Remote sensing rapid estimation method for methane carbon flux of unsubmerged hydro-fluctuation belt in reservoir area

Abstract

The invention discloses a remote sensing rapid estimation method for methane carbon flux of an unsubmerged hydro-fluctuation belt in a reservoir area. The method comprises the following steps: screening a hyperspectral remote sensing image; preprocessing the screened hyperspectral remote sensing image to obtain the spectral reflectivity of the hyperspectral image; carrying out vectorization extraction on a water body range of the reservoir area, carrying out spectrum extraction on ground monitoring points and actually measuring methane flux data; performing spectral feature extraction on the extracted spectral information of all wavebands of the ground monitoring point, and screening a spectral waveband range sensitive to methane flux change and a corresponding spectral reflectivity; and constructing a methane flux estimation model by combining a screening spectrum and geographically weighted regression, and performing inversion to obtain the methane flux of the unsubmerged hydro-fluctuation zone in the Three Gorges reservoir area. The method has the advantages of few parameters and high efficiency, can efficiently monitor the methane flux of the unsubmerged hydro-fluctuation belt in the Three Gorges reservoir area with large hydrological influence and strong spatial heterogeneity in near real time, and provides technical support for scientifically evaluating the methane flux of the hydro-fluctuation belt in the reservoir area and evaluating the source-reducing and sink-increasing benefits.

Description

Technical Field

[0001] The invention relates to the field of ecological environment monitoring, and in particular to a remote sensing rapid estimation method for methane carbon flux in an unflooded drawdown zone of a reservoir area. Background Art

[0002] As an extra-large annual regulating reservoir, the water level of the Three Gorges Reservoir changes periodically, with the water level in front of the dam changing periodically between 145m and 170m, resulting in an alternating zone (drawdown zone) with a water level drop of about 30m on both sides of the river. Environmental changes such as periodic flooding change the soil respiration in the drawdown zone, which in turn leads to dynamic evolution of methane flux. Estimating the methane flux in the unflooded drawdown zone is conducive to scientifically understanding the clean energy attributes of the Three Gorges hydropower, and is an important part of the high-quality development and ecological environmental protection of the Three Gorges Reservoir area.

[0003] The traditional monitoring method uses flux box measurement method, in which people carry instruments to measure methane flux in the study area of ​​the drawdown zone. This method is costly and inefficient, and it is difficult to obtain the methane flux of the unflooded drawdown zone in near real time. The development of hyperspectral remote sensing technology has made it possible to estimate methane flux in a large scale with high efficiency. However, the Three Gorges Reservoir is large in scope and area, and the environmental differences in the drawdown zone are large. The global regression model that ignores spatial differences is difficult to achieve accurate estimation of methane flux. Therefore, it is very necessary to study the remote sensing estimation method of methane carbon flux in the unflooded drawdown zone of the Three Gorges Reservoir area that takes into account spatial heterogeneity.

[0004] The references of background technology are as follows: [1] Hu Chunhong, Fang Chunming, Chen Xujian, Sediment Movement Laws and Simulation Technology of the Three Gorges Project[M]. 2017, Science Press; Beijing [2] Xia Zhenyao, Yan Rubing, Zhang Lun, et al. Response of root tensile properties of Bermuda grass to flooding duration[J]. Transactions of the Chinese Society of Agricultural Engineering, 2023, 39(6): 103-110. [3] Zhao Dengzhong, Tan Debao, Li Chong, et al. Temporal and spatial variation of carbon dioxide flux in Geheyan Reservoir and its influencing factors[J]. Environmental Science, 2017, 38(3): 954-963. [4] Zheng Shouren. Research and monitoring and inspection analysis on the safety and long-term operation of the Three Gorges Project reservoir dam[J]. Yangtze River Technology and Economy, 2018, 2(3): 1-9. [5] Zheng Shouren. The Three Gorges Project improves security and environmental protection for the development of the Yangtze River Economic Belt[J]. 2019,50(1): 1 6. Barbosa PM, Bodmer P, Stadler M, et al. Ecosystem Metabolism Is the Dominant Source of Carbon Dioxide in Three Young Boreal Cascade‐Reservoirs (LaRomaine Complex, Québec)[J]. Journal of Geophysical Research:Biogeosciences, 2023, 128(4): 1 21.. Summary of the invention

[0005] In view of the shortcomings of the current methane flux estimation method in the unflooded drawdown zone of the Three Gorges Reservoir, the present invention provides a remote sensing rapid estimation method for the methane carbon flux in the unflooded drawdown zone of the reservoir area, which realizes methane flux estimation with both heterogeneity and high efficiency by using a low-manpower and low-parameter monitoring framework, and provides technical support for obtaining the methane flux in a large area of ​​the unflooded drawdown zone in the Three Gorges Reservoir area in near real time.

[0006] The technical solution adopted by the present invention is: a method for rapid remote sensing estimation of methane carbon flux in the unflooded drawdown zone of a reservoir area, comprising the following steps: Step 1: According to the complete vector range of the existing reservoir drawdown zone, set the imaging time and cloud coverage, and select the hyperspectral remote sensing image; Step 2: Preprocessing the selected hyperspectral remote sensing images to obtain the spectral reflectance of the hyperspectral images, wherein the preprocessing includes orthorectification, radiation correction, and geometric correction; Step 3: Vector extraction of the water body range in the reservoir area and spectral extraction of ground monitoring points and measured methane flux data: ArcGIS 10.5 was used to vectorize the extraction of the boundary of the water body, and the vector range of the unflooded drawdown zone was clipped based on the complete vector range data of the drawdown zone; the ROI of the area of ​​interest and the longitude and latitude of the center point of the methane flux ground monitoring point were manually selected, and the spectral information of all bands of the ground monitoring point was extracted based on the longitude and latitude; the methane concentration was measured and converted into methane flux; Step 4: Use the competitive adaptive reweighted sampling CARS algorithm to extract spectral features from the spectral information of all bands of the ground monitoring points extracted in step 3, and screen the spectral band range and corresponding spectral reflectance that are sensitive to methane flux changes; Step 5: Based on the screened spectral reflectance, the local model geographically weighted regression (GWR) taking into account spatial heterogeneity is used to construct the methane flux inversion model in the drawdown zone; Step 6: Model evaluation: Calculate the determination coefficient R of the measured and estimated methane flux in the drawdown zone of the reservoir on the validation set 2, residual sum of squares RSS, modified Akaike information criterion AICc, and effective parameter ENP are used to evaluate the reliability of the model, so as to obtain the best model for methane flux inversion; Step 7: Estimation of methane flux above water in the drawdown zone of the reservoir: Extract the corresponding hyperspectral image and the longitude and latitude of all pixels according to the vector range of the unflooded drawdown zone obtained in step 3, input the longitude and latitude of all pixels and the spectral reflectance sensitive to methane flux changes obtained in step 4 into the optimal model for methane flux inversion to obtain the estimated value of methane flux in the entire unflooded drawdown zone.

[0007] Furthermore, the step 1 comprises: Step 1.1, preliminarily screen the hyperspectral remote sensing images according to the vector range of the complete drawdown zone of the existing reservoir area; Step 1.2, add screening conditions, set the cloud coverage not to exceed 25%, set the imaging time to the actual methane flux measurement time, and further screen the hyperspectral remote sensing images that meet the requirements.

[0008] Furthermore, the step 2 comprises: Step 2.1, open the ENVI5.2 orthorectification tool and perform orthorectification on the selected hyperspectral image to obtain an image without geometric distortion; Step 2.2, open the ENVI5.2 radiation correction tool, perform radiation calibration on the selected hyperspectral image, and obtain the true spectral reflectance of the surface; Step 2.3, open the ENVI5.2 geometric correction tool, call the header file of the hyperspectral remote sensing image, and set the parameters to obtain a hyperspectral image with accurate location.

[0009] Furthermore, the step 3 comprises: Step 3.1, import the preprocessed hyperspectral image into ArcGIS10.5 to vectorize the vector range of the current water body in the reservoir area; Step 3.2, import the existing complete drawdown zone vector range of the reservoir area, call the symmetrical difference tool in the ArcGIS10.5 analysis tool, and obtain the vector range of the drawdown zone of the reservoir area that is not currently flooded; Step 3.3, ArcGIS calls the mask tool (Extracted by Mask) in "Spatial Analyst Tools" to crop the hyperspectral remote sensing image of the unflooded area of ​​the reservoir drawdown zone; Step 3.4, import the hyperspectral remote sensing image of the unflooded area of ​​the reservoir drawdown zone into ENVI5.2, manually select no less than 40 ground monitoring points for methane flux, and manually select one region of interest (ROI) at each measured point, requiring that each region of interest (ROI) contain no less than 5 pixels, derive the vector range and longitude and latitude of the center point of the region of interest (ROI), obtain the methane concentration data of the reservoir drawdown zone on site and convert it into methane flux; In step 3.5, the vector ranges of all ROIs and the hyperspectral images of the unflooded range of the reservoir drawdown zone were imported into ArcGIS 10.5, and the longitude and latitude information of the center point of each ROI were extracted. The “Extract values ​​to Points” tool was used to obtain the spectral information of all bands of each ground monitoring point.

[0010] Further, the step 3.4 includes: Step 3.4.1, import the latitude and longitude files of the selected ground monitoring points into the portable GPS, and manually reach the actual measurement point of methane flux with the assistance of GPS; Step 3.4.2, use Picarro G2301 to measure the methane concentration in the unflooded drawdown zone at the measurement point, and the measurement time is 20-30 minutes; In step 3.4.3, the methane concentration is converted into methane flux according to the conversion formula.

[0011] Further, the step 4 comprises: Step 4.1, using ENVI5.2 and Matlab 2018 to perform envelope removal and differential processing on the hyperspectral spectrum of each measured point, eliminating background noise, and obtaining the spectral reflectance of each measured point after preprocessing; Step 4.2, import the preprocessed hyperspectral information of each measured point into Matlab 2018, use the competitive adaptive reweighted sampling CARS algorithm to perform preliminary dimensionality reduction on the hyperspectral information of each measured point, and extract the spectral bands sensitive to methane flux changes from all bands; Step 4.3, the number of spectral bands screened by CARS is determined. The number of spectral bands after dimensionality reduction by CARS is determined. If the number is less than 3, all bands after dimensionality reduction are retained; if the number is greater than 3, the first 3 bands after dimensionality reduction are retained.

[0012] Furthermore, the step 4.1 includes: Step 4.1.1, import the hyperspectral data of each measured point into ENVI5.2, call the envelope removal tool, and obtain the spectrum of each measured point after the envelope is removed; In step 4.1.2, the spectrum after removing the envelope of each measured point is imported into Matlab 2018, and the differential function is called to perform differential processing on the spectrum to further eliminate the noise and highlight the spectral features, and obtain the high-spectral number of each measured point after preprocessing.

[0013] Further, the step 4.2 includes: Step 4.2.1, random division of the data set, randomly divide the methane flux in the reservoir drawdown zone measured in the field in Matlab 2018; Step 4.2.2, exponential decay wavelength selection, the number of spectral bands in each iteration decreases gradually, and the ratio of the number of variables sampled in the i-th time is determined by the exponential decreasing function; Step 4.2.3, adaptive reweighted sampling, resampling is performed according to the ratio of the number of variables sampled in the i-th time determined in step 4.2.2, the partial least squares regression model is called, and the root mean square error RMSECV of the model on the validation set is calculated; Step 4.2.4, loop iteration, repeat steps 4.2.2 and 4.2.3, stop when the maximum number of iterations is reached, determine the spectral bands sensitive to methane flux changes and the priority order of these bands based on the minimum RMSECV, and extract the spectral reflectances corresponding to these bands for later use.

[0014] Further, the step 5 comprises: Step 5.1, place the latitude and longitude of the measured points and the spectral reflectance of the spectral bands sensitive to methane flux changes into a specified folder, divide the measured methane flux into a modeling set and a validation set at a ratio of 0.75:0.25, and import them into ArcGIS10.5; Step 5.2: Based on the modeling set and the spectral reflectance corresponding to the spectral bands sensitive to methane flux changes, the geographically weighted regression (GWR) algorithm that takes into account spatial heterogeneity is used to add GWRs one by one according to the priority order of the optimal spectral band set, and the methane flux inversion models of the drawdown zone that take into account spatial heterogeneity are established.

[0015] Further, the step 7 comprises: Step 7.1, according to the vector range of the hyperspectral image corresponding to the unflooded drawdown zone extracted in step 3.3, the hyperspectral image of the corresponding range is obtained by clipping, and the longitude and latitude of all pixels in the current unflooded drawdown zone are extracted in ArcGIS 10.5 as the input data of the GWR position parameters of the inversion model; Step 7.2, according to the optimal spectral band set determined in step 4, extract the spectral reflectance corresponding to the optimal spectral band of the entire unflooded drawdown zone as the input data of the GWR inversion parameters of the inversion model; Step 7.3, based on the longitude and latitude of the center point of the unflooded drawdown zone and the spectral information corresponding to the optimal spectral band, call the optimal model for methane flux inversion determined in step 6 to obtain the methane flux of the entire unflooded drawdown zone, and analyze the spatial distribution of the methane flux in the unflooded drawdown zone of the reservoir area based on the longitude and latitude location information.

[0016] The present invention proposes a remote sensing rapid estimation method for methane carbon flux in the unflooded drawdown zone of the reservoir area. The method combines the large-scale monitoring advantages of hyperspectral remote sensing satellite images and the spatial analysis capabilities of GWR that take into account heterogeneity. After envelope removal and differential preprocessing, the spectral response information of methane carbon flux in the unflooded drawdown zone of the Three Gorges Reservoir is screened using improved CARS. ArcGIS10.5, ENVI5.2, and Matlab 2018 are combined to establish an inversion model and precision verification of methane flux in the unflooded drawdown zone based on a small number of measured methane flux data. Based on the model inversion, the methane flux and spatial distribution of the unflooded drawdown zone of the entire Three Gorges Reservoir area are obtained. This method uses a small number of bands, takes into account heterogeneity, and achieves rapid estimation of methane flux with less spectral information, providing technical support for obtaining methane carbon flux in the unflooded drawdown zone of the Three Gorges Reservoir on a large scale and over a wide range using hyperspectral remote sensing satellites. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a schematic diagram of screening hyperspectral images of the drawdown zone in the Three Gorges Reservoir area according to an embodiment of the present invention; Figure 2 Schematic diagram of preprocessing of hyperspectral images of the drawdown zone of the Three Gorges Reservoir area according to an embodiment of the present invention; Figure 3 It is a schematic diagram of spectrum extraction and methane flux measurement of measured points in the non-flooded area of ​​the drawdown zone of the Three Gorges Reservoir according to an embodiment of the present invention; Figure 4 A schematic diagram of spectral dimension reduction using a competitive adaptive reweighted sampling algorithm according to an embodiment of the present invention; Figure 5 A schematic diagram of the construction and evaluation of a methane flux inversion model for the drawdown zone of the Three Gorges Reservoir area according to an embodiment of the present invention; Figure 6 This is a schematic diagram of the methane flux estimation and spatial law disclosure in the unflooded area of ​​the drawdown zone of the Three Gorges Reservoir according to an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] The embodiment of the present invention provides a method for rapid remote sensing estimation of methane carbon flux in an unflooded drawdown zone of a reservoir area, comprising the following steps: Step 1: According to the complete vector range of the drawdown zone in the Three Gorges Reservoir, set the imaging time and cloud coverage, and select hyperspectral remote sensing images such as Figure 1 The detailed implementation steps are described as follows: Step 1.1, preliminarily screen the hyperspectral remote sensing images based on the existing complete drawdown zone vector range of the Three Gorges Reservoir area; Step 1.2, add screening conditions, set the cloud coverage not to exceed 25%, set the imaging time to the actual methane flux measurement time, and further screen the hyperspectral images that meet the requirements.

[0020] Step 2: Preprocess the selected hyperspectral remote sensing images to obtain the spectral reflectance of the hyperspectral images. The preprocessing includes orthorectification, radiation correction, and geometric correction. Figure 2 The detailed implementation steps are described as follows: Step 2.1, open the ENVI5.2 orthorectification tool and perform orthorectification on the selected hyperspectral image to obtain an image without geometric distortion; Step 2.2, open the ENVI5.2 radiation correction tool, perform radiation calibration on the selected hyperspectral image, and obtain the true spectral reflectance of the surface; Step 2.3, open the ENVI5.2 geometric correction tool, call the header file of the hyperspectral remote sensing image, and set the parameters to obtain a hyperspectral image with accurate location.

[0021] Step 3: Vectorized extraction of water body range in the Three Gorges Reservoir area and spectral extraction of ground monitoring points and measured methane flux data: Use ArcGIS 10.5 to vectorize the extraction of water body boundaries, and cut out the vector range of the unflooded drawdown zone based on the own complete vector range data of the drawdown zone; manually select the ROI of the area of ​​interest of the methane flux ground monitoring point and the longitude and latitude of the center point, and extract the spectral information of all bands of the ground monitoring point based on the longitude and latitude; measure the methane concentration and convert it into methane flux. Figure 3 The detailed implementation steps are described as follows: Step 3.1, import the preprocessed hyperspectral image into ArcGIS10.5 to vectorize the vector range of the current water body of the Three Gorges Reservoir; Step 3.2, import the existing complete drawdown zone vector range of the Three Gorges Reservoir area, call the symmetrical difference tool in the ArcGIS 10.5 analysis tool, and obtain the vector range of the unflooded drawdown zone of the Three Gorges Reservoir; Step 3.3, ArcGIS calls the mask tool (Extracted by Mask) in "Spatial Analyst Tools" to crop the hyperspectral remote sensing image of the unflooded area of ​​the drawdown zone of the Three Gorges Reservoir; Step 3.4: Import the hyperspectral remote sensing image of the unflooded area of ​​the drawdown zone of the Three Gorges Reservoir into ENVI5.2, manually select no less than 40 ground monitoring points for methane flux, and manually select one ROI at each measured point, requiring that each ROI contain no less than 5 pixels, and export the vector range and central point longitude and latitude of the ROI, obtain the methane concentration data of the drawdown zone of the Three Gorges Reservoir on site and convert it into methane flux. Step 3.4 specifically includes: Step 3.4.1, import the latitude and longitude files of the selected ground monitoring points into the portable GPS, and manually reach the actual measurement point of methane flux with the assistance of GPS; Step 3.4.2, use Picarro G2301 to measure the methane concentration in the unflooded drawdown zone at the measurement point, and the measurement time is 20-30 minutes; Step 3.4.3, use the conversion formula to convert the methane concentration into methane flux according to the formula. The conversion formula is as follows: in, represents the methane flux at the grass-air interface in the unflooded drawdown zone, represents the rate of change of methane over time ( ); V is the volume of methane in the measuring instrument ( ); S is the range of the drawdown zone covered by the measuring instrument ( ); is the volume fraction of methane at standard temperature and pressure Conversion factor to mg units , F is the conversion factor from seconds to hours ( ).

[0022] In step 3.5, the vector ranges of all ROIs and the hyperspectral images of the unflooded range of the drawdown zone of the Three Gorges Reservoir were imported into ArcGIS 10.5, the longitude and latitude information of the center point of each ROI were extracted, and the spectral information of all bands of the ground monitoring points was obtained using the “Extract values ​​to Points” tool.

[0023] Step 4: Use the competitive adaptive reweighted sampling CARS algorithm to extract spectral features from the spectral information of all bands of the ground monitoring points extracted in step 3, and screen the spectral band range and corresponding spectral reflectance that are sensitive to methane flux changes. Figure 4 The detailed implementation steps are described as follows: Step 4.1: In order to highlight the reflection characteristics of different ROIs, ENVI5.2 and Matlab 2018 are used to perform envelope removal and differential processing on the hyperspectral spectrum of each measured point, eliminate background noise, and obtain the hyperspectral information of each measured point after preprocessing. Step 4.1 specifically includes: Step 4.1.1, import the hyperspectral data of each measured point into ENVI5.2, call the envelope removal tool, and obtain the spectrum of each measured point after the envelope is removed; In step 4.1.2, the spectrum after removing the envelope of each measured point is imported into Matlab 2018, and the differential function is called to perform differential processing on the spectrum to further eliminate the noise and highlight the spectral features, and obtain the high-spectral number of each measured point after preprocessing.

[0024] Step 4.2: Import the preprocessed hyperspectral information of each measured point into Matlab 2018, use the competitive adaptive reweighted sampling CARS algorithm to perform preliminary dimensionality reduction on the hyperspectral information of each measured point, and extract the spectral bands sensitive to methane flux changes from all bands. Step 4.2 specifically includes: Step 4.2.1, random division of the data set, randomly divide the methane flux in the drawdown zone of the Three Gorges Reservoir measured in the field in Matlab 2018; Step 4.2.2, exponential decay wavelength selection, the number of spectral bands in each iteration decreases gradually, and the parameters of the exponential decay function are: By constraints , and finally determine the ratio of the number of variables sampled for the i-th time: Step 4.2.3, adaptive reweighted sampling, resampling is performed according to the ratio of the number of variables sampled in the i-th time determined in step 4.2.2, the partial least squares regression model is called, and the root mean square error RMSECV of the model on the validation set is calculated; Step 4.2.4, loop iteration, repeat steps 4.2.2 and 4.2.3, stop when the maximum number of iterations is reached, determine the spectral bands sensitive to methane flux changes and the priority order of these bands based on the minimum RMSECV, and extract the spectral reflectances corresponding to these bands for later use.

[0025] Step 4.3, the number of spectral bands screened by CARS is judged. If the number is less than 3, all the bands after dimensionality reduction are retained; if the number is greater than 3, the first 3 bands after dimensionality reduction are retained.

[0026] The number and range of bands selected for the final methane flux in the drawdown zone of the Three Gorges Reservoir are shown in Table 1.

[0027] Table 1 Number and range of bands screened by competitive adaptive reweighted sampling Indicates the band is Spectral reflectance in nm; Step 5: Based on the screened spectral reflectance, a local model geographically weighted regression (GWR) that takes into account spatial heterogeneity is used to construct a methane flux inversion model in the drawdown zone. Figure 5 The detailed implementation steps are described as follows: Step 5.1, place the latitude and longitude of the measured points and the spectral reflectance of the spectral bands sensitive to methane flux changes into a specified folder, divide the measured methane flux into a modeling set and a validation set at a ratio of 0.75:0.25, and import them into ArcGIS10.5; Step 5.2: Based on the modeling set and the spectral reflectance corresponding to the spectral bands sensitive to methane flux changes, the geographically weighted regression (GWR) algorithm that takes spatial heterogeneity into account is used to add GWRs one by one according to the priority order of the optimal spectral band set, and methane flux inversion models that take spatial heterogeneity into account are established.

[0028] Step 6: Calculate the determination coefficient R of the measured and estimated methane flux in the drawdown zone of the reservoir area on the validation set 2 , residual sum of squares RSS, modified Akaike information criterion AICc, and effective parameter ENP are used to evaluate the reliability of the model and obtain the best model for methane flux inversion. Figure 5 The detailed implementation steps are described as follows: The optimal inversion model obtained by each quality index is: In the formula, is the methane flux, is the jth spectral band after dimension reduction of the i-th methane observation point (in this study, ), For The regression coefficient of the jth screening band at position, and 0.315 is the random error term.

[0029] Step 6.1: Calculate the determination coefficient R of methane flux estimation using multiple inversion models on the validation set. 2 , residual sum of squares (RSS), modified Akaike information criterion (AICc), effective parameter (ENP); Step 6.2, compare the R of each model 2, RSS, AICc, and ENP parameters, and the model with the highest R2 and low RSS, AICc, and ENP was selected as the optimal model for methane flux estimation.

[0030] Step 7: Estimation of methane flux above water in the drawdown zone of the Three Gorges Reservoir: Extract the corresponding hyperspectral image and the longitude and latitude of all pixels according to the vector range of the unflooded drawdown zone obtained in step 3, input the longitude and latitude of all pixels and the spectral reflectance sensitive to methane flux changes obtained in step 4 into the best model for methane flux inversion, and obtain the estimated value of methane flux in the entire unflooded drawdown zone. Figure 6 The detailed implementation steps are described as follows: Step 7.1, according to the hyperspectral image vector range corresponding to the unflooded drawdown zone extracted in step 3.3, the hyperspectral image of the corresponding range is obtained by clipping, and the longitude and latitude of all pixels in the current unflooded drawdown zone are extracted in ArcGIS 10.5 as the input data of the GWR position parameters of the inversion model; Step 7.2, according to the optimal spectral band set determined in step 4, extract the spectral reflectance corresponding to the optimal spectral band of the entire unflooded drawdown zone as the input data of the GWR inversion parameters of the inversion model; Step 7.3, based on the longitude and latitude of the center point of the unflooded drawdown zone and the spectral information corresponding to the optimal spectral band, call the inversion model determined in step 6 to obtain the methane flux of the entire unflooded drawdown zone, and analyze the spatial distribution of the methane flux in the unflooded drawdown zone of the reservoir area according to the longitude and latitude location information.

[0031] The present invention has few parameters and high efficiency, and can monitor the methane flux in the unflooded drawdown zone of the Three Gorges Reservoir area with large hydrological influence and strong spatial heterogeneity in near real time with high efficiency, providing technical support for scientifically evaluating the methane flux in the drawdown zone of the reservoir area and the benefit evaluation of reducing sources and increasing sinks.

[0032] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for rapid remote sensing estimation of methane carbon flux in the unflooded drawdown zone of a reservoir area, comprising the following steps: Step 1: According to the complete vector range of the existing reservoir drawdown zone, set the imaging time and cloud coverage, and select the hyperspectral remote sensing image; Step 2: Preprocessing the selected hyperspectral remote sensing images to obtain the spectral reflectance of the hyperspectral images, wherein the preprocessing includes orthorectification, radiation correction, and geometric correction; Step 3: Vector extraction of the water body range in the reservoir area and spectral extraction of ground monitoring points and measured methane flux data: ArcGIS 10.5 was used to vectorize the extraction of the boundary of the water body, and the vector range of the unflooded drawdown zone was clipped based on the complete vector range data of the drawdown zone; the ROI of the area of ​​interest and the longitude and latitude of the center point of the methane flux ground monitoring point were manually selected, and the spectral information of all bands of the ground monitoring point was extracted based on the longitude and latitude; the methane concentration was measured and converted into methane flux; Step 4: Use the competitive adaptive reweighted sampling CARS algorithm to extract spectral features from the spectral information of all bands of the ground monitoring points extracted in step 3, and screen the spectral band range and corresponding spectral reflectance that are sensitive to methane flux changes; Step 5: Based on the screened spectral reflectance, the local model geographically weighted regression (GWR) taking into account spatial heterogeneity is used to construct the methane flux inversion model in the drawdown zone; Step 6: Model evaluation: Calculate the determination coefficient R of the measured and estimated methane flux in the drawdown zone of the reservoir on the validation set 2 , residual sum of squares RSS, modified Akaike information criterion AICc, and effective parameter ENP are used to evaluate the reliability of the model, so as to obtain the best model for methane flux inversion; Step 7: Estimation of methane flux above water in the drawdown zone of the reservoir: Extract the corresponding hyperspectral image and the longitude and latitude of all pixels according to the vector range of the unflooded drawdown zone obtained in step 3, input the longitude and latitude of all pixels and the spectral reflectance sensitive to methane flux changes obtained in step 4 into the optimal model for methane flux inversion to obtain the estimated value of methane flux in the entire unflooded drawdown zone.

2. The method for rapid remote sensing estimation of methane carbon flux in the unflooded drawdown zone of a reservoir area according to claim 1 is characterized in that: The step 1 comprises: Step 1.1, preliminarily screen the hyperspectral remote sensing images according to the vector range of the complete drawdown zone of the existing reservoir area; Step 1.2, add screening conditions, set the cloud coverage not to exceed 25%, set the imaging time to the actual methane flux measurement time, and further screen the hyperspectral remote sensing images that meet the requirements.

3. According to the method for rapid remote sensing estimation of methane carbon flux in the unflooded drawdown zone of a reservoir area in claim 1, the step 2 comprises: Step 2.1, open the ENVI5.2 orthorectification tool and perform orthorectification on the selected hyperspectral image to obtain an image without geometric distortion; Step 2.2, open the ENVI5.2 radiation correction tool, perform radiation calibration on the selected hyperspectral image, and obtain the true spectral reflectance of the surface; Step 2.3, open the ENVI5.2 geometric correction tool, call the header file of the hyperspectral remote sensing image, and set the parameters to obtain a hyperspectral image with accurate location.

4. The method for rapid remote sensing estimation of methane carbon flux in the unflooded drawdown zone of a reservoir area according to claim 1 is characterized in that: The step 3 comprises: Step 3.1, import the preprocessed hyperspectral image into ArcGIS10.5 to vectorize the vector range of the current water body in the reservoir area; Step 3.2, import the existing complete drawdown zone vector range of the reservoir area, call the symmetric difference tool in the ArcGIS10.5 analysis tool, and obtain the vector range of the drawdown zone of the reservoir area that is not currently flooded; Step 3.3, ArcGIS calls the mask tool in "Spatial Analyst Tools" to crop the hyperspectral remote sensing image of the unflooded area of ​​the reservoir drawdown zone; Step 3.4, import the hyperspectral remote sensing image of the unflooded area of ​​the reservoir drawdown zone into ENVI5.2, manually select no less than 40 ground monitoring points for methane flux, and manually select one region of interest (ROI) at each measured point, requiring that each region of interest (ROI) contain no less than 5 pixels, derive the vector range and longitude and latitude of the center point of the region of interest (ROI), obtain the methane concentration data of the reservoir drawdown zone on site and convert it into methane flux; In step 3.5, the vector ranges of all ROIs and the hyperspectral images of the unflooded range of the reservoir drawdown zone were imported into ArcGIS 10.5, and the longitude and latitude information of the center point of each ROI were extracted. The spectral information of all bands of each ground monitoring point was obtained using the "Multi-value Extraction to Point" tool.

5. The method for rapid remote sensing estimation of methane carbon flux in the unflooded drawdown zone of a reservoir area according to claim 4 is characterized in that: The step 3.4 comprises: Step 3.4.1, import the latitude and longitude files of the selected ground monitoring points into the portable GPS, and manually reach the actual measurement point of methane flux with the assistance of GPS; Step 3.4.2, use Picarro G2301 to measure the methane concentration in the unflooded drawdown zone at the measurement point, and the measurement time is 20-30 minutes; In step 3.4.3, the methane concentration is converted into methane flux according to the conversion formula.

6. The method for rapid remote sensing estimation of methane carbon flux in the unflooded drawdown zone of a reservoir area according to claim 1 is characterized in that: The step 4 comprises: Step 4.1, using ENVI5.2 and Matlab 2018 to perform envelope removal and differential processing on the hyperspectral spectrum of each measured point, eliminating background noise, and obtaining the spectral reflectance of each measured point after preprocessing; Step 4.2, import the preprocessed hyperspectral information of each measured point into Matlab 2018, use the competitive adaptive reweighted sampling CARS algorithm to perform preliminary dimensionality reduction on the hyperspectral information of each measured point, and extract the spectral bands sensitive to methane flux changes from all bands; Step 4.3, the number of spectral bands screened by CARS is determined. The number of spectral bands after dimensionality reduction by CARS is determined. If the number is less than 3, all bands after dimensionality reduction are retained; if the number is greater than 3, the first 3 bands after dimensionality reduction are retained.

7. A method for rapid remote sensing estimation of methane carbon flux in the unflooded drawdown zone of a reservoir area according to claim 6, characterized in that: The step 4.1 comprises: Step 4.1.1, import the hyperspectral data of each measured point into ENVI5.2, call the envelope removal tool, and obtain the spectrum of each measured point after the envelope is removed; In step 4.1.2, the spectrum after removing the envelope of each measured point is imported into Matlab 2018, and the differential function is called to perform differential processing on the spectrum to further eliminate the noise and highlight the spectral features, and obtain the high-spectral number of each measured point after preprocessing.

8. The method for rapid remote sensing estimation of methane carbon flux in the unflooded drawdown zone of a reservoir area according to claim 6 is characterized in that: The step 4.2 comprises: Step 4.2.1, random division of the data set, randomly divide the methane flux in the reservoir drawdown zone measured in the field in Matlab 2018; Step 4.2.2, exponential decay wavelength selection, the number of spectral bands in each iteration decreases gradually, and the ratio of the number of variables sampled in the i-th time is determined by the exponential decreasing function; Step 4.2.3, adaptive reweighted sampling, resampling is performed according to the ratio of the number of variables sampled in the i-th time determined in step 4.2.2, the partial least squares regression model is called, and the root mean square error RMSECV of the model on the validation set is calculated; Step 4.2.4, loop iteration, repeat steps 4.2.2 and 4.2.3, stop when the maximum number of iterations is reached, determine the spectral bands sensitive to methane flux changes and the priority order of these bands based on the minimum RMSECV, and extract the spectral reflectances corresponding to these bands for later use.

9. The method for rapid remote sensing estimation of methane carbon flux in the unflooded drawdown zone of a reservoir area according to claim 1 is characterized in that: The step 5 comprises: Step 5.1, place the latitude and longitude of the measured points and the spectral reflectance of the spectral bands sensitive to methane flux changes into a specified folder, divide the measured methane flux into a modeling set and a validation set at a ratio of 0.75:0.25, and import them into ArcGIS10.5; Step 5.2: Based on the modeling set and the spectral reflectance corresponding to the spectral bands sensitive to methane flux changes, the geographically weighted regression (GWR) algorithm that takes into account spatial heterogeneity is used to add GWRs one by one according to the priority order of the optimal spectral band set, and the methane flux inversion models of the drawdown zone that take into account spatial heterogeneity are established.

10. The method for rapid remote sensing estimation of methane carbon flux in the unflooded drawdown zone of a reservoir area according to claim 1, characterized in that: The step 7 comprises: Step 7.1, according to the vector range of the hyperspectral image corresponding to the unflooded drawdown zone extracted in step 3.3, the hyperspectral image of the corresponding range is obtained by clipping, and the longitude and latitude of all pixels in the current unflooded drawdown zone are extracted in ArcGIS 10.5 as the input data of the GWR position parameters of the inversion model; Step 7.2, according to the optimal spectral band set determined in step 4, extract the spectral reflectance corresponding to the optimal spectral band of the entire unflooded drawdown zone as the input data of the GWR inversion parameters of the inversion model; Step 7.3, based on the longitude and latitude of the center point of the unflooded drawdown zone and the spectral information corresponding to the optimal spectral band, call the optimal model for methane flux inversion determined in step 6 to obtain the methane flux of the entire unflooded drawdown zone, and analyze the spatial distribution of the methane flux in the unflooded drawdown zone of the reservoir area based on the longitude and latitude location information.

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