Vegetation damage identification and ecological evaluation method for power transmission and transformation construction area

Abstract

The invention relates to a vegetation damage identification and ecological evaluation method for a power transmission and transformation construction area. The method comprises the following steps: data preparation and preprocessing; identifying a vegetation damaged area; optimizing and verifying a result; ecological system service value loss evaluation; and ecological restoration suggestion generation: fusing the vegetation damage degree and the spatial distribution information of the ecological system service value loss, carrying out restoration priority partitioning, providing differentiated ecological restoration measures for different partitions, and outputting a partition ecological restoration planning map and an evaluation report. Through ecological system service value evaluation, quantification of ecological economic loss is realized, and a scientific basis is provided for power transmission and transformation project ecological supervision and restoration.

Description

Technical Field

[0001] This invention relates to the field of remote sensing monitoring and ecological assessment technology, and particularly to a method for identifying and assessing vegetation damage during power transmission and transformation construction by combining novel remote sensing vegetation indices with ecosystem service value assessment. This invention is especially applicable to ecological environment supervision, ecological damage assessment, and restoration planning for long-distance linear power infrastructure construction. Background Technology

[0002] As linear infrastructure, power transmission and transformation projects inevitably cause direct damage to vegetation along the route during construction activities (such as tower foundation excavation and construction access road construction), leading to a decline in regional ecosystem service functions. Traditional vegetation damage monitoring mainly relies on field surveys, which are costly, inefficient, have limited coverage, and are difficult to quantify the overall functional loss of the ecosystem.

[0003] In recent years, although satellite remote sensing technology has been widely used for change detection, the commonly used vegetation indices have obvious limitations when applied to vegetation damage identification: these indices are usually only sensitive to a specific physiological parameter of vegetation, making it difficult to fully capture the diverse spectral characteristics of vegetation damage caused by construction; and they are easily affected by complex background environments, generating a large amount of "false change" information, resulting in low identification accuracy.

[0004] Furthermore, existing assessment methods mostly focus on the physical changes in vegetation area, failing to link physical damage to deeper losses of ecosystem service functions. This makes it impossible to provide intuitive and quantifiable economic value data for ecological compensation and restoration projects. Therefore, developing a technical method capable of accurately identifying vegetation damage and simultaneously quantifying its ecological and economic value loss is of significant practical importance. Summary of the Invention

[0005] This invention aims to solve two core problems in the existing technology: insufficient accuracy in identifying vegetation damage and difficulty in quantifying ecological and economic losses. It provides a high-precision, quantifiable, and streamlined method for identifying and assessing vegetation damage during power transmission and transformation construction.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] This application provides a method for identifying vegetation damage and conducting ecological assessment in power transmission and transformation construction areas, including the following steps:

[0008] S1: Data preparation and preprocessing: Acquire multi-temporal remote sensing images of the power transmission and transformation construction area in T1 before construction and T2 after construction, and perform preprocessing such as radiometric calibration, atmospheric correction and spatial registration; at the same time, acquire auxiliary geographic data for ecosystem service assessment.

[0009] S2: Identification of Vegetation Damage Areas: Based on the preprocessed remote sensing images, the Slope Vegetation Index (SVI) and the Three-Band Difference Vegetation Index (TBDVI) for periods T1 and T2 are calculated respectively; the difference images of DSVI and DTBDVI are obtained by the dual-temporal difference method; the difference images are initially segmented using the optimal threshold segmentation method, and the identification results based on DSVI and DTBDVI are fused to generate a preliminary vegetation damage distribution map;

[0010] S3: Result optimization and verification: Morphological post-processing is performed on the preliminary vegetation damage distribution map to remove noise, and verification and correction are performed in combination with high-resolution remote sensing images or field survey samples to generate a high-precision final vegetation damage distribution map. At the same time, producer accuracy, user accuracy and F1 score are calculated to evaluate the recognition accuracy.

[0011] S4: Ecosystem service value loss assessment: Based on the final vegetation damage distribution map, an ecosystem service assessment model is used to calculate the physical loss of habitat quality, carbon sequestration, soil conservation and water conservation services in the damaged area, and the physical loss is converted into economic value loss using a value quantification method.

[0012] S5: Ecological Restoration Recommendation Generation: Integrates spatial distribution information on vegetation damage level and ecosystem service value loss, divides the areas into restoration priority zones, proposes differentiated ecological restoration measures for different zones, and outputs zoned ecological restoration planning maps and assessment reports.

[0013] The formula for calculating SVI in step 2 of S2.1 is as follows:

[0014]

[0015] in, Indicates reflectivity, The subscripts green, nir, and swir1 represent the green light, near-infrared, and short-wave infrared band 1, respectively.

[0016] The formula for calculating TBDVI in step 2 of S2.2 is as follows:

[0017]

[0018] in, The values ​​represent reflectance, with the subscripts red, nir, and swir1 representing red light, near-infrared light, and short-wave infrared band 1, respectively.

[0019] The change detection described in step 2 of S2.3 is achieved by using a dual-temporal interpolation method to calculate the changes in SVI and TBDVI, resulting in a difference image DSVI = SVI. T1 – SVI T2And DTBDVI == TBDVI T1 – TBDVI T2 The area is currently experiencing vegetation degradation or damage.

[0020] In step 2 of S2.4, the threshold segmentation and fusion process employs an optimal threshold segmentation method to binarize the DSVI and DTBDVI images respectively, initially extracting the damaged areas. The optimal threshold is determined by selecting sample points on the high-resolution reference image, aiming to maximize the F1 score. The two binary images of the damaged areas extracted based on DSVI and DTBVI are then fused using a logical OR operation to generate a preliminary vegetation damage distribution map. This dual-index fusion strategy comprehensively utilizes the complementarity of different indices' responses to vegetation changes, improving the completeness and accuracy of the identification.

[0021] In step S4.1, the habitat quality is calculated by using the Habitat Quality module of the InVEST model, setting the vegetation-damaged area as a threat source, and calculating the change in the habitat quality of the area before and after construction.

[0022] In step S4.2, the carbon sequestration is calculated based on the area of ​​damaged vegetation and the carbon storage per unit area (biomass carbon and soil carbon) corresponding to the vegetation type, to determine the amount of carbon sequestration service loss.

[0023] In step S4.3, the soil conservation adopts the modified soil loss equation (RUSLE) or the SDR (sediment transport ratio) module of the InVEST model, combined with DEM, rainfall erosivity factor, soil erodibility factor, vegetation cover and management factor, etc., to calculate the change in soil erosion before and after construction, and the change is the change in soil conservation services.

[0024] In step S4.4, the water conservation is based on the water balance method, taking into account factors such as rainfall, evapotranspiration, and surface runoff, to assess the impact of vegetation damage on the regional water conservation capacity.

[0025] In step S4.5, the value loss is calculated using the equivalent factor method, referring to the China Terrestrial Ecosystem Unit Area Ecosystem Service Value Equivalent Table, and calculating the value loss of each service based on the ecosystem type and area of ​​the damaged area.

[0026] In step S5, the restoration priority zoning is performed by performing an equal-weighted overlay analysis of the vegetation damage level layer and the ecosystem service value loss intensity layer, dividing the area into emergency restoration zone, key restoration zone, and general maintenance zone.

[0027] Compared with the prior art, the beneficial effects of the present invention are: high recognition accuracy and strong anti-interference ability: by fusing the SVI and TBDVI dual indices, the comprehensive spectral characteristics of vegetation in the green light, near-infrared and short-wave infrared bands are fully utilized, effectively enhancing vegetation information, while suppressing the interference of background environment such as bare soil and water bodies, thereby greatly improving the recognition accuracy and robustness in complex scenarios.

[0028] The assessment is in-depth and provides strong support for decision-making: it deepens the assessment perspective from simple vegetation cover loss to the value loss of ecosystem service functions, achieving a quantitative leap from "pixels" to "wallets." The assessment results are intuitive and clear, providing a solid scientific basis for the formulation of ecological compensation standards and the cost-benefit analysis of restoration projects.

[0029] The process is automated and highly practical: It forms a standardized, batch-processable workflow that can be packaged into a software system, which greatly improves the efficiency and repeatability of the assessment work. It is suitable for rapid and large-scale ecological impact assessment and supervision of large linear projects. Attached Figure Description

[0030] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 This is an overall flowchart of the method for identifying vegetation damage and conducting ecological assessment in power transmission and transformation construction areas provided in this embodiment of the invention. Detailed Implementation

[0032] The technical solutions of the embodiments of this application will now be described with reference to the accompanying drawings. It should be noted that similar reference numerals and letters in the following drawings indicate similar items; therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0033] The terms “comprising,” “including,” or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase “comprising one…” does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0034] The terms “first,” “second,” etc., are used only to distinguish one entity or operation from another, and should not be construed as indicating or implying relative importance, nor as requiring or implying any such actual relationship or order between these entities or operations.

[0035] like Figure 1 As shown, the embodiments of this application provide the following: Step S1: Remote sensing data acquisition and preprocessing

[0036] Sentinel-2 L2A level data with cloud cover below 10% covering the target transmission and transformation lines were selected. To ensure phenological consistency, the time interval between T1 and T2 images was one year, and the same month was selected.

[0037] The supporting data included a 30-meter resolution DEM, a 1:1,000,000 soil type map, regional annual precipitation data, land use data, and high-resolution historical imagery for validation.

[0038] In SNAP software, band registration was performed on the two Sentinel-2 imagery sets to ensure complete spatial consistency. All bands were resampled to a uniform 10-meter resolution. Slope and aspect maps were generated using the DEM. All raster data were unified to the UTM-WGS84 coordinate system and cropped according to a 3-kilometer buffer zone extending outwards from the data line.

[0039] Step S2: Identification of Vegetation Damage Areas

[0040] SVI is calculated by extracting the reflectance of the B3 (green light), B8 (near infrared), and B11 (shortwave infrared 1) bands of Sentinel-2; TBDVI is calculated by extracting the reflectance of the B4 (red light), B8 (near infrared), and B11 (shortwave infrared 1) bands.

[0041] Calculate the exponential difference: DSVI = SVI T1 - SVI T2 DTBDVI = TBDVI T1 - TBDVI T2 .

[0042] Sample points are manually delineated on high-resolution images. The optimal segmentation threshold that maximizes the F1 score is determined by iterating through thresholds and calculating the F1 score. The damaged areas are then extracted using this threshold and fused by performing a logical OR operation.

[0043] A 3×3 pixel morphological opening operation is performed on the fused binary image to remove noise.

[0044] Step S3: Result Optimization and Validation

[0045] 1. Visually inspect the post-processed damaged image by overlaying it with Google Earth imagery to correct obvious misclassified or missing pixels.

[0046] 2. Accuracy verification was performed using 50 independent sample points that were not involved in threshold determination. The overall accuracy of this embodiment was verified to be 94.2%, with a Kappa coefficient of 0.88, proving the reliability of the identification results. Finally, a final vegetation damage distribution map was generated.

[0047] Step S4: Assessment of Ecosystem Service Value Loss

[0048] 1. Calculation of material loss:

[0049] In InVEST's Habitat Quality module, the damaged map is set as the threat source, and the difference in habitat quality before and after the damage is calculated.

[0050] The amount of carbon sequestration loss is calculated based on the area of ​​damaged vegetation and local carbon density parameters.

[0051] Using InVEST's SDR module, input factors such as DEM, rainfall, soil, and land use to calculate the difference in soil retention before and after damage.

[0052] By using the equivalent factor method or the market value method, the above-mentioned material losses are converted into economic value losses, and the total ecosystem service value losses are obtained by summing them up.

[0053] Step S5: Generate Ecological Restoration Suggestions

[0054] In the GIS platform, the degree of vegetation damage and the loss of ecosystem service value per unit area are weighted equally and summed. Based on the summation result, restoration priority zones are divided: emergency restoration zone, key restoration zone, and general maintenance zone.

[0055] Emergency restoration area: It is recommended to use container seedlings for high-intensity vegetation restoration after topsoil replacement, and to implement supporting engineering measures.

[0056] Key restoration areas: It is recommended to sow a mixture of herbaceous and shrub plants after leveling the land.

[0057] General maintenance area: Erect fences and warning signs, and implement closed management.

[0058] The system automatically generates a zoned ecological restoration planning map and a comprehensive report that includes loss assessment results and restoration plans.

[0059] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for identifying vegetation damage and conducting ecological assessment in power transmission and transformation construction areas, characterized in that, Includes the following steps: S1: Data preparation and preprocessing: Acquire multi-temporal remote sensing images of the power transmission and transformation construction area in T1 before construction and T2 after construction, and perform preprocessing such as radiometric calibration, atmospheric correction and spatial registration; at the same time, acquire auxiliary geographic data for ecosystem service assessment. S2: Identification of vegetation damage areas: Based on the preprocessed remote sensing images, the slope vegetation index (SVI) and the three-band difference vegetation index (TBDVI) for periods T1 and T2 are calculated respectively. The difference images of DSVI and DTBDVI were obtained by the dual-temporal difference method; the difference images were initially segmented by the optimal threshold segmentation method, and the recognition results based on DSVI and DTBDVI were fused to generate a preliminary vegetation damage distribution map. S3: Result optimization and verification: Morphological post-processing is performed on the preliminary vegetation damage distribution map to remove noise, and verification and correction are performed in combination with high-resolution remote sensing images or field survey samples to generate a high-precision final vegetation damage distribution map. At the same time, producer accuracy, user accuracy and F1 score are calculated to evaluate the recognition accuracy. S4: Ecosystem service value loss assessment: Based on the final vegetation damage distribution map, an ecosystem service assessment model is used to calculate the physical loss of habitat quality, carbon sequestration, soil conservation and water conservation services in the damaged area, and the physical loss is converted into economic value loss using a value quantification method. S5: Ecological Restoration Recommendation Generation: Integrates spatial distribution information on vegetation damage level and ecosystem service value loss, divides the areas into restoration priority zones, proposes differentiated ecological restoration measures for different zones, and outputs zoned ecological restoration planning maps and assessment reports.

2. The method according to claim 1, characterized in that, The formula for calculating SVI in step 2 is as follows: ， in, Indicates reflectivity, The subscripts green, nir, and swir1 represent the green light, near-infrared, and short-wave infrared band 1, respectively.

3. The method according to claim 1, characterized in that, The formula for calculating TBDVI in step 2 is as follows: ， in, The values ​​represent reflectance, with the subscripts red, nir, and swir1 representing red light, near-infrared light, and short-wave infrared band 1, respectively.

4. The method according to claim 1, characterized in that, In step S4, the ecosystem service assessment model includes the InVEST model and the RUSLE model; the value quantification method includes the equivalent factor method or the market value method.

5. The method according to claim 1, characterized in that, In step S2, the optimal threshold segmentation method is to select sample points on the high-resolution image and determine the optimal segmentation thresholds for DSVI and DTBDVI with the goal of maximizing the F1 score; the fusion refers to performing a logical "OR" operation on the damaged ranges extracted by DSVI and DTBDVI respectively.

6. The method according to claim 1, characterized in that, In step S5, the restoration priority zoning is performed by performing an equal-weighted overlay analysis of the vegetation damage level layer and the ecosystem service value loss intensity layer, dividing the area into emergency restoration zone, key restoration zone, and general maintenance zone.

Images