Method for determining functional connectivity threshold of road-area water bodies and its trend analysis system

Abstract

The present invention discloses a method for determining a threshold value of functional connectivity of water bodies in a road area and a trend judgment and analysis system, which belongs to the field of water body connectivity, wherein the method for determining a threshold value of functional connectivity of water bodies in a road area includes the following steps: proposing a water body identification and extraction method based on a GEE cloud platform, forming a data set of water bodies in a highway area over 30 years, calculating connectivity indexes such as water bodies in a highway area and constructing an index database; simulating and analyzing the optimal suitable distance threshold value of functional connectivity of water bodies in a highway area with or without roads; revealing the spatiotemporal dynamic change characteristics of functional connectivity of water bodies in a highway area over 30 years; establishing a method for determining the optimal distance threshold value of functional connectivity of water bodies in a highway area under road projects such as bridges and culverts, and designing and developing a method for determining a threshold value of functional connectivity of water bodies in a highway area and a trend judgment and analysis system. The present invention sets different scenarios according to the temporal change characteristics of water bodies, and evaluates water body connectivity, providing data support for water body connectivity under road area conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of water body connectivity, and in particular to a method for determining a threshold value of functional connectivity of water bodies in a road area and a trend analysis system. Background Art

[0002] In recent years, connectivity has become a focus of hydrological and ecological research, and connectivity affects the flow between landscape elements. Water body connectivity is particularly important. Water body connectivity is related to the transfer of materials, energy and organisms within or between hydrological cycle elements through water. It is an important characteristic for describing and predicting the response of water bodies to climate and landscape changes. Water body connectivity refers to the degree to which water bodies are physically connected to each other, including natural flows or artificial channels between rivers, lakes, wetlands, groundwater bodies, etc. Good water body connectivity is essential to maintaining ecosystem health. It helps to:

[0003] Species migration and gene exchange: promote the migration of aquatic organisms between different water bodies and increase biodiversity; water purification: the exchange of water flow can disperse pollutants and help the water body to purify naturally; flood regulation: connected water systems can more effectively receive and transport rainwater, reducing the risk of flood disasters; water resource management: for drinking water supply, irrigation, industrial water, etc., connectivity ensures the effective allocation and utilization of water resources.

[0004] Water body connectivity includes structural and functional connectivity. Structural connectivity refers to the state of physical connections between water bodies, including whether there are direct water flow channels, the width, depth, continuity and other physical characteristics of these channels. This involves the integrity of the river network, the condition of the riparian zone, and the presence of artificial obstacles such as dams, bridges, and roads. Functional connectivity focuses on how these physical connections affect the actual flow and exchange of water, substances (such as nutrients, sediments), and aquatic organisms between different water bodies. Even if there is a physical connection between water bodies, functional connectivity may be impaired if water flow is restricted, water quality problems or ecological barriers are affected.

[0005] Spatial heterogeneity caused by road construction, operation and human activities has reduced water connectivity and hindered normal landscape ecological processes and regulatory capabilities. Therefore, given the existing spatial distribution of water bodies and the difficulty in changing them, finding an appropriate threshold range to establish spatial ecological connections between components is an inevitable choice for maintaining ecological security and ecosystem stability. Summary of the invention

[0006] In order to solve the problems in the above-mentioned background technology, the purpose of the present invention is to provide a method for determining the threshold of functional connectivity of water bodies in road areas and a trend analysis system. Different road scenario modes are set according to the temporal change characteristics of the water bodies. On this basis, the optimal distance threshold of the connectivity of water bodies in road areas is determined, and the connectivity of water bodies is evaluated, so as to provide data support for the connectivity of water bodies under road conditions.

[0007] To achieve the above object, the present invention provides a method for determining a threshold value of functional connectivity of a road area water body, comprising the following steps:

[0008] Step S1: Propose an extraction method for water body identification based on the GEE cloud platform, form a data set of highway water bodies for several years, calculate the highway water body connectivity index and construct an index database;

[0009] Step S2: Simulate and analyze the optimal distance threshold of functional connectivity of water bodies in highway areas with or without roads;

[0010] Step S3: reveal the spatiotemporal dynamic change characteristics of water body functional connectivity in highway areas over several years;

[0011] Step S4: establishing a method for determining the optimal distance threshold of functional connectivity of water bodies in highway areas under bridge and culvert road projects;

[0012] Step S5: Through the above steps, a method for determining the road functional connectivity threshold and a trend analysis system are designed and developed.

[0013] Preferably, step S1 comprises the following steps:

[0014] S101. Computational imaging based on GEE cloud platform Index, establish road area water body dataset;

[0015] The calculation formula is:

[0016] ;

[0017] in represents the reflectivity in the near-infrared band, represents the reflectivity of the red band, is the reflectivity of the blue light band;

[0018] S102. Settings , eliminate the impact of vegetation on water bodies;

[0019] S103. Establish a water extraction model based on GEE-MNDWI-Otsu algorithm Ostu to automatically determine the optimal binarization threshold of the image;

[0020] S104. Use the processImagewater function to process the image, calculate the histogram of MNDWI and use the Otsu algorithm to determine the threshold and generate a water mask;

[0021] S105. Use the getYearlywater function to obtain a water image dataset of a highway section over the past 30 years;

[0022] S106. Import the data into ARCGIS10.7, convert the raster data of the water body into a vector file through the raster to feature tool and generate a node file and a connection file; add a number field and an attribute field to the water body through ArcGIS10.7, and generate the required node file and connection file in the ArcGIS10.7 plug-in Coneforinputs, where the node file is the area of ​​the water body patch;

[0023] S107. Determine the distance thresholds, set 9 distance thresholds of 100m, 200m, 400m, 500m, 1000m, 2000m, 5000m, 10000m and 20000m to analyze the functional connectivity of water bodies during 30 years, and expand the threshold range to set 12 distance thresholds of 100m, 200m, 400m, 500m, 750m, 1000m, 2000m, 3000m, 4000m, 5000m, 10000m and 20000m to analyze the functional connectivity of water bodies in the last year of 30 years separately;

[0024] S108. After obtaining the node file and the connection file, the functional connectivity of the water body is calculated using the software Conefor2.6 according to the distance threshold set in S105;

[0025] S109. Select the number of links, number of components, landscape overlap probability index, overall connectivity index, possible connectivity index and patch importance index to comprehensively analyze the functional connectivity of road-area water bodies and their optimal distance threshold.

[0026] Preferably, in step S109, the number of links Refers to the number of connections between water patches in the study area;

[0027] Number of components Refers to the whole composed of patches that are interconnected in function or structure. Isolated water patches will form a component and there is no functional relationship between different components. The maximum number of component patches refers to the number of components with the largest number of patches, and the maximum component area refers to the area of ​​the number of components with the largest number of patches.

[0028] Landscape overlap probability index:

[0029] ;

[0030] In the formula, is the number of components, Is a component The sum of the sizes of all patches, is the total area of ​​the study area;

[0031] Overall Connectivity Index :It is a model based on binary connection, which describes the direct connection between any two water patches;

[0032] ;

[0033] In the formula, is the number of water patches; and Plaque and area; For plaque With plaque The number of links on the shortest path between is the total area of ​​the study area, ;

[0034] Possible connectivity index : refers to the probability of possible spread of water patches in the study area, based on the probability model;

[0035] ;

[0036] In the formula, For plaque and The maximum diffusion probability, , is the total area of ​​the study area;

[0037] Patch Importance Index :

[0038] ;

[0039] In the formula, Represents the connectivity index after removing a patch.

[0040] Preferably, step S2 comprises the following steps:

[0041] S201. Use international common standards to set up scenarios with or without roads and determine the distances of buffer zones for highways of different levels: the impact domain of expressways is 1000m, national highways are 500m, provincial highways are 500m, county roads are 250m, and township roads are 100m. Use the erase command in ARCGIS to erase the road network impact domain from the landscape habitat as a scenario with roads;

[0042] S202. In the case of no road, the threshold is divided into the following four intervals:

[0043] The distance threshold is 100-500m, The value decreases, As the value increases, the number of patches of the largest component increases, and the area of ​​the largest component increases;

[0044] The distance threshold is 500-1000m, As the value decreases, the number of patches of the largest component increases, and the area of ​​the largest component increases;

[0045] The distance threshold is 1000-3000m, As the value decreases, the number of patches of the largest component increases, the area of ​​the largest component increases, and the landscape coincidence probability index increases;

[0046] When the distance threshold is >3000m, the number of components decreases, the number of maximum component patches and the maximum component area increase, and the landscape overlap probability index increases;

[0047] The functional connectivity study was conducted within the distance threshold range of 500-1000m selected through analysis;

[0048] S203. In the case of roads, , and The mean of the index decreases.

[0049] Preferably, in step S202, the distance threshold is 100-500m. The value dropped from 2223 to 1221. The value increased from 543 to 2031, the maximum number of component patches increased from 15 to 88, and the maximum component area increased from 16.967 km 2 Increased to 19.257km 2 ;

[0050] The distance threshold is 500-1000m, The value decreased from 1221 to 664, the maximum number of component patches increased from 88 to 180, and the maximum component area increased from 19.257 km 2 Increased to 29.328km 2 ;

[0051] The distance threshold is 1000-3000m, The value decreased from 664 to 157, the maximum number of component patches increased from 180 to 830, and the maximum component area increased from 29.328 km 2 Increased to 142.071km 2 , the landscape coincidence probability index increased from 0.581 to 0.826;

[0052] When the distance threshold is >3000m, the number of components decreases to 3. As the distance threshold increases, the number of components of the water patch will decrease to 1 and remain unchanged, and all patches belong to the same component.

[0053] In step S203, , and The mean values ​​of the index dropped from 0.685, 0.529 and 0.579 to 0.669, 0.526 and 0.560 respectively.

[0054] Preferably, step S3 comprises the following steps:

[0055] S301. Determine the distance threshold range to be 500-1000m, and calculate the overall connectivity index in different years and possible connectivity index To determine whether the changing trend of the index is the same under different distance thresholds;

[0056] S302. Considering the spread of wild animal and plant migration, the distance threshold is initially set at 500m for research. When the distance threshold is 500m, Increase, Increase;

[0057] S303. Using ARCGIS software, the patch importance index was calculated according to the natural break point method. It is divided into 5 levels, namely high level, higher level, medium level, lower level and low level.

[0058] Preferably, in step S302, when the distance threshold is 500m, From 0.127 to 0.429, It increased from 0.139 to 0.442; the change of component number in 30 years showed a trend of three peaks and two valleys.

[0059] Preferably, step S4 comprises the following steps:

[0060] S401. Carry out field investigation and on-site survey of water connectivity along the highway, complete on-site photography, video recording, recording, drone photography, and 3D laser scanning along the highway, and obtain on-site location data of bridges and tunnels;

[0061] S402. Import the data into ARCGIS to analyze the impact of different road layouts on water connectivity.

[0062] The present invention also provides a trend analysis system for determining a threshold value of functional connectivity of water bodies in road areas, comprising:

[0063] Data acquisition and index construction module: Based on the GEE platform, the water bodies in the road area are identified and extracted to form a water body data set for the highway section over the past 30 years, and the connectivity index of the water bodies in the road area is calculated to establish an index database;

[0064] Road-area water body functional connectivity threshold determination module: simulate and analyze the optimal suitable distance threshold of road-area water body functional connectivity in highway sections with or without roads;

[0065] Dynamic Change Module of Functional Connectivity of Road-area Water Bodies: Obtain the spatiotemporal dynamic change characteristics of functional connectivity of road-area water bodies over the past 30 years;

[0066] Threshold determination module for road engineering: establish a method to determine the optimal distance threshold for functional connectivity of water bodies in highway areas under bridge and culvert road engineering.

[0067] Therefore, the present invention adopts the above-mentioned method for determining the functional connectivity threshold of road water bodies and the trend analysis system, which has the following beneficial effects:

[0068] (1) The present invention sets different road scenario modes, based on which the optimal distance threshold of water body connectivity in the road area is determined, and the spatiotemporal dynamic change characteristics of water body connectivity in several years are revealed, as well as the important patches of water bodies for screening. Compared with the traditional water body connectivity calculation, the intrinsic connectivity of water bodies is considered from the perspective of the entire road area, and the dynamic change characteristics of water body connectivity are reflected in real time.

[0069] (2) Based on field investigation and data collection, the present invention establishes a method for determining the optimal distance threshold of functional connectivity of water bodies in highway areas, thereby forming the final optimal distance threshold of water body connectivity. Different from the current hydrological connectivity which is mostly limited to the connectivity of river networks at the macro scale and the connectivity between pore water in soil at the micro scale, the present invention starts from the actual situation to obtain the possible connectivity of water bodies in highway areas and their threshold range.

[0070] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 A schematic diagram of a process flow of an embodiment of the present invention;

[0072] Figure 2 A schematic diagram of a technical route of an embodiment of the present invention;

[0073] Figure 3 A graph of the number of links and the number of components in a roadless scenario of the Ge Na section of the Qinghai-Tibet Highway according to an embodiment of the present invention;

[0074] Figure 4A landscape overlap probability index map of the Ge Na section of the Qinghai-Tibet Highway in a road-free scenario according to an embodiment of the present invention;

[0075] Figure 5 A maximum component patch number map for a road-free scenario in the Ge Na section of the Qinghai-Tibet Highway according to an embodiment of the present invention;

[0076] Figure 6 The maximum component area diagram of the Qinghai-Tibet Highway in the case of a roadless section according to an embodiment of the present invention;

[0077] Figure 7 A road buffer setting diagram according to an embodiment of the present invention;

[0078] Figure 8 A graph of the number of links and the number of components in a scenario where there is a road on the Qinghai-Tibet Highway grid according to an embodiment of the present invention;

[0079] Fig. 9 A landscape overlap probability index map for a road-covered section of the Qinghai-Tibet Highway according to an embodiment of the present invention;

[0080] Fig.10 It is a change trend diagram of the overall connectivity index and possible connectivity index of different distance thresholds in 1990 according to an embodiment of the present invention;

[0081] Fig.11 It is a change trend diagram of the overall connectivity index and possible connectivity index of different distance thresholds in 1995 according to an embodiment of the present invention;

[0082] Fig.12 The overall connectivity index and possible connectivity index change trend diagram of different distance thresholds in 2000 according to an embodiment of the present invention;

[0083] Fig.13 is a graph showing the change trend of the overall connectivity index and possible connectivity index at different distance thresholds in 2005 according to an embodiment of the present invention;

[0084] Fig.14 is a graph showing the change trend of the overall connectivity index and possible connectivity index at different distance thresholds in 2010 according to an embodiment of the present invention;

[0085] Fig.15 It is a change trend diagram of the overall connectivity index and possible connectivity index at different distance thresholds in 2015 according to an embodiment of the present invention;

[0086] Fig.16 It is a change trend diagram of the overall connectivity index and possible connectivity index for different distance thresholds in 2020 according to an embodiment of the present invention;

[0087] Fig.17 It is a change trend diagram of the overall connectivity index and the possible connectivity index between 1990 and 2020 according to an embodiment of the present invention;

[0088] Fig.18 This is a schematic diagram of the proportion of important areas of plaques in 1990 according to an embodiment of the present invention;

[0089] Fig.19 This is a schematic diagram of the proportion of plaque importance areas in 1995 according to an embodiment of the present invention;

[0090] Fig. 20 This is a schematic diagram of the proportion of important areas of plaques in 2000 according to an embodiment of the present invention;

[0091] Fig.21 This is a schematic diagram of the proportion of plaque importance areas in 2005 according to an embodiment of the present invention;

[0092] Fig. 22 This is a schematic diagram of the proportion of plaque importance areas in 2010 according to an embodiment of the present invention;

[0093] Fig.23 This is a schematic diagram of the proportion of plaque importance areas in 2015 according to an embodiment of the present invention;

[0094] Fig.24 This is a schematic diagram of the proportion of plaque importance areas in 2020 according to an embodiment of the present invention;

[0095] Fig.25 Schematic diagram of comparison of water body connectivity under the scenario of whether there is a road or not in the Ge Na section of the Qinghai-Tibet Highway according to an embodiment of the present invention;

[0096] Fig.26 Schematic diagram of comparison of water body connectivity under the scenario of whether there is a road or not in the Ge Na section of the Qinghai-Tibet Highway according to an embodiment of the present invention;

[0097] Fig. 27 Schematic diagram of the system structure of an embodiment of the present invention. DETAILED DESCRIPTION

[0098] In order to make the purpose, technical scheme and advantages disclosed in the embodiments of the present invention clearer, the embodiments of the present invention are further described in detail in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the embodiments of the present invention and are not intended to limit the embodiments of the present invention. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application. Examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals throughout represent the same or similar elements or elements with the same or similar functions.

[0099] It should be noted that the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units that are not explicitly listed or inherent to these processes, methods, products or devices.

[0100] Like reference numerals and letters denote similar items in the following drawings, and thus, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0101] In the description of the present invention, it should be noted that the terms "upper", "lower", "inside", "outside", etc. indicate directions or positional relationships based on the directions or positional relationships shown in the accompanying drawings, or are directions or positional relationships in which the product of the invention is usually placed when in use. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore should not be understood as a limitation on the present invention.

[0102] In the description of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "setting", "installation" and "connection" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be an indirect connection through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0103] like Figure 1 , Figure 2 As shown, a method for determining a threshold value of functional connectivity of a road water body according to the present invention comprises the following steps:

[0104] Step S1: Propose a water body identification and extraction method based on the GEE cloud platform to form a water body dataset for the Qinghai-Tibet Highway in the past 30 years from 1990 to 2020, calculate the connectivity index of water bodies in the road area and construct an indicator database. The specific steps are as follows:

[0105] S101. Computational imaging based on GEE cloud platform index. The calculation formula is: ,in represents the reflectivity in the near-infrared band, represents the reflectivity of the red band, is the reflectivity of the blue light band;

[0106] S102. Settings , eliminate the impact of vegetation on water bodies;

[0107] S103. Establish a water extraction model based on GEE-MNDWI-Otsu algorithm (Ostu), where the Otsu algorithm is used to automatically determine the optimal binarization threshold of the image; the specific steps are as follows:

[0108] 1) First, get the histogram information:

[0109] var counts = ee.Array(ee.Dictionary(histogram).get('histogram'));

[0110] var means = ee.Array(ee.Dictionary(histogram).get('bucketMeans'));

[0111] var size = means.length().get([0]);

[0112] 2) Then perform the total count and sum:

[0113] var total = counts.reduce(ee.Reducer.sum(), [0]).get([0]);

[0114] var sum = means.multiply(counts).reduce(ee.Reducer.sum(), [0]).get([0]);

[0115] var mean = sum.divide(total);

[0116] 3) After looking at the histogram to calculate the between-class variance, use mean.sort(bss).get([-1]) to select the mean with the largest variance as the optimal threshold according to the sorting of the between-class variance.

[0117] S104. Use the processImagewater function to process the image, calculate the histogram of MNDWI and use the Otsu algorithm to determine the threshold and generate a water mask. The specific steps are as follows:

[0118] ①Use var his_if = image.reduceRegion({ to calculate the histogram of the image, var his_if2 = ee.Algorithms.If(his_if, his_if.get("MNDWI"), null) to determine whether the histogram exists;

[0119] ② Use get_water to calculate the Ostu threshold according to its histogram and generate a water body mask;

[0120] ③Finally, use the function var water = ee.Algorithms.If({, if the histogram exists, calculate the water mask, otherwise return Null.

[0121] S105. Use the getYearlywater function to obtain the water image dataset of the Qinghai-Tibet Highway Ge'na section for nearly 30 years from 1990 to 2020;

[0122] S106. Import the data into ARCGIS10.7, convert the raster data of the water body into a vector file through the raster to feature tool and generate a node file and a connection file. Add a number field and an attribute field to the water body through ArcGIS10.7, and generate the required node file and connection file in the ArcGIS10.7 plug-in Coneforinputs. In this method, the node file is the area of ​​the water body patch;

[0123] S107. Determine the distance threshold, which is the maximum reachable distance of ecological flow, and is used to judge the existence or strength of ecological flow between water patches in the study area. In this method, 9 distance thresholds of 100m, 200m, 400m, 500m, 1000m, 2000m, 5000m, 10000m and 20000m are set to analyze the functional connectivity of water bodies from 1990 to 2020. In order to further study the appropriate threshold, the threshold range will be broadened, and 12 distance thresholds of 100m, 200m, 400m, 500m, 750m, 1000m, 2000m, 3000m, 4000m, 5000m, 10000m and 20000m will be set to analyze the functional connectivity in 2020 separately;

[0124] S108. After obtaining the node file and the connection file, the functional connectivity of the water body is calculated using the software Conefor2.6 according to the distance threshold set in S105;

[0125] S109. Select the number of links, number of components, landscape overlap probability index, overall connectivity index, possible connectivity index and patch importance index to comprehensively analyze the functional connectivity of road-area water bodies and their optimal distance threshold.

[0126] The number of links ( ) refers to the number of connections between water patches in the study area;

[0127] Number of components ( ) refers to the whole composed of patches that are interconnected in function or structure. Isolated water patches will form a component and there is no functional relationship between different components. The maximum number of component patches refers to the number of components with the largest number of patches, and the maximum component area refers to the area of ​​the number of components with the largest number of patches.

[0128] Landscape overlap probability index:

[0129] ;

[0130] In the formula: is the number of components, Is a component The sum of the sizes of all patches, is the total area of ​​the study area;

[0131] Overall connectivity index ( ): It is a model based on binary connection, which describes the direct connection between any two water patches;

[0132] ;

[0133] Where: is the number of water patches; and Plaque and area; For plaque With plaque The number of links on the shortest path between is the total area of ​​the study area. .when =0, indicating that the water patches are not connected;

[0134] Possible connectivity index ( ): It is based on the probability model and refers to the probability of possible spread of water patches in the study area;

[0135] ;

[0136] Where: For plaque and The maximum diffusion probability between two nodes is the maximum connection probability. , the closer its value is to 1, the better the functional connectivity is. is the total area of ​​the study area;

[0137] Patch Importance Index:

[0138] ;

[0139] In the formula: Represents the connectivity index after removing a patch.

[0140] Step S2: Simulate and analyze the optimal distance threshold for functional connectivity of water bodies in the Ge Na section of the Qinghai-Tibet Highway with or without roads. The specific steps are as follows:

[0141] S201. To illustrate the impact of roads on the connectivity of water bodies in the Ge'na section of the Qinghai-Tibet Highway, internationally common standards were used to set scenarios with and without roads, and determine the buffer distances of different levels of highway impact: the impact domain of expressways was 1000m, national highways were 500m, provincial highways were 500m, county roads were 250m, and township roads were 100m. The road network impact domain was erased from the landscape habitat using the erase command in ARCGIS as a scenario with roads;

[0142] S202. In the case of no roads: With the increase of the distance threshold, the number of links NL, the landscape overlap probability index LCP, the maximum number of component patches, and the maximum component area show an increasing trend, while the number of components generally shows a decreasing trend. Based on this, the threshold is divided into the following four intervals: Figure 3 , Figure 4 , Figure 5 , Figure 6 As shown,

[0143] The distance threshold is 100-500m, The value dropped rapidly from 2223 to 1221. The value increased from 543 to 2031, the maximum number of component patches increased from 15 to 88, and the maximum component area increased from 16.967 km 2 Increased to 19.257km 2 , within this interval, The value dropped significantly. The value and the number of patches of the maximum component increase rapidly, indicating that functional connectivity is easily affected by the threshold within this distance threshold range. Functional connectivity is of great significance to the stability of the ecosystem. If the functional connectivity changes significantly due to the influence of the threshold, it means that this interval is not suitable for describing functional connectivity.

[0144] The distance threshold is 500-1000m, The value decreased from 1221 to 664, the maximum number of component patches increased from 88 to 180, and the maximum component area increased from 19.257 km 2 Increased to 29.328km 2 , in this interval, Although the value is still decreasing, the speed is slowing down, and the number of patches and the area of ​​the largest component do not change much, indicating that the functional connectivity is relatively stable in this interval, which is conducive to the analysis of functional connectivity;

[0145] The distance threshold is 1000-3000m, The value decreased from 664 to 157, the maximum number of component patches increased from 180 to 830, and the maximum component area increased from 29.328 km 2 Increased to 142.071km 2 , the landscape coincidence probability index increased from 0.581 to 0.826. In this interval, the maximum number of component patches, the maximum component area and the landscape coincidence probability index changed greatly, indicating that the internal stability was poor. The component itself is a component of the landscape, and its poor stability will affect the continuity of functional connectivity;

[0146] When the distance threshold is >3000m, the number of components gradually decreases to 3, the number of the largest component patches and the largest component area increase sharply, and the landscape overlap probability index increases slowly and approaches 1. As the distance threshold increases, the number of components of water patches will decrease to 1 and will not change. All patches belong to the same component. In this range, it means that all patches can be connected to each other and can serve as ecological habitats, but this is inconsistent with the actual situation in the study area.

[0147] S203. Through analysis, it can be concluded that the distance threshold range of 500-1000m is suitable for functional connectivity research.

[0148] S204. Figure 7 , Figure 8 , Fig. 9 As shown in the figure, in the scenario with roads, the distance threshold range does not change much and is still within the range of 500-1000m. However, after being disturbed by roads, the water body connectivity index is significantly reduced. , and The mean values ​​of the indexes decreased from 0.685, 0.529 and 0.579 to 0.669, 0.526 and 0.560 respectively. The landscape connectivity index under different distance threshold conditions showed a downward trend. The characteristics of the functional connectivity changes of road-area water bodies in the Qinghai-Tibet transport corridor with or without roads are shown in the following table.

[0149] Table 1 Changes in functional connectivity of water bodies in the road area of ​​the Qinghai-Tibet transport corridor with or without roads

[0150]

[0151] Step S3: The spatiotemporal dynamic change characteristics of the functional connectivity of road-area water bodies in the past 30 years from 1990 to 2020 are revealed. The specific steps are as follows:

[0152] S301. Taking 2020 as an example, the appropriate distance threshold range is determined to be 500-1000m. Then, the overall connectivity index of different years is calculated ( ) and possible connectivity index ( ) to determine whether the index changes in different distance thresholds have the same trend. Fig.10 , Fig.11 , Fig.12 , Fig.13 , Fig.14 , Fig.15 , Fig.16 As shown in Figure 2, the distribution of different distance thresholds from 1990 to 2020 and Although the values ​​are different, they have the same trend of change. and The overall trend is increasing, but the connectivity index may The value of is always greater than the overall connectivity index , this is due to and The main difference between them is that the former is based on a binary connectivity model, while the latter relies on a more detailed probabilistic connectivity model;

[0153] S302. As the appropriate range of the threshold has been determined and the spread of wildlife migration is taken into consideration, the distance threshold is initially set at 500m for subsequent research. Fig.17 It can be seen that when the distance threshold is 500m, the overall connectivity index ( ) and possible connectivity index ( ) shows an increasing trend, It increased from 0.127 in 1990 to 0.429 in 2020. Increased from 0.139 in 1990 to 0.442.

[0154] It can be seen from the table that a component is a unit composed of a group of connected patches. At a distance of 500m from the threshold, the change in the number of components from 1990 to 2020 shows a trend of "three peaks and two valleys", among which the number of components in 1995 is the smallest and the number of components in 1990 is the largest.

[0155] Table 2 Number of components, area and proportion of patch importance from 1990 to 2020

[0156]

[0157] S303. Functional analysis of plaques, plaque importance index It contains more spatial location information of patches, so it has more advantages. Using ARCGIS software, the patch importance index is calculated according to the natural break point method. It is divided into five levels, namely high level, relatively high level, medium level, relatively low level and low level. The high level areas are mainly distributed near the Chumar River and the Nagqu River in the study area, and the low level areas are distributed in the northern part of the study area, that is, near the Golmud River. Fig.18 , Fig.19 , Fig. 20 , Fig.21 , Fig. 22 , Fig.23 , Fig.24 The proportion of areas with low patch importance from 1990 to 2020 was relatively high, which were 66.775%, 63.161%, 56.201%, 73.323%, 49.175%, 57.093% and 55.755% respectively. Among them, the area with low patch importance in 2010 accounted for the smallest proportion, with an area of ​​298.370km 2 The largest area was in 2005, which was 602.913 km 2 The ranking of regions with high patch importance is 2020>2015>2010>2000>2005>1990>1995. The area of ​​regions with high patch importance in 2020 is 280.846 km 2 , accounting for 35.217% of the area, and the area in 1995 was 98.360km 2 , accounting for 15.107%; the area ranking of regions with higher patch importance is 2005>1990>2000>2010>1995>2020>2015; the area ranking of regions with medium patch importance is 2005>2010>1995>1990>2000>2015>2020.

[0158] Step S4: Establish a method for determining the optimal distance threshold of the functional connectivity of water bodies on the Qinghai-Tibet Highway under road projects such as bridges and culverts. The specific steps are as follows:

[0159] S401. Carry out field investigation and on-site survey of water connectivity along the Qinghai-Tibet Highway, complete on-site photography, video recording, recording, drone photography, 3D laser scanning, etc. along the highway, and obtain on-site location data of bridges and tunnels;

[0160] S402. Import the data into ARCGIS to analyze the impact of different road layout forms on water connectivity; Fig.25 , Fig.26 As shown in the figure, in the scenario where there is no road crossing, the water body connectivity is good. The presence of roads and the construction of bridges and culverts along the highway will have a certain impact on the water body connectivity, but the impact range does not exceed 1000m.

[0161] like Fig. 27 As shown, the trend analysis system of the method for determining the road functional connectivity threshold of the present invention includes:

[0162] (1) Data acquisition and index construction module: Based on the GEE platform, the water bodies in the road area are identified and extracted to form a water body dataset from 1990 to 2020, and the connectivity index of the water bodies in the road area is calculated to establish an index database;

[0163] (2) Module for determining the threshold of functional connectivity of water bodies in the road area: This module simulates and analyzes the optimal distance threshold of functional connectivity of water bodies in the Ge Na section of the Qinghai-Tibet Highway with or without roads;

[0164] (3) Dynamic change module of functional connectivity of road-area water bodies: the spatiotemporal dynamic change characteristics of functional connectivity of road-area water bodies in the past 30 years from 1990 to 2020;

[0165] (4) Threshold determination module for road projects: A method for determining the optimal distance threshold for functional connectivity of water bodies along the Qinghai-Tibet Highway under road projects such as bridges and culverts is established.

[0166] 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 method for determining the functional connectivity threshold of road-area water bodies, characterized by: The following steps are involved: Step S1: Propose an extraction method for water body identification based on the GEE cloud platform, form a data set of highway water bodies for several years, calculate the highway water body connectivity index and construct an index database; Step S2: Simulate and analyze the optimal distance threshold of functional connectivity of water bodies in highway areas with or without roads; Step S3: reveal the spatiotemporal dynamic change characteristics of water body functional connectivity in highway areas over several years; Step S4: establishing a method for determining the optimal distance threshold of functional connectivity of water bodies in highway areas under bridge and culvert road projects; Step S5: Design and develop a road functional connectivity threshold determination method and a trend analysis system through the above steps; Step S1 includes the following steps: S101. Computational imaging based on GEE cloud platform Index, establish road area water body dataset; The calculation formula is: ； in represents the reflectivity in the near-infrared band, represents the reflectivity of the red band, is the reflectivity of the blue light band; S102. Settings , eliminate the impact of vegetation on water bodies; S103. Establish a water extraction model based on GEE-MNDWI-Otsu algorithm Ostu to automatically determine the optimal binarization threshold of the image; S104. Use the processImagewater function to process the image, calculate the histogram of MNDWI and use the Otsu algorithm to determine the threshold and generate a water mask; S105. Use the getYearlywater function to obtain a water image dataset of a highway section over the past 30 years; S106. Import the data into ARCGIS10.7, convert the raster data of the water body into a vector file through the raster to feature tool and generate a node file and a connection file; add a number field and an attribute field to the water body through ArcGIS10.7, and generate the required node file and connection file in the ArcGIS10.7 plug-in Coneforinputs, where the node file is the area of ​​the water body patch; S107. Determine the distance thresholds, set 9 distance thresholds of 100m, 200m, 400m, 500m, 1000m, 2000m, 5000m, 10000m and 20000m to analyze the functional connectivity of water bodies during 30 years, and expand the threshold range to set 12 distance thresholds of 100m, 200m, 400m, 500m, 750m, 1000m, 2000m, 3000m, 4000m, 5000m, 10000m and 20000m to analyze the functional connectivity of water bodies in the last year of 30 years separately; S108. After obtaining the node file and the connection file, the functional connectivity of the water body is calculated using the software Conefor2.6 according to the distance threshold set in S105; S109. Select the number of links, number of components, landscape overlap probability index, overall connectivity index, possible connectivity index and patch importance index to comprehensively analyze the functional connectivity of road-area water bodies and their optimal distance threshold.

2. The method for determining the functional connectivity threshold of road water bodies according to claim 1, characterized in that: In step S109, the number of links Refers to the number of connections between water patches in the study area; Number of components Refers to the whole composed of patches that are interconnected in function or structure. Isolated water patches will form a component and there is no functional relationship between different components. The maximum number of component patches refers to the number of components with the largest number of patches, and the maximum component area refers to the area of ​​the number of components with the largest number of patches. Landscape overlap probability index: ； In the formula, is the number of components, Is a component The sum of the sizes of all patches, is the total area of ​​the study area; Overall Connectivity Index :It is a model based on binary connection, which describes the direct connection between any two water patches; ； In the formula, is the number of water patches; and Plaque and area; For plaque With plaque The number of links on the shortest path between is the total area of ​​the study area, ; Possible connectivity index : refers to the probability of possible spread of water patches in the study area, based on the probability model; ； In the formula, For plaque and The maximum diffusion probability, , is the total area of ​​the study area; Patch Importance Index : ； In the formula, Represents the connectivity index after removing a patch.

3. The method for determining the functional connectivity threshold of road water bodies according to claim 2, characterized in that: Step S2 includes the following steps: S201. Use international common standards to set up scenarios with or without roads and determine the distances of buffer zones for highways of different levels: the impact domain of expressways is 1000m, national highways are 500m, provincial highways are 500m, county roads are 250m, and township roads are 100m. Use the erase command in ARCGIS to erase the road network impact domain from the landscape habitat as a scenario with roads; S202. In the case of no road, the threshold is divided into the following four intervals: The distance threshold is 100-500m, The value decreases, As the value increases, the number of patches of the largest component increases, and the area of ​​the largest component increases; The distance threshold is 500-1000m, As the value decreases, the number of patches of the largest component increases, and the area of ​​the largest component increases; The distance threshold is 1000-3000m, As the value decreases, the number of patches of the largest component increases, the area of ​​the largest component increases, and the landscape coincidence probability index increases; When the distance threshold is >3000m, the number of components decreases, the number of maximum component patches and the maximum component area increase, and the landscape overlap probability index increases; The functional connectivity study was conducted within the distance threshold range of 500-1000m selected through analysis; S203. In the case of roads, , and The mean of the index decreases.

4. The method for determining the functional connectivity threshold of road water bodies according to claim 3 is characterized in that: In step S202, the distance threshold is 100-500m. The value dropped from 2223 to 1221. The value increased from 543 to 2031, the maximum number of component patches increased from 15 to 88, and the maximum component area increased from 16.967 km 2 Increased to 19.257km 2 ; The distance threshold is 500-1000m, The value decreased from 1221 to 664, the maximum number of component patches increased from 88 to 180, and the maximum component area increased from 19.257 km 2 Increased to 29.328km 2 ; The distance threshold is 1000-3000m, The value decreased from 664 to 157, the maximum number of component patches increased from 180 to 830, and the maximum component area increased from 29.328 km 2 Increased to 142.071km 2 , the landscape coincidence probability index increased from 0.581 to 0.826; When the distance threshold is >3000m, the number of components decreases to 3. As the distance threshold increases, the number of components of the water patch will decrease to 1 and remain unchanged, and all patches belong to the same component. In step S203, , and The mean values ​​of the index dropped from 0.685, 0.529 and 0.579 to 0.669, 0.526 and 0.560 respectively.

5. The method for determining the functional connectivity threshold of road water bodies according to claim 4, characterized in that: Step S3 includes the following steps: S301. Determine the distance threshold range to be 500-1000m, and calculate the overall connectivity index in different years and possible connectivity index To determine whether the changing trend of the index is the same under different distance thresholds; S302. Considering the spread of wild animal and plant migration, the distance threshold is initially set at 500m for research. When the distance threshold is 500m, Increase, Increase; S303. Using ARCGIS software, the patch importance index was calculated according to the natural break point method. It is divided into 5 levels, namely high level, higher level, medium level, lower level and low level.

6. The method for determining the functional connectivity threshold of road water bodies according to claim 5, characterized in that: In step S302, when the distance threshold is 500m, From 0.127 to 0.429, It increased from 0.139 to 0.442; the change of component number in 30 years showed a trend of three peaks and two valleys.

7. The method for determining the functional connectivity threshold of road water bodies according to claim 6, characterized in that: Step S4 includes the following steps: S401. Carry out field investigation and on-site survey of water connectivity along the highway, complete on-site photography, video recording, recording, drone photography, and 3D laser scanning along the highway, and obtain on-site location data of bridges and tunnels; S402. Import the data into ARCGIS to analyze the impact of different road layouts on water connectivity.

8. A trend analysis system for determining a threshold value of functional connectivity of road water bodies according to any one of claims 1 to 7, characterized in that: include Data acquisition and index construction module: Based on the GEE platform, the water bodies in the road area are identified and extracted to form a water body data set for the highway section over the past 30 years, and the connectivity index of the water bodies in the road area is calculated to establish an index database; Road-area water body functional connectivity threshold determination module: simulate and analyze the optimal distance threshold of road-area water body functional connectivity in highway sections with or without roads; Dynamic Change Module of Functional Connectivity of Road-area Water Bodies: Obtain the spatiotemporal dynamic change characteristics of functional connectivity of road-area water bodies over the past 30 years; Threshold determination module for road engineering: establish a method for determining the optimal distance threshold for functional connectivity of water bodies in highway areas under bridge and culvert road engineering.

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