Ecological environment surveying and mapping system and method based on remote sensing

Abstract

The invention relates to the technical field of remote sensing surveying and mapping, in particular to an ecological environment surveying and mapping system and method based on remote sensing, and the system comprises a remote sensing image acquisition module, a preprocessing module, an environmental factor analysis module, an ecological environment parameter analysis module, a data fusion module and an output module. The remote sensing image acquisition module, the preprocessing module, the environmental factor analysis module, the ecological environment parameter analysis module, the data fusion module and the output module are connected in sequence; the preprocessing module comprises a radiation correction unit, a geometric correction unit and a noise removal unit; the data fusion module comprises a multi-source data fusion unit and a precision evaluation unit, and the output module comprises a visualization unit and a report generation unit. According to the method, accurate and reliable ecological environment data are provided by utilizing fusion of remote sensing images and environment data in combination with precision evaluation, and decision makers are helped to better understand and cope with ecological environment changes through visual display and report generation.

Description

Technical Field

[0001] The present invention relates to the technical field of remote sensing mapping, and in particular to a remote sensing-based ecological environment mapping system and method. Background Art

[0002] With the increasing awareness of ecological environmental protection, ecological environmental mapping systems based on remote sensing technology have gradually become an important tool for environmental science research and management. Through remote sensing image analysis, these systems can quickly and accurately obtain detailed information on the ecological environment, providing a scientific basis for environmental monitoring, assessment and management. However, the existing ecological environmental mapping systems still have some shortcomings in data processing, environmental adaptability and accuracy, which affect the application effect of the system.

[0003] After searching, a surveying and mapping method based on remote sensing big data analysis with a publication number of CN118274805B was disclosed, and the publication date is September 24, 2024. This method analyzes the current surveying and mapping operation environment suitability coefficient of the target task point from three dimensions: wind environment, atmospheric environment, and radiation environment, and determines whether the current environment of the target task point is suitable for surveying and mapping operations. If the judgment result is no, the target task point is marked as a delayed task point and its surveying and mapping task is suspended. If the judgment result is yes, a reference image map is collected to further verify the accuracy of the current environmental suitability judgment of the target task point, effectively realize the pre-placement impact analysis of the remote sensing surveying and mapping environment, and significantly reduce the risk of surveying and mapping data distortion caused by environmental factors. However, in this technical solution, the main focus is on the analysis of environmental factors, and there is a lack of detailed analysis and processing of specific parameters of the ecological environment (such as vegetation coverage, water quality, etc.), which limits the scope and accuracy of application in ecological environment monitoring.

[0004] After searching, a method for enhancing geological mapping remote sensing images with a publication number of CN118967508B was disclosed, and the publication date is December 10, 2024. The method first obtains a multispectral image of the geological mapping area to be measured, performs color space conversion on the multispectral image, and obtains the hue channel value and brightness channel value of each pixel in the multispectral image; the multispectral image is segmented to obtain different geological areas and the categories of each geological area in the multispectral image, and the geological area is segmented into superpixels, and the edge shape characteristics of the superpixel block, as well as the distribution of the hue channel value and the change of the brightness channel value inside it, obtain the non-natural feature coefficient of the superpixel block, and then obtain the interference index of the ground object classification of each geological area, denoise the multispectral image, and obtain an enhanced image. However, this technical solution mainly focuses on the enhanced processing of geological mapping images, lacks comprehensive analysis and processing of ecological environmental parameters, and cannot fully meet the needs of ecological environmental mapping.

[0005] The above problems show that the existing remote sensing mapping methods and image enhancement technologies have certain deficiencies in the application of ecological environment monitoring. Specifically, the existing technologies mainly focus on the analysis of environmental factors and image enhancement processing, lacking detailed analysis and comprehensive processing of ecological environment parameters, resulting in limited application scope and accuracy in ecological environment monitoring. Summary of the invention

[0006] Based on the above purpose, the present invention provides an ecological environment mapping system and method based on remote sensing, an ecological environment mapping system based on remote sensing, comprising a remote sensing image acquisition module, a preprocessing module, an environmental factor analysis module, an ecological environment parameter analysis module, a data fusion module and an output module, wherein the remote sensing image acquisition module, the preprocessing module, the environmental factor analysis module, the ecological environment parameter analysis module, the data fusion module and the output module are connected in sequence; The preprocessing module includes a radiation correction unit, a geometric correction unit and a noise removal unit; The environmental factor analysis module includes a wind environment analysis unit, an atmospheric environment analysis unit, and a radiation environment analysis unit; The ecological environment parameter analysis module includes vegetation coverage analysis unit, water quality analysis unit and soil quality analysis unit; The data fusion module includes a multi-source data fusion unit and an accuracy assessment unit, and the output module includes a visualization unit and a report generation unit.

[0007] Preferably, the remote sensing image acquisition module is used to acquire a multispectral remote sensing image of the target area, the band range of the multispectral remote sensing image includes visible light, near infrared, mid-infrared and thermal infrared, the image resolution is not less than 1 meter, and the coverage range includes the terrain, landforms, vegetation, water bodies and soil of the target area; The remote sensing image acquisition module acquires multispectral remote sensing images from satellites, drones and ground sensors. The acquired multispectral remote sensing images are preliminarily preprocessed through the following steps: S1: Images with substandard quality are eliminated through image screening algorithms. The image screening algorithm includes pixel quality assessment and consistency check of image overlap areas to ensure that the quality of each image meets the requirements of subsequent processing; S2: Perform image stitching, format conversion and preliminary denoising on the qualified images. The image stitching algorithm stitches multiple images acquired at different times and angles into a complete image. At the same time, the format conversion algorithm is used to convert the images into a unified format. The preliminary denoising uses the median filtering algorithm to remove random noise in the image.

[0008] Preferably, in the preprocessing module, the pre-processed multispectral remote sensing image is used as input, and radiation correction, geometric correction and noise removal are performed to obtain a pre-processed multispectral remote sensing image; The radiation correction unit is used to eliminate the influence of atmospheric scattering and inconsistent sensor response on the image, including atmospheric correction, sensor correction and radiation calibration. The geometric correction unit is used to correct the geometric distortion of the image, including control point selection, geometric transformation and resampling; The noise removal unit is used to eliminate the noise in the image, which specifically includes median filtering, mean filtering and Gaussian filtering.

[0009] Preferably, the radiation correction unit, the geometric correction unit, and the noise removal unit specifically include the following: The atmospheric correction adopts the MODTRAN model to simulate the atmospheric transmission process, calculate the atmospheric parameters through the inversion method, and correct the atmospheric influence in the multi-spectral remote sensing image; The sensor calibration adopts dark current calibration and gain calibration; Radiometric calibration uses a standard radiation source to calibrate the radiation value of the multispectral remote sensing image to correspond to the actual surface reflectivity; The control point selection adopts a combination of automatic matching and manual verification to ensure the accuracy and reliability of the control points; The geometric transformation adopts a polynomial transformation model to establish the mapping relationship between multispectral remote sensing image coordinates and geographic coordinates through control points; The resampling adopts bilinear interpolation method to improve the quality of multispectral remote sensing images after geometric transformation; Median filtering is used to remove salt and pepper noise in images, mean filtering is used to smooth local noise in images, and Gaussian filtering is used to eliminate high-frequency noise in images.

[0010] Preferably, in the environmental factor analysis module, the pre-processed multispectral remote sensing image is used as input, and the environmental factor analysis results of the target area are obtained through wind environment analysis, atmospheric environment analysis, and radiation environment analysis; Among them, the wind environment analysis unit is used to analyze the wind speed and wind direction in the target area, including wind field modeling and wind speed distribution calculation; The atmospheric environment analysis unit is used to analyze the atmospheric environment of the target area, including the calculation of atmospheric transparency, atmospheric pressure and humidity parameters; The radiation environment analysis unit is used to analyze the radiation environment of the target area, specifically including the calculation of solar radiation, surface radiation and atmospheric radiation.

[0011] Preferably, the wind field modeling adopts a numerical meteorological model, specifically a WRF model, to simulate the wind field in the target area; The wind speed distribution calculation uses wind field data and terrain data to calculate the wind speed and direction at different locations in the target area; The atmospheric transparency is calculated using the atmospheric radiation transfer model based on parameters such as aerosols, water vapor and ozone in the atmosphere. The atmospheric pressure and humidity parameters are calculated by using meteorological observation data and humidity information in remote sensing images, and the atmospheric pressure and humidity of the target area are calculated by inversion method; Solar radiation calculation uses the solar radiation model to calculate the solar radiation intensity based on the solar altitude angle and direct solar radiation; The surface radiation calculation uses the surface reflectivity model to calculate the surface radiation according to the surface type and surface temperature; Atmospheric radiation calculation adopts the atmospheric radiation transfer model to calculate the atmospheric radiation based on parameters such as aerosols, water vapor and ozone in the atmosphere.

[0012] Preferably, in the ecological environment parameter analysis module, the pre-processed multispectral remote sensing image and the environmental factor analysis results are used as input, and the ecological environment parameter analysis results of the target area are output through vegetation coverage analysis, water quality analysis and soil quality analysis; The vegetation coverage analysis unit is used to analyze the vegetation coverage of the target area, including vegetation index calculation, vegetation classification and vegetation coverage estimation; The water quality analysis unit is used to analyze the water quality of the target area, including water reflectivity calculation, water quality parameter inversion and water classification; The soil quality analysis unit is used to analyze the soil quality of the target area, including soil reflectivity calculation, soil composition analysis and soil classification.

[0013] Preferably, the vegetation index is calculated by using the normalized difference vegetation index and the enhanced vegetation index, and the vegetation index is calculated by using the red light and near infrared band data in the remote sensing image; Vegetation classification uses a classification algorithm, specifically maximum likelihood classification, to classify vegetation in the image into different types of vegetation; Vegetation coverage estimation uses vegetation index and classification results to calculate the vegetation coverage of the target area through statistical methods; The water body reflectance is calculated by using the blue and green light band data in the remote sensing image and the water body reflectance is calculated by the inversion method; Water quality parameter inversion uses a mathematical model between water body reflectivity and water quality parameters to invert the concentration of suspended solids, chlorophyll and dissolved organic matter in the water body; Water body classification uses classification algorithms, specifically supervised classification and segmentation classification, to classify water bodies in images into different types of water bodies; The soil reflectance is calculated by using the visible light and near infrared band data in the remote sensing image and the soil reflectance is calculated by the inversion method; Soil composition analysis uses a mathematical model between soil reflectance and soil composition to invert the organic matter content, pH value and salt content in the soil; Soil classification uses a classification algorithm, specifically a support vector machine, to classify the soil in the image into different types of soil.

[0014] Preferably, in the data fusion module, the pre-processed multispectral remote sensing images, environmental factor analysis results and ecological environment parameter analysis results are used as inputs, and through multi-source data fusion and accuracy assessment, the ecological environment mapping data after multi-source data fusion and its accuracy assessment results are output; The multi-source data fusion unit is used to fuse remote sensing images, environmental factor analysis results and ecological environment parameter analysis results, including data registration, data fusion and data correction; Data registration uses control point matching and geometric correction to ensure the spatial alignment of multi-source data; data fusion uses pixel-level fusion and feature-level fusion to integrate information from multi-source data; data correction uses error models and correction algorithms to correct systematic errors and random errors in the fused data. The accuracy evaluation unit is used to evaluate the accuracy of fused data, including accuracy index calculation, error analysis and accuracy verification; The accuracy index calculation uses root mean square error and determination coefficient to evaluate the consistency between the fused data and the ground measured data; the error analysis uses error propagation model and sensitivity analysis to analyze the error sources and influencing factors in the fused data; the accuracy verification uses cross-validation and independent verification to verify the accuracy and reliability of the fused data in different regions and under different conditions.

[0015] Preferably, in the output module, the ecological environment mapping data after multi-source data fusion and its accuracy assessment results are used as input, and the ecological environment monitoring report and the visual display results are output through the visualization unit and the report generation unit; The visualization unit is used to visualize the fused data, including map generation, image display and 3D modeling; The map generation uses GIS software to generate thematic maps from fused data to display the distribution of ecological and environmental parameters in the target area; Image display uses image processing software to generate high-definition images from fused data to display the ecological environment status of the target area; 3D modeling uses 3D modeling software to generate 3D models from fused data to display the three-dimensional structure of the ecological environment in the target area; The report generation unit is used to generate ecological environment monitoring reports, including data statistics, result analysis and report output; Data statistics use statistical analysis software to conduct statistical analysis on the fused data and generate various statistical charts and statistical indicators; result analysis uses environmental science methods to conduct comprehensive analysis on the fused data and evaluate the ecological environment status and changing trends of the target area; report output uses report generation software to generate a formal ecological environment monitoring report from the analysis results and charts.

[0016] Beneficial effects of the present invention: 1. Through modular and systematic processing procedures, the remote sensing image acquisition module can provide high-quality and complete image data for subsequent ecological and environmental monitoring, thus laying a solid foundation for scientific analysis and decision-making support.

[0017] 2. Each link of the preprocessing module ensures the high-quality output of remote sensing image data, laying a solid data foundation for accurate modeling, change detection and resource monitoring of the ecological environment mapping system.

[0018] 3. The radiation correction unit ensures that the radiation value of the image is consistent with the surface reflectivity, so that the physical properties of the image data are authentic; the geometric correction unit ensures the accuracy of the image's geographical location through precise coordinate transformation; the noise removal unit improves the visual quality of the image by eliminating noise in the image.

[0019] 4. Through the orderly connection of data registration, fusion and correction, these steps together achieve the accurate fusion of multi-source data. And the accuracy assessment ensures the reliability of the fused data. This series of connections and steps can greatly improve the accuracy and stability of the ecological environment mapping system, thereby providing accurate data support for environmental protection, resource management and ecological restoration.

[0020] 5. The visualization function improves the user's perception of the ecological environment, while the report generation function provides detailed analysis and suggestions for decision makers. This comprehensive system combining data fusion, accuracy assessment, visualization and report generation can greatly improve the efficiency of ecological environment monitoring and the accuracy of decision-making, and provide strong support for environmental protection and resource management. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0022] Figure 1 is a block diagram of the system of the present invention; Figure 2A flowchart of the steps of performing preliminary preprocessing on the collected multispectral remote sensing images in the system of the present invention; Figure 3 It is a block diagram of the wind environment analysis unit, the atmospheric environment analysis unit, and the radiation environment analysis unit in the system of the present invention. DETAILED DESCRIPTION

[0023] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0024] See also Figure 1-Figure 3 The embodiment of the present invention provides an ecological environment mapping system based on remote sensing, including a remote sensing image acquisition module, a preprocessing module, an environmental factor analysis module, an ecological environment parameter analysis module, a data fusion module and an output module, wherein the remote sensing image acquisition module, the preprocessing module, the environmental factor analysis module, the ecological environment parameter analysis module, the data fusion module and the output module are connected in sequence; The preprocessing module includes a radiation correction unit, a geometric correction unit and a noise removal unit; The environmental factor analysis module includes a wind environment analysis unit, an atmospheric environment analysis unit, and a radiation environment analysis unit; The ecological environment parameter analysis module includes vegetation coverage analysis unit, water quality analysis unit and soil quality analysis unit; The data fusion module includes a multi-source data fusion unit and an accuracy assessment unit, and the output module includes a visualization unit and a report generation unit.

[0025] In one possible implementation, the remote sensing image acquisition module is responsible for acquiring high-resolution, multi-spectral remote sensing images from a variety of sensors and platforms (such as satellites, drones, etc.). These image data provide raw information for subsequent environmental analysis and are the starting point of the system. The acquired images contain information in different bands, such as visible light, near infrared, etc., covering various elements of the ground ecological environment.

[0026] Furthermore, the acquired remote sensing images usually need to be processed by radiation correction, geometric correction and noise removal. The radiation correction unit ensures the accuracy of the image's radiation value by eliminating factors such as atmospheric scattering. The geometric correction unit corrects the image's geographic coordinates to eliminate geometric distortion caused by the shooting angle or terrain. The noise removal unit uses methods such as median filtering or mean filtering to eliminate random noise in the image and improve image quality. These processes ensure the accuracy of the input data and lay the foundation for subsequent analysis.

[0027] Furthermore, the goal of the environmental factor analysis module is to evaluate the external environmental conditions of the target area. Factors that affect the ecological environment include wind, atmosphere, and radiation. The wind environment analysis unit analyzes the regional wind speed and wind direction distribution through numerical meteorological models to evaluate the impact of wind on the ecological environment. The atmospheric environment analysis unit analyzes the atmospheric composition, transparency and other information of the region based on the data of remote sensing images and the atmospheric transmission model. The radiation environment analysis unit evaluates the potential impact of lighting conditions on plant growth and water quality by calculating the intensity of solar radiation and the reflection characteristics of the surface. These analyses help to fully understand the impact of environmental factors on the ecosystem.

[0028] Furthermore, the ecological environment parameter analysis module is used for specific ecological environment characteristic analysis. The vegetation coverage analysis unit evaluates the growth status of vegetation in the area by calculating the NDVI and EVI indexes; the water quality analysis unit inverts water quality indicators such as suspended matter concentration and chlorophyll concentration through the blue and green light band data in the remote sensing image; the soil quality analysis unit inverts soil composition and its quality using remote sensing data. These analyses provide decision makers with detailed ecological environment status data.

[0029] Furthermore, the data fusion module fuses multi-source data from different analysis units to produce comprehensive analysis results. The multi-source data fusion unit combines data from different times and sources through spatial registration and data merging to form a unified ecological environment data set. The accuracy assessment unit uses cross-validation and error analysis to evaluate the accuracy of data fusion and ensure the reliability of the analysis results.

[0030] Furthermore, the output module displays the final analysis results and generates reports. The visualization unit converts the data into maps, charts or three-dimensional models to intuitively display the various indicators of the ecological environment in the region. The report generation unit automatically generates an ecological environment monitoring report based on the analysis results for reference by relevant departments.

[0031] First, the order of each module is closely connected, ensuring the integrity and accuracy of the data from acquisition to final analysis. Secondly, the preprocessing module effectively improves the quality of remote sensing images and ensures the accuracy of subsequent analysis. The combination of environmental factor analysis and ecological environmental parameter analysis provides support for a comprehensive understanding of the regional ecological status, and the data fusion module improves the reliability of the results by integrating multi-party information. Finally, the output module facilitates users to quickly understand and apply monitoring results through visualization and report generation. This efficient modular design makes the system not only powerful, but also flexible to adapt to different ecological environmental monitoring needs.

[0032] In an embodiment of the present invention, the remote sensing image acquisition module is used to acquire a multispectral remote sensing image of a target area, the band range of the multispectral remote sensing image includes visible light, near infrared, mid-infrared and thermal infrared, the image resolution is not less than 1 meter, and the coverage range includes the terrain, objects, vegetation, water bodies and soil of the target area; The remote sensing image acquisition module acquires multispectral remote sensing images from satellites, drones and ground sensors. The acquired multispectral remote sensing images are preliminarily preprocessed through the following steps: S1: Images with substandard quality are eliminated through image screening algorithms. The image screening algorithm includes pixel quality assessment and consistency check of image overlap areas to ensure that the quality of each image meets the requirements of subsequent processing; S2: Perform image stitching, format conversion and preliminary denoising on the qualified images. The image stitching algorithm stitches multiple images acquired at different times and angles into a complete image. At the same time, the format conversion algorithm is used to convert the images into a unified format. The preliminary denoising uses the median filtering algorithm to remove random noise in the image.

[0033] In one possible implementation, the main function of the remote sensing image acquisition module is to acquire multispectral remote sensing images of the target area, including visible light, near infrared, mid-infrared and thermal infrared bands. The resolution of the image is required to be no less than 1 meter, and the coverage should include the terrain, objects, vegetation, water bodies and soil of the target area. These image data are obtained from a variety of sensor sources, mainly including satellites, drones and ground sensors. Each sensor has a different acquisition range and accuracy, ensuring diversity and comprehensiveness, thereby providing comprehensive spatial data support for subsequent ecological and environmental analysis.

[0034] Furthermore, the acquired remote sensing image data needs to be quality controlled to ensure the accuracy and validity of the data. First, the image screening algorithm is used to eliminate images of substandard quality. The screening steps include: Pixel quality assessment: By evaluating the quality of each pixel, such as lighting conditions, noise level, resolution, etc., images with substandard quality due to environmental factors, sensor failure, etc. are eliminated.

[0035] Image overlap area consistency check: For images acquired from different viewing angles or at different time points, the consistency of their overlap areas is compared to determine whether the images can be used for stitching. This step ensures that the subsequent analysis will not be affected by the deviation of the overlap area when stitching the images.

[0036] Furthermore, qualified images are screened and enter the next step of processing, including image stitching and format conversion: Image stitching: stitching multiple images from different times and perspectives. The stitching algorithm needs to accurately align the images based on the spatial registration relationship between the images. This step ensures the integrity and continuity of the images, covers the entire target area, and avoids information omissions due to different acquisition angles.

[0037] Format conversion: In order to facilitate subsequent data processing and storage, the stitched images need to be converted into a unified format. This step can improve the system processing efficiency and ensure that image data from different platforms (such as satellites, drones, and ground sensors) can be smoothly integrated on the same platform for analysis.

[0038] Furthermore, after the image stitching and format conversion, preliminary denoising is performed. The median filter algorithm is used to remove random noise in the image. The median filter can effectively remove image interference caused by sensor noise, environmental interference, etc., making the image smoother and clearer. This provides more accurate input data for subsequent analysis (such as vegetation coverage, water quality monitoring, etc.).

[0039] In the embodiment of the present invention, in the preprocessing module, the multispectral remote sensing image after preliminary preprocessing is taken as input, and radiation correction, geometric correction and noise removal are performed to obtain the preprocessed multispectral remote sensing image; The radiation correction unit is used to eliminate the influence of atmospheric scattering and inconsistent sensor response on the image, including atmospheric correction, sensor correction and radiation calibration. The geometric correction unit is used to correct the geometric distortion of the image, including control point selection, geometric transformation and resampling; The noise removal unit is used to eliminate the noise in the image, which specifically includes median filtering, mean filtering and Gaussian filtering.

[0040] In a possible implementation, the main purpose of radiation correction is to eliminate the influence of atmospheric scattering, inconsistent sensor response, etc. on the image. It includes three submodules: atmospheric correction, sensor correction, and radiation calibration.

[0041] Furthermore, atmospheric correction: the MODTRAN model is used to simulate the atmospheric transmission process and invert the atmospheric parameters to eliminate the influence of the atmosphere on the remote sensing image. The mathematical expression of atmospheric correction calculates the surface reflectance (R) by correcting the atmospheric path influence in the radiant brightness. This correction process can effectively remove the interference of atmospheric factors (such as aerosols and water vapor) on the image, thereby improving the ground reflection characteristics of the image.

[0042] The mathematical expression of atmospheric correction is: ; in, is the surface reflectivity, is the radiation brightness received by the sensor, is the atmospheric path radiance, is the solar spectral irradiance, is the solar zenith angle, is the atmospheric transmittance.

[0043] Furthermore, the sensor calibration uses dark current calibration and gain calibration. Dark current calibration eliminates the background noise generated by the sensor when there is no signal, and gain calibration adjusts the sensor's responsiveness through a coefficient. The formula is: ; in, is the corrected radiance, is the dark current, is the gain coefficient. This step ensures that the radiance of the remote sensing image accurately reflects the characteristics of the ground object.

[0044] Furthermore, geometric correction mainly corrects the geometric distortion of images caused by factors such as sensor position, image acquisition angle and terrain changes. The geometric correction process ensures that the remote sensing image is aligned with the actual geographic coordinate system. Specifically, some ground control points with known coordinates are selected as references to match the pixel coordinates in the image with the actual geographic coordinates. Furthermore, according to the control points, appropriate geometric transformation methods (such as affine transformation, projection transformation, etc.) are used to achieve spatial registration and distortion correction of the image. Furthermore, after the geometric transformation, the image needs to be regenerated through a resampling algorithm (such as bilinear interpolation or nearest neighbor interpolation) to ensure that the corrected image has high spatial accuracy.

[0045] Furthermore, remote sensing images are often interfered by noise (such as sensor noise, environmental noise, etc.), which may affect the subsequent analysis results. The purpose of noise removal is to improve the quality of the image and remove these interferences. Specifically, by replacing each pixel in the image with the median of its surrounding pixels, the salt and pepper noise can be effectively removed. The image is smoothed by calculating the mean of the neighborhood pixels to reduce high-frequency noise. Using the Gaussian kernel function for weighted averaging can effectively remove high-frequency noise and maintain the clarity of the image edges.

[0046] In the embodiment of the present invention, the radiation correction unit, the geometric correction unit, and the noise removal unit specifically include the following: The atmospheric correction adopts the MODTRAN model to simulate the atmospheric transmission process, calculate the atmospheric parameters through the inversion method, and correct the atmospheric influence in the multi-spectral remote sensing image; The sensor calibration adopts dark current calibration and gain calibration; Radiometric calibration uses a standard radiation source to calibrate the radiation value of the multispectral remote sensing image to correspond to the actual surface reflectivity; The control point selection adopts a combination of automatic matching and manual verification to ensure the accuracy and reliability of the control points; The geometric transformation adopts a polynomial transformation model to establish the mapping relationship between multispectral remote sensing image coordinates and geographic coordinates through control points; The resampling adopts bilinear interpolation method to improve the quality of multispectral remote sensing images after geometric transformation; Median filtering is used to remove salt and pepper noise in images, mean filtering is used to smooth local noise in images, and Gaussian filtering is used to eliminate high-frequency noise in images.

[0047] In one possible implementation, the purpose of radiation correction is to eliminate the influence of factors such as the atmosphere and sensors, so that the radiation value of the remote sensing image corresponds to the reflectivity of the surface. The process first simulates the atmospheric transmission process through the MODTRAN model, calculates the atmospheric parameters through inversion, and then corrects the atmospheric influence in the image. Next, the sensor is calibrated, using dark current correction and gain correction to eliminate the noise and error of the sensor itself. Finally, the radiation value received by the sensor is converted into the surface reflectivity through radiation calibration. The mathematical formula of radiation calibration is as follows: ;in, is the surface reflectivity, is the radiation brightness received by the sensor, and are the minimum and maximum values ​​of the radiance, and is the minimum and maximum value of the surface reflectance. This formula realizes the conversion of the radiation brightness observed by the sensor into a standardized value corresponding to the actual surface reflectance, thereby improving the radiation accuracy of the image.

[0048] Furthermore, the core of geometric correction is to establish the mapping relationship between image coordinates and geographic coordinates through the polynomial transformation model. First, through the selection of control points, the method of combining automatic matching with manual verification is adopted to ensure the accuracy and reliability of the control points. Then, the polynomial transformation model is used for geometric correction, and the expression is: ; ;in, and is the geographic coordinate, and is the image coordinate, and are polynomial coefficients. Through this transformation, the pixel coordinates on the image can be converted into corresponding geographic coordinates to ensure the accuracy of the spatial position of the image. The accuracy of geometric correction is crucial for subsequent analysis and application, especially in ecological and environmental mapping, where the accuracy of geographic information directly affects the decision-making of environmental monitoring and resource management.

[0049] Finally, remote sensing images often contain salt and pepper noise, local noise, and high-frequency noise, which may affect the quality of the image and the analysis results. In order to improve the quality of the image, noise removal is required. Median filtering is used to remove salt and pepper noise, mean filtering is used to smooth local noise, and Gaussian filtering is used to eliminate high-frequency noise. The formula for median filtering is as follows: ; in, is the pixel value after filtering, is the radius of the filter window. This method can effectively eliminate different types of noise and improve image quality.

[0050] Radiation correction, geometric correction and noise removal complement each other in the whole system. The radiation correction unit ensures that the radiation value of the image is consistent with the surface reflectivity, so that the physical properties of the image data are authentic; the geometric correction unit ensures the accuracy of the geographical location of the image through precise coordinate transformation; the noise removal unit improves the visual quality of the image by eliminating the noise in the image. The combination of the three ultimately provides a high-quality and accurate remote sensing image, providing reliable data support for subsequent ecological environment mapping.

[0051] In the embodiment of the present invention, in the environmental factor analysis module, the pre-processed multi-spectral remote sensing image is used as input, and the environmental factor analysis results of the target area are obtained through wind environment analysis, atmospheric environment analysis, and radiation environment analysis; Among them, the wind environment analysis unit is used to analyze the wind speed and wind direction in the target area, including wind field modeling and wind speed distribution calculation; The atmospheric environment analysis unit is used to analyze the atmospheric environment of the target area, including the calculation of atmospheric transparency, atmospheric pressure and humidity parameters; The radiation environment analysis unit is used to analyze the radiation environment of the target area, specifically including the calculation of solar radiation, surface radiation and atmospheric radiation.

[0052] In a possible implementation, the wind environment analysis unit is mainly used to analyze the wind speed and wind direction in the target area. The working steps of this unit are first wind field modeling, using numerical models and remote sensing data to simulate wind flow patterns and wind field distribution. By combining the topography, climate conditions and time factors of the target area, a spatial distribution model of the wind field is established. Next, the wind speed distribution is calculated, usually using remote sensing data of wind speed and wind direction, combined with numerical models to perform spatial interpolation of wind speed, and obtain wind speed distribution maps for different regions. This analysis helps to understand the distribution of wind resources and the impact of wind speed on the ecological environment and vegetation growth.

[0053] Furthermore, the atmospheric environment analysis unit focuses on analyzing parameters such as atmospheric transparency, atmospheric pressure and humidity in the target area. First, atmospheric transparency data is obtained through remote sensing images, and the atmospheric correction method in satellite remote sensing technology is combined to estimate the aerosol concentration and water vapor content in the air, thereby obtaining a transparency index. Secondly, the atmospheric pressure and humidity parameters are calculated. These parameters can be combined with remote sensing images and ground meteorological data and calculated using atmospheric models. These atmospheric environmental parameters are crucial for assessing the ecological environment and climate conditions of the region, especially in terms of agricultural and forestry vegetation, water resource management, etc.

[0054] Furthermore, the task of the radiation environment analysis unit is to analyze the radiation environment of the target area, mainly including the calculation of solar radiation, surface radiation and atmospheric radiation. Through remote sensing image data, combined with the solar radiation model, the solar radiation intensity of the target area can be obtained. The calculation of surface radiation depends on the reflection characteristics of the surface type and the atmospheric radiation transmission model to simulate the intensity of reflected radiation from different surface objects. Atmospheric radiation is estimated by inverting the distribution of gases in the atmosphere through remote sensing data and combining it with the radiation transmission model. This analysis helps to understand the energy flow and heat distribution in the region, and then conduct in-depth analysis of the impact on ecological environment changes, climate change, land use and agricultural production.

[0055] These analysis units are closely connected and together form the core of the environmental factor analysis module. By taking remote sensing images as input, the wind, atmosphere and radiation environment analysis units provide different environmental parameters and meteorological data respectively, and the system conducts a comprehensive environmental assessment of the target area based on these results. Wind environment analysis provides basic information about air flow, which affects air quality and climate change; atmospheric environment analysis provides parameters such as atmospheric transparency and humidity, providing reference for the suitability of the ecological environment and climate change trends; radiation environment analysis helps understand the distribution of light and heat, which has an important impact on the energy balance of the ecosystem, vegetation growth and agricultural production.

[0056] In the embodiment of the present invention, the wind field modeling adopts a numerical meteorological model, specifically a WRF model, to simulate the wind field in the target area; The wind speed distribution calculation uses wind field data and terrain data to calculate the wind speed and direction at different locations in the target area; The atmospheric transparency is calculated using the atmospheric radiation transfer model based on parameters such as aerosols, water vapor and ozone in the atmosphere. The atmospheric pressure and humidity parameters are calculated by using meteorological observation data and humidity information in remote sensing images, and the atmospheric pressure and humidity of the target area are calculated by inversion method; Solar radiation calculation uses the solar radiation model to calculate the solar radiation intensity based on the solar altitude angle and direct solar radiation; The surface radiation calculation uses the surface reflectivity model to calculate the surface radiation according to the surface type and surface temperature; Atmospheric radiation calculation adopts the atmospheric radiation transfer model to calculate the atmospheric radiation based on parameters such as aerosols, water vapor and ozone in the atmosphere.

[0057] In one possible implementation, the wind field modeling uses the WRF numerical meteorological model, which can simulate the wind field in the target area, including wind speed and wind direction. This process first establishes a wind model for the target area by inputting terrain data and meteorological observation data. By simulating the flow of wind, the WRF model calculates the spatial distribution of the wind field and provides data support for subsequent wind speed distribution calculations. The wind speed distribution calculation combines wind field data and terrain information to further accurately calculate the wind speed and wind direction at different locations in the target area. Wind analysis helps to understand the potential of wind energy resources and evaluate the impact of wind speed on the ecological environment (such as vegetation growth and climate regulation).

[0058] Furthermore, atmospheric transparency is an important indicator for measuring atmospheric quality and visibility. The calculation method uses the atmospheric radiation transfer model. Through remote sensing image data and atmospheric models, the transparency of the atmosphere is calculated by considering the effects of components such as aerosols, water vapor and ozone. This process can help identify the level of atmospheric pollution and provide a reference for improvement measures.

[0059] The calculation of atmospheric pressure and humidity parameters combines meteorological observation data with humidity information in remote sensing images. Through inversion technology, remote sensing images provide the spatial distribution of atmospheric humidity, which is then combined with meteorological data to invert and calculate the atmospheric pressure and humidity of the target area. These parameters play an important role in assessing the climate conditions of the region and the suitability of the ecosystem.

[0060] Furthermore, the calculation of solar radiation uses a solar radiation model, which calculates the solar radiation intensity of the target area based on the solar altitude angle and direct solar radiation. This step can help evaluate the lighting conditions of the area, which in turn affects ecological factors such as agriculture and vegetation growth.

[0061] The calculation of surface radiation depends on the surface reflectivity model. The surface type and temperature information are obtained through remote sensing images, and the radiation intensity of different surfaces is calculated. Surface radiation helps to understand the heat changes of different objects (such as forests, farmlands, cities, etc.) under solar radiation, which has important guiding significance for energy balance and land use planning.

[0062] The calculation of atmospheric radiation uses the atmospheric radiation transfer model, combined with components such as aerosols, water vapor and ozone, to simulate the absorption and scattering process of radiation by the atmosphere. These atmospheric components are inverted through remote sensing data, and atmospheric radiation is further calculated. Atmospheric radiation has a key impact on the greenhouse effect, climate change and energy balance.

[0063] In the embodiment of the present invention, the ecological environment parameter analysis module takes the preprocessed multispectral remote sensing image and the environmental factor analysis results as input, and outputs the ecological environment parameter analysis results of the target area through vegetation coverage analysis, water quality analysis and soil quality analysis; The vegetation coverage analysis unit is used to analyze the vegetation coverage of the target area, including vegetation index calculation, vegetation classification and vegetation coverage estimation; The water quality analysis unit is used to analyze the water quality of the target area, including water reflectivity calculation, water quality parameter inversion and water classification; The soil quality analysis unit is used to analyze the soil quality of the target area, including soil reflectivity calculation, soil composition analysis and soil classification.

[0064] In a possible implementation, first, the system inputs preprocessed multispectral remote sensing images and environmental factor analysis results. The preprocessed remote sensing images contain data from multiple bands, and are processed by radiation correction, atmospheric correction, etc. to ensure the accuracy and reliability of the data. The environmental factor analysis results may include meteorological data, terrain data, land use information, etc. These input data together provide a basis for subsequent analysis.

[0065] Furthermore, the ecological environment parameter analysis module performs analysis according to different submodules (vegetation coverage analysis, water quality analysis, soil quality analysis). The specific steps are as follows: First, the vegetation coverage is analyzed. Specifically, the vegetation index (such as NDVI) is calculated using the near-infrared band in the remote sensing image to reflect the vegetation health status of the target area. Furthermore, the classification algorithm is used to classify different areas in the image according to vegetation types, which may include different vegetation types such as forests, grasslands, and farmlands. Furthermore, based on the vegetation classification and vegetation index calculation results, the vegetation coverage of the target area is estimated, thereby reflecting the degree of greening in the ecological environment.

[0066] Furthermore, the water quality is analyzed. Specifically, the reflectivity information in the remote sensing image is used to calculate the reflection characteristics of the water body to distinguish the water body from other landforms. Furthermore, water quality related parameters such as dissolved oxygen, suspended solids, algae concentration, etc. are inverted through remote sensing technology, which are important indicators for measuring water quality. Furthermore, the image data and water quality parameters are combined to classify the water bodies in the target area, such as fresh water, salt water, polluted water, etc., so as to evaluate the health of the water body.

[0067] Furthermore, soil quality analysis, specifically, uses reflectivity information in remote sensing images to analyze soil reflection characteristics to identify different soil types and soil conditions. Furthermore, combined with ground sampling data, the proportion of different components in the soil (such as organic matter content, mineral content, etc.) is inverted, which helps to assess the fertility and health of the soil. Furthermore, based on the reflection characteristics of the soil and the results of its component analysis, the soil type of the target area is classified, such as sand, clay, loam, etc.

[0068] These analysis modules are independent but interrelated. The results of each module provide the necessary input data for the subsequent modules. For example, vegetation coverage and water quality analysis can affect the overall assessment of the ecological environment, while soil quality is directly related to the sustainable use of land. By combining these analysis results, the system can comprehensively evaluate the ecological environment of the target area.

[0069] This modular analysis structure helps to achieve accurate ecological and environmental assessments and improve the flexibility and scalability of the system. For example, if you only need to analyze a specific aspect (such as water quality), you can only run the relevant analysis module without having to process other modules, saving computing resources.

[0070] In the embodiment of the present invention, the vegetation index is calculated using NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index), and the vegetation index is calculated through the red light and near infrared band data in the remote sensing image; Vegetation classification uses a classification algorithm, specifically maximum likelihood classification, to classify vegetation in the image into different types of vegetation; Vegetation coverage estimation uses vegetation index and classification results to calculate the vegetation coverage of the target area through statistical methods; The water body reflectance is calculated by using the blue and green light band data in the remote sensing image and the water body reflectance is calculated by the inversion method; Water quality parameter inversion uses a mathematical model between water body reflectivity and water quality parameters to invert the concentration of suspended solids, chlorophyll and dissolved organic matter in the water body; Water body classification uses classification algorithms, specifically supervised classification and segmentation classification, to classify water bodies in images into different types of water bodies; The soil reflectance is calculated by using the visible light and near infrared band data in the remote sensing image and the soil reflectance is calculated by the inversion method; Soil composition analysis uses a mathematical model between soil reflectance and soil composition to invert the organic matter content, pH value and salt content in the soil; Soil classification uses a classification algorithm, specifically a support vector machine, to classify the soil in the image into different types of soil.

[0071] In a possible implementation, first, the NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index) are calculated using the red and near-infrared band data in the remote sensing image. NDVI mainly evaluates the health of vegetation by reflecting the difference between the greenness of vegetation and the land, while EVI enhances the response to vegetation on the basis of NDVI and can better suppress atmospheric interference. The combination of the two can more comprehensively evaluate the state of vegetation. Furthermore, after obtaining the vegetation index, the maximum likelihood classification algorithm is used to classify the vegetation in the image. This method classifies different types of vegetation (such as forests, grasslands, farmlands, etc.) in the image based on statistical principles, thereby providing basic data for further vegetation coverage estimation. Furthermore, combined with the vegetation index and classification results, a statistical method (such as pixel counting method or weighted average method) is used to calculate the vegetation coverage of the target area, thereby evaluating the greening degree of the area.

[0072] Furthermore, the reflectivity of water bodies is calculated by inversion method through the blue and green band data in remote sensing images. These bands are very sensitive to the reflectivity characteristics of water bodies, which helps to extract water body information and accurately distinguish water bodies from other landforms. Furthermore, there is a certain mathematical model relationship between water body reflectivity and water quality parameters (such as suspended matter concentration, chlorophyll concentration, dissolved organic matter concentration, etc.). Through the inversion method, water quality parameters are calculated based on water body reflectivity data, which are important indicators for evaluating water quality. Furthermore, supervised classification or segmentation classification algorithms are used to classify water bodies into different types (such as fresh water, salt water or polluted water bodies). This helps to provide decision support for water quality management and environmental protection.

[0073] Furthermore, the soil reflectance is calculated by inversion method through the visible light and near-infrared band data in the remote sensing image. The reflectance characteristics of the soil can reveal the soil type and its properties, laying the foundation for the subsequent soil composition analysis. Furthermore, combined with the mathematical model of soil reflectance and soil composition, the organic matter content, pH value, salt content and other information in the soil are inverted. These soil parameters directly affect the fertility and suitability of the soil. Furthermore, the support vector machine (SVM) algorithm is used to classify the soil in the image. SVM can effectively process nonlinear data, classify soil into different types (such as sand, clay, loam, etc.), and further provide soil type distribution information.

[0074] The calculation results of vegetation index provide a data basis for vegetation classification and coverage estimation, while the vegetation classification results further provide spatial distribution information for ecological environment assessment. Similarly, the calculation of water body reflectance provides a basis for water quality inversion, and the inversion results of water quality parameters can provide accurate water body type information for water body classification. Soil reflectance calculation and soil composition analysis provide a basis for soil type and quality assessment.

[0075] Through this modular design, the system can comprehensively consider a variety of ecological and environmental factors and provide comprehensive analysis results for the regional ecological environment. In addition, the modular analysis method also makes the system more flexible, and can select specific analysis modules according to different needs, which improves the adaptability and accuracy of the system. This integration method also ensures that the system can achieve efficient and accurate ecological and environmental monitoring, thereby supporting decision makers to make scientific and effective decisions in land use, resource protection and environmental governance.

[0076] In the embodiment of the present invention, the data fusion module takes the preprocessed multispectral remote sensing image, the environmental factor analysis result and the ecological environment parameter analysis result as input, and outputs the ecological environment mapping data after multi-source data fusion and accuracy evaluation through multi-source data fusion and accuracy evaluation; The multi-source data fusion unit is used to fuse remote sensing images, environmental factor analysis results and ecological environment parameter analysis results, including data registration, data fusion and data correction; Data registration uses control point matching and geometric correction to ensure spatial alignment of multi-source data. Data fusion uses pixel-level fusion and feature-level fusion to integrate information from multi-source data. Data correction uses error models and correction algorithms to correct systematic and random errors in the fused data. The accuracy evaluation unit is used to evaluate the accuracy of fused data, including accuracy index calculation, error analysis and accuracy verification; The accuracy index calculation uses root mean square error and determination coefficient to evaluate the consistency between the fused data and the ground measured data. Error analysis uses error propagation model and sensitivity analysis to analyze the error sources and influencing factors in the fused data. Accuracy verification uses cross-validation and independent verification to verify the accuracy and reliability of the fused data in different regions and under different conditions.

[0077] In one possible implementation, data registration is the first step in data fusion, and its purpose is to ensure that data from different sources are aligned in space. Specific methods include control point matching and geometric correction: Among them, control point matching: by selecting a set of common points (control points) in remote sensing images and other data sources, and calculating the spatial coordinate relationship between them, the image is geometrically adjusted. Geometric correction: Based on the control point matching results, the image is corrected using a geometric transformation algorithm to eliminate geometric errors caused by factors such as sensor angles and terrain undulations. Ensure the spatial consistency of remote sensing data, environmental factor analysis results, and ecological environmental parameter analysis results.

[0078] Through data registration, it is possible to ensure the precise alignment of multi-source data in the same coordinate system, providing a reliable spatial foundation for subsequent data fusion.

[0079] Furthermore, data fusion is the core step to integrate information from multiple sources. Specifically, pixel-level fusion: by analyzing each pixel, the data information of remote sensing images, environmental factors, and ecological environment parameters are integrated. For example, the spectral data in the remote sensing image is combined with parameters such as soil, water, and vegetation to form a new image data set.

[0080] Feature-level fusion: Generate more comprehensive environmental feature information by integrating feature information from different data sources (such as terrain, climate, soil type, etc.). This fusion method can enhance the expressiveness of data and thus improve the accuracy of subsequent analysis.

[0081] Data fusion can effectively integrate different types of data, improve the comprehensive utilization of data, and provide more comprehensive and detailed ecological and environmental information.

[0082] In the process of data fusion, some systematic errors and random errors may occur, so data correction is required, including: Error model: According to the characteristics of the sensor, environmental changes and other factors, an error model is established to quantitatively analyze the source of the error. Correction algorithm: The fused data is corrected through the correction algorithm to eliminate systematic errors and random errors. For example, the correction method based on ground measured data is used to further adjust the deviation in the fused data and improve the accuracy of the data. Data correction ensures the quality of the fusion results, reduces the deviation of environmental monitoring results caused by errors, and thus improves the reliability of the system.

[0083] Furthermore, the accuracy assessment unit conducts a comprehensive analysis of the accuracy of the fused data. Specifically, by calculating RMSE (root mean square error) and R (coefficient of determination), the consistency between the fused data and the ground-based measured data is quantified. RMSE reflects the average size of the data error, and R characterizes the goodness of model fit. Furthermore, through the error propagation model and sensitivity analysis, the impact of different factors on the fused data is analyzed, the source of error is identified, and a reference is provided for subsequent improvements. Furthermore, through cross-validation and independent validation methods, the performance of the fused data in different regions and conditions is verified to ensure its wide applicability and reliability.

[0084] In the embodiment of the present invention, the output module takes the ecological environment mapping data after multi-source data fusion and its accuracy assessment results as input, and outputs the ecological environment monitoring report and the visual display results through the visualization unit and the report generation unit; The visualization unit is used to visualize the fused data, including map generation, image display and 3D modeling; The map generation uses GIS software to generate thematic maps from fused data to display the distribution of ecological and environmental parameters in the target area; Image display uses image processing software to generate high-definition images from fused data to display the ecological environment status of the target area; 3D modeling uses 3D modeling software to generate 3D models from fused data to display the three-dimensional structure of the ecological environment in the target area; The report generation unit is used to generate ecological environment monitoring reports, including data statistics, result analysis and report output; Data statistics use statistical analysis software to conduct statistical analysis on the fused data and generate various statistical charts and statistical indicators; result analysis uses environmental science methods to conduct comprehensive analysis on the fused data and evaluate the ecological environment status and changing trends of the target area; report output uses report generation software to generate a formal ecological environment monitoring report from the analysis results and charts.

[0085] In a possible implementation, first, the input module receives raw data from multiple sources such as remote sensing sensors, ground monitoring, and climate data. The data fusion module aligns, fuses, and corrects these data to generate a high-precision ecological environment data set. The fused data includes environmental parameters (such as vegetation coverage, soil quality, water pollution, etc.) and monitoring accuracy information.

[0086] Furthermore, the accuracy assessment module performs quality inspection on the fused data and verifies the accuracy of the data by calculating accuracy indicators such as error and standard deviation. The accuracy assessment results will guide the subsequent analysis and report generation to ensure that the data display and report results are highly reliable.

[0087] Furthermore, the fused and evaluated data will be input into the visualization unit, which uses different software tools to display the data in various forms. Map generation uses GIS software to create thematic maps that reflect the distribution of various ecological environmental parameters. This allows users to intuitively see the spatial distribution of different ecological factors in the target area. The image display function generates high-definition images through image processing software to show the current status of the ecological environment in the target area. The 3D modeling function further enhances the visualization of the data, using 3D modeling software to generate a 3D stereo model of the target area, reflecting the three-dimensional structure and dynamic changes of the environment. In this way, users can fully and clearly understand the ecological and environmental conditions of the target area.

[0088] At the same time, the report generation unit uses statistical analysis software to perform statistical processing on the fused data and generate various statistical charts and indicators, such as the changing trend of ecological environment quality and the distribution law of various environmental parameters. The result analysis module uses environmental science methods to conduct in-depth analysis of the data, evaluate the ecological environment status of the target area, and predict its changing trend. These analysis results will be automatically integrated into a formal ecological environment monitoring report and output through the report generation software. The report will combine charts and text descriptions to provide detailed information on ecological environment changes, cause analysis, and future forecasts.

[0089] Through this series of steps, the system realizes the complete process from multi-source data acquisition to the final output report and visualization results. The connection and data flow of each module complement each other, so that ecological environment monitoring not only has high-precision data support, but also can be presented to users in an intuitive and easy-to-understand form. The visualization function enhances the user's perception of the ecological environment status, while the report generation function provides decision makers with detailed analysis and suggestions. This comprehensive system combining data fusion, accuracy assessment, visualization and report generation can greatly improve the efficiency of ecological environment monitoring and the accuracy of decision-making, and provide strong support for environmental protection and resource management.

[0090] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0091] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. An ecological environment mapping system based on remote sensing, characterized in that: It includes a remote sensing image acquisition module, a preprocessing module, an environmental factor analysis module, an ecological environment parameter analysis module, a data fusion module and an output module, wherein the remote sensing image acquisition module, the preprocessing module, the environmental factor analysis module, the ecological environment parameter analysis module, the data fusion module and the output module are connected in sequence; The preprocessing module includes a radiation correction unit, a geometric correction unit and a noise removal unit; The environmental factor analysis module includes a wind environment analysis unit, an atmospheric environment analysis unit, and a radiation environment analysis unit; The ecological environment parameter analysis module includes vegetation coverage analysis unit, water quality analysis unit and soil quality analysis unit; The data fusion module includes a multi-source data fusion unit and an accuracy assessment unit, and the output module includes a visualization unit and a report generation unit.

2. A remote sensing-based ecological environment mapping method, applied to the remote sensing-based ecological environment mapping system as claimed in claim 1, characterized in that: The remote sensing image acquisition module is used to acquire multispectral remote sensing images of the target area. The band range of the multispectral remote sensing images includes visible light, near infrared, mid-infrared and thermal infrared. The image resolution is not less than 1 meter. The coverage includes the terrain, objects, vegetation, water bodies and soil of the target area. The remote sensing image acquisition module acquires multispectral remote sensing images from satellites, drones and ground sensors. The acquired multispectral remote sensing images are preliminarily preprocessed through the following steps: S1: Images with substandard quality are eliminated through image screening algorithms. The image screening algorithm includes pixel quality assessment and consistency check of image overlap areas to ensure that the quality of each image meets the requirements of subsequent processing; S2: Perform image stitching, format conversion and preliminary denoising on the qualified images. The image stitching algorithm stitches multiple images acquired at different times and angles into a complete image. At the same time, the format conversion algorithm is used to convert the images into a unified format. The preliminary denoising uses the median filtering algorithm to remove random noise in the image.

3. The method for ecological environment mapping based on remote sensing according to claim 2, characterized in that: In the preprocessing module, the pre-processed multispectral remote sensing image is taken as input, and subjected to radiation correction, geometric correction and noise removal to obtain a pre-processed multispectral remote sensing image; The radiation correction unit is used to eliminate the influence of atmospheric scattering and inconsistent sensor response on the image, including atmospheric correction, sensor correction and radiation calibration. The geometric correction unit is used to correct the geometric distortion of the image, including control point selection, geometric transformation and resampling; The noise removal unit is used to eliminate the noise in the image, which specifically includes median filtering, mean filtering and Gaussian filtering.

4. The method for ecological environment mapping based on remote sensing according to claim 3 is characterized in that: The radiation correction unit, the geometric correction unit, and the noise removal unit specifically include the following: The atmospheric correction adopts the MODTRAN model to simulate the atmospheric transmission process, calculate the atmospheric parameters through the inversion method, and correct the atmospheric influence in the multi-spectral remote sensing image; The sensor calibration adopts dark current calibration and gain calibration; Radiometric calibration uses a standard radiation source to calibrate the radiation value of the multispectral remote sensing image to correspond to the actual surface reflectivity; The control point selection adopts a combination of automatic matching and manual verification to ensure the accuracy and reliability of the control points; The geometric transformation adopts a polynomial transformation model to establish the mapping relationship between multispectral remote sensing image coordinates and geographic coordinates through control points; The resampling adopts bilinear interpolation method to improve the quality of multispectral remote sensing images after geometric transformation; Median filtering is used to remove salt and pepper noise in images, mean filtering is used to smooth local noise in images, and Gaussian filtering is used to eliminate high-frequency noise in images.

5. The method for ecological environment mapping based on remote sensing according to claim 4, characterized in that: In the environmental factor analysis module, the pre-processed multi-spectral remote sensing image is used as input, and the environmental factor analysis results of the target area are obtained through wind environment analysis, atmospheric environment analysis, and radiation environment analysis; Among them, the wind environment analysis unit is used to analyze the wind speed and wind direction in the target area, including wind field modeling and wind speed distribution calculation; The atmospheric environment analysis unit is used to analyze the atmospheric environment of the target area, including the calculation of atmospheric transparency, atmospheric pressure and humidity parameters; The radiation environment analysis unit is used to analyze the radiation environment of the target area, specifically including the calculation of solar radiation, surface radiation and atmospheric radiation.

6. The method for ecological environment mapping based on remote sensing according to claim 5, characterized in that: The wind field modeling adopts a numerical meteorological model, specifically a WRF model, to simulate the wind field in the target area; The wind speed distribution calculation uses wind field data and terrain data to calculate the wind speed and direction at different locations in the target area; The atmospheric transparency is calculated using the atmospheric radiation transfer model based on parameters such as aerosols, water vapor and ozone in the atmosphere. The atmospheric pressure and humidity parameters are calculated by using meteorological observation data and humidity information in remote sensing images, and the atmospheric pressure and humidity of the target area are calculated by inversion method; Solar radiation calculation uses the solar radiation model to calculate the solar radiation intensity based on the solar altitude angle and direct solar radiation; The surface radiation calculation uses the surface reflectivity model to calculate the surface radiation according to the surface type and surface temperature; Atmospheric radiation calculation adopts the atmospheric radiation transfer model to calculate the atmospheric radiation based on parameters such as aerosols, water vapor and ozone in the atmosphere.

7. The method for ecological environment mapping based on remote sensing according to claim 6, characterized in that: In the ecological environment parameter analysis module, the pre-processed multispectral remote sensing image and the environmental factor analysis results are used as input, and the ecological environment parameter analysis results of the target area are output through vegetation coverage analysis, water quality analysis and soil quality analysis; The vegetation coverage analysis unit is used to analyze the vegetation coverage of the target area, including vegetation index calculation, vegetation classification and vegetation coverage estimation; The water quality analysis unit is used to analyze the water quality of the target area, including water reflectivity calculation, water quality parameter inversion and water classification; The soil quality analysis unit is used to analyze the soil quality of the target area, including soil reflectivity calculation, soil composition analysis and soil classification.

8. The method for ecological environment mapping based on remote sensing according to claim 7, characterized in that: The vegetation index is calculated using the normalized difference vegetation index and enhanced vegetation index, using the red and near-infrared band data in remote sensing images. Vegetation classification uses a classification algorithm, specifically maximum likelihood classification, to classify vegetation in the image into different types of vegetation; Vegetation coverage estimation uses vegetation index and classification results to calculate the vegetation coverage of the target area through statistical methods; The water body reflectance is calculated by using the blue and green light band data in the remote sensing image and the water body reflectance is calculated by the inversion method; Water quality parameter inversion uses a mathematical model between water body reflectivity and water quality parameters to invert the concentration of suspended solids, chlorophyll and dissolved organic matter in the water body; Water body classification uses classification algorithms, specifically supervised classification and segmentation classification, to classify water bodies in images into different types of water bodies; The soil reflectance is calculated by using the visible light and near infrared band data in the remote sensing image and the soil reflectance is calculated by the inversion method; Soil composition analysis uses a mathematical model between soil reflectance and soil composition to invert the organic matter content, pH value and salt content in the soil; Soil classification uses a classification algorithm, specifically a support vector machine, to classify the soil in the image into different types of soil.

9. The method for ecological environment mapping based on remote sensing according to claim 8, characterized in that: In the data fusion module, the pre-processed multispectral remote sensing images, environmental factor analysis results and ecological environment parameter analysis results are used as inputs, and through multi-source data fusion and accuracy evaluation, the ecological environment mapping data after multi-source data fusion and its accuracy evaluation results are output; The multi-source data fusion unit is used to fuse remote sensing images, environmental factor analysis results and ecological environment parameter analysis results, including data registration, data fusion and data correction; Data registration uses control point matching and geometric correction to ensure the spatial alignment of multi-source data; data fusion uses pixel-level fusion and feature-level fusion to integrate information from multi-source data; data correction uses error models and correction algorithms to correct systematic errors and random errors in the fused data; The accuracy evaluation unit is used to evaluate the accuracy of fused data, including accuracy index calculation, error analysis and accuracy verification; The accuracy index calculation uses root mean square error and determination coefficient to evaluate the consistency between the fused data and the ground measured data; Error propagation model and sensitivity analysis are used for error analysis to analyze the sources of errors and influencing factors in the fused data. Cross-validation and independent validation are used for accuracy verification to verify the accuracy and reliability of the fused data in different regions and under different conditions.

10. The method for ecological environment mapping based on remote sensing according to claim 9, characterized in that: In the output module, the ecological environment mapping data after multi-source data fusion and its accuracy assessment results are used as input, and the ecological environment monitoring report and the visual display results are output through the visualization unit and the report generation unit; The visualization unit is used to visualize the fused data, including map generation, image display and 3D modeling; The map generation uses GIS software to generate thematic maps from fused data to display the distribution of ecological and environmental parameters in the target area; Image display uses image processing software to generate high-definition images from fused data to display the ecological environment status of the target area; 3D modeling uses 3D modeling software to generate 3D models from fused data to display the three-dimensional structure of the ecological environment in the target area; The report generation unit is used to generate ecological environment monitoring reports, including data statistics, result analysis and report output; Data statistics use statistical analysis software to conduct statistical analysis on the fused data and generate various statistical charts and statistical indicators; result analysis uses environmental science methods to conduct comprehensive analysis on the fused data and evaluate the ecological environment status and changing trends of the target area; report output uses report generation software to generate a formal ecological environment monitoring report from the analysis results and charts.

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