CN110472357B - Construction method and application method of remote sensing comprehensive ecological model RSIEI for evaluating differential effect of surface thermal environment in mining development dense area - Google Patents
Construction method and application method of remote sensing comprehensive ecological model RSIEI for evaluating differential effect of surface thermal environment in mining development dense area Download PDFInfo
- Publication number
- CN110472357B CN110472357B CN201910772939.2A CN201910772939A CN110472357B CN 110472357 B CN110472357 B CN 110472357B CN 201910772939 A CN201910772939 A CN 201910772939A CN 110472357 B CN110472357 B CN 110472357B
- Authority
- CN
- China
- Prior art keywords
- band
- rsiei
- remote sensing
- ecological
- lst
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
Abstract
The invention relates to a construction method and application of a remote sensing comprehensive ecological model RSIEI for evaluating the differential effect of the surface heat environment of a mining development intensive area. The construction method comprises the following steps: inverting the surface temperature of the research area by using a radiation conduction equation method; characterizing surface vegetation biomass with photosynthetic vegetation coverage parameters and non-photosynthetic vegetation coverage parameters; characterizing soil moisture content with soil moisture index parameters; the bare soil and the building index parameters are used for representing the density degree of the bare soil and the building; and integrating four basic ecological parameters based on a principal component analysis method to construct RSIEI. The application of the method comprises the following steps: by means of a statistical method, the contributions of four ecological parameters to the differential effect of the surface heat environment in mining development dense areas are quantitatively analyzed. The invention is not only suitable for evaluating the thermal environment differential effect of mining development dense cities, but also is equally feasible for evaluating the surface thermal environment differential effect of small-scale mining development dense villages and towns taking administrative boundaries as boundaries.
Description
Technical Field
The invention relates to an ecological assessment model based on a remote sensing technology, in particular to a construction method and application of a remote sensing comprehensive ecological model RSIEI for assessing the differential effect of the earth surface thermal environment in a mining development intensive area.
Background
Rapid industrialization and urbanization have become a major phenomenon worldwide in recent decades, and although little research has been done on the heat island effect as an independent land form by the industrial site, its overall contribution to urban heat island values is limited, the industrial site tends to be the most surface temperature concentration zone, and the hot zones inside the city are essentially concentrated in the industrial zone.
Cities rising by energy are on the way of producing facilities such as mining areas, industrial parks and the like in large quantity, and meanwhile, the quick development of local economy, culture and the like is promoted, and meanwhile, ecological environment problems are brought. For example, in mining development dense areas, a great deal of environmental problems are caused and induced by development of mineral resources, vegetation is destroyed, land resources are severely reduced, the atmospheric environment and the water environment are worsened, the changes reduce vegetation evapotranspiration, the absorption of solar heat radiation by impermeable materials is increased, and the occurrence of the surface heat environment gathering effect is caused.
The mining development dense area refers to an area which is relatively concentrated in terms of the number of mine enterprises and the amount of land occupied by the mine enterprises. In recent years, the analysis and research of the surface thermal environment are attracting more and more attention as an important component for improving the ecological environment in mining development dense areas. Therefore, if the surface temperature and the surface related parameters of the mining development dense region can be accurately and rapidly obtained, the regional ecological thermal environment differentiating effect can be quantitatively evaluated, and important guiding significance can be provided for the industrial layout, mineral development, ecological protection and sustainable development of the mining development dense city.
Surface Temperature (LST) is a manifestation of surface energy balance and is widely used in the study of urban thermal environments. Previously, many researchers have used ground observation stations or expensive precision instruments to measure the surface temperature, while using the remote sensing thermal infrared band to detect the surface temperature is a simple and cost effective method.
The variation of the surface temperature in urban areas is mainly related to different land utilization/land coverage classifications and land surface ecology conditions. Many scholars have focused on studying quantitative relationships between land surface biophysical parameters and the surface thermal environment. In the past, the earth surface heat environment is evaluated by single or multiple ecological parameters, for example, the research based on the relation between vegetation index or vegetation coverage (Vegetation Fractional Coverage, VFC) and earth surface temperature is quite large, and the research conclusion is basically consistent, namely, the vegetation and earth surface temperature are in a negative correlation relation, and the vegetation has the function of reducing the earth surface temperature. There are also scholars studying the relationship between the surface temperature of an area and several surface biophysical parameters, and the result shows that there is a strong relationship between the surface temperature and the surface biophysical parameters, wherein the study of normalized building index (NDBI), normalized vegetation index (NDVI), normalized water index (NDWI) and the like is more common. However, since the thermal properties of land surfaces are closely related to numerous biophysical properties, it is not sufficient to study the effect of only a single surface biophysical parameter on the surface temperature. In the prior study, four biophysical parameters are selected to construct a remote sensing ecological model to evaluate the case of regional ecological environment quality, and the model uses the surface temperature as a parameter to construct the model, so that the aim of evaluating the regional surface thermal environment by combining the four biophysical parameters can not be achieved. Therefore, by selecting four indexes which are different from the model and do not contain surface temperature parameters, a remote sensing comprehensive ecological model (RSIEI) is constructed, and research on the comprehensive effect of various types of biophysical components on the differential effect of the thermal environment of the evaluation area is necessary. The invention takes the mining development dense region as a research region, is helpful for better understanding the comprehensive relationship between various types of biophysical components and the differential effect of the surface heat environment in the mining development dense region, and helps decision makers to formulate effective surface heat environment treatment policies.
Disclosure of Invention
The invention aims to solve the technical problem of providing a construction method and application of a remote sensing comprehensive ecological model RSIEI for evaluating the differential effect of the surface thermal environment of a mining development dense area based on remote sensing data and technology. The model is not only suitable for evaluating the thermal environment differential effect of mining development dense cities, but also is feasible for evaluating the surface thermal environment differential effect of small-scale mining development dense villages and towns taking administrative boundaries as boundaries.
The technical scheme adopted for solving the technical problems is as follows:
the construction method of the remote sensing comprehensive ecological model RSIEI for evaluating the differential effect of the surface thermal environment of the mining development dense area comprises the following steps:
step S1: preprocessing the remote sensing image of the research area;
step S2: inverting the surface temperature LST of the research area by using a radiation conduction equation method;
step S3: selecting a photosynthetic vegetation coverage fPV parameter and a non-photosynthetic vegetation coverage fNPV parameter to characterize surface vegetation biomass;
in the step S3, the method selects the photosynthetic vegetation coverage fPV parameter and the non-photosynthetic vegetation coverage fNPV parameter to characterize the surface vegetation biomass, and includes the steps of:
(3-1) inverting fPV and fNPV using a pel three-part model, wherein non-photosynthetic vegetation NPV information is extracted from the dry fuel index DFI and verifying its suitability in the study area; photosynthetic vegetation PV is represented by a more mature normalized vegetation index NDVI; establishing a feature space with NDVI and DFI as feature parameters, and then extracting feature values of PV and NPV ends; the formula is as follows:
(3-2)NDVI＝(Band(NIR)-Band(R))/(Band(NIR)+Band(R))
wherein: band (NIR) is the spectral reflectance in the near infrared Band); band (R) is the spectral reflectance of the red Band;
(3-3)DFI＝100*(1-Band(SWIR2)/Band(SWIR1))*Band(R)/Band(NIR)
wherein: band (SWIR 2) is the spectral reflectance of the short wave infrared 2 Band; band (SWIR 1) is the spectral reflectance of the short wave infrared 1 Band;
(3-4) eliminating noise influence existing in each wave band of the multispectral image by adopting a minimum noise separation method, extracting end member characteristic values by utilizing a relatively mature pure pixel index method, and reducing errors caused by manual selection;
step S4: the soil moisture index parameter 1-TVDI is used for representing the soil moisture content, and the TVDI is the temperature vegetation drought index;
step S5: the bare soil and the building density are characterized by the bare soil and a building index parameter NDBI;
step S6: integrating four basic ecological parameters based on a principal component analysis method to construct a remote sensing comprehensive ecological model RSIEI;
in the step S6, integrating four basic ecological parameters based on a principal component analysis method to construct a remote sensing comprehensive ecological model RSIEI, including the steps of:
(6-1) first for four basic ecological parameters: the photosynthetic vegetation coverage fPV, the non-photosynthetic vegetation coverage fNPV, the soil humidity 1-TVDI, bare soil and the building concentration NDBI inversion result are standardized, so that the value range of the result is fixed within the range of [0,1], and the formula is as follows:
NI＝(I-Imin)/(Imax-Imin)
wherein I is the numerical value of four basic ecological parameters;
imax and Imin are the maximum value and the minimum value of four basic ecological parameters in the image respectively;
(6-2) performing band combination on the four basic ecological parameter inversion results subjected to the standardization treatment;
(6-3) performing principal component analysis on the result of the combination of the four basic ecological parameter bands by using a principal component analysis method PCA;
(6-4) storing the first principal component PCA1 of the obtained principal component analysis result, and obtaining an initial remote sensing comprehensive ecological model by using 1-PCA1 in order to enable pixels with large values in the result to represent areas with good ecological environment quality;
and (6-5) carrying out standardization treatment on the initial remote sensing comprehensive ecological model, and fixing the value range within the range of [0,1] to obtain the final remote sensing comprehensive ecological model RSIEI.
An application method of a remote sensing comprehensive ecological model RSIEI for evaluating the differential effect of the surface thermal environment of a mining development dense area, wherein the remote sensing comprehensive ecological model RSIEI adopts the construction method as claimed in claim 1, and comprises the following steps:
(1) Quantitatively analyzing contributions of four basic ecological parameters to the differential effect of the surface heat environment in mining development dense areas by means of a statistical method;
(2) Quantitatively analyzing the applicability of the constructed remote sensing comprehensive ecological model RSIEI to the evaluation of the differential effect of the surface heat environment in mining development dense areas by means of a statistical method;
(3) By means of a statistical method, the applicability and feasibility of the established remote sensing comprehensive ecological model RSIEI for evaluating the dense villages and towns of small-scale mining development are verified.
Compared with the prior art, the invention adopting the technical scheme has the following beneficial effects:
the contribution degree of each ecological parameter to the differential effect of the earth surface heat environment of the research area is quantitatively analyzed by a regression analysis method, and the result shows that the four selected ecological parameters have close relations with the differential effect of the earth surface heat environment of the mining development dense area, fPV and (1-TVDI) have obvious linear negative correlation relations (P < 0.01) with LST on 0.01 level (double sides), and fNPV and NDBI have obvious linear positive correlation relations (P < 0.01) with LST on 0.01 level (double sides), so that the differential effect of the earth surface heat environment of the mining development dense area can be completely integrated with the four ecological parameters to construct a remote sensing comprehensive ecological model to evaluate the differential effect of the earth surface heat environment of the mining development dense area;
the remote sensing comprehensive ecological model constructed by the principal component analysis method has the advantages that the weight of each ecological parameter is not set manually, but is determined according to the contribution degree of each parameter to the first principal component (PCA 1), and the method for determining the index weight is objective and reasonable;
the constructed remote sensing comprehensive ecological model (RSIEI) and LST inversion result spatial distribution can be known, a high-temperature area in a research area is positioned in a mining area, corresponds to an area with poor ecological environment quality on an RSIEI model inversion result image, and a low-temperature area is Yu Lin-ground, corresponds to an area with good ecological environment quality on an RSIEI model inversion result image, so that the RSIEI and LST have the characteristic of spatial inverse correlation, and the mining development intensive area surface thermal environment differentiation effect can be completely and effectively evaluated by using the model;
the RSIEI model established based on the principal component analysis method is verified to be suitable for evaluation of the thermal environment diversity effect of mining development dense cities, and is also feasible for evaluation of the surface thermal environment diversity effect of small-scale mining development dense villages and towns taking administrative boundaries as boundaries.
Further, the preferred embodiment of the present invention is as follows:
in the step S1, the preprocessing of the remote sensing image includes the steps of:
(1-1) performing radiometric calibration and FLAASH atmospheric correction on a visible light wave band, converting a pixel gray value into a radiance value, and eliminating atmospheric influence;
(1-2) performing independent radiometric calibration on the 6 th band of the Landsat5 TM image and the 10 th band of the Landsat8 TIRS image, and storing the thermal infrared bands as BSQ format for LST inversion;
and (1-3) utilizing the village and town vector boundary files in the research area to cut the pretreatment result in batches to obtain the images of each research area.
In the step S2, inversion of the surface temperature LST by using a radiation conduction equation method includes the steps of:
(2-1) expression of a thermal infrared radiation luminance value lλ received by the satellite sensor:
Lλ＝[εB(LST)+(1-ε)L↓]τ+L↑
wherein epsilon is the emissivity of the earth surface; LST is the surface temperature, and the unit is K; b (LST) is black body heat radiation brightness, unit W/(m 2 x μm x sr); τ is the transmittance of the atmosphere in the thermal infrared band; l ∈and L ∈are the uplink and downlink radiation brightness of the atmosphere, respectively, and the unit W/(m 2 × μm sr); the atmospheric profile parameters are obtained by a website provided by NASA;
(2-2) emissivity of the earth's surface: epsilon=0.004 x vfc+0.986
In the calculation of the emissivity epsilon of the earth surface, inverting the vegetation coverage VFC by adopting a pixel three-part model, namely, assuming that a mixed pixel consists of three parts of photosynthetic vegetation PV, non-photosynthetic vegetation NPV and bare soil BS;
(2-3) assuming that the earth's surface, the atmosphere, has lambertian properties to thermal radiation, the black body at temperature T has a radiance B (LST) in the thermal infrared band of: b (LST) = [ Llambda-L ≡ -tau (1-epsilon) L
(2-4) the ground temperature LST in the above formula is obtained as a function of the Planck formula:
LST＝K2/ln(K1/B(LST)+1)
for band6 of the Landsat5 TM image, k1= 607.76W/(m2×μm×sr), k2= 1260.56K;
for the 10 th band of the Landsat8 TIRS image, k1= 774.89W/(m2×μm×sr), k2= 1321.08K.
In the step S4, the soil moisture content is represented by selecting a soil moisture index parameter 1-TVDI, and the method comprises the following steps:
(4-1) inverting TVDI by using a temperature vegetation drought index method;
TVDI＝(TS－TSmin)/(TSmax－TSmin)
in the above formula, TS is the surface temperature of any pixel, TSmin represents the lowest surface temperature under the condition of the same normalized vegetation index NDVI, and TSmax represents the highest surface temperature under the condition of the same normalized vegetation index NDVI;
(4-2)TSmin＝a1+b1*NDVI
(4-3)TSmax＝a2+b2*NDVI
the above formula is respectively called a wet side equation and a dry side equation, wherein a1 and b1 are coefficients of the wet side equation, and a2 and b2 are coefficients of the dry side equation; according to the TVDI principle, the larger the TVDI is, the closer to the dry edge of the feature space is, and the lower the soil humidity is; otherwise, the greater the soil humidity; in order to make the value of TVDI large represent that the soil humidity is high, the calculated TVDI is further subtracted by 1 to obtain the soil humidity parameter.
In the step S5, bare soil and building index parameter NDBI is selected to represent bare soil and building density parameters, including the steps of:
(5-1) characterizing bare soil and building parameters using NDBI index capable of enhancing bare surface information of bare soil and buildings in the research area;
the calculation formula is as follows:
(5-2)NDBI＝(Band(MIR1)-Band(NIR))/(Band(MIR1)+Band(NIR))
wherein Band (MIR 1) and Band (NIR) represent spectral reflectivities in the mid-infrared and near-infrared bands, respectively.
The contribution of four basic ecological parameters to the differential effect of the surface thermal environment is quantitatively analyzed by a statistical method, and the method comprises the following steps:
(1.1) extracting 500 random points on four basic ecological parameters and LST inversion result images respectively by Arcgis10.2 software, wherein the constraint distance is more than 60 meters to prevent the points from overlapping;
(1.2) regression analysis was performed using the data correlation analysis tool of the SPSS22.0 software with LST as the dependent variable and four basic ecological parameters as independent variables, respectively.
The applicability of the remote sensing comprehensive ecological model RSIEI constructed by means of quantitative analysis of a statistical method to the evaluation of the differential effect of the surface thermal environment comprises the following steps:
(2.1) extracting 500 random points on the RSIEI and LST inversion result image by using Arcgis10.2 software, wherein the constraint distance is more than 60 meters to prevent the points from overlapping;
(2.2) regression analysis was performed using the data correlation analysis tool of SPSS22.0 software with LST as the dependent variable and RSIEI as the independent variable.
By means of a statistical method, verifying the applicability and feasibility of the established remote sensing comprehensive ecological model RSIEI for evaluating the dense villages and towns developed by the small-scale mining industry, comprising the following steps:
(3.1) selecting 8 mining development dense villages and towns in a research area, extracting four ecological parameters in 8 small-scale areas and random points on LST inversion result images by using Arcgis10.2 software with administrative boundaries as boundaries, wherein each parameter extracts 500 random points, and the constraint distance is more than 60 meters for preventing the points from overlapping; carrying out regression analysis by taking LST as a dependent variable and four basic ecological parameters as independent variables by using a data correlation analysis tool of SPSS22.0 software;
(3.2) selecting 8 mining development dense villages and towns in a research area, extracting random points on RSIEI and LST inversion result images in 8 small-scale areas by using Arcgis10.2 software with administrative boundaries as boundaries, extracting 500 random points from each parameter, and restricting the distance to be more than 60 meters for preventing the points from overlapping; and carrying out regression analysis by using the data correlation analysis tool of the SPSS22.0 software and taking LST as a dependent variable and four basic ecological parameters as independent variables respectively.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a flow chart of inversion of the RSIEI of the remote sensing integrated ecological model of the present invention;
FIG. 3 is a plot of the NDVI-DFI feature space scatter of the study area 2008-2018 at inversion fPV and fNPV in the present invention;
FIG. 4 is a plot of dry and wet edge fits for study area 2008-2018 at inversion (1-TVDI) in the present invention;
FIG. 5 is a diagram of the spatial distribution characteristics of normalized LST and normalized RSIEI in the research area 2018 in the present invention;
FIG. 6 is a graph of normalized RSIEI versus normalized LST regression analysis of study area 2000-2008 in the present invention;
fig. 7 is a graph of RSIEI versus normalized LST regression analysis of 2018 small scale study area in accordance with the present invention.
Detailed Description
The technical scheme of the present description is further described below with reference to the accompanying drawings and examples.
A construction method of a remote sensing comprehensive ecological model RSIEI for evaluating the differential effect of the surface thermal environment of a mining development dense region comprises the steps of firstly inverting four selected ecological parameters by utilizing a remote sensing technology, and standardizing inversion results of the four ecological parameters so that respective value ranges are fixed in a range of [0,1 ]. And then, carrying out wave band combination on the four ecological parameter inversion results, then, carrying out principal component analysis by utilizing the wave band combination results, and storing the first principal component. In order to enable a large numerical value to represent a result with good ecological environment quality, subtracting the first main component from 1, and carrying out standardization processing on the result to obtain the constructed remote sensing comprehensive ecological model RSIEI.
Referring to fig. 1, the specific steps are as follows:
step S1: preprocessing the remote sensing image of the research area;
step S2: inversion of the surface temperature LST of the investigation region by using a radiation conduction equation method (Radiative Transfer Equation-RTE, also called an atmospheric correction method);
step S3: selecting a photosynthetic vegetation coverage fPV parameter and a non-photosynthetic vegetation coverage fNPV parameter to characterize surface vegetation biomass;
step S4: soil moisture index parameter 1-TVDI to characterize soil moisture content;
step S5: the bare soil and the building density are characterized by the bare soil and a building index parameter NDBI;
step S6: and integrating four basic ecological parameters based on a principal component analysis method (PCA, principal component analysis) to construct a remote sensing comprehensive ecological model (RSIEI).
In this embodiment, the remote sensing image of the research area is preprocessed, which specifically includes the steps of:
(1-1) performing radiometric calibration and FLAASH atmospheric correction on the visible light wave band, converting the gray scale value of the pixel into a radiance value, and eliminating the atmospheric influence.
(1-2) the 6 th band of the Landsat5 TM image and the 10 th band of the Landsat8 TIRS image (the radiometric calibration deviation of band 11 is larger, so the LST is inverted here by the 10 th band) are independently radiometric calibrated, and saved as BSQ format for LST inversion.
And (1-3) utilizing the village and town vector boundary files in the research area to cut the pretreatment result in batches to obtain the images of each research area.
In this embodiment, the surface temperature LST of the investigation region is inverted by using a radiation conduction equation method (Radiative Transfer Equation-RTE, also called an atmospheric correction method), which specifically comprises the following steps:
(2-1) expression of the thermal infrared radiation luminance value lλ received by the satellite sensor (radiation transmission equation):
Lλ＝[εB(LST)+(1-ε)L↓]τ+L↑
wherein epsilon is the emissivity of the earth surface; LST is the ground truth temperature (K); b (LST) is black body heat radiation brightness, unit W/(m 2 x μm x sr); τ is the transmittance of the atmosphere in the thermal infrared band. L ∈and L ∈are the upstream and downstream radiation brightness of the atmosphere, respectively, and the unit W/(m 2. Mu.m sr). Atmospheric profile parameters are entered into the imaging time, center longitude and latitude, and other parameter information acquisition at the website (http:// atm corr. Gsfc. NASA gov /) provided by NASA.
Table 1 shows the atmospheric profile parameters of the 3-phase images.
| Atmospheric profile parameters | 2008-9-12 | 2013-9-26 | 2018-9-8 |
| Atmospheric transmittance tau in thermal infrared band | 0.80 | 0.93 | 0.83 |
| On the atmosphereLine radiance L ∈ (unit: W/( |
1.51 | 0.50 | 1.27 |
| Atmospheric downlink radiance L ∈ (unit: W/(m2×μm×sr)) | 2.51 | 0.88 | 2.14 |
(2-2) calculating the emissivity of the earth: epsilon=0.004 x vfc+0.986
In the calculation of the emissivity epsilon of the earth surface, the NDVI threshold method proposed by Sobrino is studied in the past, namely, a simplified vegetation coverage (VFC) calculation model inverts the VFC. When the model calculates the NDVI values of vegetation and bare soil, a certain confidence coefficient is needed to be taken to obtain the maximum and minimum NDVI values, and the process has larger subjectivity and has a certain influence on the inversion result precision. For this purpose, the present study inverted VFC using a pel three-part model, i.e., assuming that the blended pel consists of three parts, PV, NPV and BS.
(2-3) assuming that the earth surface and the atmosphere have lambertian properties on heat radiation, the calculation formula of the radiation brightness B (LST) of the black body with the temperature T in the thermal infrared band is as follows: b (LST) = [ Llambda-L ≡ -tau (1-epsilon) L
(2-4) the ground truth temperature LST in the above equation may be obtained as a function of the Planck equation:
LST＝K2/ln(K1/B(LST)+1)
for Landsat5 TM Band6, k1= 607.76W/(m2×μm×sr), k2= 1260.56K;
for Landsat8 TIRS Band10, k1= 774.89W/(m2×μm×sr), k2= 1321.08K.
In this embodiment, the photosynthetic vegetation coverage fPV parameter and the non-photosynthetic vegetation coverage fNPV parameter are selected to characterize the surface vegetation biomass, which specifically comprises the following steps:
(3-1) inversion fPV and fNPV using the pixel three-part model proposed by Guerschman et al. Wherein NPV information is extracted from the dry fuel index DFI and its suitability in this area is verified. PV is represented by the more mature normalized vegetation index NDVI. Feature space with NDVI and DFI as feature parameters is established, and then feature values of PV and NPV ends are extracted, as shown in figure 3. The 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) because severe noise exists in each wave band of the multispectral image, firstly, a minimum noise separation method (Minimum Noise Fraction, MNF) is adopted to eliminate the influence of the severe noise, and the minimum noise transformation (MNF) method is adopted to reduce the dimension of the 3-stage image data, so that a large number of random test vectors penetrating through the data set are generated. And then extracting end member characteristic values by using a mature pure pixel index method (Pixel Purity Index method, PPI), projecting 6 main wave bands, setting the iteration number to be 2000, setting the threshold coefficient to be 3, regarding pixels with PPI indexes larger than 3 as pure end members, and taking the average value of the pure end members of each vertex of the NDVI-DFI characteristic space scatter diagram triangle as the characteristic value of the corresponding end member.
In the embodiment, the soil moisture content is represented by selecting a soil moisture index parameter 1-TVDI, and the specific steps are as follows:
(4-1) inversion of TVDI was first performed using the temperature vegetation drought index method proposed by Sandholt et al.
TVDI＝(TS－TSmin)/(TSmax－TSmin)
In the above formula, TS is the surface temperature of any pixel, TSmin represents the lowest surface temperature under the condition of the same NDVI, and TSmax represents the highest surface temperature under the condition of the same NDVI.
(4-2)TSmin＝a1+b1*NDVI
(4-3)TSmax＝a2+b2*NDVI
The above equations are called wet-side and dry-side equations, respectively. Where a1 and b1 are coefficients of the wet-side equation and a2 and b2 are coefficients of the dry-side equation. According to the TVDI principle, the larger the TVDI is, the closer to the dry edge of the feature space is, and the lower the soil humidity is; conversely, the greater the soil moisture.
And respectively utilizing the NDVI and LST inversion results to perform the dry-wet edge fitting of the Ts-NDVI characteristic space to obtain a dry-wet edge fitting diagram and a fitting equation of the three-phase image, as shown in figure 4. In order to make the TVDI value large represent that the soil humidity is high, the calculated TVDI is further subtracted by 1 to obtain the soil humidity parameter of the present invention.
In the embodiment, bare soil and a building index NDBI are selected to represent bare soil and building density parameters, and the concrete steps are as follows:
(5-1) characterizing bare soil and building parameters using NDBI index that enhances bare surface information of bare soil and buildings in the investigation region.
The calculation formula is as follows:
(5-2)NDBI＝(Band(MIR1)-Band(NIR))/(Band(MIR1)+Band(NIR))
wherein Band (MIR 1) and Band (NIR) represent the mid-infrared Band and near-infrared Band in the image, respectively.
In this embodiment, the integrated construction of the remote sensing integrated ecological model (RSIEI) is performed on four basic ecological parameters based on the principal component analysis method (PCA, principal component analysis), as shown in fig. 2, specifically including the steps of:
(6-1) firstly, carrying out inversion result standardization treatment on four basic ecological parameters (photosynthetic vegetation coverage fPV, non-photosynthetic vegetation coverage fNPV, soil humidity 1-TVDI, bare soil and building aggregation NDBI) so as to fix the value range of the result within the range of [0,1], wherein the formula is as follows:
NI＝(I-Imin)/(Imax-Imin)
wherein I is the numerical value of the index;
imax and Imin are the maximum and minimum values of the index in the image, respectively.
(6-2) performing band combination on the four basic ecological parameter inversion results subjected to the standardization treatment;
(6-3) performing principal component analysis on the result of the combination of the four ecological parameter bands by using a principal component analysis method PCA;
(6-4) storing the first principal component of the obtained principal component analysis result, and obtaining an initial remote sensing comprehensive ecological model by using (1-PCA 1) in order to enable the pixels with large values in the result to represent the areas with good ecological environment quality;
and (6-5) carrying out standardization treatment on the initial remote sensing comprehensive ecological model, and fixing the value range within the range of [0,1] to obtain the final remote sensing comprehensive ecological model RSIEI.
The application of the remote sensing integrated ecological model RSIEI for evaluating the differential effect of the surface heat environment in the mining development dense area in the embodiment comprises the following steps:
(1) Quantitatively analyzing contributions of four ecological parameters to the differential effect of the surface heat environment in mining development dense areas by means of a statistical method;
(2) Quantitatively analyzing the applicability of the constructed remote sensing comprehensive ecological model RSIEI to the evaluation of the differential effect of the surface heat environment in mining development dense areas by means of a statistical method;
(3) By means of a statistical method, the applicability and feasibility of the established remote sensing comprehensive ecological model RSIEI for evaluating the dense villages and towns of small-scale mining development are verified.
In order to verify the applicability and feasibility of the model for evaluating the differential effect of the earth surface thermal environment in the mining development dense region, four ecological parameters of a research region and random points on an inversion result image of the remote sensing comprehensive ecological model and the earth surface temperature are respectively extracted, and the relationship between the four ecological parameters and the earth surface temperature and the relationship between the remote sensing comprehensive ecological model and the earth surface temperature are respectively analyzed by using a regression analysis method in statistics.
In order to verify the applicability and feasibility of the model in a small-scale area, four ecological parameters of 8 mining development dense villages and towns and random points on a remote sensing comprehensive ecological model and ground surface temperature inversion result image in the scale range of an administrative unit in a research area are respectively extracted, and the relationship between the four ecological parameters in the small-scale area and the ground surface temperature and the relationship between the remote sensing comprehensive ecological model and the ground surface temperature are respectively analyzed by a regression analysis method in statistics.
Referring to fig. 1, the specific application method of the above model is as follows:
(1) The contributions of four ecological parameters of a research area to the differential effect of the surface heat environment are quantitatively analyzed by means of a statistical method, and the method comprises the following specific steps:
(1.1) extracting 500 random points on four basic ecological parameters and LST inversion result images respectively by Arcgis10.2 software, wherein the constraint distance is more than 60 meters to prevent the points from overlapping;
(1.2) regression analysis was performed using the data correlation analysis tool of the SPSS22.0 software with LST as the dependent variable and four ecological parameters as independent variables, respectively. The results are shown in Table 2.
And (1.3) regression analysis results show that the four ecological parameters are closely related to the differential effect of the surface heat environment. From the regression relation equation of four ecological parameters and LST in 3 years, fPV and LST are in a remarkable linear negative correlation (P < 0.01) on the level of 0.01 (two sides), which shows that the increase of photosynthetic vegetation coverage can reduce the surface temperature, and the surface temperature can be correspondingly reduced by 10.95-18.47 ℃ every 10% of photosynthetic vegetation coverage; the fNPV and LST have a remarkable linear positive correlation (P < 0.01) on the level of 0.01 (bilateral), and the surface temperature can be correspondingly increased by 5.70-9.22 ℃ when the non-photosynthetic vegetation coverage is increased by 10%; the (1-TVDI) and the LST are in a remarkable linear negative correlation (P < 0.01) on the level of 0.01 (two sides), which shows that the increase of the soil humidity can lower the surface temperature, and the increase of the soil humidity by 10 percent can correspondingly lower the surface temperature by 6.23-11.31 ℃; NDBI and LST are in a significant linear positive correlation (P < 0.01) at the 0.01 level (double-sided), indicating that an increase in bare earth and building area exacerbates the rise in surface temperature, and that each 10% increase in bare earth and building area increases the surface temperature by 4.30-6.37 ℃.
(2) The applicability of the remote sensing integrated ecological model (RSIEI) constructed by means of quantitative analysis of a statistical method to the evaluation of the differential effect of the surface thermal environment comprises the following specific steps:
(2.1) extracting 500 random points on the RSIEI and LST inversion result image by using Arcgis10.2 software, wherein the constraint distance is more than 60 meters to prevent the points from overlapping;
(2.2) regression analysis was performed using the data correlation analysis tool of SPSS22.0 software with LST as the dependent variable and RSIEI as the independent variable. The regression analysis results are shown in FIG. 6.
(2.3) for further comparative analysis of correlation between RSIEI and LST, it is known from normalized RSIEI and normalized LST spatial distribution images of 2018 study area (as shown in fig. 5), where there is obvious thermal environment differentiation effect, high temperature area represented by dark red area is mainly distributed in mining area stope and town village residential area, and middle low temperature area represented by light red and white area is mainly distributed in woodland, grassland, garden land and water-irrigated land area; the dark green areas in fig. 5 (b) represent areas with good ecological environment, mostly in woodlands, grasslands, garden lands and waterlands, while the light green and white areas represent areas with poor ecological environment, mainly distributed in mining sites and town village residents. From the spatial distribution characteristics of both, the overall spatial distribution of the normalized RSIEI has diametrically opposite distribution characteristics to the overall spatial distribution of the normalized LST. Vegetation has a relief effect on the surface heat effect, while mining land and town resident land have a greater contribution to the surface heat accumulation effect. The high temperature region in fig. 5 (a) corresponds to the region of poor ecological environment in fig. 5 (b), and the low temperature region in fig. 5 (a) corresponds to the region of good ecological environment in fig. 5 (b), both of which have characteristics of inverse spatial correlation. For 8 mining developments bordering on town administrative units in the research area, dense towns are developed, both of which have similar distribution characteristics.
(3) By means of a statistical method, verifying the applicability and feasibility of the established remote sensing comprehensive ecological model (RSIEI) for evaluating the dense villages and towns developed by small-scale mining industry, and specifically comprises the following steps:
and (3.1) selecting 8 mining development dense villages and towns in a research area, extracting four ecological parameters in 8 small-scale areas and random points on LST inversion result images by using Arcgis10.2 software with administrative boundaries as boundaries, wherein each parameter extracts 500 random points, and the constraint distance is more than 60 meters for preventing the points from overlapping. And carrying out regression analysis by using the data correlation analysis tool of the SPSS22.0 software and taking LST as a dependent variable and four ecological parameters as independent variables respectively.
Regression results of four ecological parameters and LST of 8 small-scale mining development dense villages and towns in 2018 show that the four ecological parameters and the earth surface thermal environment of the village and township administrative unit scale are closely related in different effects, fPV and LST are in a remarkable linear negative correlation (P < 0.01) on the level of 0.01 (two sides), so that the increase of photosynthetic vegetation coverage can reduce the earth surface temperature, and the earth surface temperature can be correspondingly reduced by 8.51-14.94 ℃ every 10% of photosynthetic vegetation coverage; the fNPV and LST have a remarkable linear positive correlation (P < 0.01) on the level of 0.01 (double sides), and the surface temperature can be correspondingly increased by 4.01-12.08 ℃ when the non-photosynthetic vegetation coverage is increased by 10%; the (1-TVDI) and the LST are in a remarkable linear negative correlation (P < 0.01) on the level of 0.01 (two sides), which shows that the increase of the soil humidity can lower the surface temperature, and the increase of the soil humidity by 10 percent can correspondingly lower the surface temperature by 6.73-9.71 ℃; NDBI and LST show a significant linear positive correlation (P < 0.01) at the 0.01 level (double-sided), indicating that an increase in bare earth and building area exacerbates the rise in surface temperature, and that every 10% increase in bare earth and building area causes a corresponding rise in surface temperature of 3.36-6.03 ℃.
And (3.3) selecting 8 small-scale mining development dense villages and towns in a research area, extracting random points on RSIEI and LST inversion result images in the 8 small-scale areas by using Arcgis10.2 software with administrative boundaries as boundaries, extracting 500 random points from each parameter, and restricting the distance to be more than 60 meters for preventing the points from overlapping. And carrying out regression analysis by using the data correlation analysis tool of the SPSS22.0 software and taking LST as a dependent variable and four ecological parameters as independent variables respectively. As shown in fig. 7.
Regression results of RSIEI and LST of 8 mining developments in 2018 dense villages and towns show that RSIEI has close relationship with the differential effect of the surface heat environment of the village and township administrative unit scale. From the regression equation of the RSIEI and LST, the RSIEI and LST of fig. 7a show a significant linear negative correlation (P < 0.01) at the 0.01 level (double-sided), with a corresponding decrease in surface temperature of 6.50 ℃ for every 10% increase in RSIEI value; the RSIEI and LST of fig. 7b are in a significant linear negative correlation (P < 0.01) at the 0.01 level (double sided), with a corresponding drop in surface temperature of 4.81 ℃ for every 10% increase in RSIEI value; the RSIEI and LST of fig. 7c are in a significant linear negative correlation (P < 0.01) at the 0.01 level (double sided), with a corresponding decrease in surface temperature of 4.58 ℃ for every 10% increase in RSIEI value; the RSIEI and LST of fig. 7d are in a significant linear negative correlation (P < 0.01) at the 0.01 level (double sided), with a corresponding drop in surface temperature of 4.91 ℃ for every 10% increase in RSIEI value; the RSIEI and LST of fig. 7e are in a significant linear negative correlation (P < 0.01) at the 0.01 level (double sided), with a corresponding decrease in surface temperature of 3.57 ℃ for every 10% increase in RSIEI value; the RSIEI and LST of fig. 7f are in a significant linear negative correlation (P < 0.01) at the 0.01 level (double sided), with a corresponding drop in surface temperature of 2.81 ℃ for every 10% increase in RSIEI value; FIG. 7g of RSIEI and LST show a significant linear negative correlation (P < 0.01) at the 0.01 level (double-sided), with a corresponding decrease in surface temperature of 2.09 ℃ for every 10% increase in RSIEI value; the RSIEI and LST of fig. 7h are significantly linearly inversely correlated (P < 0.01) at the 0.01 level (double-sided), with a corresponding drop in surface temperature of 1.25 ℃ for every 10% increase in RSIEI value.
The conclusion above shows that it is effective and feasible to evaluate mining development dense cities by using a remote sensing comprehensive model created by integrating four ecological parameters and to evaluate the earth surface thermal environment differentiation effect of small-scale mining development dense villages and towns by taking administrative boundaries as boundaries.
The above embodiments are merely illustrative of the spirit of the invention. Those skilled in the art may make various modifications or additions to the described embodiments or substitutions thereof without departing from the spirit of the invention or exceeding the scope of the invention as defined in the accompanying claims.
Claims (9)
1. The construction method of the remote sensing comprehensive ecological model RSIEI for evaluating the differential effect of the surface thermal environment of the mining development dense area is characterized by comprising the following steps:
step S1: preprocessing the remote sensing image of the research area;
step S2: inverting the surface temperature LST of the research area by using a radiation conduction equation method;
step S3: selecting a photosynthetic vegetation coverage fPV parameter and a non-photosynthetic vegetation coverage fNPV parameter to characterize surface vegetation biomass;
in the step S3, the method selects the photosynthetic vegetation coverage fPV parameter and the non-photosynthetic vegetation coverage fNPV parameter to characterize the surface vegetation biomass, and includes the steps of:
(3-1) inverting fPV and fNPV using a pel three-part model, wherein non-photosynthetic vegetation NPV information is extracted from the dry fuel index DFI and verifying its suitability in the study area; photosynthetic vegetation PV is represented by a more mature normalized vegetation index NDVI; establishing a feature space with NDVI and DFI as feature parameters, and then extracting feature values of PV and NPV ends; the formula is as follows:
(3-2)NDVI＝(Band(NIR)-Band(R))/(Band(NIR)+Band(R))
wherein: band (NIR) is the spectral reflectance in the near infrared Band; band (R) is the spectral reflectance of the red Band;
(3-3)DFI＝100*(1-Band(SWIR2)/Band(SWIR1))*Band(R)/Band(NIR)
wherein: band (SWIR 2) is the spectral reflectance of the short wave infrared 2 Band; band (SWIR 1) is the spectral reflectance of the short wave infrared 1 Band;
(3-4) eliminating noise influence existing in each wave band of the multispectral image by adopting a minimum noise separation method, extracting end member characteristic values by utilizing a relatively mature pure pixel index method, and reducing errors caused by manual selection;
step S4: the soil moisture index parameter 1-TVDI is used for representing the soil moisture content, and the TVDI is the temperature vegetation drought index;
step S5: the bare soil and the building density are characterized by the bare soil and a building index parameter NDBI;
step S6: integrating four basic ecological parameters based on a principal component analysis method to construct a remote sensing comprehensive ecological model RSIEI;
in the step S6, integrating four basic ecological parameters based on a principal component analysis method to construct a remote sensing comprehensive ecological model RSIEI, including the steps of:
(6-1) first for four basic ecological parameters: the photosynthetic vegetation coverage fPV, the non-photosynthetic vegetation coverage fNPV, the soil humidity 1-TVDI, bare soil and the building concentration NDBI inversion result are standardized, so that the value range of the result is fixed within the range of [0,1], and the formula is as follows:
NI＝(I-Imin)/(Imax-Imin)
wherein I is the numerical value of four basic ecological parameters;
imax and Imin are the maximum value and the minimum value of four basic ecological parameters in the image respectively;
(6-2) performing band combination on the four basic ecological parameter inversion results subjected to the standardization treatment;
(6-3) performing principal component analysis on the result of the combination of the four basic ecological parameter bands by using a principal component analysis method PCA;
(6-4) storing the first principal component PCA1 of the obtained principal component analysis result, and obtaining an initial remote sensing comprehensive ecological model by using 1-PCA1 in order to enable pixels with large values in the result to represent areas with good ecological environment quality;
and (6-5) carrying out standardization treatment on the initial remote sensing comprehensive ecological model, and fixing the value range within the range of [0,1] to obtain the final remote sensing comprehensive ecological model RSIEI.
2. The method for constructing the RSIEI of the remote sensing integrated ecological model for assessing the differential effect of the surface thermal environment in mining development dense areas according to claim 1, wherein in the step S1, the remote sensing image is preprocessed, and the method comprises the steps of:
(1-1) performing radiometric calibration and FLAASH atmospheric correction on a visible light wave band, converting a pixel gray value into a radiance value, and eliminating atmospheric influence;
(1-2) performing independent radiometric calibration on the 6 th band of the Landsat5 TM image and the 10 th band of the Landsat8 TIRS image, and storing the thermal infrared bands as BSQ format for LST inversion;
and (1-3) utilizing the village and town vector boundary files in the research area to cut the pretreatment result in batches to obtain the images of each research area.
3. The method for constructing the remote sensing integrated ecological model RSIEI for assessing the differential effect of the surface thermal environment in mining development dense areas according to claim 1, wherein in said step S2, the surface temperature LST is inverted by using the radiation conduction equation method, comprising the steps of:
(2-1) expression of a thermal infrared radiation luminance value lλ received by the satellite sensor:
Lλ＝[εB(LST)+(1-ε)L↓]τ+L↑
wherein epsilon is the emissivity of the earth surface; LST is the surface temperature, and the unit is K; b (LST) is black body heat radiation brightness, unit W/(m 2 x μm x sr); τ is the transmittance of the atmosphere in the thermal infrared band; l ∈and L ∈are the uplink and downlink radiation brightness of the atmosphere, respectively, and the unit W/(m 2 × μm sr); the atmospheric profile parameters are obtained by a website provided by NASA;
(2-2) emissivity of the earth's surface: epsilon=0.004 x vfc+0.986
In the calculation of the emissivity epsilon of the earth surface, inverting the vegetation coverage VFC by adopting a pixel three-part model, namely, assuming that a mixed pixel consists of three parts of photosynthetic vegetation PV, non-photosynthetic vegetation NPV and bare soil BS;
(2-3) assuming that the earth's surface, the atmosphere, has lambertian properties to thermal radiation, the black body at temperature T has a radiance B (LST) in the thermal infrared band of: b (LST) = [ Llambda-L ≡ -tau (1-epsilon) L
(2-4) the ground temperature LST in the above formula is obtained as a function of the Planck formula:
LST＝K2/ln(K1/B(LST)+1)
for band6 of the Landsat5 TM image, k1= 607.76W/(m2×μm×sr), k2= 1260.56K;
for the 10 th band of the Landsat8 TIRS image, k1= 774.89W/(m2×μm×sr), k2= 1321.08K.
4. The method for constructing the remote sensing integrated ecological model RSIEI for assessing the differential effect of the surface thermal environment in mining development dense areas according to claim 1, wherein in the step S4, the soil moisture content is represented by selecting the soil moisture index parameter 1-TVDI, comprising the steps of:
(4-1) inverting TVDI by using a temperature vegetation drought index method;
TVDI＝(TS－TSmin)/(TSmax－TSmin)
in the above formula, TS is the surface temperature of any pixel, TSmin represents the lowest surface temperature under the condition of the same normalized vegetation index NDVI, and TSmax represents the highest surface temperature under the condition of the same normalized vegetation index NDVI;
(4-2)TSmin＝a1+b1*NDVI
(4-3)TSmax＝a2+b2*NDVI
the above formula is respectively called a wet side equation and a dry side equation, wherein a1 and b1 are coefficients of the wet side equation, and a2 and b2 are coefficients of the dry side equation; according to the TVDI principle, the larger the TVDI is, the closer to the dry edge of the feature space is, and the lower the soil humidity is; otherwise, the greater the soil humidity; in order to make the value of TVDI large represent that the soil humidity is high, the calculated TVDI is further subtracted by 1 to obtain the soil humidity parameter.
5. The method for constructing the remote sensing integrated ecological model RSIEI for assessing the differential effect of the surface thermal environment in mining development dense areas according to claim 1, wherein in the step S5, bare soil and building index parameter NDBI are selected to represent bare soil and building dense degree parameters, comprising the steps of:
(5-1) characterizing bare soil and building parameters using NDBI index capable of enhancing bare surface information of bare soil and buildings in the research area;
the calculation formula is as follows:
(5-2)NDBI＝(Band(MIR1)-Band(NIR))/(Band(MIR1)+Band(NIR))
wherein Band (MIR 1) and Band (NIR) represent spectral reflectivities in the mid-infrared and near-infrared bands, respectively.
6. An application method of a remote sensing comprehensive ecological model RSIEI for evaluating the differential effect of the surface thermal environment of a mining development dense area, wherein the remote sensing comprehensive ecological model RSIEI adopts the construction method as set forth in claim 1, and the application method is characterized by comprising the following steps:
(1) Quantitatively analyzing contributions of four basic ecological parameters to the differential effect of the surface heat environment in mining development dense areas by means of a statistical method;
(2) Quantitatively analyzing the applicability of the constructed remote sensing comprehensive ecological model RSIEI to the evaluation of the differential effect of the surface heat environment in mining development dense areas by means of a statistical method;
(3) By means of a statistical method, the applicability and feasibility of the established remote sensing comprehensive ecological model RSIEI for evaluating the dense villages and towns of small-scale mining development are verified.
7. The application method of the remote sensing integrated ecological model RSIEI for evaluating the differential effect of the surface heat environment in mining development dense areas according to claim 6, wherein the contribution of four basic ecological parameters to the differential effect of the surface heat environment is quantitatively analyzed by means of a statistical method, and the application method comprises the following steps:
(1.1) extracting 500 random points on four basic ecological parameters and LST inversion result images respectively by Arcgis10.2 software, wherein the constraint distance is more than 60 meters to prevent the points from overlapping;
(1.2) regression analysis was performed using the data correlation analysis tool of the SPSS22.0 software with LST as the dependent variable and four basic ecological parameters as independent variables, respectively.
8. The application method of the remote sensing integrated ecological model RSIEI for evaluating the differential effect of the surface thermal environment in the mining development dense area according to claim 6, wherein the applicability of the constructed remote sensing integrated ecological model RSIEI to the evaluation of the differential effect of the surface thermal environment is quantitatively analyzed by a statistical method, comprising the steps of:
(2.1) extracting 500 random points on the RSIEI and LST inversion result image by using Arcgis10.2 software, wherein the constraint distance is more than 60 meters to prevent the points from overlapping;
(2.2) regression analysis was performed using the data correlation analysis tool of SPSS22.0 software with LST as the dependent variable and RSIEI as the independent variable.
9. The application method of the remote sensing comprehensive ecological model RSIEI for evaluating the differential effect of the surface heat environment in the mining development dense area according to claim 6, wherein the application method for verifying the applicability and feasibility of the established remote sensing comprehensive ecological model RSIEI for evaluating the mining development dense villages and towns by means of a statistical method comprises the following steps:
(3.1) selecting 8 mining development dense villages and towns in a research area, extracting four basic ecological parameters in 8 small-scale areas and random points on LST inversion result images by using Arcgis10.2 software with administrative boundaries as boundaries, wherein each parameter extracts 500 random points, and the constraint distance is more than 60 meters for preventing the points from overlapping; carrying out regression analysis by taking LST as a dependent variable and four basic ecological parameters as independent variables by using a data correlation analysis tool of SPSS22.0 software;
(3.2) selecting 8 mining development dense villages and towns in a research area, extracting random points on RSIEI and LST inversion result images in 8 small-scale areas by using Arcgis10.2 software with administrative boundaries as boundaries, extracting 500 random points from each parameter, and restricting the distance to be more than 60 meters for preventing the points from overlapping; and carrying out regression analysis by using the data correlation analysis tool of the SPSS22.0 software and taking LST as a dependent variable and four basic ecological parameters as independent variables respectively.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910772939.2A CN110472357B (en) | 2019-08-21 | 2019-08-21 | Construction method and application method of remote sensing comprehensive ecological model RSIEI for evaluating differential effect of surface thermal environment in mining development dense area |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910772939.2A CN110472357B (en) | 2019-08-21 | 2019-08-21 | Construction method and application method of remote sensing comprehensive ecological model RSIEI for evaluating differential effect of surface thermal environment in mining development dense area |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110472357A CN110472357A (en) | 2019-11-19 |
| CN110472357B true CN110472357B (en) | 2023-05-02 |
Family
ID=68513123
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910772939.2A Active CN110472357B (en) | 2019-08-21 | 2019-08-21 | Construction method and application method of remote sensing comprehensive ecological model RSIEI for evaluating differential effect of surface thermal environment in mining development dense area |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110472357B (en) |
Families Citing this family (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111398182A (en) * | 2020-02-17 | 2020-07-10 | 华北理工大学 | Quantitative evaluation method for space-time differential effect of earth surface thermal environment based on pixel three-component model |
| CN112710613B (en) * | 2020-12-18 | 2022-04-01 | 太原理工大学 | Photosynthetic vegetation parameter remote sensing estimation index determination method under three-component state |
| CN112712437B (en) * | 2021-01-18 | 2022-08-26 | 河南工业大学 | Mining area environment change comprehensive evaluation method |
| CN113269429B (en) * | 2021-05-19 | 2022-03-18 | 青岛星科瑞升信息科技有限公司 | Ecological environment quality evaluation method based on water ecological benefits |
| 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 |
| 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 |
| 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 |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106599601A (en) * | 2016-12-29 | 2017-04-26 | 中国科学院遥感与数字地球研究所 | Remote sensing assessment method and system for ecosystem vulnerability |
| CN107478611A (en) * | 2017-08-02 | 2017-12-15 | 珠江水利委员会珠江水利科学研究院 | A kind of method for calculating the exposed rate of rock |
Family Cites Families (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6018587A (en) * | 1991-02-21 | 2000-01-25 | Applied Spectral Imaging Ltd. | Method for remote sensing analysis be decorrelation statistical analysis and hardware therefor |
| US5764819A (en) * | 1991-10-18 | 1998-06-09 | Dekalb Genetics Corporation | Methods for classifying plants for evaluation and breeding programs by use of remote sensing and image analysis technology |
| US5566069A (en) * | 1994-03-07 | 1996-10-15 | Monsanto Company | Computer network for collecting and analyzing agronomic data |
| CN104298828B (en) * | 2014-10-13 | 2017-05-03 | 河海大学 | Method for simulating influence of urban green space patterns on thermal environments |
| CN104820754A (en) * | 2015-05-13 | 2015-08-05 | 南京信息工程大学 | Space statistical downscaling rainfall estimation method based on geographical difference analysis method |
| CN105279604A (en) * | 2015-09-25 | 2016-01-27 | 同济大学 | Mine-area land resource change cooperative analysis method |
| CN109358162B (en) * | 2018-11-06 | 2021-06-22 | 福州大学 | Novel remote sensing ecological index construction method based on space geometric principle |
| CN109581372B (en) * | 2018-12-10 | 2021-01-05 | 北京航天泰坦科技股份有限公司 | Ecological environment remote sensing monitoring method |
-
2019
- 2019-08-21 CN CN201910772939.2A patent/CN110472357B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106599601A (en) * | 2016-12-29 | 2017-04-26 | 中国科学院遥感与数字地球研究所 | Remote sensing assessment method and system for ecosystem vulnerability |
| CN107478611A (en) * | 2017-08-02 | 2017-12-15 | 珠江水利委员会珠江水利科学研究院 | A kind of method for calculating the exposed rate of rock |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110472357A (en) | 2019-11-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110472357B (en) | Construction method and application method of remote sensing comprehensive ecological model RSIEI for evaluating differential effect of surface thermal environment in mining development dense area | |
| Zhang et al. | Empirical models for estimating daily maximum, minimum and mean air temperatures with MODIS land surface temperatures | |
| Lesaignoux et al. | Influence of soil moisture content on spectral reflectance of bare soils in the 0.4–14 μm domain | |
| CN109115995B (en) | Soil moisture information acquisition methods, device, computer equipment and storage medium | |
| CN111368817B (en) | Method and system for quantitatively evaluating thermal effect based on earth surface type | |
| Yang et al. | The impact analysis of water body landscape pattern on urban heat island: a case study of Wuhan City | |
| Rahman et al. | A comparison of four relative radiometric normalization (RRN) techniques for mosaicing H-res multi-temporal thermal infrared (TIR) flight-lines of a complex urban scene | |
| CN111398182A (en) | Quantitative evaluation method for space-time differential effect of earth surface thermal environment based on pixel three-component model | |
| Zareie et al. | Derivation of land surface temperature from Landsat Thematic Mapper (TM) sensor data and analysing relation between land use changes and surface temperature | |
| Hardin et al. | The influence of surface type on the absorbed radiation by a human under hot, dry conditions | |
| CN113553907A (en) | Forest ecological environment condition evaluation method based on remote sensing technology | |
| Shen et al. | Spatial–temporal land-use/land-cover dynamics and their impacts on surface temperature in Chongming Island of Shanghai, China | |
| CN114581791A (en) | Inversion method and system for atmospheric water vapor content based on MODIS data | |
| Park et al. | Marine biological feedback associated with Indian Ocean Dipole in a coupled ocean/biogeochemical model | |
| Wen et al. | Parametrized BRDF for atmospheric and topographic correction and albedo estimation in Jiangxi rugged terrain, China | |
| Srivanit et al. | Thermal infrared remote sensing for urban climate and environmental studies: An application for the city of Bangkok, Thailand | |
| Hingerl et al. | Spatio‐temporal variability of water and energy fluxes–a case study for a mesoscale catchment in pre‐alpine environment | |
| Li et al. | The variation of vegetation productivity and its relationship to temperature and precipitation based on the GLASS-LAI of different African ecosystems from 1982 to 2013 | |
| CN114005048A (en) | Multi-temporal data-based land cover change and thermal environment influence research method | |
| Chang et al. | Assessing variability of optimum air temperature for photosynthesis across site-years, sites and biomes and their effects on photosynthesis estimation | |
| Fahmy et al. | Investigating land use land cover changes and their effects on land surface temperature and urban heat islands in Sharqiyah Governorate, Egypt | |
| He et al. | Spatiotemporal change patterns and driving factors of land surface temperature in the Yunnan-Kweichow Plateau from 2000 to 2020 | |
| Zhou et al. | Analysis of surface radiation budget during the summer and winter in the metropolitan area of Beijing, China | |
| Palmer et al. | Aboveground biomass production of a semi-arid southern African savanna: Towards a new model | |
| Byambakhuu et al. | Spectral unmixing model to assess land cover fractions in Mongolian steppe regions |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |