Ecological restoration management supervision and effect evaluation system and method based on satellite remote sensing cloud service

Abstract

The invention discloses an ecological restoration management supervision and effect evaluation system and method based on a satellite remote sensing cloud service, and the method comprises the steps: constructing a natural ecological resource element sample set based on a cloud service platform massive satellite image, training YOLOV5 and STANet deep learning algorithms based on the sample set, and carrying out the training of the YOLOV5 and STANet deep learning algorithms in combination with a field investigation result and the precision of an iterative optimization algorithm. Performing classification and change detection on natural ecological elements by using the intelligent interpretation model obtained by training; the method comprises the following steps: establishing a natural ecological element key index set on the basis of an intelligently interpreted surface feature classification result, calculating an initial value according to an empirical formula of each index, then fitting and correcting the initial value by using field sampling data, and further dynamically monitoring quality features of elements such as water and vegetation; based on remote sensing spectrum analysis and intelligent interpretation results, dynamic supervision and effect evaluation are carried out on project implementation progress and completion conditions, and the supervision efficiency and level are practically improved while application of the satellite remote sensing technology is deepened.

Description

Technical Field

[0001] The present invention relates to an ecological restoration management supervision and effectiveness evaluation system and method based on satellite remote sensing cloud services, belonging to the technical field of ecological restoration dynamic monitoring. Background Art

[0002] Dynamic supervision of the implementation process of ecological restoration work can timely discover problems in the work and take effective measures to improve them. After the ecological restoration work is completed, scientific and objective evaluation of the results of ecological restoration work will help further standardize the supervision of ecological restoration work, thereby promoting the standardization and institutionalization of ecological restoration supervision, and using the evaluation of excellent ecological restoration work experience as an important reference for guiding subsequent related ecological restoration work.

[0003] In recent years, my country's ecological restoration work has shifted from focusing on a single natural ecological element to a comprehensive and systematic restoration of all elements, including mountains, rivers, forests, fields, lakes, grasslands, and sand seas. The larger spatial scope, more project types, and higher restoration levels have put forward upgraded requirements for traditional ecological restoration supervision and effectiveness evaluation methods. Remote sensing technology has played a role in the supervision and effectiveness evaluation of ecological restoration projects in the past, relying on its advantages such as objective and accurate survey results and strong macro-monitoring capabilities. However, there are still problems such as insufficient ability to obtain spatiotemporal information and low levels of intelligence and automation. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide an ecological restoration management supervision and effectiveness evaluation system and method based on satellite remote sensing cloud services, introduce satellite remote sensing cloud services into ecological restoration supervision and effectiveness evaluation, and construct an automated and intelligent monitoring mode for the supervision of the implementation progress and completion effectiveness evaluation of ecological protection and restoration projects, so as to effectively improve the efficiency and level of supervision.

[0005] The present invention adopts the following technical solutions to solve the above technical problems:

[0006] An ecological restoration management supervision and effectiveness evaluation system based on satellite remote sensing cloud services, the system comprising: a satellite remote sensing cloud service platform, a sample construction module, a first model training module, a second model training module, a land feature classification module, a land feature change detection module, an effectiveness evaluation index construction module, an index calculation and model fitting module, and an effectiveness evaluation module;

[0007] The satellite remote sensing cloud service platform is used to store satellite remote sensing images, and is also used to receive the ground object change detection results uploaded by the ground object change detection module and the monitoring results uploaded by the effectiveness evaluation module, and feed back the results to the user;

[0008] The sample construction module is used to obtain a remote sensing image sample set including five types of land objects, including water bodies, farmland, woodland, grassland and buildings from the satellite remote sensing cloud service platform, and manually annotate the remote sensing image sample set, and divide the annotated remote sensing image sample set into a training set, a verification set and a test set;

[0009] The first model training module is used to train and verify the YOLOV5 model according to the training set and the verification set to obtain a trained land object classification model;

[0010] The second model training module is used to train and verify the STANet model according to the training set and the verification set to obtain a trained ground object change detection model;

[0011] The object classification module is used to test the test set using the trained object classification model to obtain the object classification result;

[0012] The ground object change detection module is used to perform ground object change detection on the ground object classification results using the trained ground object change detection model, obtain the ground object change detection results, and upload them to the satellite remote sensing cloud service platform;

[0013] The effectiveness evaluation index construction module is used to construct a monitoring index set corresponding to the type of ground feature to be evaluated;

[0014] The indicator calculation and model fitting module is used to calculate the value of each indicator in the monitoring indicator set according to the monitoring indicator set constructed by the effectiveness evaluation indicator construction module, and perform correlation fitting between the calculated value of each indicator and the actual value to obtain the fitting model corresponding to each indicator;

[0015] The effectiveness evaluation module is used to dynamically monitor the fitting values ​​of various indicators and the fitting values ​​of base period indicators during the implementation of ecological restoration, and upload the monitoring results to the satellite remote sensing cloud service platform.

[0016] As a preferred solution of the system of the present invention, the types of land objects to be evaluated include water bodies and forest land, wherein the monitoring index set corresponding to the water body includes chlorophyll concentration, total nitrogen concentration, total phosphorus concentration, ammonia nitrogen concentration, suspended solids concentration and chemical oxygen demand, a total of 6 indicators; the calculation formula of each indicator is as follows:

[0017] Chla (μg / L) =A*((b1-b2) / (b1+b2))-B

[0018] TN (mg / L) =A*exp(B*((b1-b2) / (b1+b2))-C)

[0019] TP (mg / L) =A*exp(B*((b1-b2) / (b1+b2)))

[0020] NH3-N (mg / L) =A*Chla 2 +B*Chla-C

[0021] TSS (mg / L) =A*(b1 / b2)-C

[0022] COD (mg / L) =A*Chla 2 -B*Chla-C

[0023] Among them, Chla, TN, TP, NH3-N, TSS and COD represent chlorophyll concentration, total nitrogen concentration, total phosphorus concentration, ammonia nitrogen concentration, suspended solids concentration and chemical oxygen demand respectively, μg / L represents micrograms per liter, mg / L represents milligrams per liter; A, B and C are all coefficients; b1 is the wavelength in the green spectrum band, and b2 is the wavelength in the red spectrum band;

[0024] The monitoring indicator set corresponding to forest land includes four indicators: vegetation normalized index, vegetation coverage, net primary productivity and leaf area index; the calculation formula of each indicator is as follows:

[0025] NDVI = (b3-b2) / (b3+b2)

[0026] FVC=(NDVI-NDVI soil ) / (NDVI veg -NDVI soil )

[0027] NPP=APAR×ε

[0028] LAI=A*EVI-B

[0029] Among them, NDVI, FVC, NPP and LAI represent the normalized difference vegetation index, vegetation coverage, net primary productivity and leaf area index respectively; b3 is the wavelength in the near-infrared spectrum band; NDVI soil NDVI is the vegetation index for naked areas; veg is the vegetation index for areas covered with complete vegetation; APAR is the photosynthetically active radiation absorbed by vegetation; ε is the actual light energy utilization rate of vegetation; and EVI is the enhanced vegetation index.

[0030] As a preferred solution of the system of the present invention, the calculated value of each indicator is subjected to correlation fitting with the actual value to obtain a fitting model corresponding to each indicator, which is specifically as follows:

[0031] For the area to be evaluated, select several points. On the basis of band selection, calculate the calculated value of each point indicator according to the calculation formula of each indicator. At the same time, use the equipment to collect the actual value of each point indicator. Draw a spatial point distribution map according to the calculated value, actual value and corresponding point. According to the spatial distribution trend, select linear, exponential or polynomial model for fitting, and establish the fitting model corresponding to each indicator.

[0032] As a preferred solution of the system of the present invention, the fitting values ​​of various indicators during the implementation of ecological restoration and the fitting values ​​of the base period indicators are dynamically monitored, as follows:

[0033] According to the fitting models corresponding to each indicator, the fitting values ​​corresponding to each indicator in the base period are obtained. At the same time, the fitting values ​​of each monitoring indicator corresponding to water bodies and forest land in any period during the implementation of ecological restoration are calculated through the fitting models corresponding to each indicator. The fitting values ​​of the base period are compared with the fitting values ​​of any period to judge the ecological restoration status of water bodies and forest land.

[0034] The ecological restoration governance supervision and effectiveness evaluation method based on satellite remote sensing cloud service is implemented based on the ecological restoration governance supervision and effectiveness evaluation system based on satellite remote sensing cloud service. The method is specifically as follows:

[0035] A remote sensing image sample set including five types of land objects, including water bodies, farmland, woodland, grassland and buildings, was obtained from the satellite remote sensing cloud service platform, and was manually annotated. The annotated remote sensing image sample set was divided into a training set, a validation set and a test set;

[0036] Train and verify the YOLOV5 model according to the training set and verification set to obtain a trained object classification model;

[0037] Use the trained object classification model to test the test set and obtain the object classification results;

[0038] The STANet model is trained and verified according to the training set and the verification set to obtain a trained ground object change detection model;

[0039] Use the trained object change detection model to perform object change detection on the object classification results, obtain the object change detection results, and upload them to the satellite remote sensing cloud service platform, which will then feed back to the user;

[0040] Construct a monitoring indicator set corresponding to the type of land feature to be evaluated, calculate the value of each indicator in the monitoring indicator set, and perform correlation fitting between the calculated value of each indicator and the actual value to obtain the fitting model corresponding to each indicator;

[0041] The fitting values ​​of various indicators during the implementation of ecological restoration and the fitting values ​​of the base period indicators are dynamically monitored, and the monitoring results are uploaded to the satellite remote sensing cloud service platform, which then feeds back to users.

[0042] A computer device includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor. When the processor executes the computer program, the steps of the ecological restoration management supervision and effectiveness evaluation method based on satellite remote sensing cloud service are implemented.

[0043] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the ecological restoration management supervision and effectiveness evaluation method based on satellite remote sensing cloud services.

[0044] Compared with the prior art, the present invention adopts the above technical solution and has the following technical effects:

[0045] 1. The present invention uses a remote sensing cloud service model to coordinate the management of satellite remote sensing images of ecological restoration projects, and obtains the main monitoring indicators and surface coverage and change characteristics of the ecological restoration project area through remote sensing image spectral analysis and intelligent interpretation technology based on the satellite image resources of the cloud service platform, thereby supervising the progress of project implementation and compliance with the plan, and evaluating the project effectiveness.

[0046] 2. The present invention manages remote sensing images in the form of cloud services, dynamically accesses major domestic remote sensing satellite image sources, and supplements historical collections and other commercial satellite image resources. On the basis of integrated management, it organically combines intelligent interpretation with spectral calculations, uses deep learning algorithms to achieve land feature classification and change monitoring, and builds a major terrestrial ecological monitoring indicator set. In view of the wide spatial distribution, multiple types, and strong correlation of ecological restoration projects, the workload and work efficiency of field verification are reduced, and remote sensing technology is used to enable the implementation supervision and effectiveness evaluation of ecological restoration projects. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is the overall architecture diagram of the ecological restoration management supervision and effectiveness evaluation system based on satellite remote sensing cloud services of the present invention;

[0048] Figure 2 This is an example of convenient online image retrieval for satellite remote sensing cloud services;

[0049] Figure 3 It is an example of local image management for satellite remote sensing cloud services;

[0050] Figure 4 This is an example of YOLOV5 object classification, where (a) is cultivated land and (b) is forest land;

[0051] Figure 5 This is an example of STANet ground object change detection, where (a) is before the change and (b) is after the change;

[0052] Figure 6 This is an example of water quality indicator monitoring results (ammonia nitrogen content);

[0053] Figure 7 It is an example of forest and grassland quality indicator monitoring results (NPP);

[0054] Figure 8 This is an example of superimposing field measurement points and remote sensing interpretation results;

[0055] Fig. 9 This is an example of fitting field measurement points with remote sensing interpretation results;

[0056] Fig.10 The following are examples of the comparison of remote sensing interpretation results before and after correction, where (a) is before correction and (b) is after correction. DETAILED DESCRIPTION

[0057] The embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be interpreted as limiting the present invention.

[0058] like Figure 1As shown, the present invention proposes a cloud service supervision and effectiveness evaluation model for ecological protection and restoration projects based on satellite remote sensing. By integrating satellite remote sensing, intelligent interpretation and information technology, an integrated system for satellite image acquisition, management, information mining and decision-making assistance for ecological protection and restoration supervision and effectiveness evaluation is constructed. The system mainly includes four parts: (1) Accessing online satellite images and uploading local satellite images through the cloud service platform to achieve coordinated management of image resources, forming a multi-source satellite image data warehouse, and providing data query and retrieval, online browsing and comparative analysis, and download and download services. In addition, the cloud service platform supports the uploading and warehousing of monitoring results. For areas with abnormal supervision, the cloud service platform can send abnormal monitoring results and rectification requirements to the ecological restoration unit to form project supervision. and implementation closed loop; (2) Based on the massive satellite images of the cloud service platform, a sample set of natural ecological resource elements is constructed, and on this basis, the YOLOV5 and STANet deep learning algorithms are trained. Combined with the results of field surveys, the algorithm accuracy is iteratively optimized, and the trained intelligent interpretation model is used to classify and detect changes in natural ecological elements; (3) Based on the classification results of land objects obtained by intelligent interpretation, a set of key indicators of natural ecological elements is established based on remote sensing spectral analysis technology, and the initial values ​​are calculated according to the empirical formulas of each indicator. Then, the initial values ​​are fitted and corrected using field sampling data, and dynamic monitoring of the quality characteristics of water bodies, vegetation and other elements is carried out; (4) Based on the results of remote sensing spectral analysis and intelligent interpretation, dynamic supervision and effectiveness evaluation of project implementation progress and completion status are carried out.

[0059] Based on the above system, the present invention proposes an ecological restoration management supervision and effectiveness evaluation method based on satellite remote sensing cloud service, which includes the following steps:

[0060] ① Overall management of satellite remote sensing image resources. Satellite remote sensing image resources are divided into two parts. One part is satellite images automatically accessed through the cloud service platform, including two types: automatically issued by the superior competent department and obtained through cooperation. Based on the cloud service platform, management operations such as retrieval, query, quick browsing and download of this part of satellite remote sensing images can be realized; the other part is satellite remote sensing images collected and purchased, which can be conveniently managed through the upload and release function of the cloud service platform. The resolution of satellite images managed by the platform is better than 3 meters; the acquisition frequency is better than monthly; and the storage capacity is TB level. Figure 2 and Figure 3 They are examples of convenient online image retrieval of satellite remote sensing cloud services and local image management of satellite remote sensing cloud services.

[0061] ② Intelligent interpretation of natural ecological resource elements. Before conducting intelligent interpretation of natural ecological resource elements, it is necessary to first establish a set of high-quality ground feature sample sets. Based on the massive satellite remote sensing images of the cloud service platform, a sample set of five types of features, including water bodies, farmland, woodland, grassland, and buildings, was established by manual annotation. The number of samples of each type of ground feature is 100,000. Then, the YOLOV5 and STANet model deep learning algorithms are used to classify and detect changes in natural resource elements. Figure 4 (a) and (b) are the cultivated land and forest land obtained by YOLOV5 classification. Figure 5 (a) and (b) are the changes of forest land before and after detection by STANet. The accuracy of ground object classification reached 90%; the accuracy of change detection reached 80%. The intelligent interpretation results were verified in the field and the network model was updated iteratively.

[0062] ③ Construction and calculation of key indicator sets for natural ecological resources. The current ecological protection and restoration projects are aimed at the systematic and comprehensive management and restoration of mountains, rivers, forests, farmlands, lakes, grasslands and sands. It is necessary to realize the characteristics and status analysis of all elements by constructing key indicator sets. Based on the results of intelligent element land object classification, combined with remote sensing spectral analysis technology, for water quality monitoring, chlorophyll concentration (Chla), total nitrogen concentration (TN), total phosphorus concentration (TP), ammonia nitrogen concentration (NH3-N), suspended solids concentration (TSS) and chemical oxygen demand (COD) are used to form a water quality monitoring indicator set (WQ_I). Figure 6 It is the monitoring result of water quality indicators taking ammonia nitrogen concentration as an example; for forest and grass vegetation monitoring, four indicators including Normalized Difference Vegetation Index (NDVI), Fractional Vegetation Cover (FVC), Net Primary Productivity (NPP) and Leaf Area Index (LAI) are used to form the vegetation monitoring indicator set (VQ_I). Figure 7 This is the monitoring result of forest and grass quality indicators, taking net primary productivity as an example. For the calculation of indicators, first select the appropriate spectral bands of satellite remote sensing images, such as chlorophyll concentration using green band (b1) and red band (b2); NDVI using near infrared band (b3) and red band (b2). The calculation formulas for each indicator are as follows:

[0063]

[0064] Among them, A, B, C are coefficients; b1, b2, b3 are the wavelengths of the selected spectral bands; NDVI soil NDVI is the vegetation index for naked areas; veg is the vegetation index for areas covered with complete vegetation; APAR is the photosynthetically active radiation absorbed by vegetation; ε is the actual light energy utilization rate of vegetation; and EVI is the enhanced vegetation index.

[0065] On the basis of band selection, the initial index value is calculated according to the empirical formula of each index. Figure 8 This is an example of superimposing the field measurement points and remote sensing interpretation results; finally, the initial index value is correlated with the actual value collected at the field fixed point (the two sets of values ​​are plotted using Matlab software to draw a spatial point distribution map, and then the most appropriate data model such as linear, exponential, polynomial, etc. is selected according to the spatial distribution trend to calculate the model index of the two sets of data. Fig. 9 It is an example of fitting the field measured points and remote sensing interpretation results), establish the final index calculation model, and complete the calculation of the index value based on the model (the two groups of data, the initial index value and the actual value collected at the fixed point in the field, are divided into a model calculation group, a numerical correction group and a verification group in a ratio of 4:4:2, where the model calculation group is used to establish the aforementioned calculation model; the numerical correction group is to bring the initial index value into the calculation model to calculate a new value. For water quality monitoring, the correction values ​​of 6 indicators, including Chla, TN, TP, NH3-N, TSS and COD, are calculated in turn, and the values ​​are compared with the field values ​​in the verification group to judge the reliability of the model; for forest and grassland, the same processing process is used. Fig.10 (a) and (b) are comparison examples of remote sensing interpretation results before and after correction, respectively. The reliability of the calculated results is further confirmed by field verification. 基 、VQ_I 基 For reference, the fitting values ​​WQ_Ii and VQ_Ii of the i-th (monthly / quarterly / annual) period during the implementation of ecological restoration were calculated and compared to determine the improvement in water quality and vegetation coverage. The monitoring results were uploaded to the cloud service platform for management. For ecological restoration areas with abnormalities, the monitoring results were sent to the relevant project units through the cloud service platform for verification and rectification.

[0066] ④ Regular monitoring during the project implementation phase and effectiveness evaluation during the completion phase. During the project implementation phase, in combination with the project implementation cycle, dynamic monitoring of the project implementation area is carried out at multiple time frequencies such as monthly / quarterly / annual, to obtain the spatiotemporal distribution and change characteristics of the objects in the work area and the main indicator parameters of key ecological elements such as water bodies, forests and grasslands. The progress of the project is evaluated by comparing indicators and objects in different time phases, as well as comparing with the project design. For projects that have unreasonable deviations from the plan, timely regulatory warnings are issued to urge the identification of the causes and the adoption of remedial measures. During the project completion and warranty period, the overall completion of the project is evaluated against the expected project effect map.

[0067] Based on the same inventive concept, an embodiment of the present application provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the aforementioned method for ecological restoration management supervision and effectiveness evaluation based on satellite remote sensing cloud services are implemented.

[0068] Based on the same inventive concept, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the aforementioned ecological restoration management supervision and effectiveness evaluation method based on satellite remote sensing cloud services.

[0069] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0070] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0071] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0072] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0073] The above embodiments are only for illustrating the technical idea of ​​the present invention, and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the present invention.

Claims

1. The ecological restoration management supervision and effectiveness evaluation system based on satellite remote sensing cloud services is characterized by: The system includes: a satellite remote sensing cloud service platform, a sample construction module, a first model training module, a second model training module, a ground object classification module, a ground object change detection module, an effectiveness evaluation index construction module, an index calculation and model fitting module, and an effectiveness evaluation module; The satellite remote sensing cloud service platform is used to store satellite remote sensing images, and is also used to receive the ground object change detection results uploaded by the ground object change detection module and the monitoring results uploaded by the effectiveness evaluation module, and feed back the results to the user; The sample construction module is used to obtain a remote sensing image sample set including five types of land objects, including water bodies, farmland, woodland, grassland and buildings from the satellite remote sensing cloud service platform, and manually annotate the remote sensing image sample set, and divide the annotated remote sensing image sample set into a training set, a verification set and a test set; The first model training module is used to train and verify the YOLOV5 model according to the training set and the verification set to obtain a trained land object classification model; The second model training module is used to train and verify the STANet model according to the training set and the verification set to obtain a trained ground object change detection model; The object classification module is used to test the test set using the trained object classification model to obtain the object classification result; The ground object change detection module is used to perform ground object change detection on the ground object classification results using the trained ground object change detection model, obtain the ground object change detection results, and upload them to the satellite remote sensing cloud service platform; The effectiveness evaluation index construction module is used to construct a monitoring index set corresponding to the type of ground feature to be evaluated; The indicator calculation and model fitting module is used to calculate the value of each indicator in the monitoring indicator set according to the monitoring indicator set constructed by the effectiveness evaluation indicator construction module, and perform correlation fitting between the calculated value of each indicator and the actual value to obtain the fitting model corresponding to each indicator; The effectiveness evaluation module is used to dynamically monitor the fitting values ​​of various indicators and the fitting values ​​of base period indicators during the implementation of ecological restoration, and upload the monitoring results to the satellite remote sensing cloud service platform.

2. The ecological restoration management supervision and effectiveness evaluation system based on satellite remote sensing cloud service according to claim 1 is characterized in that: The types of land features to be evaluated include water bodies and forest land. The monitoring indicator set corresponding to water bodies includes chlorophyll concentration, total nitrogen concentration, total phosphorus concentration, ammonia nitrogen concentration, suspended solids concentration and chemical oxygen demand, a total of 6 indicators; the calculation formula of each indicator is as follows: <h2 style=";text-align:left;direction:ltr">Chla<h2 style=";text-align:left;direction:ltr"> (μg / L) <h2 style=";text-align:left;direction:ltr"> (A*((b1-b2) / (b1+b2))-B) TN (mg / L) =A*exp(B*((b1-b2) / (b1+b2))-C) TP (mg / L) =A*exp(B*((b1-b2) / (b1+b2))) NH3-N (mg / L) =A*Chla 2 +B*Chla-C TSS (mg / L) =A*(b1 / b2)-C CODE (mg / L) =A*Chla 2 -B*Chla-C Among them, Chla, TN, TP, NH3-N, TSS and COD represent chlorophyll concentration, total nitrogen concentration, total phosphorus concentration, ammonia nitrogen concentration, suspended solids concentration and chemical oxygen demand respectively, μg / L represents micrograms per liter, mg / L represents milligrams per liter; A, B and C are all coefficients; b1 is the wavelength in the green spectrum band, and b2 is the wavelength in the red spectrum band; The monitoring indicator set corresponding to the forest land includes four indicators: vegetation normalized index, vegetation coverage, net primary productivity and leaf area index; the calculation formula of each indicator is as follows: NDVI = (b3-b2) / (b3+b2) FVC=(NDVI-NDVI soil ) / (NDVI veg -NDVI soil ) NPP=APAR×ε LAI=A*EVI-B Among them, NDVI, FVC, NPP and LAI represent the normalized difference vegetation index, vegetation coverage, net primary productivity and leaf area index respectively; b3 is the wavelength in the near-infrared spectrum band; NDVI soil NDVI is the vegetation index for naked areas; veg is the vegetation index for areas covered with complete vegetation; APAR is the photosynthetically active radiation absorbed by vegetation; ε is the actual light energy utilization rate of vegetation; and EVI is the enhanced vegetation index.

3. The ecological restoration management supervision and effectiveness evaluation system based on satellite remote sensing cloud service according to claim 2 is characterized in that: The calculated value of each indicator is fitted with the actual value to obtain the fitting model corresponding to each indicator, which is as follows: For the area to be evaluated, select several points. On the basis of band selection, calculate the calculated value of each point indicator according to the calculation formula of each indicator. At the same time, use the equipment to collect the actual value of each point indicator. Draw a spatial point distribution map according to the calculated value, actual value and corresponding point. According to the spatial distribution trend, select linear, exponential or polynomial model for fitting, and establish the fitting model corresponding to each indicator.

4. The ecological restoration management supervision and effectiveness evaluation system based on satellite remote sensing cloud service according to claim 3 is characterized in that: The dynamic monitoring of the fitting values ​​of each indicator during the implementation of ecological restoration and the fitting values ​​of the base period indicators is as follows: According to the fitting models corresponding to each indicator, the fitting values ​​corresponding to each indicator in the base period are obtained. At the same time, the fitting values ​​of each monitoring indicator corresponding to water bodies and forest land in any period during the implementation of ecological restoration are calculated through the fitting models corresponding to each indicator. The fitting values ​​of the base period are compared with the fitting values ​​of any period to judge the ecological restoration status of water bodies and forest land.

5. The ecological restoration management supervision and effectiveness evaluation method based on satellite remote sensing cloud services is implemented based on the ecological restoration management supervision and effectiveness evaluation system based on satellite remote sensing cloud services according to any one of claims 1 to 4, characterized in that: The method is specifically as follows: A remote sensing image sample set including five types of land objects, including water bodies, farmland, woodland, grassland and buildings, was obtained from the satellite remote sensing cloud service platform, and was manually annotated. The annotated remote sensing image sample set was divided into a training set, a validation set and a test set; Train and verify the YOLOV5 model according to the training set and verification set to obtain a trained object classification model; Use the trained object classification model to test the test set and obtain the object classification results; The STANet model is trained and verified according to the training set and the verification set to obtain a trained ground object change detection model; Use the trained object change detection model to perform object change detection on the object classification results, obtain the object change detection results, and upload them to the satellite remote sensing cloud service platform, which will then feed back to the user; Construct a monitoring indicator set corresponding to the type of land feature to be evaluated, calculate the value of each indicator in the monitoring indicator set, and perform correlation fitting between the calculated value of each indicator and the actual value to obtain the fitting model corresponding to each indicator; The fitting values ​​of various indicators during the implementation of ecological restoration and the fitting values ​​of the base period indicators are dynamically monitored, and the monitoring results are uploaded to the satellite remote sensing cloud service platform, which then feeds back to users.

6. A computer device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that: When the processor executes the computer program, it implements the steps of the method for ecological restoration management supervision and effectiveness evaluation based on satellite remote sensing cloud service as described in claim 5.

7. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by the processor, the steps of the method for ecological restoration management supervision and effectiveness evaluation based on satellite remote sensing cloud service as described in claim 5 are implemented.

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