A method for quantitatively evaluating ecological benefits of a road area damaged ecosystem restoration technology

Abstract

The application provides a kind of road area damaged ecosystem restoration technology ecological benefit promotion quantitative evaluation method, comprising: by obtaining the multi-modal data of target road full life cycle, the multi-dimensional evaluation index system covering ecosystem structure, ecological environment quality and ecosystem function is constructed;Utilize fuzzy analytic hierarchy process to couple multiple indexes to generate comprehensive road area ecological index;By constructing the double reference system dynamic benchmark containing historical conditions and minimal interference, the non-engineering interference factors such as climate fluctuation are stripped from the time and space dimensions, the index difference before and after restoration is compared and analyzed, and the net ecological benefit promotion amount brought by restoration technology is accurately quantified.The problems that natural background fluctuation interference is difficult to eliminate and the cause is unknown in the prior art are overcome, accurate and dynamic evaluation of the restoration effect of road area damaged habitat is realized, and scientific basis is provided for green transportation construction decision-making.

Description

Technical Field

[0001] This invention relates to the field of ecological benefit assessment, and in particular to a quantitative assessment method for improving the ecological benefits of roadside damaged ecosystem restoration technology. Background Technology

[0002] As a typical linear infrastructure, road construction and operation inevitably have a lasting impact on the surrounding ecosystems, especially in ecologically sensitive and vulnerable areas, easily leading to a series of ecological problems such as landscape fragmentation, habitat degradation, soil erosion, and decline in ecological functions. With the increasing demands for ecological civilization construction and green transportation development, various roadside ecosystem restoration technologies, such as vegetation restoration, slope ecological protection, and ecological corridor construction, have been gradually implemented to address the ecological damage caused during road construction. How to scientifically and objectively assess the degree of ecological benefit improvement brought about by these restoration technologies has become a key technical issue that urgently needs to be addressed in the fields of transportation engineering and ecological protection.

[0003] Existing methods for assessing the ecological effects of roads mainly include single-indicator methods and comprehensive index methods. Single-indicator methods typically select indicators such as vegetation cover, normalized difference vegetation index (NDVI), net primary productivity, soil erosion, or landscape fragmentation to quantitatively analyze the impact of road engineering on a specific ecological element. While this type of method has certain advantages in revealing changes in local ecological processes, because road ecosystems are driven by the coupling of multiple ecological elements, a single indicator cannot comprehensively reflect the combined impact of road construction and restoration measures on the overall structure, ecological environment quality, and ecosystem function, often resulting in biased evaluation results.

[0004] Comprehensive index assessment methods, by integrating multiple ecological indicators, provide a holistic characterization of ecosystem status and have been widely applied in regional ecological environment quality assessment. Among these, the Remote Sensing Ecological Index (RSEI) method, based on remote sensing data, utilizes indicators such as greenness, humidity, aridity, and heat to comprehensively evaluate the ecological environment, and has also found some application in road ecological effect studies. However, existing RSEI methods primarily target the assessment of ecological environment status at the area scale, making it difficult to fully characterize the direct impact of linear road engineering on landscape patterns and ecological connectivity, especially lacking the ability to express landscape fragmentation characteristics at the road area scale.

[0005] Furthermore, traditional Remote Sensing Ecological Indices (RSEI) methods typically employ Principal Component Analysis (PCA) for dimensionality reduction and weighting of indicators. These weights are derived from the indicator variance contribution rate, reflecting statistical characteristics rather than ecological significance, and thus failing to capture the actual importance of different ecological indicators within the ecosystem. Simultaneously, PCA weights are highly dependent on data distribution and sensitive to outliers and changes in sample structure. In long-term dynamic assessments spanning multiple time phases and regions, this can easily lead to unstable evaluation results and poor repeatability. While the traditional Analytic Hierarchy Process (AHP) can incorporate expert experience, its judgment method based on precise scaling struggles to express the uncertainties and ambiguities in expert cognition. Moreover, consistency constraints often require corrections to the original judgments, easily introducing new subjective biases.

[0006] From the perspective of evaluation scale and objectives, existing technologies primarily focus on identifying and assessing the negative impacts of road engineering on ecosystems, and on the overall evaluation of the implementation effects of major ecological projects at the regional or watershed scale. Quantitative evaluation methods for the ecological benefits brought about by specific ecological restoration technologies at the road basin scale remain relatively lacking. Most existing methods directly compare the ecological state before and after construction, failing to effectively eliminate interference from non-engineering factors such as regional climate change and interannual climate fluctuations. In ecologically fragile regions like the Qinghai-Tibet Plateau, the impact of climate fluctuations is particularly significant, resulting in a large amount of "noise" mixed into the observed ecological changes. Existing methods generally struggle to effectively distinguish between changes caused by road construction and ecological restoration measures and regional-scale natural fluctuations, leading to unclear attribution of ecological benefits and limited credibility of evaluation results. Summary of the Invention

[0007] To address the aforementioned problems, this invention aims to provide a quantitative assessment method for the ecological benefits of roadside damaged ecosystem restoration technologies. This method overcomes the difficulties in eliminating natural background fluctuations and unclear ecological benefit attribution in existing methods. It enables an objective, stable, and accurate assessment of the ecological benefits of roadside damaged ecosystem restoration technologies, providing reliable technical support for the optimization of green road construction and ecological restoration technologies.

[0008] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A quantitative assessment method for the ecological benefit improvement of roadside damaged ecosystem restoration technology includes the following steps: Step 1: Acquire and integrate multimodal data of the target road during the pre-construction, construction, and operation and repair phases to build a database; Step 2: Based on the database from Step 1, construct a multi-dimensional evaluation index system; Step 3: Based on the multi-dimensional evaluation index system in Step 2, combined with fuzzy hierarchical analysis, construct a comprehensive road area ecological index; Step 4: Construct a historical condition reference system and a minimal disturbance reference system based on the database from Step 1; Step 5: Based on the road area ecological index constructed in Step 3 and the historical condition reference system and minimal disturbance reference system established in Step 4, compare and analyze the differences in the changes of the road area ecological index before and after the restoration of the target road section, and obtain the amount of ecological benefit improvement brought about by the restoration technology.

[0009] Furthermore, in step 2, the multi-dimensional evaluation indicator system includes a primary indicator system and a secondary indicator system; The primary indicator system includes indicators for ecosystem structure, ecological environment quality, and ecosystem function. The secondary indicator system includes the indicators of patch cohesion, landscape diversity index (SHDI), land dryness index (NDBSI), land surface heat index (LST), vegetation cover index (FVC), and water conservation capacity index (WET).

[0010] Furthermore, step 3 includes: Step 3-1: Evaluate the relative importance of each indicator in the multi-dimensional evaluation indicator system in Step 2 within the evaluation system, and construct a fuzzy judgment matrix; Step 3-2: Based on the fuzzy judgment matrix in Step 3-1, calculate the fuzzy weights of each indicator in the primary and secondary indicator systems, perform consistency checks on the fuzzy weights, and use the centroid method to defuzzify them to obtain the deterministic weights. Step 3-3: Perform dimensionless processing on each indicator in the secondary indicator system constructed in Step 2 to obtain standardized indicator values; Step 3-4: The deterministic weights obtained in Step 3-2 and the standardized index values ​​obtained in Step 3-3 are weighted and summed to obtain a comprehensive road area ecological index.

[0011] Furthermore, in step 4, the process of constructing the minimal disturbance reference frame is as follows: Based on the database in step 1, a reference area is delineated within the same climatic and ecological geographical unit where the target road is located; From the reference area range, filter out reference areas with minimal interference; At the same time scale as the target road segment, extract the road ecological index of the minimal disturbance reference area; The minimal disturbance reference area and its corresponding road ecological index are used as spatial benchmarks to form a minimal disturbance reference system.

[0012] Furthermore, the screening criteria for the minimal interference reference region are as follows: Areas more than 5 km from the centerline of the road and major residential areas, and; Areas where land use types are consistent with the ecological baseline of the target road area, and; Regions where no type transformation has occurred due to non-natural factors.

[0013] Furthermore, in step 4, the process of constructing the historical condition reference system is as follows: Extract road area ecological index or secondary indicator system data corresponding to the target road segment in historical periods from the database constructed in step 1; Statistical analysis was conducted on the road ecological index or secondary indicator system data during historical periods to obtain the multi-year average of the road ecological index or secondary indicator system data, which was used as the ecological baseline value of the target road section before road construction. The historical ecological baseline values ​​and their corresponding time reference intervals are used as stable ecological benchmarks for the target road area before road activities occur, forming a historical condition reference system.

[0014] Furthermore, step 5 includes: Step 5-1: Based on the minimal disturbance reference system, perform ecological scale correction on the road area ecological index constructed in Step 3 to obtain the corrected value after removing the influence of regional scale natural fluctuations. Step 5-2: Based on the historical condition reference system and minimal disturbance reference system of Step 4 and the correction value of Step 5-1, calculate the difference between the current correction value and the background correction value of the road area ecological index corresponding to the target road section during the construction period, and obtain the net ecological effect caused by road construction and restoration activities. Step 5-3: Based on the net ecological effect in Step 5-2, compare and analyze the difference in net ecological effect between the target road section that adopts road damage ecosystem restoration technology and the traditional road section that does not adopt restoration technology, and derive the ecological benefit improvement of the restoration technology.

[0015] Furthermore, the current status correction value in step 5-2 is the road area ecological index correction value during the construction period, and the background correction value is the road area ecological index correction value before construction.

[0016] Compared with the prior art, the beneficial effects of the present invention are: This application proposes a quantitative assessment method for the ecological benefits of roadside ecosystem restoration technologies. While existing methods focus on evaluating the negative impacts of roads on the surrounding environment, this application proposes that road construction may also have positive ecological benefits, and that the magnitude of these benefits can be quantified. A corresponding evaluation method is proposed. Specifically, a database is constructed by integrating multimodal data from before construction, during construction, and during operation and restoration. Based on this database, a multi-dimensional evaluation index system is developed, and a comprehensive roadside ecological index is constructed using fuzzy hierarchical analysis. This index, as a core quantitative tool, can systematically capture the complex characteristics of ecological changes. Simultaneously, a historical condition reference system and a minimal disturbance reference system are established using the database. The former reflects the baseline of natural evolution, while the latter simulates the scenario of minimal human disturbance. Together, they provide a standardized benchmark for ecological comparison before and after restoration. By comparing and analyzing the differences in roadside ecological index changes before and after restoration of the target road section, this method significantly improves the scientific rigor and operability of ecological benefit assessment, enabling precise quantification of the ecological benefits brought by restoration technologies. This provides a generalizable evaluation framework for roadside ecological restoration projects.

[0017] This invention employs Fuzzy Analytic Hierarchy Process (FAHP) coupled with multiple indicators to generate a comprehensive roadside ecological index, replacing traditional Principal Component Analysis (PCA) and Analytic Hierarchy Process (AHP). Compared to AHP, which introduces inherent subjective bias and information loss by forcibly converting fuzzy linguistic judgments into precise numerical values, FAHP more naturally and realistically quantifies expert knowledge and judgments through linguistic variables and fuzzy numbers, intrinsically expressing the fuzziness and uncertainty of expert cognition. Its output fuzzy weight range not only reflects the expected value of indicator importance but also intuitively reveals its credibility range, fundamentally enhancing the robustness and interpretability of the weight results. Furthermore, addressing the fundamental flaw of PCA as an unsupervised data-driven method, whose weights rely entirely on data distribution, reflecting statistical variance rather than intrinsic ecological importance, and easily leading to ambiguous weight meanings and underestimating indicators with low variance but high ecological importance, the indicator weights obtained by FAHP in this invention directly embody ecological significance. Furthermore, by comparing fuzzy weight intervals, this invention can clearly reveal the statistical significance or degree of overlap of the differences in importance of different indicators, significantly improving the reliability, stability, scientificity and interpretive depth of the assessment results, and better serving the ecological benefit assessment objectives of roadside damaged ecosystems.

[0018] Regarding the establishment of reference systems, this invention innovatively constructs a historical condition reference system and a minimal disturbance reference system, significantly improving the accuracy and robustness of ecological benefit assessment from both spatial and temporal scales. On the temporal scale, the "historical condition reference system" uses the multi-year average values ​​of key indicators before and during construction as baseline and current values, effectively smoothing out short-term climate fluctuations and incidental event interference, enhancing the representativeness and trend stability of the data, and thus reliably depicting the long-term evolutionary patterns of the ecosystem. On the spatial scale, the "minimal disturbance reference system" selects reference areas with similar ecological baselines and minimal disturbance, and eliminates common disturbance factors such as regional climate fluctuations through ratio processing, achieving accurate attribution of the effects of road construction and ecological restoration, and objectively reflecting the optimal potential and actual effects of ecological restoration. The synergistic application of the two reference systems jointly ensures the scientific rigor, robustness, and decision-making guidance value of the assessment results.

[0019] This invention deeply integrates Fuzzy Hierarchical Analysis (FAHP) with a "historical condition reference system" and a "minimum disturbance reference system," achieving synergistic effects and significantly improving the scientific rigor and practicality of road ecological benefit assessment. FAHP, through fuzzy number processing and expert judgment, provides the assessment system with high-fidelity indicator weights that are ecologically meaningful and encompass uncertainty, laying a reliable theoretical foundation. The dual reference systems, from both temporal and spatial dimensions, ensure the robustness and attribution accuracy of the indicator data—the "historical condition reference system" effectively smooths short-term fluctuations using multi-year averages, depicting long-term ecological trends, while the "minimum disturbance reference system" eliminates common disturbance factors such as climate through spatial ratios, highlighting the true effects of road disturbance and human restoration. FAHP and the reference systems are not simply superimposed but organically combined: FAHP endows the indicator system with ecologically credible weights, while the reference systems ensure accurate measurement and attribution of indicator changes, jointly ensuring that the final quantified ecological benefit improvement has both high credibility and strong explanatory power, clearly supporting decisions on the optimization and promotion of restoration technologies. Furthermore, this invention relies on remote sensing and GIS technologies, possessing strong operability and low implementation costs. It not only provides scientific verification of the ecological restoration effect but also provides a reliable basis for its optimization and large-scale application, effectively promoting the coordinated development of transportation infrastructure and ecological protection. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating the quantitative assessment method for improving the ecological benefits of the roadside damaged ecosystem restoration technology of the present invention. Figure 2 Spatial distribution map of roads and related technical measures provided in embodiments of the present invention; Figure 3 A schematic diagram illustrating the setting of the evaluation buffer and minimal interference reference area provided in an embodiment of the present invention; Figure 4The graph shows the changes in road ecological effects under different buffer distances, as provided in the embodiments of the present invention. Detailed Implementation

[0021] To enable those skilled in the art to better understand the technical solutions of the present invention, the technical solutions of the present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0022] Please see Figures 1-4 This application discloses a quantitative assessment method for improving the ecological benefits of roadside damaged ecosystem restoration technology, including the following steps: Step 1: Acquire and integrate multimodal data of the target road during the pre-construction, construction, and operation and repair phases to build a database; Specifically, the multimodal data in step 1 includes at least: road project design data and spatial distribution data as a spatial benchmark, environmental geographic auxiliary data such as digital elevation models reflecting the regional background and administrative boundary data, and multi-source, multi-temporal remote sensing image data for dynamic monitoring. This constructs a spatiotemporal assessment database covering the entire lifecycle.

[0023] Step 2: Based on the database in Step 1, construct a multi-dimensional evaluation indicator system; the multi-dimensional evaluation indicator system includes a primary indicator system and a secondary indicator system; The primary indicator system includes indicators for ecosystem structure, ecological environment quality, and ecosystem function. The secondary indicator system includes the following indicators: patch cohesion, landscape diversity index (SHDI), land aridity index (NDBSI), land surface heat index (LST), vegetation coverage index (FVC), and water conservation capacity index (WET). In step 2, the meaning and calculation formula of each indicator in the secondary indicator system are as follows: Patch cohesion measures the degree of natural connectivity of landscape patches, reflecting the degree of habitat fragmentation. A high value indicates good connectivity of landscape patches and low degree of habitat fragmentation; a decreasing index indicates reduced connectivity between patches.

[0024]

[0025] Where i represents the landscape type; j represents the number of patches; n represents the sum of patches in landscape type i; m represents the sum of landscape types; a ij The area of ​​the patch; l ij A is the perimeter of the patch; A is the total landscape area.

[0026] Landscape diversity (SHDI): Reflects landscape heterogeneity and is highly sensitive to the uneven distribution of different patch types within a landscape. In a landscape system, the richer the land use and the higher the degree of fragmentation, the greater the content of uncertain information, and the higher the calculated value.

[0027]

[0028] Where Pi is the proportion of the area occupied by landscape type i; n is the number of landscape types.

[0029] Land Aridity Index (NDBSI): Represented by the average of the Bare Soil Index (SI) and the Engineering and Construction Index (IBI), it measures soil degradation and disturbance caused by man-made infrastructure. To some extent, it reflects the reduction of ecological land and embodies the spatial changes in ecological space. The higher the value, the more exposed land surface and man-made structures there are in the area, and the greater the negative disturbance to the ecosystem.

[0030]

[0031]

[0032]

[0033] Among them, b2, b3, b4, b8, b 11 The reflectance of Sentinel-2A in the blue band, green band, red band, near-infrared band, and shortwave infrared 1 band.

[0034] Land surface heat (LST): Reflects the energy flow and material exchange of the soil-vegetation-atmosphere system. It is a key indicator for measuring the energy balance and greenhouse effect on the Earth's surface and can reflect the spatial distribution characteristics of the underlying surface temperature field in a relatively detailed manner.

[0035]

[0036] Among them, B 10 For Landsat 8 thermal infrared band 10.

[0037] Combined vegetation cover (FVC): This is usually expressed as the weighted average of the vegetation cover of each vegetation type in a certain area and its proportion of the area occupied. It is an important parameter for describing vegetation communities and ecosystems, and also a major factor influencing soil erosion and water loss. FVC uses a pixel-based binary model to invert the vegetation cover of the road section under study.

[0038]

[0039] Wherein, NDVI is the calculated NDVI value of the pixel; NDVIv is the NDVI value of a pure plant pixel, NDVIv is defined as an annual cumulative NDVI change frequency of 95%, and FVC with a cumulative frequency of 95% or more is 1; NDVIs is the NDVI value of a bare soil or unvegetated pixel. In this application, NDVIs is defined as an NDVI cumulative frequency of 5%, and vegetation cover with a cumulative frequency of less than 5% is 0.

[0040] Water conservation capacity (WET): The water conservation capacity of ecosystems such as forests, grasslands, and wetlands is expressed by water conservation capacity (WET), which refers to the water content of soil and vegetation. It directly reflects the water content of the ecosystem. The higher the value, the more abundant the water content of soil and vegetation, the stronger the water conservation capacity of the ecosystem, and the stronger the stability and resilience of the ecosystem.

[0041]

[0042] Among them, b2, b3, b4, b8, b 11 b 12 These are the reflectance values ​​for the Sentinel-2A blue band, green band, red band, near-infrared band, shortwave infrared 1 band, and shortwave infrared 2 band, respectively. The coefficients in the WET index are derived from the Tasselled Cap-wetness formula published on the Index DataBase website.

[0043] Both patch cohesion and landscape diversity indicators are based on land use / cover data and calculated using Fragstats software. The specific technical steps are as follows: Based on the land use / cover type, a class description text document is created and renamed with the suffix "fcd" to describe the attribute category (patch type). Each record contains a numerical class (patch type) value, a character description of the category, a logical state indicator, and a local description. An edge depth table file is created and renamed with the suffix "fsq" to specify the edge depth, edge contrast, and similarity coefficient for use in the corresponding functional indicator analysis. In Fragstats software, first import the raster data of land use types in the study area by "Add layer". In the "Common tables" window, import the files with the .fcd and .fsq extensions from step 2 into the "Class descriptors" and "Edge depth" columns, respectively. In the "Landscape metrics" function block, select "Shannon's Diversity Index (SHDI)" in the "Diversity" column to calculate the landscape diversity index, and select "Patch Cohesion Index (COHESION)" in the "Aggregation" column to calculate the patch cohesion index.

[0044] Four indicators—land aridity, surface heat, comprehensive vegetation coverage, and water conservation capacity—can be obtained through inversion on the Google Earth Engine (GEE) cloud computing platform.

[0045] Step 3: Based on the multi-dimensional evaluation index system in Step 2, combined with fuzzy hierarchical analysis, construct a comprehensive road area ecological index; Step 3 specifically includes: Step 3-1: Evaluate the relative importance of each indicator in the multi-dimensional evaluation indicator system in Step 2 within the evaluation system, and construct a fuzzy judgment matrix; Step 3-2: Based on the fuzzy judgment matrix in Step 3-1, calculate the fuzzy weights of each indicator in the primary and secondary indicator systems, perform consistency checks on the fuzzy weights, and use the centroid method to defuzzify them to obtain the deterministic weights. Step 3-3: Perform dimensionless processing on each indicator in the secondary indicator system constructed in Step 2 to obtain standardized indicator values; Step 3-4: The deterministic weights obtained in Step 3-2 and the standardized index values ​​obtained in Step 3-3 are weighted and summed to obtain a comprehensive road area ecological index.

[0046] In step 3-1, the fuzzy judgment matrix is ​​constructed as follows: Fuzzy Hierarchical Analysis (FAHP) introduces linguistic variables such as triangular fuzzy numbers to transform the fuzzy linguistic judgments of experts on the relative importance of each indicator system into fuzzy numbers that can be mathematically calculated, thereby constructing a fuzzy judgment matrix and calculating a fuzzy weight interval that can reflect the importance of the indicators and their uncertainty range. This makes the weight assignment process closer to human thinking and the results more robust and interpretable.

[0047] like Figure 1 The diagram shown illustrates an embodiment of the quantitative assessment method for ecological benefit enhancement of the present invention. This method is based on the concept of "key indicators + dynamic reference system" and includes: By obtaining accurate administrative division vector boundaries and environmental geographic auxiliary data such as DEM through the National Geomatics Center of China, the baseline conditions of the region can be clarified. We obtained linear engineering plan drawings, project feasibility study reports, project design specifications, project environmental impact assessment reports, project construction organization designs, high-resolution maps of the route and road spatial distribution data such as the boundaries of nature reserves, project design materials, and ecological protection and restoration technical measures from the construction design unit and the regional forestry and grassland bureau. We then used ArcGIS software for spatial mapping to further clarify the research objects and analyze the data. We acquired Sentinel-2A and Landsat-8 datasets through the Google Earth Engine (GEE) cloud computing platform, and performed preprocessing operations such as atmospheric correction, cloud cover filtering, cloud removal, resampling, pixel median synthesis, mosaicking, cropping, and water body masking using an improved Normalized Difference Water Index (MNDWI). At the same time, we resampled the raster data to a resolution of 10m×10m and acquired land use / cover data through the Dynamic World dataset. We integrated these data to form multi-source remote sensing data, providing a solid high-resolution data foundation for the quantitative assessment of road ecological benefits.

[0048] Based on the principles of scientific rigor, comprehensiveness, operability, and effectiveness, and combined with the ecological impact mechanisms and characteristics of land transportation, this application draws on the factor settings in domestic and international road impact assessment research and environmental impact assessment practices for construction projects. It also refers to the retrospective analysis of the disturbance to the surrounding ecological environment in highway and railway environmental protection acceptance reports. Taking into account the direct effects of spatial disturbances from road construction on ecosystem structure, as well as the indirect effects on ecological quality and function, a road area ecological index is constructed based on remote sensing ecological indices, with ecosystem structure, ecological environment quality, and ecosystem function as primary comprehensive evaluation indicators.

[0049] More specifically, the weight setting in step 3 adopts the fuzzy hierarchical analysis method. The opinions of more than 80 experts with long-term experience in the fields of highways, railways and environmental protection were collected through questionnaires, and the road area ecological index indicator system shown in Table 1 was finally formed.

[0050] Table 1. Indicator System for Constructing the Road Area Ecological Index

[0051] It should be noted that the weights of the primary and secondary indicator systems are calculated using fuzzy analytic hierarchy process (AHP), and a comprehensive road ecological index is constructed by weighted summation. The application of fuzzy AHP aims to overcome the information loss problem in the traditional AHP when dealing with fuzziness in judgment, and solves the fundamental defect of principal component analysis (PCA), which, although an objective method, only reflects the statistical variance of data and cannot directly reflect the inherent ecological importance of the indicators.

[0052] Step 4: Construct a historical condition reference system and a minimal disturbance reference system based on the database from Step 1; Specifically, a dynamic evaluation benchmark with a dual reference system is constructed from both temporal and spatial dimensions, using the pre-construction ecological baseline as a historical condition reference system and the minimal disturbance reference system with the minimally disturbed area as a spatial reference system, so as to achieve accurate attribution of the net ecological benefits generated in road construction and ecological restoration. The definition of the minimal disturbance reference system is as follows: within the same climate-ecological geographical unit as the target road, a region with extremely low human activity disturbance, no direct impact from road construction, and similar ecological background conditions is selected as a reference. By performing synchronous ratio processing of key ecological indicators between the target road area and the reference area, a spatial reference system is constructed to separate regional-scale natural fluctuations and common external disturbance factors.

[0053] The process of constructing a minimal disturbance reference frame is as follows: Based on the database in step 1, a reference area is delineated within the same climatic and ecological geographical unit where the target road is located; From the reference area range, filter out reference areas with minimal interference; At the same time scale as the target road segment, extract the road ecological index of the minimal disturbance reference area; The minimal disturbance reference area and its corresponding road ecological index are used as spatial benchmarks to form a minimal disturbance reference system.

[0054] The selection criteria for the minimal interference reference region are as follows: Areas more than 5 km from the centerline of the road and major residential areas, and; Areas where land use types are consistent with the ecological baseline of the target road area, and; Regions where no type transformation has occurred due to non-natural factors.

[0055] The historical condition reference system is defined as follows: In the quantitative assessment of the ecological benefits of damaged ecosystems in road areas, the ecosystem state before the target road construction activities is used as the time dimension benchmark. It integrates the statistical characteristics of key ecological indicators over multiple consecutive years prior to construction to construct a time reference system reflecting the natural variation range and stability level of the road area's ecosystem. In other words, the historical condition reference system uses the ecosystem state before the target road construction activities as the time dimension benchmark; it is not a single static point in time, but rather a comprehensive calculation based on multiple historical years, multiple growing seasons, or multiple observation periods. It is used to assess the net impact of road construction and ecological restoration activities on the road area's ecosystem.

[0056] More specifically, the process of constructing a historical reference system is as follows: Extract road area ecological index or secondary indicator system data corresponding to the target road segment in historical periods from the database constructed in step 1; Statistical analysis was conducted on the road ecological index or secondary indicator system data during historical periods to obtain the multi-year average of the road ecological index or secondary indicator system data, which was used as the ecological baseline value of the target road section before road construction. The historical ecological baseline values ​​and their corresponding time reference intervals are used as stable ecological benchmarks for the target road area before road activities occur, forming a historical condition reference system.

[0057] Based on the above operations, the combined impact of non-engineering disturbances (such as climate change) on the target area and the reference area is eliminated to a certain extent, and the calculation results in the spatial dimension only reflect the relative ecological response caused by road ecological restoration activities.

[0058] Based on the commonalities in the responses of the two regions to regional climate fluctuations, such common interfering factors can be effectively eliminated.

[0059] It should be noted that setting up a dynamic reference system to assess the complex effects of road construction on the ecosystem involves combining historical condition reference systems and minimal disturbance reference systems to scientifically measure the dynamic changes of key indicators on both spatial and temporal scales.

[0060] In terms of spatial scale, since high-altitude and ecologically fragile areas are sensitive to climate fluctuations, directly providing original indicators may be influenced by climate fluctuations. Therefore, a minimal disturbance reference system is introduced, which is a region with minimal road construction disturbances during the study period and an ecological background similar to the area to be assessed. By ratioing the key indicators of the assessment area to those of the minimal disturbance reference system, corrected values ​​for the indicators are obtained, thereby eliminating the disturbance of climate fluctuations to the calculation results.

[0061] In terms of time scale, a historical reference system is selected, defining two periods: "before construction" and "construction period". The multi-year average of key indicators in the "before construction" period is used as the baseline correction value, and the multi-year average of key indicators in the "construction period" period is used as the current status correction value. The changes of key indicators in time scale are calculated.

[0062] In practical application, taking the 1km road area on both sides of the road as an example, the 1km road area on both sides of the road is divided into 10 buffer zones of 100m each to analyze the spatial heterogeneity of the improvement of road ecological benefits at different buffer distances. At the same time, a 500m wide buffer zone 5km away from the road, which is similar to the ecological background and natural conditions of the study area, is selected as the minimal disturbance reference area.

[0063] Because the dimensions of the various indicators are not uniform, in order to improve the scientificity and consistency of the data, and to eliminate the interference of the spatiotemporal differences in construction time and space of different road sections on comparability, it is first necessary to normalize the five indicators except for the comprehensive vegetation cover, and standardize their values ​​to the range of 0 to 1. The processing formula is as follows:

[0064] in, It is the standardized value of the i-th evaluation index. It is the original value of the i-th evaluation index. and Let represent the maximum and minimum values ​​of the i-th evaluation indicator, respectively. To ensure consistency across different years and regions... and To maintain consistency and reduce interference from outliers, and to be consistent with the vegetation coverage normalization method, this application uses the 95th quartile (P95) and 5th quartile (P5) of multi-year data as the global maximum and minimum values, further improving the scientific validity and effectiveness of the data range.

[0065] Given the sensitive response of ecologically fragile areas to climate fluctuations, the ecological effects measured directly using the original values ​​of regional indicators would include both road disturbances and climate fluctuations. Therefore, this invention introduces a "minimal disturbance reference system," using the following calculation formula to replace the original indicator values. Through a quantitative algorithm based on eliminating the influence of regional climate fluctuations, the interference of non-road factors in the original indicators is effectively removed, thus obtaining a more accurate ecological response caused by road activities:

[0066] in, It is the correction value for the i-th evaluation index. It is the standardized value of the i-th evaluation index. It is the median of the standardized values ​​of the i-th evaluation index under a minimal disturbance reference system.

[0067] The ecological effects of roads are measured using the following formula:

[0068] Where, Δ E For ecological effect quantity, This indicates the ecological baseline value of the assessment indicators under the "historical condition reference system" during the "pre-construction" period; This indicates the ecological status value of the assessment indicator during the "construction period". For positive indicators, a negative value indicates that road construction has a negative effect on the ecology, and vice versa; for negative indicators, a positive value indicates that road construction has a negative ecological disturbance, and vice versa.

[0069] The ecological benefit improvement of relevant technical measures is measured by the following formula:

[0070] Where, Δ B The increase in ecological benefits is calculated as a percentage; Δ E g This indicates the ecological effect of green road sections that utilize emerging roadside ecosystem restoration technologies; Δ Et This indicates the ecological impact of traditional road sections that have not adopted emerging roadside ecosystem restoration technologies.

[0071] Step 5: Based on the road area ecological index constructed in Step 3 and the historical condition reference system and minimal disturbance reference system established in Step 4, compare and analyze the differences in the changes of the road area ecological index before and after the restoration of the target road section, and obtain the amount of ecological benefit improvement brought about by the restoration technology.

[0072] Step 5 includes: Step 5-1: Based on the minimal disturbance reference system, perform ecological scale correction on the road area ecological index constructed in Step 3 to obtain the corrected value after removing the influence of regional scale natural fluctuations. Step 5-2: Based on the historical condition reference system and minimal disturbance reference system of Step 4 and the correction value of Step 5-1, calculate the difference between the current correction value and the background correction value of the road area ecological index corresponding to the target road section during the construction period, and obtain the net ecological effect caused by road construction and restoration activities. Among them, the current status correction value is the road area ecological index correction value during the construction period, and the background correction value is the road area ecological index correction value before construction. Step 5-3: Based on the net ecological effect in Step 5-2, compare and analyze the difference in net ecological effect between the target road section that adopts road damage ecosystem restoration technology and the traditional road section that does not adopt restoration technology, and derive the ecological benefit improvement of the restoration technology.

[0073] After determining the amount of ecological benefit improvement, a series of continuous buffer zones of equal width can be set on both sides of the road, with the road centerline as the benchmark. The amount of ecological benefit improvement in each buffer zone can be calculated, and a curve of the amount of ecological benefit improvement as a function of buffer distance can be generated. The relationship between the amount of ecological benefit improvement and the distance from the road centerline can be quantitatively evaluated based on the curve. Example

[0074] The Tongsai Expressway is located in Huangnan Tibetan Autonomous Prefecture, Qinghai Province, at the junction of the Qinghai-Tibet Plateau and the Loess Plateau. It traverses the Maixiu sub-region of the Sanjiangyuan National Nature Reserve, with an average altitude between 2,700 and 3,800 meters. The ecological environment there is fragile, sensitive, and unstable. During the design and construction process, a number of emerging ecological protection and restoration technologies were applied, making it an ideal sample for studying the ecological effects of roads and the ecological benefits of green construction.

[0075] In this embodiment, as Figure 2As shown, the Tongren-Sai Expressway is divided into traditional road sections and green road sections based on the application of green technologies. The traditional road section, namely the Tongren South-Xibusha section, is 14.24km long. Construction began in 2018 and was completed in 2021, but no new green technologies were adopted. The green road section, which began construction in 2021 and was basically completed in 2023, adopted several new ecological protection and restoration technologies, including the fishing method for prefabricated bridge construction, turf and topsoil protection and utilization, a freight cable car without access roads, sowing of native plants, and vegetation restoration using plant fiber blankets. These technologies aim to achieve the green goal of "low-disturbance construction and timely restoration." It is further subdivided into Green Road Section 1 (i.e., the Tongren-Tongren South section, located in a lower-altitude urban agricultural area) and Green Road Section 2 (i.e., the Xibusha-Zeku section, located in a higher-altitude forest and pastoral area).

[0076] This embodiment uses the quantitative assessment method for ecological benefit improvement of roadside damaged ecosystem restoration technology of this application for quantitative assessment, and the specific implementation is as follows: Step 1: For the Tongsai Expressway, multimodal data from the pre-construction and construction phases were acquired and integrated, and a database was constructed; The multimodal data includes: (1) Environmental geographic auxiliary data: The administrative division vector boundary of the study area was obtained through the National Geomatics Center of China, and the 12.5m resolution digital elevation model (DEM) data was obtained based on the ALOS topographic dataset of NASA Earth Observation System Data Center.

[0077] (2) Road spatial distribution data and road project design data: The high-resolution map of the route plan of Tongsai Expressway, construction design drawings and construction organization design data were integrated and vectorized using ArcGIS software to determine the road centerline and road boundary.

[0078] (3) Multi-source remote sensing data: Sentinel-2A and Landsat-8 imagery data from 2016 to 2023 (covering both traditional and green road sections before and during construction) were selected using the Google Earth Engine (GEE) cloud computing platform. Atmospheric correction, cloud removal, mosaicking, and resampling were performed on the images, and the resolution was unified to 10m×10m. Dynamic World land use data at 10m resolution for the corresponding years were also acquired.

[0079] Step 2: Based on the database obtained in Step 1, an indicator system suitable for the characteristics of the Tongsai Expressway area was constructed. The primary indicators were established as ecosystem structure, ecological environment quality, and ecosystem function. At the secondary indicator level, using Fragstats software, patch cohesion and landscape diversity index (SHDI) were calculated based on land use data to characterize ecosystem structure; land aridity index (NDBSI) and land surface heat index (LST) were retrieved using the GEE platform to characterize ecological environment quality; the combined vegetation cover (FVC) was retrieved using a pixel-based bisection model; and water conservation capacity (WET) was calculated using the tasseled cap transformation formula to characterize ecosystem function.

[0080] Step 3: A structured questionnaire was distributed to 80 experts in relevant fields to construct a fuzzy judgment matrix. The weights of each indicator were calculated using the fuzzy hierarchical analysis (FAHP) method (see Table 1), with ecosystem structure weighting 0.40, ecological environment quality weighting 0.22, and ecosystem function weighting 0.38. Dimensionless standardization (95th quantile normalization) was performed on all secondary indicators along the Tongsai Expressway, and a weighted sum was generated by combining the deterministic weights to produce a raster dataset of the Road Area Ecological Index (RAEI) for each year along the Tongsai Expressway from 2016 to 2023.

[0081] Step 4: To eliminate the significant climate fluctuation interference from the Tibetan Plateau, the following dual reference system was constructed: Minimal Disturbance Reference System (Spatial Baseline): Along both sides of the Tongsai Expressway, based on the principle of "same climate-ecological geographical unit," a strip-shaped area 5km away from the road centerline and 500m wide was delineated using ArcGIS. Patches within this area whose land use type has not changed and are far from residential areas were selected as "minimal disturbance reference areas" for subsequent spatial ratio correction. Figure 3 The diagram shows the setting of the assessment area and the minimal disturbance reference area provided in the embodiment of the quantitative assessment method for ecological benefit improvement of the present invention. The assessment area consists of 10 road buffer zones, each 100m wide, totaling 1km wide on both sides of the road, to analyze the spatial heterogeneity of the ecological benefit improvement of the road at different buffer distances. At the same time, a 500m wide buffer zone, 5km away from the road and similar to the ecological background and natural conditions of the study area, is selected as the minimal disturbance reference area.

[0082] Historical conditions reference system (time benchmark): The RAEI index and its sub-indices for three consecutive years before the construction of the Tongsai Expressway (2016-2018 for traditional road sections and 2018-2020 for green road sections) were extracted, and their multi-year average was calculated as the "ecological baseline value" of the area.

[0083] Step 5: Using contemporaneous data from a minimal disturbance reference system, the RAEI values ​​of the target road segment (green road segment) and the traditional road segment are adjusted by ratio to eliminate the impact of regional climate fluctuations. The difference between the current state correction value and the pre-construction (background correction value) for each road segment is calculated to quantify the net ecological effect of the road. By calculating and comparing the effect quantities of the traditional road segment and the green road segment, the ecological benefit improvement of the corresponding technical measures is obtained, such as... Figure 4 As shown in Table 2.

[0084] Specifically, such as Figure 4 The curves showing the changes in road ecological effects at different buffer distances and the data in Table 2 indicate that, based on the RAEI assessment, the overall disturbance level of the Tongsai Expressway is relatively low, with the ecological effects mainly concentrated in the 0–100m range. Within this range, Green Road Section 1 exhibits a positive ecological effect (RAEI change value +0.025), while Green Road Section 2 and the traditional road section show negative effects (-0.084 and -0.074, respectively). Compared to the traditional road section, Green Road Section 1 achieves an ecological benefit improvement of 0.098. Outside the 100m range, the ecological effect value of Green Road Section 1 fluctuates significantly, but within the 800m range, its negative effect remains lower than that of the traditional road section, maintaining an ecological benefit higher than 0.015. The negative effects of both Green Road Section 2 and the traditional road section decrease with increasing buffer distance, and the negative effect of Green Road Section 2 is consistently lower than that of the traditional road section, with its ecological benefit improvement increasing from 0.014 to 0.056 with increasing distance.

[0085] The analysis of each sub-indicator shows that within the 0–100m road area, except for surface heat, all other indicators of Green Road Section 1 exhibit positive effects, indicating that the restoration measures taken in this section have a positive effect on improving the regional ecological condition. For Green Road Section 2 and the traditional road section, except for surface heat, the effect values ​​of all indicators peak within the 0–100m range. Overall, except for patch cohesion and comprehensive vegetation cover, the negative effects of the Green Road section are lower than those of the traditional road section, and it shows certain positive effects in several indicators. Furthermore, within the 0–100m range, the positive effects of Green Road Section 1 are generally higher than those of Green Road Section 2, showing a more significant improvement in ecological benefits.

[0086] Table 2. Ecological benefits of the Tongsai Expressway buffer zone and the improvement in ecological benefits of green road section technical measures

[0087] The above analysis results indicate that as the road area increases, the ecological effects and benefits of Green Road Section 1, affected by urban human activities, fluctuate significantly, while the negative ecological effects of Green Road Section 2 and the traditional road section show a weakening trend. Outside the 200m road area, the negative effects of Green Road Section 2 are consistently lower than those of the traditional road section, and the corresponding ecological benefits remain consistently positive, indicating that the relevant measures have a certain ecological improvement effect on a larger spatial scale. Further analysis shows that the road ecological effects are concentrated within the 0–100m road area. Green Road Section 1, with its poor ecological background, produces a positive effect of 0.025 under the constraints of human activities and the concentrated implementation of technology, achieving an ecological benefit improvement of 0.098 compared to the traditional road section. In contrast, the traditional road section, without corresponding ecological restoration measures, exhibits a negative ecological effect of -0.074 within the 0–100m range; Green Road Section 2, influenced by its forest and grassland ecological background conditions and the time scale of the restoration process, also exhibits a negative ecological effect of -0.084 within this range. Based on the above results, in the subsequent protection and restoration process, it is necessary to differentiate the ecological restoration measures according to the ecological background conditions and spatial scale characteristics of different road sections.

[0088] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A quantitative assessment method for the ecological benefit improvement of roadside damaged ecosystem restoration technology, characterized in that, Includes the following steps: Step 1: Acquire and integrate multimodal data of the target road during the pre-construction, construction, and operation and repair phases to build a database; Step 2: Based on the database from Step 1, construct a multi-dimensional evaluation index system; Step 3: Based on the multi-dimensional evaluation index system in Step 2, combined with fuzzy hierarchical analysis, construct a comprehensive road area ecological index; Step 4: Construct a historical condition reference system and a minimal disturbance reference system based on the database from Step 1; Step 5: Based on the road area ecological index constructed in Step 3 and the historical condition reference system and minimal disturbance reference system established in Step 4, compare and analyze the differences in the changes of the road area ecological index before and after the restoration of the target road section, and obtain the amount of ecological benefit improvement brought about by the restoration technology.

2. The method for quantitatively evaluating the ecological benefits of roadside damaged ecosystem restoration technology according to claim 1, characterized in that, In step 2, the multi-dimensional evaluation indicator system includes a primary indicator system and a secondary indicator system; The primary indicator system includes indicators for ecosystem structure, ecological environment quality, and ecosystem function. The secondary indicator system includes the indicators of patch cohesion, landscape diversity index (SHDI), land dryness index (NDBSI), land surface heat index (LST), vegetation cover index (FVC), and water conservation capacity index (WET).

3. The method for quantitatively evaluating the ecological benefits of roadside damaged ecosystem restoration technology according to claim 2, characterized in that, Step 3 includes: Step 3-1: Evaluate the relative importance of each indicator in the multi-dimensional evaluation indicator system in Step 2 within the evaluation system, and construct a fuzzy judgment matrix; Step 3-2: Based on the fuzzy judgment matrix in Step 3-1, calculate the fuzzy weights of each indicator in the primary and secondary indicator systems, perform consistency checks on the fuzzy weights, and use the centroid method to defuzzify them to obtain the deterministic weights. Step 3-3: Perform dimensionless processing on each indicator in the secondary indicator system constructed in Step 2 to obtain standardized indicator values; Step 3-4: The deterministic weights obtained in Step 3-2 and the standardized index values ​​obtained in Step 3-3 are weighted and summed to obtain a comprehensive road area ecological index.

4. The method for quantitatively evaluating the ecological benefits of roadside damaged ecosystem restoration technology according to claim 1, characterized in that, In step 4, the process of constructing the minimal disturbance reference frame is as follows: Based on the database in step 1, a reference area is delineated within the same climatic and ecological geographical unit where the target road is located; From the reference area range, filter out reference areas with minimal interference; At the same time scale as the target road segment, extract the road ecological index of the minimal disturbance reference area; The minimal disturbance reference area and its corresponding road ecological index are used as spatial benchmarks to form a minimal disturbance reference system.

5. The method for quantitatively evaluating the ecological benefits of roadside damaged ecosystem restoration technology according to claim 4, characterized in that, The selection criteria for the minimal interference reference region are: Areas more than 5 km from the centerline of the road and major residential areas, and; Areas where land use types are consistent with the ecological baseline of the target road area, and; Regions where no type transformation has occurred due to non-natural factors.

6. The method for quantitatively evaluating the ecological benefits of roadside damaged ecosystem restoration technology according to claim 1, characterized in that, In step 4, the process of constructing the historical condition reference system is as follows: Extract road area ecological index or secondary indicator system data corresponding to the target road segment in historical periods from the database constructed in step 1; Statistical analysis was conducted on the road ecological index or secondary indicator system data during historical periods to obtain the multi-year average of the road ecological index or secondary indicator system data, which was used as the ecological baseline value of the target road section before road construction. The historical ecological baseline values ​​and their corresponding time reference intervals are used as stable ecological benchmarks for the target road area before road activities occur, forming a historical condition reference system.

7. The method for quantitatively evaluating the ecological benefits of roadside damaged ecosystem restoration technology according to claim 1, characterized in that, Step 5 includes: Step 5-1: Based on the minimal disturbance reference system, perform ecological scale correction on the road area ecological index constructed in Step 3 to obtain the corrected value after removing the influence of regional scale natural fluctuations. Step 5-2: Based on the historical condition reference system and minimal disturbance reference system of Step 4 and the correction value of Step 5-1, calculate the difference between the current correction value and the background correction value of the road area ecological index corresponding to the target road section during the construction period, and obtain the net ecological effect caused by road construction and restoration activities. Step 5-3: Based on the net ecological effect in Step 5-2, compare and analyze the difference in net ecological effect between the target road section that adopts road damage ecosystem restoration technology and the traditional road section that does not adopt restoration technology, and derive the ecological benefit improvement of the restoration technology.

8. The method for quantitatively evaluating the ecological benefits of roadside damaged ecosystem restoration technology according to claim 7, characterized in that, The current status correction value in step 5-2 is the road area ecological index correction value during the construction period, and the background correction value is the road area ecological index correction value before construction.

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