CN-121980282-A - Ecological fragile area identification and ecological restoration method and system based on remote sensing image

CN121980282ACN 121980282 ACN121980282 ACN 121980282ACN-121980282-A

Abstract

The application relates to an ecological fragile area identification and ecological restoration method and system based on remote sensing images. The method comprises the steps of collecting multi-source remote sensing data of an optical radar and a synthetic aperture radar, calculating spatial correction parameters to complete image registration, adopting time sequence interpolation to fill a cloud cover shielding area to generate a complete remote sensing image, inputting the complete image into a soil humidity inversion model to generate humidity time sequence data, simultaneously extracting vegetation parameters to determine a change trend, analyzing the correlation between the two parameters and the trend to match the trend to define an ecological degradation area, extracting space-time distribution characteristics of the degradation area to judge an abnormal area, adopting a spatial interpolation algorithm to process the data of the abnormal area, combining a multi-level risk standard to quantitatively evaluate degradation risk level, verifying an ecological restoration effect based on the risk level, and comparing initial monitoring data to generate a standardized restoration effect index. By adopting the method, the pertinence and the effectiveness of the identification and repair of the ecological fragile area can be improved, and the cost and the period of manual investigation are reduced.

Inventors

LIU JIARU
ZHANG FUCUN
DONG CAIXIA

Assignees

青海理工学院

Dates

Publication Date: 20260505
Application Date: 20260123

Claims (9)

1. The method for identifying and ecologically repairing the ecologically vulnerable area based on the remote sensing image is characterized by comprising the following steps: Acquiring multi-source remote sensing data comprising an optical image and a synthetic aperture radar image, calculating correction parameters according to the multi-source remote sensing data, adjusting the spatial position of the image, and if a cloud cover area exists, filling pixel values by adopting a time sequence interpolation method to generate a complete remote sensing image; Inputting the complete remote sensing image into a constructed soil humidity inversion model to generate humidity time sequence data, determining a vegetation change trend, performing correlation analysis and trend matching on the vegetation change trend and the humidity time sequence data, and determining an ecological degradation area; Extracting space-time distribution characteristics of the ecological degradation region, judging an abnormal region according to the space-time distribution characteristics, interpolating the abnormal region by adopting a spatial interpolation algorithm, and evaluating degradation risk level by combining a multi-level risk standard; And verifying the restoration effect of the ecological restoration area based on the degradation risk level, and obtaining a restoration effect index by combining initial monitoring data and comparative analysis.
2. The method of claim 1, wherein the calculating correction parameters according to the multi-source remote sensing data adjusts the spatial position of the image, and if there is a cloud cover area, performing pixel value filling by using a time sequence interpolation method to generate a complete remote sensing image, comprising: extracting image registration error data corresponding to the multi-source remote sensing data, calculating an error distribution matrix for the image registration error data by adopting a least square fitting algorithm, and generating error correction parameters; performing spatial position adjustment on the original optical image and the synthetic aperture radar image of the multi-source remote sensing data by adopting the error correction parameters to generate a multi-source image set after registration optimization; Performing uniform correction of radiation scale and imaging mechanism difference compensation processing on the optical image and the synthetic aperture radar image in the multi-source image set respectively, and outputting standardized image data; Determining the spectrum, texture and topography characteristics of the multi-source image based on the standardized image data, and executing time sequence consistency correction processing by combining the time sequence images corresponding to the image characteristics to generate a correction data set; performing radiometric calibration rechecking pretreatment on the correction data set, judging whether the radiation precision meets a preset precision threshold, and if so, generating a pretreated image set; performing pixel-level fusion processing on the preprocessed image set based on a weighted fusion algorithm to obtain fused image data; And detecting a cloud layer shielding region in the fused image data, and if the cloud layer shielding region exists, filling pixel values in the shielding region by adopting a time sequence interpolation method to generate a complete remote sensing image.
3. The method of claim 1, wherein the inputting the complete remote sensing image into the constructed soil humidity inversion model generates humidity time series data and determines a vegetation change trend, performing correlation analysis and trend matching on the vegetation change trend and the humidity time series data, and determining an ecological degradation area comprises: Acquiring multi-temporal image data corresponding to the complete remote sensing image, extracting near infrared band reflectivity data and red band reflectivity data in the multi-temporal image data, and performing difference operation and ratio operation on the band reflectivity data to generate vegetation index data; Extracting time sequence features based on the vegetation index data, and carrying out trend analysis on the time sequence features by adopting a preset time sequence analysis algorithm to determine vegetation change trend; Acquiring image data corresponding to the complete remote sensing image, and performing inversion operation processing on the image data based on a preset soil humidity inversion model to generate soil humidity time sequence data; Performing correlation analysis and trend matching treatment on the time sequence characteristics of the soil humidity time sequence data and the vegetation change trend, and judging an ecological evolution state to obtain a corresponding ecological dynamic change mode; Judging whether the ecological dynamic change mode shows a continuous descending trend of vegetation index and soil humidity and is lower than a preset ecological degradation trend threshold value, and if so, determining an ecological degradation area.
4. The method according to claim 1, wherein the extracting the spatiotemporal distribution feature of the ecologically degraded area, determining an abnormal area according to the spatiotemporal distribution feature, interpolating the abnormal area by a spatial interpolation algorithm, and evaluating the degradation risk level in combination with a multi-level risk criterion, comprises: acquiring a variation trend parameter of the ecological degradation region, and sequentially performing outlier rejection and data normalization on the variation trend parameter to generate a normalized trend data set; Performing time sequence feature fitting on the trend data set by adopting a fitting algorithm, performing spatial feature cluster analysis on the fitted data set by adopting a clustering algorithm, and integrating to obtain space-time distribution features; Judging whether the space-time distribution characteristics exceed a preset distribution threshold interval, and if so, generating corresponding alarm signal data; Based on the threshold triggering condition of the alarm signal data, extracting abnormal distribution characteristics which are completely matched with an alarm threshold in the ecological degradation area, and generating an abnormal area identifier; And carrying out interpolation operation on the space-time data corresponding to the abnormal region identification by adopting a spatial interpolation algorithm, generating a space-time variation mode of the ecological degradation region, and quantitatively evaluating degradation risk level by combining with a preset multi-level degradation risk level dividing standard.
5. The method according to claim 1, wherein verifying the repair effect of the ecological repair area based on the degradation risk level, and combining initial monitoring data with comparative analysis to obtain a repair effect index, comprises: Extracting key ecological characteristic parameters associated with the corresponding ecological restoration areas according to the degradation risk level, and generating a characteristic data set; Respectively carrying out time sequence trend fitting and space dissimilarity degree measurement and calculation on each key ecological characteristic parameter of the characteristic data set, and generating a preliminary value of a restoration effect index by combining with the weighted fusion of a preset restoration target dynamic adaptation coefficient; Performing difference value operation and deviation quantitative analysis on the initial value of the repair effect index and the initial ecological monitoring data of the corresponding repair area to generate repair effect deviation data; And if the repair effect deviation data exceeds the deviation threshold range, dynamically adjusting the weight distribution parameters of the characteristic data set based on the deviation degree, and re-calculating to generate a repair effect index.
6. The method of claim 5, wherein the preliminary repair effect index value is calculated by the following formula: Wherein, the Represents the preliminary value of the repair effect index, Representing the total number of key ecological feature parameters, Represent the first The importance weights of the key ecological characteristic parameters are dynamically distributed according to the degradation risk level, Represent the first The time sequence adaptation coefficient of each parameter is dynamically adjusted according to the repair stage, Represent the first The spatial adaptation coefficients of the individual parameters, Represent the first The goodness of time-series trend fit of the individual parameters, Represent the first The spatial dissimilarity of the individual parameters is calculated by a coefficient of variation method, , Represent the first The dynamic adaptation coefficient of the repair target of each parameter is calculated according to the preset repair target value, 。
7. An ecological fragile area identification and ecological restoration system based on remote sensing images, which is characterized by comprising: The image processing module is used for acquiring multi-source remote sensing data comprising an optical image and a synthetic aperture radar image, calculating correction parameters according to the multi-source remote sensing data, adjusting the spatial position of the image, and filling pixel values by adopting a time sequence interpolation method to generate a complete remote sensing image if a cloud layer shielding area exists; The degradation identification module is used for inputting the complete remote sensing image into a constructed soil humidity inversion model to generate humidity time sequence data and determine vegetation change trend, carrying out correlation analysis and trend matching on the vegetation change trend and the humidity time sequence data, and determining an ecological degradation area; the risk assessment module is used for extracting the space-time distribution characteristics of the ecological degradation region, judging an abnormal region according to the space-time distribution characteristics, interpolating the abnormal region by adopting a spatial interpolation algorithm, and assessing degradation risk level by combining with a multi-level risk standard; And the restoration verification module is used for verifying the restoration effect of the ecological restoration area based on the degradation risk level and obtaining a restoration effect index by combining initial monitoring data contrast analysis.
8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 7 when the computer program is executed.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.

Description

Ecological fragile area identification and ecological restoration method and system based on remote sensing image Technical Field The invention belongs to the field of ecological environment monitoring, and particularly relates to an ecological fragile area identification and ecological restoration method and system based on remote sensing images. Background The current dynamic monitoring and accurate restoration of the ecological fragile area become one of the core demands in the field of ecological environment protection, and the remote sensing technology is widely applied to the identification and evaluation work of the ecological fragile area by virtue of the observation advantages of a large range, all weather and multiple time phases. The prior art is mainly based on single optical remote sensing images or synthetic aperture radar images to develop ecological parameter inversion and fragile region division, however, single data sources are easily interfered by factors such as cloud cover, topographic shadow and the like, so that the integrity of image data is insufficient, the space-time evolution rule of an ecological fragile region is difficult to accurately capture, meanwhile, the traditional fragile region identification method is mostly dependent on static threshold judgment, lacks space-time coupling analysis on key ecological parameters such as soil humidity and vegetation coverage, and is easy to cause the problems of fuzzy boundary definition and low identification precision of the fragile region, and in addition, the prior ecological restoration effect verification is mostly based on manual field investigation or single-point monitoring data, so that the defects of long period, high cost and limited coverage range exist, and a standardized quantitative evaluation system is lacking, so that the dynamic adjustment and optimization of a restoration scheme are difficult to realize. Disclosure of Invention Based on the above, it is necessary to provide a method and a system for identifying and repairing an ecologically vulnerable area based on remote sensing images, which can improve the pertinence and effectiveness of ecological restoration work, reduce the cost and period of manual field investigation. In a first aspect, the application provides an ecological fragile area identification and ecological restoration method based on remote sensing images, which comprises the following steps: And acquiring multi-source remote sensing data comprising an optical image and a synthetic aperture radar image, calculating correction parameters according to the multi-source remote sensing data, adjusting the spatial position of the image, and if a cloud cover area exists, filling pixel values by adopting a time sequence interpolation method to generate a complete remote sensing image. And (3) inputting the complete remote sensing image into a constructed soil humidity inversion model to generate humidity time sequence data, determining a vegetation change trend, carrying out correlation analysis and trend matching on the vegetation change trend and the humidity time sequence data, and determining an ecological degradation area. And extracting the space-time distribution characteristics of the ecological degradation region, judging the abnormal region according to the space-time distribution characteristics, interpolating the abnormal region by adopting a spatial interpolation algorithm, and evaluating the degradation risk level by combining with a multi-level risk standard. And verifying the repairing effect of the ecological repairing area based on the degradation risk level, and obtaining the repairing effect index by combining initial monitoring data and comparative analysis. In one embodiment, the method for calculating correction parameters according to multi-source remote sensing data to adjust the spatial position of an image, and if a cloud cover area exists, filling pixel values by adopting a time sequence interpolation method to generate a complete remote sensing image comprises the following steps: And extracting image registration error data corresponding to the multi-source remote sensing data, calculating an error distribution matrix by adopting a least square fitting algorithm for the image registration error data, and generating error correction parameters. And performing spatial position adjustment on the original optical image of the multi-source remote sensing data and the synthetic aperture radar image by adopting error correction parameters to generate a multi-source image set after registration optimization. And respectively performing uniform correction of radiation scale and imaging mechanism difference compensation processing on the optical image and the synthetic aperture radar image in the multi-source image set, and outputting standardized image data. And determining the spectrum, texture and topography characteristics of the multi-source image based on the standardized image dat