in a concomitant increase in surface temperature.
S10: analyzing the correlation between the surface temperature change and the change of the photosynthetic vegetation coverage, the non-photosynthetic vegetation coverage and the bare soil coverage during the research period, and performing correlation analysis on the surface temperature difference result and the vegetation coverage difference result during the research period by using a correlation analysis method;
the correlation matrix results show that the earth surface temperature change and the vegetation coverage (f) in 2000-2018PV、fNPV、fBS) There is a strong correlation between the changes in. It is composed ofMiddle 19 years fPV、fNPVAnd fBSThe correlation with the change of the earth surface temperature is-0.81, 0.72 and 0.90 respectively, which shows that the change of the vegetation coverage has a significant influence on the change of the earth surface temperature. During 19 years fPV、fNPV、fBSThe influence degrees of the change degrees on the surface temperature are ranked from large to small as fBS>fPV>fNPV. The development of mining industry causes large-area bare earth surface, and is a main influence factor of high-temperature aggregation effect in the earth surface heat environment of research area in 19 years
S11: and (2) quantitatively analyzing a response rule of a space-time differential effect on plant coverage degree and space-time change in a ground surface thermal environment of a mining development compact area, and randomly extracting 500 sample points on mean images of photosynthetic vegetation coverage, non-photosynthetic vegetation coverage and bare soil coverage in 2000-2018 by utilizing a random point creating function of Arcgis10.2 software, so that a constraint distance for preventing points from being overlapped is more than 60 meters. Using the data correlation analysis tool of SPSS22.0 software, taking the mean value of the earth surface temperature in 2000-2018 as a dependent variable and the mean values of the photosynthetic vegetation coverage, the non-photosynthetic vegetation coverage and the bare soil coverage as independent variables to perform regression analysis
The results show that fPV、fNPVAnd fBSThe method is closely related to the differential effect of the surface thermal environment, and the regression equations are respectively as follows:
LST＝-5.16×fPV+29.79
LST＝6.05×fNPV+24.64
LST＝9.79×fBS+26.45
the correlation coefficients r are-0.60, 0.47 and 0.65, respectively. From the regression equation obtained, fPV、fNPVAnd fBSRespectively presenting an extremely significant negative correlation, an insignificant positive correlation and an extremely significant positive correlation with the earth surface temperature; study area fPVWhen the ground surface temperature is increased by 10 percent, the ground surface temperature is correspondingly reduced by about 0.52 ℃, fNPVWhen the ground surface temperature is increased by 10 percent, the ground surface temperature is correspondingly increased by about 0.61 ℃, fBSEvery 10% of the elevation will correspondingly raise the earth's surface temperature by about 0.98 deg.C.
The quantitative analysis result shows that the photosynthetic vegetation has the effect of reducing the temperature of the earth surface, and the non-photosynthetic vegetation and the bare soil have the effect of increasing the temperature of the earth surface. The cooling effect of the photosynthetic vegetation on the ground surface temperature is less than the heating effect of the non-photosynthetic vegetation and the bare soil on the ground surface temperature. The non-photosynthetic vegetation has less warming effect on the surface temperature than bare soil. Therefore, in order to improve the high-temperature aggregation effect of the ground surface thermal environment in the research area and improve the quality of the overall ecological environment in the mining development intensive area, a land regulation measure of mining and reclamation is adopted for industrial and mining land, a large amount of drought-resistant and high-temperature-resistant green vegetation or economic crops are planted in large-area exposed ground surfaces such as a closed mining area, a tailing pond and the like, non-photosynthetic vegetation such as withered vegetation, litter, crop stubbles and the like is cleaned in time, the proportion of the non-photosynthetic vegetation associated with the components of the photosynthetic vegetation is reduced as much as possible, and the effect of reducing the high-temperature aggregation effect of the ground surface thermal environment by the photosynthetic vegetation is. Meanwhile, the small-area bare earth surface in the reclamation area is timely replanted with green vegetation so as to achieve the purposes of reducing the bare earth area to the maximum extent and avoiding the high-temperature heat effect aggregation phenomenon.
The above results show that the method for quantitatively evaluating the time-space differential effect of the earth surface thermal environment of the mining development compact area based on the pixel element three-component model is effective and feasible.
Although the invention has been described herein with reference to a number of illustrative embodiments thereof, it should be understood that numerous other modifications and embodiments can be devised by those skilled in the art that will fall within the spirit and scope of the principles of this disclosure. More specifically, various variations and modifications are possible in the component parts and/or arrangements of the subject combination arrangement within the scope of the disclosure, the drawings and the appended claims. In addition to variations and modifications in the component parts and/or arrangements, other uses will also be apparent to those skilled in the art.
Claims (8)
1. A quantitative evaluation method for a space-time differential effect of a surface thermal environment based on a pixel trisection model is characterized by comprising the following steps:
s1, preprocessing the remote sensing images with the resolution in L andsat acquired at different times in a research area;
s2: carrying out land utilization classification on land features in a research area;
s3: carrying out precision verification on the land utilization classification result;
s4: inverting the ground surface temperature of the research area by using a radiation transmission equation method;
s5: performing precision verification on the surface temperature inversion result;
s6: inverting the photosynthetic vegetation coverage, the non-photosynthetic vegetation coverage and the bare soil coverage based on the NDVI-DFI pixel three-section model;
s7: performing precision verification on the photosynthetic vegetation coverage, the non-photosynthetic vegetation coverage and the bare soil coverage obtained in the step S6;
s8: calculating the mean value of the ground surface temperature during the research period according to the multi-period ground surface temperature obtained in the step S4, and calculating the variation index of the thermal field of the mean value of the ground surface temperature during the research period by using a variation index formula of the thermal field, wherein the variation index formula of the thermal field is as follows:
HI＝(T-Tmean)/Tmean
in the formula, HI is a thermal field variation index, T is the surface temperature (DEG C), and Tmean is the surface temperature mean value (DEG C);
dividing the calculation result of the thermal field variation index of the ground surface temperature mean value in the research period into 5 grades, and comparing and analyzing the calculation result with the vegetation coverage mean value spatial distribution in the research period;
s9: obtaining vegetation coverage and earth surface temperature change images of the research area in 3 time periods by using a red-blue-difference image method, and analyzing the time-space differential effect of the earth surface thermal environment of the research area along with the change of the vegetation coverage;
s10: analyzing the correlation between the surface temperature change and the change of the photosynthetic vegetation coverage, the non-photosynthetic vegetation coverage and the bare soil coverage during the research period, and performing correlation analysis on the surface temperature difference result and the vegetation coverage difference result during the research period by using a correlation analysis method;
s11: the response rule of the time-space differential effect of the ground surface thermal environment of the mining development compact area on the time-space change of the vegetation coverage is quantitatively analyzed, 500 sample points are respectively and randomly extracted on mean images of the photosynthetic vegetation coverage, the non-photosynthetic vegetation coverage and the bare soil coverage during the research period by utilizing the random point creating function of Arcgis10.2 software, a data correlation analysis tool of SPSS22.0 software is utilized, the mean value of the ground surface temperature during the research period is taken as a dependent variable, and the mean values of the photosynthetic vegetation coverage, the non-photosynthetic vegetation coverage and the bare soil coverage are respectively taken as independent variables for regression analysis.
2. The quantitative evaluation method for the spatiotemporal differentiation effect of the earth surface thermal environment based on the pixel trisection model as claimed in claim 1, wherein in step S1, preprocessing the L andsat medium resolution remote sensing images acquired at different times in the research area according to the following steps:
s1-1, performing radiometric calibration and F L AASH atmospheric correction on visible light wave bands of resolution remote sensing images in L andsat acquired at different times, converting pixel gray values into radiance values, eliminating atmospheric influence and using the radiance values for inversion of vegetation coverage;
s1-2, performing radiometric calibration on the thermal infrared band of the remote sensing image with the resolution in L andsat acquired at different times for inversion of the earth surface temperature;
s1-3: and cutting the preprocessing result by using the vector boundary file of the research area to obtain an image of the research area.
3. The quantitative evaluation method for the spatiotemporal differentiation effect of the earth surface thermal environment based on the pixel trisection model according to claim 1, wherein in step S2, the land utilization classification of the land features in the research area is specifically:
and dividing the ground objects in the research area into 6 categories of forest land, cultivated land, industrial and mining land, residential land, bare land and water area by using a random forest algorithm based on grid search parameter optimization.
4. The quantitative evaluation method for the spatiotemporal differentiation effect of the earth surface thermal environment based on the pixel trisection model as claimed in claim 1, wherein in said step S3, the accuracy verification is performed on the land utilization classification result according to the following steps:
s3-1: acquiring a GF-1 image with the spatial resolution of 8m and the same period as the last period of remote sensing image for preprocessing;
s3-2: and (4) carrying out precision verification on the classification result by utilizing the preprocessed GF-1 image, and calculating the overall classification precision and the kappa coefficient.
5. The quantitative evaluation method for the time-space diversity effect of the earth surface thermal environment based on the pixel three-component model according to claim 1, wherein in the step S4, the inversion of the earth surface temperature by using the radiation transmission equation method specifically comprises:
radiation transmission equation of thermal infrared radiation brightness value received by the satellite sensor:
Lλ＝[B(T)+(1-)Ld]τ+Lu
formula (III) LλRadiance value W/(m) obtained after radiometric calibration of thermal infrared band2μ m · sr); the surface emissivity is used; b (T) is the black body heat radiation brightness W/(m)2·μm·sr)；LuAnd LdRespectively the atmospheric up and down radiation brightness W/(m)2Mum sr), tau is the transmittance of the atmosphere in the thermal infrared band, and atmospheric profile parameters tau, L can be obtained by inputting the longitude and latitude of the image center, the imaging time and other parameter information in the website provided by NASA (http:// atmcorruAnd Ld；
Surface emissivity formula:
＝0.004Pv+0.986
Pvfor vegetation coverage of each pixel, a pixel trisection model is used for inverting PvSubstituting the result into a formula to obtain; assuming that the atmosphere and the earth surface have Lambertian properties for thermal radiation, the radiation brightness B (T) of a black body with a temperature T in the thermal infrared band is
B(T)＝[Lλ-Lu-τ(1-)Ld]/τ
Inverting the ground real temperature T according to the Planck formula
T＝K2/ln(K1/B(T)+1)
For the 6 th band, K, of L andsat5TM image1＝607.76W/(m2·μm·sr)，K21260.56K, K for the 10 th band of L andsat8 TIRS images1＝774.89W/(m2·μm·sr)，K2＝1321.08K。
6. The quantitative evaluation method for the time-space diversity effect of the earth surface thermal environment based on the pixel three-component model according to claim 1, wherein in step S5, the accuracy verification is performed on the inversion result of the earth surface temperature according to the following steps:
firstly, acquiring air temperature data of a meteorological station in a research area on the same day as each period of image, then comparing and analyzing the air temperature data with the surface temperature inversion value obtained in the step S3, if the surface temperature inversion value is consistent with the actually measured temperature trend of the meteorological station, indicating that the surface temperature inversion value is consistent with the actual temperature and the inversion precision is higher, otherwise, indicating that the inversion precision is low and the result is unreliable.
7. The quantitative evaluation method for the space-time differential effect of the earth surface thermal environment based on the pixel three-component model according to claim 1, wherein in step S6, the photosynthetic vegetation coverage, the non-photosynthetic vegetation coverage and the bare soil coverage are inverted according to the following steps:
s6-1: establishing a feature space with NDVI and DFI as feature parameters;
NDVI is calculated as:
NDVI＝(Rnir-Rred)/(Rnir+Rred)
in the formula RredAnd RnirSpectral reflectances of a red band and a near-infrared band, respectively;
DFI calculation formula:
DFI＝100×(1-Rswir2/Rswir1)×Rred/Rnir
in the formula Rswir1、Rswir2、RredAnd RnirSpectral reflectances of a short wave infrared 1 band, a short wave infrared 2 band, a red band and a near infrared band, respectively;
s6-2: adopting a minimum noise separation transformation tool to carry out dimension reduction on image data of each stage, selecting the first 6 wave bands of each stage of image, setting the iteration frequency to be 2000 and the threshold coefficient to be 3;
s6-3: utilizing the MNF Rotation calculation result to generate a pure pixel index PPI; and taking the pixel with PPI larger than 5 and close to each vertex of the feature space scatter diagram as a pure end member, and taking the average index value of each vertex pure end member as the characteristic value of the corresponding end member.
S6-4: and respectively substituting the 3 end member characteristic values into a pixel trisection model tool of an ENVI5.3 remote sensing image processing software platform, and performing inversion to obtain a vegetation coverage RGB space distribution diagram of the research area.
8. The method for quantitatively evaluating the space-time differential effect in the earth surface thermal environment based on the pixel three-component model according to claim 1, wherein in step S6, the photosynthetic vegetation coverage, the non-photosynthetic vegetation coverage and the bare soil coverage inverted based on the NDVI-DFI pixel three-component model are subjected to precision verification by using a measurement method.
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