Method for monitoring vegetation recovery after an earthquake

Abstract

The application provides a post-earthquake vegetation recovery monitoring method, comprising the following steps: selecting an earthquake vegetation damaged area to generate an earthquake area remote sensing image, and establishing an earthquake area remote sensing image library in different seasons and different years; using the fact that vegetation is quite different from water body, bare soil and other non-vegetation ground objects in spectral characteristics, using a DSCAN density clustering algorithm, extracting a preliminary vegetation existing area from the earthquake area image, defining the vegetation sparsity in the earthquake area, quantitatively showing the recovery of various vegetation and the overall vegetation, more accurately analyzing the current vegetation recovery degree, and obviously comparing which indicators need to be improved, thereby providing data support for the next vegetation recovery measures.

Description

Technical Field

[0001] This invention relates to the field of vegetation restoration monitoring, and in particular to a method for monitoring post-earthquake vegetation restoration. Background Technology

[0002] An earthquake, also known as a seismic event or ground shaking, is a natural phenomenon caused by the rapid release of energy in the Earth's crust, generating seismic waves. The collision and compression between tectonic plates, causing faulting and fracturing along plate boundaries and within plates, is the primary cause of earthquakes. This is especially true in mountainous areas, where earthquakes can severely damage vegetation and alter local vegetation patterns. Therefore, post-earthquake vegetation recovery is a major concern. Summary of the Invention

[0003] This invention provides a method for monitoring post-earthquake vegetation restoration to solve at least one of the above-mentioned technical problems.

[0004] To address the above problems, as one aspect of the present invention, a method for monitoring post-earthquake vegetation restoration is provided, comprising the following steps:

[0005] Step 1: Select earthquake-damaged vegetation areas to generate remote sensing images of earthquake areas, and establish a database of remote sensing images of earthquake areas in different seasons and years;

[0006] Step 2, vegetation component analysis and extraction, including steps 21 and 22:

[0007] Step 21, Preliminary classification: Taking advantage of the significant differences in spectral characteristics between vegetation, water bodies, bare soil, and other non-vegetated land features, the DSCAN density clustering algorithm is used to extract the areas with preliminary vegetation from the earthquake area image and define the vegetation sparseness within the earthquake area.

[0008] Step 22, detailed classification of vegetation cover area: The K-means algorithm is used to cluster the vegetation cover area to extract the distribution area of ​​trees, shrubs, herbs and other plants in the earthquake area;

[0009] Step 3, vegetation damage assessment, including steps 31 and 32:

[0010] Step 31: Analyze remote sensing images of the earthquake area in different time periods and quantify the damage to vegetation in the earthquake area based on sparsity.

[0011] Step 32, evaluation of damage to various vegetation components: Based on K-means clustering, vegetation is classified in the vegetation-covered area, and the proportion of each vegetation species is calculated to more accurately analyze the vegetation recovery.

[0012] Step 4, construct an AHP vegetation restoration evaluation model: Take vegetation sparsity and the ratios of arbors, shrubs, and herbaceous plants as evaluation indicators of the analytic hierarchy process, assign weights according to the importance of each indicator, set three fuzzy indicators of good restoration, medium restoration, and low restoration for fuzzy evaluation result values, and construct an AHP-based vegetation optimization fuzzy model according to the vegetation restoration evaluation indicators and the degree of damaged vegetation;

[0013] Step 5, vegetation restoration suggestions: Use the genetic algorithm to solve the gaps between each indicator and the existing indicators under the condition of good vegetation restoration, and put forward suggestions on which indicator to improve as the key point of the next step of vegetation restoration.

[0014] Preferably, before step 1, it further includes: obtaining a remote sensing image with a scale of 2-5m in the earthquake area through a remote sensing satellite, and correcting the spectral information of the remote sensing image through a correction program to process a small amount of cloud cover, improving the usability of the remote sensing image.

[0015] Preferably, in step 22, the method of using weights is adopted to weight each band, change the distribution of various vegetation in the spectral space, increase the spectral feature differences between various vegetation, and make the clustering results more accurate.

[0016] Preferably, the weighting formula is:

[0017] λwi = λi * ωi

[0018] Different weights are assigned according to the different proportions of vegetation in each spectral band.

[0019] Preferably, in step 31, the vegetation damage in the earthquake area is divided into three levels: severely damaged, moderately damaged, and slightly damaged, and the sparsity (S) is defined by the following formula to quantify the vegetation damage in the earthquake area:

[0020]

[0021] Among them, S>75% indicates that the vegetation is severely damaged, 25%<S<75% indicates that the vegetation is moderately damaged, and S<25% indicates that the vegetation is slightly damaged.

[0022] Preferably, the ratio of various vegetation is calculated by the following formula:

[0023] <00正确的权重分配是提高模型准确性的关键因素之一。

[0024]

[0025] [[ID=3所提出的植被恢复建议为实际操作提供了明确的方向。

[0026] Due to the adoption of the above technical solutions, the present invention has the following innovative points:

[0027] (1) Observe the vegetation recovery in earthquake-stricken areas using remote sensing images.

[0028] Using remote sensing satellites to perform remote sensing imaging of earthquake areas allows for a holistic observation of the earthquake zone and a comprehensive understanding of vegetation distribution information. This avoids misleading analyses of vegetation recovery in earthquake-affected areas due to incomplete information on vegetation distribution.

[0029] (2) The distribution of various vegetation types was analyzed from remote sensing images.

[0030] By using a stepwise extraction method, the distribution of non-vegetation, trees, shrubs, and herbaceous plants in remote sensing images can be analyzed, providing a better understanding of the distribution of various vegetation types within the earthquake zone.

[0031] (3) Define vegetation density and the ratio of each type of vegetation

[0032] By analyzing the distribution of various vegetation types, defining the concept of vegetation density and the ratio of each type of vegetation, the recovery status of each type of vegetation and the overall vegetation can be quantitatively displayed.

[0033] (4) Vegetation restoration evaluation indicators were established.

[0034] By introducing the analytic hierarchy process (AHP) and using vegetation density and the percentage of each type of vegetation as evaluation indicators, we can more accurately analyze the current state of vegetation recovery.

[0035] (5) Recommendations for vegetation restoration

[0036] By introducing a genetic algorithm and using the analytic hierarchy process (AHP) as the core, we can solve for the standards that various indicators need to meet for vegetation restoration. By comparing these standards with the current evaluation indicators, we can clearly identify which indicators need improvement and provide data support for the next vegetation restoration measures. Attached Figure Description

[0037] Figure 1 A flowchart illustrating the vegetation restoration assessment process is shown schematically.

[0038] Figure 2 A flowchart illustrating the optimization of vegetation restoration evaluation indicators is shown. Detailed Implementation

[0039] The embodiments of the present invention will be described in detail below, but the present invention can be implemented in many different ways as defined and covered by the claims.

[0040] Currently, vegetation recovery after an earthquake is an important indicator for post-earthquake observation. Therefore, this invention designs and establishes a vegetation recovery detection and evaluation model after an earthquake, which uses remote sensing images to analyze the vegetation distribution and determine the vegetation recovery status.

[0041] This invention provides a method for monitoring post-earthquake vegetation restoration. It primarily involves analyzing remote sensing satellite data and periodically interpreting satellite imagery of disaster areas to monitor the effectiveness of vegetation restoration in damaged habitats based on large-scale parameters such as forest stand changes and landscape stability. The method also periodically monitors the habitat restoration effectiveness in different years and under different restoration and management models after vegetation restoration in earthquake-damaged habitats. An evaluation index system is designed based on the vegetation monitoring content, forming an evaluation model. This model is then combined with expert evaluation and the analytic hierarchy process (AHP) to evaluate vegetation restoration. The evaluation structure is displayed on a remote sensing analysis map using color coding.

[0042] The specific implementation process of the present invention will be described in detail below with a specific embodiment.

[0043] 1. Data Acquisition

[0044] 1) Remote sensing images of earthquake regions at a scale of 2-5m are acquired via remote sensing satellites, and the spectral information of the remote sensing images is corrected using a correction program to handle minor cloud cover. This improves the usability of the remote sensing images.

[0045] 2) Select earthquake-damaged vegetation areas, generate remote sensing images of the earthquake areas, and establish a database of remote sensing images of earthquake areas in different seasons and years.

[0046] 2. Vegetation component analysis and extraction

[0047] 1) Preliminary classification

[0048] Taking advantage of the significant differences in spectral characteristics between vegetation, water bodies, bare soil, and other non-vegetated features, the DSCAN density clustering algorithm is used to extract preliminary areas with vegetation from earthquake region images. The vegetation sparsity within the earthquake region can then be defined.

[0049] 2) Detailed classification of vegetation cover areas

[0050] After extracting vegetation distribution areas using the DBSAC density clustering algorithm, the K-means algorithm is used to cluster the vegetation cover areas to extract the distribution areas of trees, shrubs, and herbaceous plants within the earthquake zone. Since the differences in spectral characteristics among different vegetation types are very small, a weighted approach is used to weight each band, altering the spectral spatial distribution of each vegetation type and increasing the differences between them to make the clustering results more accurate. The weighting formula is as follows:

[0051] λwi=λi*ωi

[0052] Where λ i ω represents the value of each band in a remotely sensed image. i λ represents the weight of each band. ωi The weighted values ​​for each band.

[0053] Different weights are assigned according to the proportion of vegetation in each spectral band.

[0054] 3. Vegetation damage assessment

[0055] By analyzing remote sensing images in the earthquake area during different time periods, the vegetation damage in the earthquake area is divided into three levels: severely damaged, moderately damaged, and slightly damaged; the damage situation of vegetation in the earthquake area is quantified by defining the sparsity (S).

[0056] The sparsity formula is as follows:

[0057]

[0058] Among them, the damage level is divided as follows:

[0059] When S > 75%, it indicates that the vegetation is severely damaged;

[0060] When 25% < S < 75%, it indicates that the vegetation is moderately damaged;

[0061] When S < 25%, it indicates that the vegetation is slightly damaged.

[0062] In the above formula, Surface vegetation represents the vegetation coverage area, and Surface habitar represents the damaged habitat area.

[0063] The evaluation method for the damage of various vegetation components is as follows: Based on K-means clustering for vegetation classification in the vegetation coverage area, the ratio of various vegetation can be calculated, and the vegetation recovery situation can be analyzed more precisely.

[0064] Among them, the calculation formula for the ratio of various vegetation:

[0065]

[0066]

[0067]

[0068] In the above formula, Surface tree represents the coverage area of arbor, Surface shrub represents the coverage area of shrub, and Surface herbaceous represents the area of herbaceous plants.

[0069] 4. AHP vegetation recovery evaluation model

[0070] Vegetation sparseness and the ratio of trees, shrubs, and herbaceous plants were used as evaluation indicators using the analytic hierarchy process (AHP). Weights were assigned to each indicator according to its importance, and fuzzy evaluation result values ​​were set: good, medium, and low restoration. Based on the vegetation restoration evaluation indicators and the degree of damaged vegetation, an AHP-based fuzzy vegetation optimization model was constructed.

[0071] 5. Recommendations for Vegetation Restoration

[0072] By using genetic algorithms to identify the gaps between various indicators and existing indicators when vegetation restoration is progressing well, it is possible to suggest which indicator should be prioritized for improvement in the next stage of vegetation restoration.

[0073] Due to the adoption of the above technical solution, the present invention has the following innovations:

[0074] (1) Observe the vegetation recovery in earthquake-stricken areas using remote sensing images.

[0075] Using remote sensing satellites to perform remote sensing imaging of earthquake areas allows for a holistic observation of the earthquake zone and a comprehensive understanding of vegetation distribution information. This avoids misleading analyses of vegetation recovery in earthquake-affected areas due to incomplete information on vegetation distribution.

[0076] (2) The distribution of various vegetation types was analyzed from remote sensing images.

[0077] By using a stepwise extraction method, the distribution of non-vegetation, trees, shrubs, and herbaceous plants in remote sensing images can be analyzed, providing a better understanding of the distribution of various vegetation types within the earthquake zone.

[0078] (3) Define vegetation density and the ratio of each type of vegetation

[0079] By analyzing the distribution of various vegetation types, defining the concept of vegetation density and the ratio of each type of vegetation, the recovery status of each type of vegetation and the overall vegetation can be quantitatively displayed.

[0080] (4) Vegetation restoration evaluation indicators were established.

[0081] By introducing the analytic hierarchy process (AHP) and using vegetation density and the percentage of each type of vegetation as evaluation indicators, we can more accurately analyze the current state of vegetation recovery.

[0082] (5) Recommendations for vegetation restoration

[0083] By introducing a genetic algorithm and using the analytic hierarchy process (AHP) as the core, we can solve for the standards that various indicators need to meet for vegetation restoration. By comparing these standards with the current evaluation indicators, we can clearly identify which indicators need improvement and provide data support for the next vegetation restoration measures.

[0084] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for monitoring post-earthquake vegetation restoration, characterized in that, Includes the following steps: Step 1: Select earthquake-damaged vegetation areas to generate remote sensing images of earthquake areas, and establish a database of remote sensing images of earthquake areas in different seasons and years; Step 2, vegetation component analysis and extraction, including steps 21 and 22: Step 21, Preliminary classification: Taking advantage of the significant differences in spectral characteristics between vegetation, water bodies, bare soil, and other non-vegetated land features, the DSCAN density clustering algorithm is used to extract the areas with preliminary vegetation from the earthquake area image and define the vegetation sparseness within the earthquake area. Step 22, detailed classification of vegetation cover area: The K-means algorithm is used to cluster the vegetation cover area to extract the distribution areas of trees, shrubs and herbaceous plants in the earthquake area; Step 3, vegetation damage assessment, including steps 31 and 32: Step 31: Analyze remote sensing images of the earthquake area in different time periods and quantify the damage to vegetation in the earthquake area based on sparsity. Step 32, evaluation of damage to various vegetation components: Based on K-means clustering, vegetation is classified in the vegetation-covered area, and the proportion of each vegetation species is calculated to more accurately analyze the vegetation recovery. Step 4, construct the AHP vegetation restoration evaluation model: use vegetation sparseness and the ratio of trees, shrubs and herbaceous plants as evaluation indicators of the analytic hierarchy process, and assign weights according to the importance of each indicator. Set three fuzzy indicators of good, medium and low restoration to evaluate the fuzzy evaluation results. Based on the vegetation restoration evaluation indicators and the degree of damaged vegetation, construct a vegetation optimization fuzzy model based on AHP. Step 5, Vegetation Restoration Recommendations: Using a genetic algorithm, we can identify the gaps between various indicators and existing indicators when vegetation restoration is progressing well, and propose suggestions on which indicator should be prioritized for further vegetation restoration.

2. The post-earthquake vegetation restoration monitoring method according to claim 1, characterized in that, Before step 1, the process also includes: acquiring remote sensing images of the earthquake region at a scale of 2m to 5m using remote sensing satellites, correcting the spectral information of the remote sensing images through a correction procedure, processing a small amount of cloud cover, and improving the usability of the remote sensing images.

3. The post-earthquake vegetation restoration monitoring method according to claim 1, characterized in that, In step 22, weights are applied to each band to change the distribution of each vegetation in the spectral space, increase the differences in spectral characteristics between different vegetation types, and make the clustering results more accurate.

4. The post-earthquake vegetation restoration monitoring method according to claim 3, characterized in that, The weighting formula is: , Different weights are assigned based on the proportion of vegetation in each band of the spectrum.

5. The post-earthquake vegetation restoration monitoring method according to claim 1, characterized in that, In step 31, vegetation damage within the earthquake zone is categorized into three levels: severe damage, moderate damage, and minor damage. The degree of vegetation damage within the earthquake zone is quantified using the following formula: Sparseness (S). , In the formula, Surface vegetation Indicates the area of ​​vegetation cover. Surface habitar Indicates damaged habitat areas; Among them, S > 75% indicates severe vegetation damage, 25% < S < 75% indicates moderate vegetation damage, and S < 25% indicates mild vegetation damage.

6. The post-earthquake vegetation restoration monitoring method according to claim 1, characterized in that, In step 32, the proportion of each vegetation type is calculated using the following formula: , ， ， In the formula, Surface tree Indicates the area covered by trees. Surface vegetation Indicates the area of ​​vegetation cover. Surface shrub Indicates the area covered by shrubs. Surface herbaceous This indicates the area of ​​herbaceous plants.

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