Coastal zone land utilization sea-land zone pattern extraction method

Abstract

The invention discloses a coastal zone land utilization sea-land zone pattern extraction method, and relates to the technical field of coastal zone sea-land zone pattern extraction. Comprising the following steps: constructing a coastal zone section line by utilizing coastal zone coastline baseline data; obtaining coastal zone section line land utilization data; performing sequential numbering processing on the coastal zone section line land utilization data; defining a coastal zone land utilization sea-land gradient pattern sequence similarity index; constructing a coastal zone land utilization sea-land gradient pattern clustering model; and obtaining a coastal zone land utilization sea-land zone pattern through the coastal zone land utilization sea-land gradient pattern clustering model. According to the method, the sea-land zone pattern of the regional coastal zone can be effectively revealed.

Description

Technical Field

[0001] The invention relates to the technical field of extracting the sea-land zonal pattern of coastal zones, and more specifically to a method for extracting the sea-land zonal pattern of coastal zone land use. Background Art

[0002] The coastal zone is a transition zone between the ocean and land and an ecologically fragile zone that responds to global changes. It is also one of the areas with the most concentrated human activities. However, due to excessive human development and utilization, the sea-land zonal pattern of the coastal zone has changed significantly, causing various ecological and environmental problems. For example, excessive port expansion has caused irreversible changes in the coastline, directly leading to a sharp shrinkage of coastal wetlands; coastal organisms have also reduced their diversity due to habitat loss; at the same time, abnormal longshore currents caused by shoreline changes will also increase the risk of coastal erosion. The excessive expansion of near-shore artificial aquaculture will also encroach on coastal wetlands, and the wastewater discharged will aggravate the eutrophication of offshore waters. The emergence of these problems is closely related to the excessive land use and development activities in the region and the failure to follow the sea-land zonal distribution and its changing laws of land use, which seriously threatens the sustainable development of the regional coastal zone.

[0003] However, the currently commonly used coastal strip segmentation and zoning methods subjectively divide the coastal zone into regular strips and small areas, which may break the correlation between the strips or sub-areas; at the same time, the regularized sub-strips or sub-areas are contrary to the heterogeneity of the sea-land zonal pattern, and it is not easy to correctly characterize the sea-land zonal characteristics of land use. Therefore, constructing a method to accurately extract the sea-land zonal pattern of coastal land use has become an urgent problem to be solved in the study of sea-land zonal characteristics. Solving this problem is of great scientific significance for understanding the sea-land interaction characteristics of the coastal zone under human disturbance.

[0004] Therefore, it is an urgent problem for technicians in this field to propose a method for extracting the sea-land zonal pattern of coastal land use to solve the difficulties existing in the prior art. Summary of the invention

[0005] In view of this, the present invention provides a method for extracting the sea-land zonal pattern of coastal land use. In order to achieve the above-mentioned purpose, the present invention provides the following technical solutions:

[0006] A method for extracting the sea-land zonal pattern of coastal land use comprises the following steps:

[0007] Construct coastal profiles using coastal shoreline baseline data;

[0008] Obtain land use data for coastal profiles;

[0009] The land use data of the coastal profiles are numbered sequentially;

[0010] Define the sequential similarity index of the sea-land gradient pattern of coastal land use;

[0011] Construct a clustering model of the sea-land gradient pattern of coastal land use;

[0012] The sea-land zonal pattern of coastal land use was obtained through the sea-land gradient pattern clustering model of coastal land use.

[0013] The above method optionally includes the following specific contents for constructing the coastal profile line:

[0014] The strip-shaped coastal zone is sampled perpendicular to the coastline into coastal profile lines that are equally spaced and perpendicular to the coastline.

[0015] The above method optionally obtains the specific content of the coastal zone profile land use data as follows:

[0016] The coastal profile vector data are superimposed with the remote sensing images of the region, and the coastal profile land use data are obtained through visual interpretation.

[0017] The above method optionally includes the following steps of sequentially numbering the land use data of the coastal profile line:

[0018] The land use elements on each profile line in the coastal profile land use data are numbered in sequence from sea to land as 1, 2, 3...n, and the numbers are assigned to the corresponding land use elements as attribute information.

[0019] The above method optionally defines the specific content of the coastal land use sea-land gradient pattern sequence similarity index as follows:

[0020] The calculation formula of the order similarity index of the sea-land gradient pattern of coastal land use is as follows:

[0021]

[0022] Among them, SSI ij is the similarity value of the land use sea-land gradient pattern between profile i and profile j; LCS(i,j) is the maximum identical subsequence length between profile i and profile j; C i and C j are the number of land use elements of profile line i and profile line j respectively.

[0023] The above method can optionally construct a clustering model of the sea-land gradient pattern of coastal land use as follows:

[0024] The initial cluster number k and cluster center of the coastal land use sea-land gradient pattern clustering model are determined by the decision diagram constructed by the sample point density ρ and the minimum distance δ to the high-density point, where ρ is the constant cutoff distance d required for calculation c It is defined as the 3% position of all sample points in descending order of distance.

[0025] In the above method, the calculation formula of sample point density ρ and minimum distance δ to high density points is as follows:

[0026]

[0027] Among them, ρ i is the local density of sample point i; d ij is the distance between sample point i and sample point j; d c is the predefined cutoff distance, when x<0, then χ(x)=1, otherwise χ(x)=0, δ i It is the minimum distance from sample point i to other high-density sample points.

[0028] In the above method, the optimal clustering result discrimination index is optionally characterized by the Davies–Bouldin (DB) index, and the calculation formula is as follows:

[0029]

[0030] DB(k) is the verification value of the cluster number k; w i or j It is the distance from all sample points in cluster i or j to the cluster center C i or C j The average distance of ij is the cluster center C i With C j The distance between.

[0031] The above method can optionally obtain the specific content of the sea-land zonal pattern of coastal land use through the sea-land gradient pattern clustering model of coastal land use:

[0032] The sea-land zonal pattern of coastal land use is determined by the proportion of land use coverage of a single cluster in the clustering results and the spatial position of each land use in the sea-land sequence direction. The sea-land gradient pattern of cluster land use is determined, and the sea-land zonal pattern of regional coastal land use is obtained from the land use structure and arrangement.

[0033] The above method is optional. The calculation method of land use coverage ratio of a single cluster is as follows:

[0034]

[0035] Among them, P T is the coverage ratio of land use type T in a certain cluster; C is the total number of elements of land use type T in the cluster; L T,N is the length of the Nth element of land use T in the cluster; L is the length of the profile line; M is the total number of coastal profile lines contained in the cluster;

[0036] The spatial position of each land use in the sea-land sequence is characterized by the distance from the land use to the sea, calculated as follows:

[0037]

[0038] Among them, D T is the average distance from land use type T to the coastal sea in a certain cluster; C is the total number of elements of land use type T in the cluster; D T,N is the distance from the Nth element of land use T in cluster to the sea area; N T is the total number of elements of land use T in the cluster.

[0039] It can be seen from the above technical solutions that, compared with the prior art, the present invention discloses a method for extracting the sea-land zonal pattern of coastal land use, which has the following beneficial effects:

[0040] 1) The coastal zone is innovatively simplified into equally spaced coastal zone profile lines. The coastal zone profile lines not only retain the zonal characteristics of the coastal zone in the sea-land direction (vertical direction), but also the spatial differences in the sea-land zonal characteristics in the horizontal direction can be represented by the differences in the profile lines, which can effectively reveal the sea-land zonal pattern of the regional coastal zone;

[0041] 2) In addition, in view of the horizontal differences in the sea-land gradient pattern of land use, the present invention constructs a similarity index of the order of the sea-land gradient pattern of coastal land use, which makes it possible to extract the sea-land zonal pattern of coastal land use;

[0042] 3) In addition, the clustering model of coastal land use sea-land gradient pattern mainly uses the inherent characteristics of the data to explore the typical coastal land use sea-land gradient pattern, avoiding subjective interference from humans and making the results of the coastal sea-land zonal pattern more objective;

[0043] 4) The processing method and technical process based on coastal profile lines can also be used to explore the expansion trend of human activities on the sea-land gradient, and to extract the evolution speed and process of the coastline. It provides a new way for the overall study of the coastal zone with a coordinated sea-land approach, and has important practical and scientific significance for understanding the sea-land interaction characteristics of the coastal zone under human interference. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0045] Figure 1 A flow chart of a method for extracting the sea-land zonal pattern of coastal land use provided by the present invention;

[0046] Figure 2 This is a schematic diagram of the implementation principle of the method for extracting the sea-land zonal pattern of coastal land use provided by the present invention. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] See also Figure 1 As shown, the present invention discloses a method for extracting the sea-land zonal pattern of coastal land use, comprising the following steps:

[0049] Construct coastal profiles using coastal shoreline baseline data;

[0050] Obtain land use data for coastal profiles;

[0051] The land use data of the coastal profiles are numbered sequentially;

[0052] Define the sequential similarity index of the sea-land gradient pattern of coastal land use;

[0053] Construct a clustering model of the sea-land gradient pattern of coastal land use;

[0054] The sea-land zonal pattern of coastal land use was obtained through the sea-land gradient pattern clustering model of coastal land use.

[0055] Furthermore, the specific contents of constructing the coastal profile are as follows:

[0056] The strip-shaped coastal zone is sampled perpendicular to the coastline into coastal profile lines that are equally spaced and perpendicular to the coastline.

[0057] Specifically, the length of the coastal profile line is consistent with the width of the coastal zone (the sum of the width of the sea and land). If the width of the coastal zone is 20 kilometers, the length of the coastal profile line is 20 kilometers, ensuring that the coastal profile line can cover the entire sea and land area of ​​the coastal zone. The interval distance between profile lines (also known as sampling distance) is determined by the spatial resolution of the remote sensing data used to interpret land use information, which is generally 3 times the spatial resolution. For example, if the spatial resolution of the remote sensing image is 30 meters, the interval distance is 90 meters (30*3).

[0058] Furthermore, the specific contents of obtaining the land use data of the coastal profile are as follows:

[0059] The coastal profile vector data are superimposed with the remote sensing images of the region, and the coastal profile land use data are obtained through visual interpretation.

[0060] Furthermore, the specific contents of the sequential numbering of the coastal profile land use data are as follows:

[0061] The land use elements on each profile line in the coastal profile land use data are numbered in sequence from sea to land as 1, 2, 3...n, and the numbers are assigned to the corresponding land use elements as attribute information.

[0062] Specifically, the sequence number of each land use element reflects its position on the sea-land sequence gradient, and the total number n reflects the diversity characteristics of the sea-land zonal pattern of land use in the region.

[0063] Furthermore, the specific content of the coastal land use sea-land gradient pattern sequence similarity index is defined as follows:

[0064] The calculation formula of the order similarity index of the sea-land gradient pattern of coastal land use is as follows:

[0065]

[0066] Among them, SSI ij is the similarity value of the land use sea-land gradient pattern between profile i and profile j; LCS(i,j) is the maximum identical subsequence length between profile i and profile j; C i and C j are the number of land use elements of profile line i and profile line j respectively.

[0067] Furthermore, the specific contents of constructing the clustering model of the sea-land gradient pattern of coastal land use are as follows:

[0068] The initial cluster number k and cluster center of the coastal land use sea-land gradient pattern clustering model are determined by the decision diagram constructed by the sample point density ρ and the minimum distance δ to the high-density point, where ρ is the constant cutoff distance d required for calculation c It is defined as the 3% position of all sample points in descending order of distance.

[0069] Furthermore, the calculation formulas for the sample point density ρ and the minimum distance δ to the high-density point are as follows:

[0070]

[0071] Among them, ρ i is the local density of sample point i; d ij is the distance between sample point i and sample point j; d c is the predefined cutoff distance, when x<0, then χ(x)=1, otherwise χ(x)=0, δ i It is the minimum distance from sample point i to other high-density sample points.

[0072] Furthermore, the optimal clustering result discrimination index is characterized by the Davies–Bouldin (DB) index, and the calculation formula is as follows:

[0073]

[0074] DB(k) is the verification value of the cluster number k; w i or j It is the distance from all sample points in cluster i or j to the cluster center C i or C j The average distance of ij is the cluster center C i With C j The distance between.

[0075] Specifically, the sample point density ρ and the minimum distance δ to the high-density point are used as the x-axis and y-axis to make a scatter plot, where ρ and δ are both larger and farther away from the x-axis and the isolated points on the y-axis are the best cluster centers, and the number of these isolated points is the initial number of clusters;

[0076] The principle of the center point clustering algorithm is as follows:

[0077] 1) Randomly select k objects from the sample data set as initial cluster centers;

[0078] 2) Based on the selected k cluster centers, calculate the distance or similarity between other objects and the cluster center, and assign the objects to the cluster center with the closest distance or the most similarity until all other objects are assigned. At this point, the data set is divided into k clusters;

[0079] 3) Calculate the average distance from each object in the k clusters to other objects respectively, take the object with the smallest distance as the new cluster center, and then repeat step 2) to compare the previous cluster center with the current cluster center. The calculation ends when the cluster center object does not change;

[0080] 4) The last k clusters are the final clustering results.

[0081] Furthermore, the specific content of the sea-land zonal pattern of coastal land use is obtained through the clustering model of the sea-land gradient pattern of coastal land use:

[0082] The sea-land zonal pattern of coastal land use is determined by the proportion of land use coverage of a single cluster in the clustering results and the spatial position of each land use in the sea-land sequence direction. The sea-land gradient pattern of cluster land use is determined, and the sea-land zonal pattern of regional coastal land use is obtained from the land use structure and arrangement.

[0083] Furthermore, the calculation method for the proportion of land use coverage of a single cluster is as follows:

[0084]

[0085] Among them, P T is the coverage ratio of land use type T in a certain cluster; C is the total number of elements of land use type T in the cluster; L T,N is the length of the Nth element of land use T in the cluster; L is the length of the profile line; M is the total number of coastal profile lines contained in the cluster;

[0086] The spatial position of each land use in the sea-land sequence is characterized by the distance from the land use to the sea, calculated as follows:

[0087]

[0088] Among them, D T is the average distance from land use type T to the coastal sea in a certain cluster; C is the total number of elements of land use type T in the cluster; D T,N is the distance from the Nth element of land use T in cluster to the sea area; N T is the total number of elements of land use T in the cluster.

[0089] In a specific embodiment, see Figure 2 As shown, in view of the subjective interference and uncertainty of the results of traditional coastal zone zonal pattern identification, this embodiment provides a method for extracting the sea-land zonal pattern of coastal land use, which specifically includes the following steps:

[0090] (1) Constructing coastal profile lines using coastal coastline baseline data:

[0091] The coastline baseline data is the coastline data of the initial year of the regional coastal zone, which is mainly determined through manual visual interpretation.

[0092] The construction of the coastal zone profile line requires the presetting of the length L of the profile line and the spacing d between the profile lines. The length of the profile line is mainly determined by the width of the coastal zone (i.e. the total length of the sea and land areas). Generally speaking, the sea area is a 10km buffer zone extending from the coastline baseline to the sea area, and the land area is a 30km buffer zone extending to the land area, so L is generally set to 40km. The spacing d of the profile lines is generally determined based on the spatial resolution R of the interpreted coastal land use remote sensing image, and is generally 3 times R, in order to ensure that each coastal land use element in the area is on the coastal profile line. If the spatial resolution of the remote sensing image is 30m, then d is 90m.

[0093] (2) Constructing a coastal profile land use dataset:

[0094] The coastal profile data generated in step (1) is used in combination with the regional coastal remote sensing image to obtain the coastal profile land use data through visual interpretation.

[0095] (3) The land use data of the coastal profiles are numbered sequentially based on the sea-land gradient:

[0096] Based on the land use data of the coastal profile interpreted in step (2), the land use elements on each profile in the data are numbered sequentially from the sea to the land. For example, if a profile has the following sequence in the sea-land direction: ACADBDBED (A, B, C, D, E represent different land use types respectively), then the sequence numbers are: 1 (A), 2 (C), 3 (A), 4 (D), 5 (B), 6 (D), 7 (B), 8 (E), 9 (D).

[0097] (4) Constructing a clustering model of the sea-land gradient pattern of coastal land use:

[0098] The clustering model of coastal land use sea-land gradient pattern mainly consists of three parts, namely, the order similarity index of coastal land use sea-land gradient pattern, cluster center and number discrimination index and clustering algorithm. Each part is introduced in detail as follows:

[0099] The coastal land use sea-land gradient pattern sequence similarity index is based on the principle that the more similar the land use types and their sequence arrangements on the two profiles are, the more similar the coastal land use patterns are. The largest common subsequence (LCS) index is used to construct the coastal land use sea-land gradient pattern sequence similarity index. The calculation formula is as follows:

[0100]

[0101] Among them, SSI ij is the similarity value of the land use sea-land gradient pattern between profile i and profile j; LCS(i,j) is the maximum identical subsequence length between profile i and profile j; C i and C j are the number of land use elements of profile line i and profile line j respectively.

[0102] The cluster center and number are mainly calculated by the sample point density ρ and the minimum distance δ index to the high-density point. The calculation formulas are as follows:

[0103]

[0104] Among them, ρ i is the local density of sample point i; d ij is the distance between sample point i and sample point j; d c is the predefined cutoff distance, when x<0, then χ(x)=1, otherwise χ(x)=0, δ i It is the minimum distance from sample point i to other high-density sample points.

[0105] A scatter plot is made with ρ and δ as the x-axis and y-axis respectively, where ρ and δ are both large and far away from the x and y axes. The isolated points are the best cluster centers, and the number of these isolated points is the initial number of clusters.

[0106] The clustering algorithm selects the center point clustering algorithm, and the algorithm principle is as follows:

[0107] 1) Randomly select k objects from the sample data set as initial cluster centers;

[0108] 2) Based on the selected k cluster centers, calculate the distance or similarity between the object and the cluster center, and assign the object to the cluster center with the closest distance or the most similarity until all other objects are assigned. At this point, the data set is divided into k clusters;

[0109] 3) Calculate the average distance between each object in the k clusters and other objects respectively, take the object with the smallest distance as the new cluster center, and then repeat step 2); compare the previous cluster center with the current cluster center, and the calculation ends when the cluster center object does not change;

[0110] 4) The last k clusters are the final clustering results.

[0111] In addition, in order to obtain the optimal clustering results, this method selects the Davies–Bouldin (DB) index for judgment, and its calculation formula is as follows:

[0112]

[0113] DB(k) is the verification value of the cluster number k; w i or j It is the distance from all sample points in cluster i or j to the cluster center C i or C j The average distance of ij is the cluster center C i With C j The distance between.

[0114] By using the clustering model of the sea-land gradient pattern of coastal land use, this method can effectively extract the preliminary clustering results of the sea-land gradient pattern of coastal land use.

[0115] (5) The sea-land zonal pattern of coastal land use based on land use structure:

[0116] Based on the preliminary clustering results extracted by the model in step (4), this method mainly analyzes each clustering result from two structural indicators: the proportion of land use area and the distance from land use to the sea, so as to quantitatively analyze the sea-land gradient pattern of land use and reveal the typical sea-land zonal pattern of land use in the regional coastal zone.

[0117] The calculation method of land use coverage ratio of a single cluster is as follows:

[0118]

[0119] Among them, P T is the coverage ratio of land use type T in a certain cluster; C is the total number of elements of land use type T in the cluster; L T,N is the length of the Nth element of land use T in the cluster; L is the length of the profile line; M is the total number of coastal profile lines contained in the cluster;

[0120] The spatial position of each land use in the sea-land sequence is characterized by the distance from the land use to the sea, calculated as follows:

[0121]

[0122] Among them, D T is the average distance from land use type T to the coastal sea in a certain cluster; C is the total number of elements of land use type T in the cluster; D T,N is the distance from the Nth element of land use T in cluster to the sea area; N T is the total number of elements of land use T in the cluster.

[0123] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0124] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for extracting the sea-land zonal pattern of coastal land use, characterized in that: The following steps are involved: Construct coastal profiles using coastal shoreline baseline data; Obtain land use data for coastal profiles; The land use data of the coastal profiles are numbered sequentially; Define the sequential similarity index of the sea-land gradient pattern of coastal land use; Construct a clustering model of the sea-land gradient pattern of coastal land use; The sea-land zonal pattern of coastal land use was obtained through the sea-land gradient pattern clustering model of coastal land use.

2. The method for extracting the sea-land zonal pattern of coastal land use according to claim 1, characterized in that: The specific contents of constructing the coastal profile are as follows: The strip-shaped coastal zone is sampled perpendicular to the coastline into coastal profile lines that are equally spaced and perpendicular to the coastline.

3. The method for extracting the sea-land zonal pattern of coastal land use according to claim 1, characterized in that: The specific contents of obtaining coastal profile land use data are as follows: The coastal profile vector data are superimposed with the remote sensing images of the region, and the coastal profile land use data are obtained through visual interpretation.

4. The method for extracting the sea-land zonal pattern of coastal land use according to claim 1, characterized in that: The specific contents of the sequential numbering of the coastal profile land use data are as follows: The land use elements on each profile line in the coastal profile land use data are numbered in sequence from sea to land as 1, 2, 3...n, and the numbers are assigned to the corresponding land use elements as attribute information.

5. The method for extracting the sea-land zonal pattern of coastal land use according to claim 1, characterized in that: The specific contents of defining the similarity index of the sea-land gradient pattern of coastal land use are as follows: The calculation formula of the order similarity index of the sea-land gradient pattern of coastal land use is as follows: Among them, SSI ij is the similarity value of the land use sea-land gradient pattern between profile i and profile j; LCS(i,j) is the maximum identical subsequence length between profile i and profile j; C i and C j are the number of land use elements of profile line i and profile line j respectively.

6. The method for extracting the sea-land zonal pattern of coastal land use according to claim 1, characterized in that: The specific contents of constructing the clustering model of the sea-land gradient pattern of coastal land use are as follows: The initial cluster number k and cluster center of the coastal land use sea-land gradient pattern clustering model are determined by the decision diagram constructed by the sample point density ρ and the minimum distance δ to the high-density point, where ρ is the constant cutoff distance d required for calculation c It is defined as the 3% position of all sample points in descending order of distance.

7. The method for extracting the sea-land zonal pattern of coastal land use according to claim 6, characterized in that: The calculation formulas for sample point density ρ and the minimum distance δ to high-density points are as follows: Among them, ρ i is the local density of sample point i; d ij is the distance between sample point i and sample point j; d c is the predefined cutoff distance, when x<0, then χ(x)=1, otherwise χ(x)=0, δ i It is the minimum distance from sample point i to other high-density sample points.

8. The method for extracting the sea-land zonal pattern of coastal land use according to claim 1, characterized in that: The optimal clustering result discrimination index is characterized by the Davies–Bouldin (DB) index, and the calculation formula is as follows: DB(k) is the verification value of the cluster number k; w i or j It is the distance from all sample points in cluster i or j to the cluster center C i or C j The average distance of ij is the cluster center C i With C j The distance between.

9. The method for extracting the sea-land zonal pattern of coastal land use according to claim 1, characterized in that: The specific content of the sea-land zonal pattern of coastal land use obtained through the sea-land gradient pattern clustering model of coastal land use is as follows: The sea-land zonal pattern of coastal land use is determined by the proportion of land use coverage of a single cluster in the clustering results and the spatial position of each land use in the sea-land sequence direction. The sea-land gradient pattern of cluster land use is determined, and the sea-land zonal pattern of regional coastal land use is obtained from the land use structure and arrangement.

10. The method for extracting the sea-land zonal pattern of coastal land use according to claim 9, characterized in that: The calculation method of land use coverage ratio of a single cluster is as follows: Among them, P T is the coverage ratio of land use type T in a certain cluster; C is the total number of elements of land use type T in the cluster; L T,N is the length of the Nth element of land use T in the cluster; L is the length of the profile line; M is the total number of coastal profile lines contained in the cluster; The spatial position of each land use in the sea-land sequence is characterized by the distance from the land use to the sea, calculated as follows: Among them, D T is the average distance from land use type T to the coastal sea in a certain cluster; C is the total number of elements of land use type T in the cluster; D T,N N is the distance from the Nth element of land use T in cluster to the sea area; T is the total number of elements of land use T in the cluster.

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