Road area wetland ecological evaluation early warning method and system based on risk prediction

Abstract

The invention belongs to the technical field of ecological risk prediction, and discloses a road area wetland ecological evaluation early warning method and system based on risk prediction, and the method comprises the following steps: S1, obtaining multi-source data of a research area, and carrying out the processing and information fusion of the data; s2, constructing a landscape ecological risk evaluation model by using the landscape interference degree and the landscape fragility; s3, carrying out statistics on ecological risk gradient changes of the transverse distance and the longitudinal distance of the road, and analyzing a landscape pattern; s4, constructing a landscape ecological risk index based on the landscape interference degree and the landscape vulnerability index; and S5, based on a result of the ecological risk assessment model, designing and developing a road domain wetland landscape ecological risk monitoring and early warning system. According to the road area wetland ecological evaluation early warning method and system based on risk prediction, the ecological risk state of the wetland landscape is monitored in real time, early warning is given out when the risk value exceeds the preset threshold value, and timely information is provided for wetland protection and management decision making.

Description

Technical Field

[0001] The present invention relates to the technical field of ecological risk prediction, and in particular to a road wetland ecological assessment and early warning method and system based on risk prediction. Background Art

[0002] The Qinghai-Tibet Plateau is known as the "Asian Water Tower". It is the region with the largest distribution of alpine wetlands in China. It has extremely important ecological functions, such as ecological water storage, water supply and climate regulation. It plays a key role in adapting to global climate change and preventing global water crises. However, it is also one of the most vulnerable ecosystems. Under the combined influence of climate change and human activities, once it is destroyed, it is difficult to recover in the short term. As one of the important forms of human activities, road construction plays an important role in it, and it also causes changes in the dynamic landscape along the route.

[0003] On the one hand, road construction often crosses natural landscapes, and the direct occupation of them will inevitably divide the original landscape patches, so that the originally continuous natural landscape is cut into multiple smaller fragments, affecting the spatial configuration between different landscapes. On the other hand, human activities brought about by road construction increase the human interference with the natural landscape. Among them, the gradually accumulated traffic, road runoff and road slope erosion on high-grade roads such as national highways may affect the landscape processes such as wetland hydrological connectivity, aggravate the overall changes in the landscape, and cause medium- and long-term cumulative impacts on the natural landscape.

[0004] At present, the research on the impact of road landscape mainly focuses on the impact of roads on the overall ecosystem, while further analysis of different types of wetland landscapes is relatively rare. Therefore, quantitatively exploring the changes in different wetland landscape types in the road area will help clarify the distribution patterns of different wetlands within the road area and provide a scientific basis for road management and wetland health maintenance.

[0005] Landscape ecological risk refers to the adverse consequences that may arise from the interaction between landscape pattern and ecological processes under the influence of natural or human factors. It can effectively analyze and reflect the changes in regional ecological processes and functions. Road construction has caused the fragmentation of natural landscape patches such as wetlands, increased the fragility of the landscape level, and changed the ecological risk of the area around the road. At present, most of the research on the impact of roads on ecological risks is carried out at the road network level, while there are fewer studies at the road area level, focusing on ecological risk analysis and change patterns.

[0006] In the study of road impacts in road areas, it can usually be divided into two situations. One is the study of landscapes without roads, which reflects the indirect changes in the landscapes along the roads caused by human activities and economic development; the other is the study of landscapes with roads, which highlights the direct cutting of the road landscape on the surrounding landscapes on the basis of the former, and highlights the superposition of the impact of roads on landscape ecological risks. However, existing studies usually only focus on analyzing the ecological risks caused by changes in the landscape around roads, and rarely explore the role of direct road cutting on ecological risks. There is a lack of comparison of ecological risks under two different road situations. It is urgent to quantitatively explore the magnitude of the superposition of landscape ecological risks caused by roads as landscape elements. Summary of the invention

[0007] The purpose of the present invention is to provide a road wetland ecological assessment and early warning method and system based on risk prediction, which can monitor the ecological risk status of wetland landscape in real time and issue an early warning when the risk value exceeds a preset threshold, so as to provide timely information for wetland protection and management decision-making.

[0008] To achieve the above object, the present invention provides a road wetland ecological assessment and early warning method based on risk prediction, comprising the following steps:

[0009] S1. Obtain multi-source data of the study area, and process and fuse the data;

[0010] S2. Use landscape disturbance and landscape vulnerability to construct a landscape ecological risk assessment model;

[0011] S3. Analyze the landscape pattern by statistically analyzing the changes in ecological risk gradients along the horizontal and vertical distances of the road;

[0012] S4. Construct a landscape ecological risk index based on landscape disturbance and landscape vulnerability index;

[0013] S5. Based on the results of the ecological risk assessment model, design and develop an ecological risk monitoring and early warning system for roadside wetland landscapes.

[0014] Preferably, in step S1, the spatiotemporal variation data of the wetland landscape in the study area is obtained based on satellite remote sensing technology; the terrain and wetland spatial structure information is obtained based on three-dimensional laser technology; high-resolution images and video data of the wetland landscape are obtained based on drone technology; and historical land use and road vector data are analyzed based on historical data.

[0015] Preferably, in step S2, a landscape ecological risk assessment model is constructed using landscape disturbance degree and landscape vulnerability to conduct landscape ecological risk assessment;

[0016] Among them, the landscape disturbance degree E iThe degree to which the ecosystem is affected by external interference. The stronger the external interference, the greater the ecological risk of the system, as shown below:

[0017] E i =aC i +bQ i +cDQ i

[0018] Among them, a, b, and c represent weights, which are 0.5, 0.3, and 0.2 respectively; C i is the landscape fragmentation, Q i is the landscape separation, DQ i Landscape dominance.

[0019] Preferably, the landscape fragmentation C i , as shown below:

[0020]

[0021] Landscape separation Q i , as shown below:

[0022]

[0023] Landscape dominance DQ i , as shown below:

[0024]

[0025] Among them, n i represents the number of patches of landscape type i, A i represents the area of ​​landscape type i, N represents the total number of patches of all landscape types, A represents the total area of ​​the landscape, B i represents the number of plots where patch i appears, B represents the total number of plots, S i represents the area of ​​patch i, and S represents the total area of ​​the sample plot.

[0026] Preferably, in step S4, the landscape ecological risk index ERI represents the ecological risk of the landscape after being disturbed by the outside world, and is calculated using Fragstats 4.2 as shown below:

[0027]

[0028] Among them, S ki represents the area of ​​landscape type i in the kth sampling area, S k is the total area of ​​the kth sampling area, E i is the disturbance degree of landscape type i, F i is the vulnerability of landscape type i, and N is the total number of landscape types.

[0029] Preferably, a road wetland ecological assessment and early warning system based on risk prediction adopts a classic three-layer architecture model, including a presentation layer, a business logic layer, and a data access layer;

[0030] The presentation layer is responsible for interacting with users and providing an intuitive UI interface, allowing users to easily configure parameters, view data, and receive warnings;

[0031] The business logic layer handles the core functions of the system to ensure the accuracy and timeliness of data processing;

[0032] The data access layer manages the storage, retrieval and maintenance of data to ensure data consistency and integrity.

[0033] Preferably, the early warning function is implemented as follows:

[0034] The early warning module, based on preset risk assessment standards and real-time monitoring data, automatically determines whether the wetland landscape faces potential risks. When it is detected that the risk indicator exceeds the safety threshold, the system will immediately trigger the early warning mechanism and send real-time alerts to relevant managers and decision makers via email, SMS, and application notifications. The early warning module has the function of recording early warning history, allowing users to review and analyze early warning events to optimize future monitoring and response strategies.

[0035] Therefore, the present invention adopts the above-mentioned road wetland ecological assessment and early warning method and system based on risk prediction to monitor the ecological risk status of the wetland landscape in real time, and issues an early warning when the risk value exceeds a preset threshold, providing timely information for wetland protection and management decisions.

[0036] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a flow chart of a road wetland ecological assessment and early warning method based on risk prediction of the present invention;

[0038] Figure 2 It is a flow chart of data acquisition and preprocessing of the present invention;

[0039] Figure 3 This is a flow chart for realizing the ecological risk monitoring and early warning function of wetland landscape in road area of ​​the present invention;

[0040] Figure 4 is an overview of the study area in the embodiment of the present invention;

[0041] Figure 5 Schematic diagram of road area buffer zone analysis in an embodiment of the present invention;

[0042] Figure 6Schematic diagram of the Yangtze River source park and road risk area in an embodiment of the present invention;

[0043] Figure 7 : is the wetland area percentage change characteristic under the road distance gradient of the road area in the embodiment of the present invention; wherein, (a) is a river channel; (b) is a lake; (c) is a beach; (d) is a saline-alkali land; (e) is a swamp;

[0044] Figure 8 is the ecological risk distribution of scenario 1 in the embodiment of the present invention;

[0045] Fig. 9 is the ecological risk distribution of scenario 2 in the embodiment of the present invention;

[0046] Fig.10 is the difference in ecological risk level area between the one with and without road landscape in the embodiment of the present invention;

[0047] Fig.11 is the change of ecological risk under the road distance gradient in the embodiment of the present invention;

[0048] Fig.12 It is the change of ecological risk of longitudinal gradient of the road in the embodiment of the present invention; among them, (a) is National Highway G109; (b) is National Highway G215; (c) is Provincial Highway S313. DETAILED DESCRIPTION

[0049] The technical solution of the present invention is further described below through the accompanying drawings and embodiments.

[0050] like Figure 1 As shown, the present invention provides a road wetland ecological assessment and early warning method based on risk prediction,

[0051] S1. Obtain multi-source data of the study area, and process and fuse the data, such as Figure 2 shown.

[0052] S11. Data acquisition based on satellite remote sensing technology, 3D laser technology, UAV technology and historical data.

[0053] (1) Use satellite remote sensing technology to obtain temporal and spatial variation data of wetland landscapes in the study area. By analyzing remote sensing images at different time points, monitor and record changes in wetland area, type, and distribution. Satellite remote sensing data plays an important role in the dynamic monitoring of wetland ecosystems due to its wide coverage and strong timeliness.

[0054] (2) Use three-dimensional laser radar (LiDAR) technology to obtain more accurate information on terrain and wetland spatial structure. Through LiDAR scanning, high-precision topographic maps and wetland terrain models are generated, which helps to analyze wetland terrain changes and their impact on ecological processes.

[0055] (3) UAV technology is used to obtain high-resolution images and video data of wetland landscapes. The cameras and sensors carried by drones provide more detailed wetland landscape features, including vegetation cover, water distribution and other information, providing support for the assessment and monitoring of wetland ecosystems.

[0056] (4) The study includes the analysis of historical land use and road vector data. Land use data comes from the China Land Use Status Remote Sensing Monitoring Database of the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences (http: / / www.resdc.cn / ). According to the "Wetland Classification (GB / T24708-2009)" and regional characteristics, it is divided into wetland landscapes: lakes, canals, beaches, saline-alkali land and swamps; non-wetland landscapes: grasslands, unused land, and construction land. Road vector data are obtained from OSM (http: / / www.openstreetmap.org).

[0057] S12. Process and fuse the multi-source data.

[0058] By comprehensively utilizing the above-mentioned multiple data sources and using the Geographic Information System (GIS) platform, multi-source data are pre-processed, classified and integrated to ensure data consistency and accuracy.

[0059] Based on data acquisition and processing, wetland and non-wetland landscapes are classified according to the standard of Wetland Classification (GB / T24708-2009). Combined with regional characteristics, wetland landscapes are divided into lakes, canals, beaches, saline-alkali land and swamps. At the same time, non-wetland landscapes such as grasslands, unused land and construction land are identified, which is helpful for in-depth analysis of the ecological risks of wetland landscapes and their interactions with non-wetland landscapes.

[0060] S2. Use landscape disturbance and landscape vulnerability to construct a landscape ecological risk assessment model.

[0061] When conducting ecological risk assessment of road-related landscapes, two different scenarios are simulated: one with no roads and one with roads. The one with no roads is an ideal natural landscape without any road interference. This scenario is achieved by removing all roads from the landscape type data. The one with roads retains the road network data and overlays it with the landscape type data to reflect the segmentation of the landscape by the current road network.

[0062] Based on the wetland landscape and non-wetland landscape data under two scenarios, a landscape ecological risk assessment model was constructed using landscape disturbance degree and landscape vulnerability. Landscape ecological risk assessment was carried out to quantify the impact of roads as landscape elements on landscape ecological risks.

[0063] Among them, the landscape disturbance degree Ei The degree to which the ecosystem is affected by external interference. The stronger the external interference, the greater the ecological risk of the system, as shown below:

[0064] E i =aC i +bQ i +cDQ i

[0065] Among them, a, b, and c represent weights, which are 0.5, 0.3, and 0.2 respectively; C i is the landscape fragmentation, Q i is the landscape separation, DQ i is the landscape dominance, and the calculation formula is as follows:

[0066]

[0067] Among them, n i represents the number of patches of landscape type i, A i represents the area of ​​landscape type i, N represents the total number of patches of all landscape types, A represents the total area of ​​the landscape, B i represents the number of plots where patch i appears, B represents the total number of plots, S i represents the area of ​​patch i, and S represents the total area of ​​the sample plot. i The larger the value, the worse the stability of the landscape and the ecosystem; i The larger the value, the more dispersed the landscape types are and the more frequent the conversion between landscape types is; i Indicates the magnitude of the impact of patches on the formation and change of landscape pattern.

[0068] Landscape vulnerability F i It reflects the ability of each landscape type to resist external interference. The smaller the resistance of a landscape type, the greater its vulnerability and the higher the ecological risk. The reference literature and actual situation of landscape vulnerability assign values ​​of 0.30, 0.50, 0.20, 0.10, 0.38, 0.45, 0.48, 0.58, 0.55, 0.53 and 0.60 to grassland, unused land, forest land, construction land, lakes, canals, beaches, reservoirs and ponds, saline-alkali land, swamps and glaciers respectively.

[0069] S3. Analyze the landscape pattern by statistically analyzing the changes in ecological risk gradients along the lateral and longitudinal distances of the road.

[0070] Distance gradient analysis is an effective method in landscape ecology research. It uses the buffer analysis function of the geographic information system (GIS) to evaluate the spatial pattern changes of the target landscape type within different distance ranges. First, the research object landscape type is determined, and the relevant environmental elements are selected as reference sources. Then, a series of buffer distance gradients are set around the reference source. In each buffer zone, the landscape index of the target landscape type is calculated, such as the richness of landscape components, degree of fragmentation, and diversity. The changing trends of these indexes at different distance gradients are analyzed to reveal the influence of distance on landscape pattern.

[0071] The present invention statistically analyzes the ecological risk gradient changes in the horizontal and vertical distances of the road, analyzes the ecological risk change trends in the horizontal and vertical dimensions under road construction, studies the distribution law of ecological risks in the road wetland landscape, and then proposes targeted protection measures.

[0072] S4. Construct a landscape ecological risk index based on landscape disturbance and landscape vulnerability index.

[0073] The landscape ecological risk index ERI indicates the ecological risk of the landscape after being disturbed by the outside world. It is calculated using Fragstats4.2 as shown below:

[0074]

[0075] Among them, S ki represents the area of ​​landscape type i in the kth sampling area, S k is the total area of ​​the kth sampling area, E i is the disturbance degree of landscape type i, F i is the vulnerability of landscape type i, and N is the total number of landscape types.

[0076] S5. Based on the results of the ecological risk assessment model, design and develop a roadside wetland ecological assessment and early warning system based on risk prediction.

[0077] S51. System requirements analysis.

[0078] System requirements analysis is a key step to ensure the successful implementation of the road-area wetland landscape ecological risk monitoring and early warning system, which involves an in-depth understanding of the target user groups, system functions, performance standards, security requirements and user interface requirements. Target users include environmental protection agencies, wetland managers and scientific researchers, who need a system that can monitor wetland changes in real time, assess ecological risks and issue early warnings in a timely manner. System functional requirements include data collection, processing, display, risk assessment and early warning notifications. Performance requirements ensure that the system responds quickly and is stable and reliable when processing large amounts of data. Security requirements protect data from unauthorized access, and user interface requirements require that the system be intuitive and easy to use, and easy for non-technical users to operate. Through precise requirements analysis, clear guidance is provided for system design and development.

[0079] S52. System architecture design.

[0080] System architecture design is the basis for building a road-area wetland landscape ecological risk monitoring and early warning system. Based on the C# platform, the system adopts a classic three-layer architecture model, including the presentation layer, business logic layer, and data access layer.

[0081] The presentation layer is responsible for interacting with users and providing an intuitive UI interface that enables users to easily configure parameters, view data, and receive warnings.

[0082] The business logic layer handles the core functions of the system, such as data analysis, risk assessment, and early warning triggering, ensuring the accuracy and timeliness of data processing.

[0083] The data access layer manages the storage, retrieval and maintenance of data to ensure data consistency and integrity.

[0084] In addition, the system also needs to consider modular design to facilitate future functional expansion and maintenance.

[0085] S53. Design and implementation of early warning function.

[0086] like Figure 3 As shown in the figure, the early warning module is the core part of the road wetland landscape ecological risk monitoring and early warning system. It automatically determines whether the wetland landscape is facing potential risks based on preset risk assessment standards and real-time monitoring data. Once the risk index is detected to exceed the safety threshold, the system will immediately trigger the early warning mechanism and send real-time alerts to relevant managers and decision makers through email, SMS or application notifications. The module also has the function of recording early warning history, allowing users to review and analyze early warning events to optimize future monitoring and response strategies. The early warning module is designed to achieve a rapid response to changes in wetland ecosystems, so that timely measures can be taken to protect the fragile wetland environment.

[0087] Example

[0088] like Figure 4 As shown in Figure 1, the Yangtze River source area is taken as the research area, including three roads: National Highway G109, G215 and Provincial Highway S313, with a total length of about 314 kilometers. National Highway G109 and G215 are usually two-way two-lane roads with relatively large traffic volume. Among them, G109 has more than 3,000 passenger and freight vehicles per day during the peak tourist season, while Provincial Highway S313 has relatively less traffic volume. Because it is built on the hinterland of the Qinghai-Tibet Plateau, which is high and cold and permafrost is widely distributed, there are many wetlands around the road, mainly including rivers, lakes and beaches. For example, Salt Lake and Chumar River are representative lakes and rivers. The terrain conditions along the route are complex and the ecological environment is fragile. Many small villages such as Wudaoliang and Yege Township are distributed among them.

[0089] In the study of the spatial distribution characteristics of wetland landscapes in road-adjacent areas, the roads were used as reference sources to establish buffer zones of different distances, such as 30m, 60m, 120m, 200m, 300m, 400m, 500m, 600m, 1000m and 2000m, and GIS analysis tools were used to analyze the dynamic changes of wetland landscapes in adjacent areas, such as Figure 5 shown.

[0090] The average patch area of ​​the road landscape is 1036.04 hm 2 According to the principles of landscape ecology, the road area grid (risk area) is set to 2.5 km × 2.5 km, which is 2 to 5 times the average landscape patch area, with a total of 4609 grids. Figure 6 As shown in Figure 1, the ecological risk distribution map is obtained by performing Kriging interpolation on the risk index values ​​at the center of the grid.

[0091] 1. Dynamic analysis of wetland landscape within the road area.

[0092] The percentage of wetland area under the road area distance was statistically analyzed through buffer zone analysis, and the dynamics of wetland landscape under the road area spatial gradient were analyzed, such as Figure 7 As shown. From 1990 to 2020, with the increase of road distance, the area of ​​river channels and beach lands first decreased and then increased. The river channels decreased within 400m, and increased with the increase of road distance outside 400m; the beach land decreased within 120m of the road area, and increased with the increase of road distance from 200m to 600m, and then fluctuated; the lake area decreased slightly with the increase of time outside 300m of the road in 30 years, and the percentage of lake area gradually increased with the increase of road distance, and there was a certain positive correlation with the road distance; the saline-alkali land increased with the increase of road distance, showing a strong positive correlation with the road distance, with a small increase trend within 600m of the road area and an obvious increase trend outside 600m; the percentage of swamp area was in a fluctuating state with the increase of road distance.

[0093] 2. Landscape ecological risk analysis within the road area.

[0094] Based on two scenarios, one without adding road landscape (scenario 1) and one with adding road landscape (scenario 2), the road-area landscape ecological risk assessment was conducted. The natural breakpoint method was used to divide the landscape ecological risk assessment results into five levels. The results are as follows: Figure 8 and Fig. 9 The spatial distribution of ecological risks in the two scenarios is roughly the same. The high-risk areas are located along the National Highway G109, G215 and the upper half of the Provincial Highway S313. The high-risk areas in the G215 area are significantly reduced. The ecological risk distribution of the two scenarios is compared from the areas (a) to (d) on the right side of the figure. (a) is the township center, (b) and (c) are the densely populated areas of beaches and rivers, and (d) is the intersection of national highways and the densely populated areas of saline-alkali land. In (a), the town center road network is dense, which has a greater impact on the landscape, causing the town center in scenario 2 to change from a low-risk area to a high-risk area; in (b) and (c), the road crosses the river or beach, which is more sensitive than other land types. The direct cutting of the road leads to a further increase in ecological risk, and some beach and river areas change from high-risk areas to high-risk areas; (d) The intersection area of ​​China Road changes from a low-risk area to a high-risk area, indicating that the superposition of the road cutting the landscape will lead to an increase in ecological risk, especially in the saline-alkali land areas on both sides of G109, where the road cutting leads to a further increase in ecological risk. Although these areas are in high-risk areas and have little change, road cutting undoubtedly increases ecological vulnerability, indicating that road construction has a certain negative impact on these sensitive areas and increases the ecological risk along the road.

[0095] In order to explore the difference in landscape ecological risk level areas under the two scenarios, the ecological risk level areas of scenario one and scenario two were subtracted, such as Fig.10 As shown. It can be seen that the difference between scenario one and scenario two is mainly in the area of ​​high-risk areas and low-risk areas, which is manifested as the overall ecological risk level of scenario two being higher than that of scenario one. Among them, the area of ​​low ecological risk areas in scenario one is between 2% and 4% higher than that in scenario two, and the area of ​​medium ecological risk areas and high ecological risk areas is lower than that in scenario two; taking 2020 as an example, for scenario one, that is, without considering the road landscape, the area of ​​low ecological risk areas increased by about 996.12km2, and its change area accounted for about 4%, the area of ​​high ecological risk areas decreased by about 1.6%, and the area of ​​medium ecological risk areas decreased by about 1.8%. It can be found that when there is no road landscape impact in the road area, the area of ​​medium and high ecological risk areas is significantly reduced. It can be seen that road construction will further aggravate the ecological risk of road area landscape, and greatly increase the area of ​​medium ecological risk and high ecological risk.

[0096] 3. Analysis of changing trends of ecological risks.

[0097] Buffer zone analysis was used to statistically analyze the average ecological risk of wetland landscape in the road area at a horizontal distance of 10,000 m from the road. Fig.11 As shown in the figure, along the road distance buffer zone, the two scenarios have different changing trends. In scenario 1, the landscape ecological risk decreases slightly within 200m with the increase of road distance, and then increases slowly beyond 200m; in scenario 2, the landscape ecological risk gradually decreases with the increase of road distance, and decreases slowly within 1000m, and the decreasing trend increases rapidly beyond 1000m. After 10000m, the landscape ecological risk is basically the same as scenario 1. The road distance and ecological risk show a significant negative correlation, with a correlation of -0.93. In general, road construction within the road area will increase the ecological risk of the area along the road. Within a certain range of the road, the closer the distance to the road, the higher the ecological risk, and the more serious the damage to the surrounding landscape caused by the road.

[0098] In order to more intuitively observe the changes in ecological risk along the longitudinal direction of the road, the road was divided into equal parts to obtain different sections. The ecological risks at different sections were statistically analyzed to reflect the changing trend of ecological risk under the longitudinal distance of the road. The results are as follows: Fig.12 As shown. It can be found that among the three roads, when the roads pass through wetlands or township areas, the ecological risk has an upward trend. The ecological risk in the wetland and township areas is higher than that in the surrounding areas, with an obvious "peak", among which the ecological risk in the wetland area is the highest. From the perspective of a single road, the National Highway G109 has more sections with high longitudinal ecological risks than the other two roads, and the ecological risks in saline-alkali land, lakes and river wetlands have reached peaks; the ecological risk in the township area is higher in the first half of the National Highway G215, and the ecological risk in the wetland area in the second half is higher than that in the township area in the first half; the change in the longitudinal ecological risk of Provincial Highway S313 is similar to that of National Highway G215, the ecological risk in the second half is higher than that in the first half, and the ecological risk in the wetland area is the highest. This shows that when roads in high-altitude areas pass through townships and wetlands, the ecological risk will have a trend of rising rapidly to a peak, and the ecological risk of roads passing through wetland areas is higher than that of township areas, and the ecological risk of road construction passing through wetlands is greater.

[0099] In summary, road construction changes the spatial composition of wetland landscapes along the roads, affecting the magnitude of ecological risks. Taking linear roads in the road area as the research object, the dynamics of wetland landscapes and changes in ecological risks within the road area were analyzed. The impact of roads as landscape elements on the distribution of ecological risks was quantified by scenario analysis, and the following conclusions were obtained:

[0100] (1) As the distance from the road increases, the river channels and beaches first decrease and then increase. The area proportions of lakes and saline-alkali land show a significant positive correlation with the road distance. The area proportion of swamps fluctuates with the increase in road distance. In terms of the gradient change of landscape ecological risk, the closer to the road, the higher the ecological risk. The road will cause more serious damage to the surrounding landscape. When crossing towns and wetlands, the ecological risk will have a rapid upward trend, and the ecological risk of road construction crossing wetlands is even greater.

[0101] (2) Road construction will further aggravate the ecological risk of the road area landscape, and the area of ​​medium ecological risk area and high ecological risk area will increase greatly. In the ecological risk assessment under the road scenario, by comparing the ecological risks under two different scenarios, it was found that the high-risk area was mainly concentrated along the National Highway G109, G215 and the upper half of Provincial Highway S313 on the west side of the road area; road construction has a certain negative impact on township centers, beach and river canal dense areas, national highway intersections and saline-alkali land dense areas, increasing the ecological risks along the roads in these areas.

[0102] Therefore, the present invention adopts the above-mentioned road wetland ecological assessment and early warning method and system based on risk prediction to monitor the ecological risk status of the wetland landscape in real time, and issues an early warning when the risk value exceeds a preset threshold, providing timely information for wetland protection and management decisions.

[0103] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A road wetland ecological assessment and early warning method based on risk prediction, characterized in that: The following steps are involved: S1. Obtain multi-source data of the study area, and process and fuse the data; S2. Use landscape disturbance and landscape vulnerability to construct a landscape ecological risk assessment model; S3. Analyze the landscape pattern by statistically analyzing the changes in ecological risk gradients along the horizontal and vertical distances of the road; S4. Construct a landscape ecological risk index based on landscape disturbance and landscape vulnerability index; S5. Based on the results of the ecological risk assessment model, design and develop an ecological risk monitoring and early warning system for roadside wetland landscapes.

2. The method for early warning of road wetland ecological assessment based on risk prediction according to claim 1 is characterized by: In step S1, the spatiotemporal variation data of the wetland landscape in the study area are obtained based on satellite remote sensing technology; the terrain and wetland spatial structure information are obtained based on three-dimensional laser technology; high-resolution images and video data of the wetland landscape are obtained based on drone technology; and historical land use and road vector data are analyzed based on historical data.

3. The method for early warning of road wetland ecological assessment based on risk prediction according to claim 1 is characterized by: In step S2, a landscape ecological risk assessment model is constructed using landscape disturbance degree and landscape vulnerability to conduct landscape ecological risk assessment; Among them, the landscape disturbance degree E i The degree to which the ecosystem is affected by external interference. The stronger the external interference, the greater the ecological risk of the system, as shown below: Yes i =aC i +bQ i +cDQ i Among them, a, b, and c represent weights, which are 0.5, 0.3, and 0.2 respectively; C i is the landscape fragmentation, Q i is the landscape separation, DQ i Landscape dominance.

4. The method for early warning of road wetland ecological assessment based on risk prediction according to claim 3 is characterized in that: Landscape fragmentation C i , as shown below: Landscape separation Q i , as shown below: Landscape dominance DQ i , as shown below: Among them, n i represents the number of patches of landscape type i, A i represents the area of ​​landscape type i, N represents the total number of patches of all landscape types, A represents the total area of ​​the landscape, B i represents the number of plots where patch i appears, B represents the total number of plots, S i represents the area of ​​patch i, and S represents the total area of ​​the sample plot.

5. The method for early warning of road wetland ecological assessment based on risk prediction according to claim 1 is characterized in that: In step S4, the landscape ecological risk index ERI represents the ecological risk of the landscape after being disturbed by the outside world and is calculated using Fragstats 4.2 as follows: Among them, S ki represents the area of ​​landscape type i in the kth sampling area, S k is the total area of ​​the kth sampling area, E i is the disturbance degree of landscape type i, F i is the vulnerability of landscape type i, and N is the total number of landscape types.

6. The early warning system of the road wetland ecological assessment early warning method based on risk prediction according to claim 1 is characterized by: Adopt the classic three-tier architecture model, including presentation layer, business logic layer and data access layer; The presentation layer is responsible for interacting with users and providing an intuitive UI interface, allowing users to easily configure parameters, view data, and receive warnings; The business logic layer handles the core functions of the system to ensure the accuracy and timeliness of data processing; The data access layer manages the storage, retrieval and maintenance of data to ensure data consistency and integrity.

7. The road wetland ecological assessment and early warning system based on risk prediction according to claim 6 is characterized in that: The early warning function implementation process is as follows: The early warning module based on preset risk assessment standards and real-time monitoring data automatically determines whether the wetland landscape faces potential risks; when it detects that the risk index exceeds the safety threshold, the system will immediately trigger the early warning mechanism and send real-time alerts to relevant managers and decision makers via email, SMS, and application notifications; The early warning module has the function of recording early warning history, allowing users to review and analyze early warning events to optimize future monitoring and response strategies.

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