Lake wetland ecological restoration method in dry season

Abstract

The invention relates to the technical field of ecological restoration, in particular to a lake wetland ecological restoration method in a dry season, which is executed by a processor and comprises the following steps: establishing a simulated lake wetland ecological environment, deploying optimal environmental factors and obtaining an optimal ecological environment; performing condition factor simulation on the optimal ecological environment to obtain a mapping ecological environment, and storing data information of the mapping ecological environment in a database to form a mapping data set; acquiring at least the plant type and the change water level of the target lake wetland according to the current environment information of the target lake wetland to form a restoration target data set; inputting a differentiation calculation model into the repaired target data set to obtain a difference value data set; comparing the difference value data set with the mapping data set to obtain a wetland restoration range; and performing remote sensing image-based restoration on the wetland restoration range. The problems that in the prior art, the repairing effect is poor and the repairing cost is too high due to direct implementation in the target environment are solved.

Description

Technical Field

[0001] The present application relates to the field of ecological restoration technology, and specifically, to a method for ecological restoration of lake wetlands during the dry season. Background Art

[0002] Lake wetlands are one of the important ecosystems on the earth and have multiple ecological functions, such as regulating climate, purifying water quality, and providing biological habitats. However, due to the influence of human activities and natural factors, lake wetlands are facing serious ecological degradation problems. Through ecological restoration, the ecological functions of lake wetlands can be restored and the health and stability of the ecosystem can be maintained, which is of great significance for protecting biodiversity and maintaining ecological balance.

[0003] The patent that has been published is a Chinese patent named "Ecological restoration method for lake wetlands affected by rising water levels during the dry season", with publication number CN114049520B. Although the method disclosed in this patent can achieve ecological restoration, it is carried out directly in the environment that actually needs to be restored. This makes it easy for the implementation effect to deviate from the ideal goal and causes a sharp increase in the restoration cost. Summary of the invention

[0004] The purpose of the present application is to provide a method for ecological restoration of lake wetlands during the dry season, which solves the problems of poor restoration effect and excessively high restoration cost caused by direct implementation in the target environment in the prior art.

[0005] The technical solution of this application:

[0006] The present application provides a method for ecological restoration of lake wetlands during the dry season, the method being executed by a processor and comprising:

[0007] Establish a simulated lake wetland ecological environment, deploy the best environmental factors, and obtain the best ecological environment;

[0008] Simulating the conditional factors of the optimal ecological environment to obtain a mapped ecological environment, and storing the mapped ecological environment data information in a database to form a mapped data set;

[0009] The current environmental information of the target lake wetland is at least used to obtain the plant types and water level changes of the target lake wetland to form a restoration target data set;

[0010] Input the difference calculation model into the repair target data set to obtain the difference value data set;

[0011] Compare the difference value dataset with the mapping dataset to obtain the wetland restoration scope;

[0012] Perform remote sensing image-based restoration on the wetland restoration area.

[0013] In some embodiments, the establishment of a simulated lake wetland ecological environment, deployment of optimal environmental factors, and acquisition of an optimal ecological environment include:

[0014] Establish data on wetland plant communities, waterbird community structure, and lake topography;

[0015] The established wetland plant community, water bird community structure and lake topography data are deployed based on excellent conditions to optimize the intermediate ecological environment;

[0016] Eliminate interference factors from the intermediate ecological environment to obtain the best ecological environment.

[0017] In some embodiments, the method of simulating the optimal ecological environment to obtain a mapped ecological environment and storing the mapped ecological environment data information in a database to form a mapped data set includes:

[0018] At least deploy factors for heavy rain, strong winds, high temperatures, and drought conditions;

[0019] Based on the conditional factors, the mapped ecological environment affected by the conditional factors is obtained;

[0020] The mapped ecological environment data information is stored in a database to form a mapped data set.

[0021] In some embodiments, the current environmental information of the target lake wetland at least performs the acquisition of the plant type and the change of water level of the target lake wetland to form a restoration target data set, including:

[0022] The plant types of the target lake wetland should be at least submerged plants, rooted floating-leaf plants, emergent plants and wetland plants in the dry season;

[0023] The changed water level of the target lake wetland shall be at least the water level after rainfall in the dry season and the water level without rainfall in the dry season.

[0024] In some embodiments, inputting a differential calculation model into the repair target data set to obtain a differential value data set includes:

[0025] The plant type data and the water level change data in the dry season are transmitted to the differential calculation model to perform differential calculation, wherein the differential calculation model is:

[0026] M=M 目标湖泊数据值 -Μ 预设湖泊数据值

[0027] Wherein, M is the difference value; 目标湖泊数据值 Plant type data and water level change data during the dry season; 预设湖泊数据值 Preset plant type data and changing water level data for dry season;

[0028] When M is equal to 0, it means there is no abnormal difference, and when M is greater than or equal to 0, it means there is an abnormal situation;

[0029] Data aggregation is performed on the anomalies to form a difference value dataset.

[0030] In some embodiments, comparing the difference value dataset with the mapping dataset to obtain the wetland restoration scope includes:

[0031] Compare the corresponding contents in the difference value dataset and the mapping dataset to obtain the difference comparison range, which is as follows:

[0032] The plant type data and water level change data in the dry season corresponding to the difference value greater than or equal to 0 are compared with the corresponding pre-simulated content in the mapping data set. When the comparison result is more than 90% consistent, the corresponding plant type data and water level change data in the dry season are determined as the target wetland restoration range; otherwise, it is determined as the non-restoration range of the target wetland.

[0033] In some embodiments, the remote sensing image-based restoration of the wetland restoration scope includes: determining high-resolution remote sensing images based on remote sensing technology, interpreting high-resolution remote sensing images using decision tree classification, and determining the specific spatial location of the target wetland to be restored.

[0034] The technical solution of the present application has at least the following advantages and beneficial effects: the present application establishes the optimal wetland ecological environment in advance, simulates the dry season lake ecology, maps the simulated ecological environment, and then compares the difference between the actual dry season lake ecological environment and the preset value with the simulated ecological environment, so as to accurately determine the objects that need to be repaired in the actual wetland lake, and at the same time assists with remote sensing technology to accurately locate the spatial position, thereby greatly improving the repair efficiency and effectively reducing the repair cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is a schematic diagram of the process of this application. DETAILED DESCRIPTION

[0036] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0037] Example

[0038] It should be noted that when existing technologies are used to carry out ecological restoration of lake wetlands, operations are often carried out directly in the environment that actually needs to be restored. This makes it easy for the implementation effect to deviate from the ideal goal and causes a sharp increase in restoration costs.

[0039] Based on the above content, the present application provides a method for ecological restoration of lake wetlands during the dry season, and specifically provides a method that can accurately determine the ecological restoration area and restoration method of lake wetlands during the dry season. By pre-establishing the optimal wetland ecological environment, the dry season lake ecology is simulated, and then the simulated ecological environment is mapped. Based on the difference between the actual dry season lake ecological environment and the preset value, the difference is compared with the simulated ecological environment, so as to accurately determine the objects that need to be restored in the actual wetland lake, and at the same time, assist remote sensing technology to accurately locate the spatial position, greatly improve the restoration efficiency and effectively reduce the restoration cost.

[0040] Please refer to Figure 1 In detail, the present application provides a method for ecological restoration of lake wetlands during the dry season, the method being executed by a processor and comprising:

[0041] First, establish a simulated lake wetland ecological environment, deploy the best environmental factors, and obtain the best ecological environment. Specifically:

[0042] Establish data on wetland plant communities, waterbird community structure, and lake topography;

[0043] The established wetland plant community, water bird community structure and lake topography data are deployed based on excellent conditions to optimize the intermediate ecological environment;

[0044] Eliminate interference factors from the intermediate ecological environment to obtain the best ecological environment.

[0045] Next, conditional factor simulation is performed on the optimal ecological environment to obtain a mapped ecological environment, and the mapped ecological environment data information is stored in a database to form a mapped data set, where:

[0046] At least deploy factors for heavy rain, strong winds, high temperatures, and drought conditions;

[0047] Based on the conditional factors, the mapped ecological environment affected by the conditional factors is obtained;

[0048] The mapped ecological environment data information is stored in a database to form a mapped data set.

[0049] Next, the current environmental information of the target lake wetland is at least used to obtain the plant type and changing water level of the target lake wetland to form a restoration target data set, wherein: the plant types of the target lake wetland are at least submerged plants, rooted floating-leaf plants, emergent plants and wetland plants in the dry season; the changing water level of the target lake wetland is at least the water level after rainfall in the dry season and the water level without rainfall in the dry season.

[0050] Then, the differential calculation model is input into the restoration target data set to obtain the differential value data set, specifically: the plant type data and the change water level data in the dry season are transmitted to the differential calculation model to perform differential calculation, wherein the differential calculation model is:

[0051] M=M 目标湖泊数据值 -Μ 预设湖泊数据值

[0052] Wherein, M is the difference value; 目标湖泊数据值 Plant type data and water level change data during the dry season; 预设湖泊数据值 Preset plant type data and changing water level data for dry season;

[0053] Based on the above formula, when M is equal to 0, it means there is no abnormal difference, and when M is greater than or equal to 0, it means there is an abnormal situation;

[0054] Data aggregation is performed on the anomalies to form a difference value dataset.

[0055] Then, the difference value dataset is compared with the mapping dataset to obtain the wetland restoration scope, wherein: the difference value dataset is compared with the corresponding content in the mapping dataset to obtain the difference comparison scope, specifically: the plant type data and the change water level data in the dry season corresponding to the difference value greater than or equal to 0 are compared with the corresponding pre-simulated content in the mapping dataset. When the comparison result has a consistency greater than 90%, the corresponding plant type data and the change water level data in the dry season are determined to be the target wetland restoration scope; otherwise, it is determined to be the non-restoration scope of the target wetland.

[0056] Finally, remote sensing image restoration is performed on the wetland restoration scope, including: determining high-resolution remote sensing images based on remote sensing technology, interpreting high-resolution remote sensing images using decision tree classification method, and determining the specific spatial location of the target wetland to be restored.

[0057] Furthermore, in order to better understand this plan, we will take Poyang Lake as an example to illustrate the implementation process of this plan:

[0058] Firstly, the relevant data of the plant community, the water bird community structure and the topography of Poyang Lake are obtained, and a simulation model of the existing ecological environment of Poyang Lake is established based on the data. According to the simulation model of the existing ecological environment of Poyang Lake, the excellent conditions deployment based on Poyang Lake is executed to obtain a preliminary restoration model of Poyang Lake, and the interference factors of the preliminary restoration environment are eliminated to obtain the optimal ecological restoration simulation model. Through the established preliminary restoration model and the optimal ecological restoration simulation model, the multi-level step-by-step restoration process can quickly discover problems when performing performance testing and effect prediction on the model, and make effective adjustments and repairs at the stage where the problems are located, so that the entire ecological restoration simulation process is more efficient and avoids the situation where the actual implementation effect is very poor due to erroneous simulation results.

[0059] Next, we deployed rainstorm, strong wind, high temperature and drought conditions for Poyang Lake, and based on these conditions, we performed restoration simulation exercises on the optimal ecological restoration simulation model to obtain the mapping ecological environment exercise results under the influence of the above conditions. To ensure that the simulation results are more realistic, the entire simulation process was repeated many times, and all the exercise results were stored to form a mapping data set.

[0060] Next, we obtained plant type data under various conditions, including submerged plants, rooted floating-leaf plants, emergent plants, and hygrophytes in the dry season of Poyang Lake. We also obtained the changing water level data of the water level after rainfall in the dry season and the water level without rainfall in the dry season, thus forming the Poyang Lake restoration data set.

[0061] Then, the Poyang Lake restoration dataset was input into the differential calculation model, and the differential calculation was performed based on the following formula until a result without abnormal differences was obtained. All data in the differential calculation process were aggregated to form a differential value dataset:

[0062] M=M 目标湖泊数据值 -Μ 预设湖泊数据值

[0063] Wherein, M is the difference value; 目标湖泊数据值 Plant type data and water level change data during the dry season; 预设湖泊数据值 Preset plant type data and changing water level data for the dry season.

[0064] Then, the difference value dataset was compared with the mapping dataset to determine the restoration scope of Poyang Lake.

[0065] Finally, based on remote sensing technology, high-resolution remote sensing images are determined, and the decision tree classification method is used to interpret the high-resolution remote sensing images, the specific spatial location of Poyang Lake to be restored is determined, and actual restoration measures are implemented at that location.

[0066] Although some specific embodiments of the present disclosure have been described in detail by way of examples, it should be understood by those skilled in the art that the above examples are for illustration only and are not intended to limit the scope of the present disclosure. It should be understood by those skilled in the art that the above embodiments may be modified or some technical features may be replaced by equivalents without departing from the scope and spirit of the present disclosure. The scope of the present disclosure is defined by the appended claims.

Claims

1. A method for ecological restoration of lake wetlands during the dry season, the method being executed by a processor, characterized in that: include: Establish a simulated lake wetland ecological environment, deploy the best environmental factors, and obtain the best ecological environment; Simulating the conditional factors of the optimal ecological environment to obtain a mapped ecological environment, and storing the mapped ecological environment data information in a database to form a mapped data set; The current environmental information of the target lake wetland is at least used to obtain the plant types and water level changes of the target lake wetland to form a restoration target data set; Input the difference calculation model into the repair target data set to obtain the difference value data set; Compare the difference value dataset with the mapping dataset to obtain the wetland restoration scope; Perform remote sensing image-based restoration on the wetland restoration area.

2. The ecological restoration method according to claim 1, characterized in that: The establishment of a simulated lake wetland ecological environment, deployment of optimal environmental factors, and acquisition of an optimal ecological environment include: Establish data on wetland plant communities, waterbird community structure, and lake topography; The established wetland plant community, water bird community structure and lake topography data are deployed based on excellent conditions to optimize the intermediate ecological environment; Eliminate interference factors from the intermediate ecological environment to obtain the best ecological environment.

3. The ecological restoration method according to claim 1, characterized in that: The method of simulating the optimal ecological environment to obtain a mapped ecological environment and storing the mapped ecological environment data information in a database to form a mapped data set includes: At least deploy factors for heavy rain, strong winds, high temperatures, and drought conditions; Based on the conditional factors, the mapped ecological environment affected by the conditional factors is obtained; The mapped ecological environment data information is stored in a database to form a mapped data set.

4. The ecological restoration method according to claim 1, characterized in that: The current environmental information of the target lake wetland at least performs the acquisition of the plant type and the change of water level of the target lake wetland to form a restoration target data set, including: The plant types of the target lake wetland should be at least submerged plants, rooted floating-leaf plants, emergent plants and wetland plants in the dry season; The changed water level of the target lake wetland shall be at least the water level after rainfall in the dry season and the water level without rainfall in the dry season.

5. The ecological restoration method according to claim 1, characterized in that: The difference calculation model is input into the repair target data set to obtain the difference value data set, including: The plant type data and the water level change data in the dry season are transmitted to the differential calculation model to perform differential calculation, wherein the differential calculation model is: M=M 目标湖泊数据值 -M 预设湖泊数据值 Wherein, M is the difference value; 目标湖泊数据值 Plant type data and water level change data during the dry season; 预设湖泊数据值 Preset plant type data and changing water level data for dry season; When M is equal to 0, it means there is no abnormal difference, and when M is greater than or equal to 0, it means there is an abnormal situation; Data aggregation is performed on the anomalies to form a difference value dataset.

6. The ecological restoration method according to claim 5, characterized in that: The difference value dataset is compared with the mapping dataset to obtain the wetland restoration scope, including: Compare the corresponding contents in the difference value dataset and the mapping dataset to obtain the difference comparison range, which is as follows: The plant type data and water level change data in the dry season corresponding to the difference value greater than or equal to 0 are compared with the corresponding pre-simulated content in the mapping data set. When the comparison result is more than 90% consistent, the corresponding plant type data and water level change data in the dry season are determined as the target wetland restoration range; otherwise, it is determined as the non-restoration range of the target wetland.

7. The ecological restoration method according to claim 1, characterized in that: The wetland restoration scope is to be restored based on remote sensing images, including: Based on remote sensing technology, high-resolution remote sensing images are determined, and the decision tree classification method is used to interpret high-resolution remote sensing images to determine the specific spatial location of the target wetland to be restored.

Images