An intelligent monitoring method for ecological corridor construction effect evaluation

Abstract

The application discloses an ecological corridor construction effect evaluation intelligent monitoring method, and belongs to the technical field of ecological restoration and wild animal protection. In view of the fact that the existing evaluation relies on manual patrol, and the monitoring range is limited, the index is single, the hidden obstacles are difficult to identify, the subjectivity is strong, and the dynamic tracking cannot be realized, the method builds a space-air-ground integrated monitoring network, fuses remote sensing, unmanned aerial vehicle, ground and infrared monitoring data, sets six evaluation dimensions, identifies the traffic obstacles and key ecological nodes in combination with a migration resistance model, divides the corridor function area, determines the construction effect according to good, general and poor, and outputs the rectification management and protection scheme. The method realizes the full-time automatic quantitative evaluation of the corridor, is suitable for various corridors and terrestrial wild animals, and can be used for ecological engineering such as national parks, nature reserves and traffic ecological compensation.

Description

Technical Field

[0001] This invention relates to the fields of ecological restoration project evaluation, wildlife protection, integrated air-space-ground remote sensing monitoring, artificial intelligence recognition, landscape ecology, and geographic information systems (GIS), specifically to an intelligent monitoring method for evaluating the construction effects of ecological corridors. This method is mainly applied to the completion acceptance, long-term operation and maintenance monitoring, ecological effect assessment, obstacle identification and optimization of projects such as terrestrial wildlife migration corridors, vegetation connectivity corridors, road underpasses / overpasses for animals, and wetland ecological corridors. It is suitable for various engineering projects such as nature reserves, national parks, territorial space ecological restoration, and transportation ecological compensation. Background Technology

[0002] Ecological corridors are core engineering facilities for mitigating habitat fragmentation, ensuring wildlife migration, maintaining gene exchange among populations, and enhancing regional biodiversity. GB / T 43646-2024, "Design Requirements for Terrestrial Wildlife Corridors," clearly requires continuous monitoring and effectiveness evaluation for at least three years after corridor construction. Currently, the evaluation and monitoring of ecological corridor construction effectiveness in China suffers from several shortcomings: traditional monitoring methods with insufficient coverage; a one-sided evaluation index system with low quantification; difficulty in identifying hidden barriers and ecological obstacles; lack of dynamic tracking capabilities; and inaccurate species mobility assessment. While technologies such as remote sensing, drones, infrared AI recognition, and landscape resistance models are gradually being applied to ecological monitoring, a standardized intelligent monitoring and evaluation method integrating data collection, preprocessing, multi-index calculation, zoning evaluation, barrier identification, level determination, and optimization suggestions has not yet been established. To meet the industry's needs for scientific evaluation and refined operation and maintenance of ecological corridors, this invention proposes an integrated intelligent monitoring and evaluation method. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of traditional ecological corridor evaluation, such as strong reliance on manual labor, limited monitoring range, single indicators, low quantitative accuracy, difficulty in identifying hidden obstacles, and inability to dynamically track data. By integrating integrated air-space-ground monitoring, AI intelligent recognition, landscape ecology models, and circuit theory algorithms, a full-process intelligent monitoring and evaluation method is constructed. This method enables automatic calculation of multi-dimensional indicators, corridor zoning evaluation, intelligent obstacle identification, effect grading judgment, and automated operation and maintenance suggestions, thereby improving the objectivity, comprehensiveness, and timeliness of the evaluation and providing scientific data support for ecological corridor acceptance, long-term management, and dynamic transformation.

[0004] This invention relates to an intelligent monitoring method for evaluating the construction effects of ecological corridors. The method comprises seven core steps: data acquisition, data preprocessing, index calculation, zoning evaluation, obstacle identification, level determination, and operation and maintenance optimization. It is supported by an integrated air-space-ground monitoring hardware system and a multi-dimensional evaluation index system and algorithm model. The specific technical solution is as follows: The S1 integrated air-space-ground multi-source data acquisition system establishes a three-layer monitoring network: the satellite layer acquires time-series data on the landscape pattern, land use, and vegetation cover of the corridor and its surrounding area; the UAV layer conducts full-coverage inspections according to preset routes and altitudes, carrying multispectral cameras to collect data on vegetation communities, topography, and small obstacles; the ground layer deploys IoT sensors, infrared camera arrays, and acoustic signature acquisition devices. Sensors monitor environmental factors such as temperature, humidity, soil, water quality, noise, and light, while infrared cameras and acoustic signature devices identify wildlife species, passage frequency, activity periods, and group characteristics. The acquisition range includes the main corridor and the surrounding buffer zone, ensuring data integrity.

[0005] The S2 multi-source data fusion preprocessing performs step-by-step processing on various types of raw data: remote sensing images undergo radiometric correction, geometric correction, and mosaicking and cropping; UAV images undergo orthophoto mosaicking and vegetation classification and interpretation; ground sensor data are denoised using filtering algorithms to remove outliers; wildlife species, individuals, and behaviors are identified based on deep learning AI models, and travel patterns are statistically analyzed; finally, all data are uniformly rasterized and spatiotemporally registered to form a standardized corridor dataset, eliminating spatiotemporal biases and equipment errors in multi-source data.

[0006] The S3 evaluation indexes are calculated hierarchically based on a standardized dataset, automatically solving for six categories of quantitative indicators: Landscape pattern indicators: corridor width, patch density, sprawl, clustering, and landscape connectivity index; Habitat quality indicators: vegetation coverage, proportion of native vegetation, water connectivity, and habitat continuity index; Species access indicators: target species pass rate, passage volume per unit time, and route selection frequency index; Human interference indicators: noise, strong light, human activities, and the range and intensity of traffic interference; Ecosystem service indicators: biodiversity index, habitat carrying capacity; Seasonal adaptation indicators: stability of passage throughout the four seasons and functional degradation rate under extreme weather conditions. Simultaneously, the migration resistance coefficient of landscape units is calculated using \(R_i=\sum_{k}V_{ik} \times Q_k\) to quantify the degree of obstruction to species migration. The S4 Corridor Functional Zoning Evaluation is divided into three areas based on the functional characteristics of ecological corridors: core access area, edge buffer zone, and connecting transition zone. The evaluation is differentiated by the functional focus of each area: the core area focuses on assessing species accessibility, migration resistance, and vegetation integrity; the buffer zone focuses on assessing human disturbance and edge effects; and the transition zone focuses on assessing the connectivity between the corridor and the habitat. The scores for each zone are output separately. The S5 obstacle and ecological pinch point intelligent identification introduces circuit theory and moving window algorithm, calls BarrierMapper to identify first-level / second-level / third-level migration obstacles, and distinguishes four types: hard obstacles, vegetation loss, disturbance overload, and terrain fracture; calls Pinchpoint Mapper to identify corridor ecological pinch points (key connectivity nodes), and marks the obstacle location, impact range and hazard level to provide precise positioning for transformation. The S6 comprehensive effect level is determined by calculating the corridor's comprehensive score (total score 100 points) using the weighted summation method. It is divided into three levels according to the GB / T 43646-2024 standard: a total score ≥80 points is good, indicating smooth corridor passage, high utilization rate, and low animal injury rate; 60~79 points is fair, indicating normal passage but with local defects; and <60 points is poor, indicating obstructed passage and high risk of functional failure. S7's dynamic operation and maintenance and iterative optimization automatically match transformation and maintenance plans based on evaluation level and obstacle type: Level 1 severe obstacles output emergency remediation plans, and Level 2 and 3 obstacles output phased operation and maintenance plans; the system is set up with monthly and quarterly automatic retesting mechanisms to track the trend of indicator changes in the long term, and at the same time, it adaptively optimizes indicator weights and algorithm parameters based on historical measured data to form a closed loop of "monitoring-evaluation-transformation-remonitoring". This method can flexibly adjust the index weights and scoring thresholds according to different target species such as small mammals, medium-sized ungulates, large mammals, and birds, and adapt to different types of ecological corridors. With a wide monitoring range and all-weather operation, the integrated air-ground system breaks through the spatial and temporal limitations of manual patrols, covering complex areas such as dense forests and mountains, and achieving 24-hour uninterrupted data collection, significantly improving the data sample size and integrity. The evaluation system is comprehensive and quantitatively objective, constructing a multi-level indicator system with six dimensions. It combines landscape ecology models and resistance algorithms, and automatically calculates the data throughout the process, eliminating subjective scoring by humans. The evaluation results meet current national standards and are highly comparable. Accurate identification of hidden obstacles and ecological choke points relies on professional spatial algorithms to identify hidden obstacles and key ecological choke points that are difficult to detect by manual inspections, clarify the level of obstacles and the scope of their impact, and make the transformation plan highly targeted. The evaluation is divided into zones, which are based on the functional characteristics of the corridor. The evaluation is conducted in a zoned manner, targeting the core area, buffer zone and transition zone. This approach can accurately identify the functional shortcomings of different sections and avoid the problem of local issues being masked by an average evaluation of the entire area. Dynamic closed-loop management enables long-term automatic retesting and tracking of corridor temporal changes, timely detection of functional degradation issues, and iterative optimization of algorithms to continuously improve evaluation accuracy. With strong versatility and adaptability to various scenarios, it can be adapted to various types such as vegetation corridors, ecological sky bridges, animal culverts, and waterfront corridors. It can adaptively adjust parameters for different wildlife species and can be widely used in engineering acceptance, daily operation and maintenance, and ecological scientific research. Attached Figure Description Figure 1 This is a schematic diagram of the overall monitoring and evaluation process of the present invention; Figure 2 A schematic diagram of the layout of an integrated air-space-ground monitoring network; Figure 3 A schematic diagram illustrating the working principle of multi-source data preprocessing; Figure 4 A hierarchical structure diagram of the six evaluation indicators; Figure 5 Schematic diagram illustrating the working principle of obstacle and ecological pinch point identification; Figure 6 This is a diagram illustrating the principles of zoning evaluation and comprehensive grade determination. Detailed Implementation This invention provides an intelligent monitoring method for evaluating the construction effects of ecological corridors. It relies on an integrated air-space-ground monitoring system, a multi-dimensional evaluation index system, a species migration resistance model, and an obstacle identification algorithm to achieve intelligent evaluation throughout the entire process. The method is described in detail below, using four typical application scenarios, different types of ecological corridors, and target wild animals. All implementation cases strictly follow the seven steps from S1 to S7, and the hardware deployment, parameter configuration, data calculation, evaluation judgment, and operation and maintenance plans are complete and feasible. Example 1: Application Scenarios and Basic Overview of a Mountain Linear Vegetation Ecological Corridor (Mainly for Medium-Sized Ungulates). This case study evaluates a mountain linear vegetation ecological corridor with a total length of 3.2 km and a width of 25-40 m. The main species served are medium-sized terrestrial ungulates such as roe deer and red deer. Located in a mountainous forest area, the corridor connects to two nature reserves and serves as a core migration route in the region. This evaluation is a comprehensive assessment of the corridor's completion and acceptance, with continuous 24-hour monitoring for 7 consecutive days. The steps are executed (corresponding to the entire process of claim 1). S1 Integrated Space-Air-Ground Monitoring Network Construction and Data Acquisition. Satellite Remote Sensing Unit: Retrieves high-resolution satellite imagery of the area, collecting data on land use, annual vegetation temporal changes, and macroscopic landscape patterns along the corridor and its surrounding 500m buffer zone; Multispectral UAV: ​​Sets flight routes to cover the entire corridor, flying at an altitude of 80m and a speed of 3m / s, conducting patrols three times daily (morning, noon, and evening) to collect data on vegetation community distribution, surface topography, and small fallen obstacles; Ground Integrated Monitoring Unit: Deploys an integrated device every 100m along the corridor's centerline, including IoT-enabled temperature and humidity sensors, soil moisture sensors, and noise sensors. Infrared cameras and acoustic signature acquisition devices are also deployed at corridor entrances and key nodes in the middle section to monitor environmental parameters, wildlife passage frequency, and diurnal activity patterns 24 / 7. The collected data includes six categories of raw data: landscape, vegetation, soil and water environment, meteorology, animals, and human disturbance. The S2 multi-source data fusion preprocessing performs geometric correction, radiometric correction, and cropping on satellite imagery; orthophoto stitching and vegetation type interpretation on UAV imagery; ground sensor data uses a moving average filtering algorithm to remove outliers caused by wind, rain, and equipment vibration; wildlife species and population size identification is performed based on a lightweight deep learning model, distinguishing between roe deer, red deer, and associated small mammals; finally, the entire domain data is rasterized and time-series aligned with minute-level timestamps to generate a standardized corridor dataset. S3 stratified evaluation index calculation + resistance model calculation. Six categories of evaluation indicators are calculated: landscape pattern indicators (average corridor width 32m, minimum width 25m, patch density, landscape connectivity index meets standards); habitat quality indicators (vegetation coverage 91%, native tree ratio 83%, good water connectivity); species access indicators (average daily passage of 26 medium-sized mammals); human disturbance indicators (low frequency of forest patrol personnel activities, no vehicle or high-decibel noise interference); ecosystem service indicators (biodiversity index, habitat carrying capacity meets design standards); seasonal adaptation indicators (good passage stability in summer and winter). Substituting these into the migration resistance model Ri = ∑ kVik × Qk, combined with the weighting of medium-sized ungulate habit calibration factors, the average resistance coefficient for the entire area is 0.18 (the lower the resistance, the stronger the passage capacity). The S4 functional zoning evaluation divides the corridor into three areas: core access area, edge buffer zone, and connecting transition zone. Core access area (2.0km in the middle of the corridor): focuses on assessing accessibility and vegetation integrity, with a single score of 85 points; Edge buffer zone (50m on both sides of the corridor): there is a small amount of forest visitor activity, and human disturbance slightly exceeds the standard, with a single score of 72 points; Connecting transition zone (the section connecting the two ends of the corridor with the habitat): the vegetation and terrain are smoothly connected, and the connectivity is excellent, with a single score of 84 points; the comprehensive zoning weighted score is 81 points. S5 Obstacle and Ecological Pinch Identification: Obstacle identification tool and ecological pinch identification tool were used for full-area scanning: 2 secondary vegetation loss obstacles (local shrubs fallen) and 1 tertiary topographic small protrusion obstacle were identified; no hard artificial obstacles or severely disturbed obstacles were found; 2 key ecological pinch points in the whole area (narrow sections of corridors, which are necessary nodes for migration) were marked. The S6 comprehensive rating score is 81 points. According to the scoring criteria (≥80 points is good), the construction effect of the mountain vegetation ecological corridor is rated as good. S7 Dynamic Operation and Maintenance and Model Iteration. Obstacle Management: For areas with missing secondary vegetation, a 30-day phased replanting plan is developed; local leveling operations are carried out for tertiary terrain obstacles; Interference Control: Warning signs are added to buffer zones to limit the scope of tourist activities; Retesting Setup: The system automatically sets a quarterly retesting plan and conducts a full-process monitoring and evaluation every 3 months; Model Optimization: The monitoring data is summarized, the vegetation factor weights in mountainous forest areas are fine-tuned, and the resistance model parameters are optimized. Implementation Results. The corridor's overall passage function is complete, with only minor local defects. After rectification, it can fully meet the year-round migration needs of medium-sized ungulates. The monitoring data is highly consistent with the results of on-site manual verification, and the evaluation conclusions are objective and reliable. Example 2: Underpass for Wildlife (General Type, Multiple Species Coexistence) Application Scenarios and Basic Overview. The evaluation object is a highway underpass for wildlife. It is a single-span structure, 45m long and 6m wide, primarily used by small mammals, birds, and amphibians, while also accommodating a small number of medium-sized mammals. This corridor is adjacent to a national highway, with traffic flow and headlights being the main sources of interference. This evaluation is based on quarterly routine operation and maintenance monitoring. The steps are executed (corresponding to the entire process of claim 1). The S1 data acquisition satellite focuses on collecting data on land use and road network distribution around the culvert; drones inspect the terrain and vegetation at the culvert entrances and exits; temperature, humidity, noise, and light sensors are densely deployed inside the culvert, and multiple infrared cameras are deployed inside and outside the culvert entrance to monitor traffic noise, nighttime strong light, and animal passage data. S2 data preprocessing focuses on filtering out interfering data such as instantaneous vehicle noise and sudden changes in lighting. AI identifies and distinguishes the passage records of small mammals, birds, and amphibians, and completes time-series registration. The S3 index solution shows that the temperature and humidity inside the cave meet the standards; the vegetation index (cave entrance shading vegetation) score is low; the two types of human interference indicators, noise and light, have significant deductions; the daily average number of small mammals and birds passing through is stable; and the average migration resistance coefficient for the entire area is 0.35. The S4 functional zoning evaluation shows that the core traffic area (inside the culvert) scored 70 points; the edge buffer zone (20m on both sides of the culvert entrance) was affected by traffic lights and scored 65 points; the connecting transition zone (woodland on both sides of the road) scored 76 points, and the overall zoning score was 71 points. The S5 obstacle recognition system identified a level 2 light interference obstacle (strong light from vehicles shining directly into the tunnel entrance at night) and a level 2 traffic noise obstacle. There were no major obstacles of hard material or terrain, and the tunnel itself was free of defects. The S6 overall rating score is 71 points, which is between 60 and 80 points, and the corridor effect is rated as average. S7 Operation and Maintenance Plan. Emergency Rectification: Plant tall, shading trees on both sides of the culvert entrance and exit, and add soundproof barriers to reduce light and noise interference; Retesting Cycle: Set up monthly retesting to continuously track the effect of interference improvement; Parameter Adjustment: For traffic corridors, increase the weight of human interference indicators. Implementation Results. The main structure and traffic conditions of the culvert are up to standard. The main defect is traffic-related interference. After the rectification, the frequency of animal passage is expected to increase by more than 20%, which meets the requirements for the operation and maintenance of highway wildlife passages.

Claims

1. An intelligent monitoring method for evaluating the construction effect of ecological corridors, characterized in that, Includes the following steps: S1. Establish an integrated air-space-ground monitoring network, employing a combination of satellite remote sensing, multispectral UAVs, ground-based IoT sensors, infrared camera arrays, and acoustic signature acquisition equipment to synchronously collect multi-source data across the entire ecological corridor and its surrounding buffer zones. The collected data includes landscape data, vegetation data, environmental parameters, wildlife images and acoustic signatures, and data on human disturbance. S2. Perform fusion and preprocessing on the multi-source raw data, completing remote sensing image correction, UAV image stitching, sensor data noise reduction, AI species and behavior recognition, and spatiotemporal registration to generate a standardized corridor dataset. S3. Based on the standardized dataset, perform hierarchical calculations of six categories of evaluation indicators, including landscape pattern indicators, habitat quality indicators, species mobility indicators, human disturbance indicators, ecosystem service indicators, and seasonal adaptation indicators. S4. Combine landscape connectivity models and species migration resistance models to conduct functional zoning evaluations of the ecological corridor, dividing it into core access areas, edge buffer zones, and transitional zones, and calculating individual scores and overall scores for each zone. S5. Using circuit theory and moving window algorithm, the system identifies migration obstacles, ecological pinch points, and weak sections within the corridor, and marks the obstacle type and impact level. S6 Based on a preset comprehensive scoring threshold, the system classifies the ecological corridor construction effect into three levels: good, average, and poor, completing the overall effect comprehensive judgment. S7 Combining the evaluation results and the distribution of obstacle points, the system automatically outputs differentiated optimization suggestions for corridor operation and maintenance, vegetation restoration, obstacle modification, and interference control, and establishes a long-term dynamic monitoring and iterative evaluation mechanism.

2. The intelligent monitoring method for evaluating the construction effect of ecological corridors according to claim 1, characterized in that, In step S1, satellite remote sensing acquires overall land use, landscape pattern, and vegetation cover time series data of the corridor; multispectral UAVs conduct full-coverage inspections along preset routes, collecting data on vegetation communities, surface topography, and small obstacles; ground-based IoT sensors are deployed inside linear corridors, ecological overpasses, and culverts to monitor temperature, humidity, water quality, soil, noise, and light parameters; infrared camera arrays and acoustic fingerprint devices are deployed along migration routes to identify wildlife species, frequency of passage, group size, and activity periods.

3. The intelligent monitoring method for evaluating the construction effect of ecological corridors according to claim 1, characterized in that, In step S2, the AI ​​species identification module, based on deep learning algorithms, distinguishes between terrestrial mammals, birds, and amphibians and reptiles, and counts the frequency of passage and daily / seasonal activity patterns; it also performs unified rasterization and spatiotemporal registration on data from different time series and spatial dimensions to eliminate equipment errors and environmental interference.

4. The intelligent monitoring method for evaluating the construction effect of ecological corridors according to claim 1, characterized in that, In step S3, the landscape pattern indicators include average corridor width, minimum corridor width, patch density, spread, aggregation, and landscape connectivity index; the habitat quality indicators include vegetation cover, proportion of native vegetation, water connectivity, and habitat continuity index; the species access indicators include target species pass-through rate, number of species passing per unit time, and migration route selection frequency index; the anthropogenic disturbance indicators include noise intensity, light disturbance, frequency of human activities, and traffic impact range; the ecosystem service indicators include biodiversity index, soil and water conservation capacity, and habitat carrying capacity; and the seasonal adaptation indicators include corridor access stability in different seasons and access capacity decay rate under extreme weather conditions.

5. The intelligent monitoring method for evaluating the construction effect of ecological corridors according to claim 1 uses a migration resistance model to calculate the migration resistance coefficient Ri, with the formula Ri = ∑ k Vik × Qk, where Vik is the resistance value of the k-th habitat factor in the i-th landscape unit, and Qk is the weight of the corresponding factor. The weight is calibrated by combining expert consultation with measured data; the lower the resistance coefficient, the stronger the corridor's passage capacity.

6. The intelligent monitoring method for evaluating the construction effect of ecological corridors according to claim 1, characterized in that, The functional zoning evaluation rules in step S4 are as follows: the core passage area focuses on assessing species passability, migration resistance, and vegetation integrity; the edge buffer zone focuses on assessing the intensity of human disturbance and the range of edge effects; and the connecting transition zone focuses on assessing the connectivity between corridors and habitats.

7. In the intelligent monitoring method for evaluating the construction effect of ecological corridors according to claim 1, in step S5, the link mapper and obstacle mapper tools are used to identify obstacle points, and the bottleneck point mapper r is used to identify ecological pinch points; the obstacle types are divided into four categories: hard artificial obstacles, vegetation loss, disturbance overload, and terrain faults, and are further divided into level one, level two, and level three obstacles according to their severity.

8. The intelligent monitoring method for evaluating the construction effect of ecological corridors according to claim 1, characterized in that, In step S6, the comprehensive score adopts the weighted summation method, with a total score of 100 points; a total score ≥ 80 points is considered good, indicating smooth passage of the target species, high utilization rate, and low injury rate; a total score ≤ 80 points is considered average, indicating that the species can pass but the utilization rate is low and there are local obstacles; a total score < 60 points is considered poor, indicating that the species has difficulty passing normally and there are prominent obstacles.

9. The intelligent monitoring method for evaluating the construction effect of ecological corridors according to claim 1, characterized in that, In step S7, an emergency renovation plan is output for level 1 obstacles, and a phased operation and maintenance plan is output for level 2 and level 3 obstacles; the system automatically starts retesting and evaluation on a monthly / quarterly basis, tracks the trend of corridor indicators, and dynamically updates the evaluation model and weight parameters.

10. The intelligent monitoring method for evaluating the construction effect of ecological corridors according to claim 1, characterized in that, This method is applicable to various types of corridors, such as linear vegetation corridors, ecological overpasses across highways, wildlife culverts, and waterfront corridors. It can adaptively adjust the index weights and scoring thresholds for different target species, including small mammals, medium-sized ungulates, large mammals, and birds.

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