Forest gap and forest growth cycle stage extraction method based on spatial topology

Abstract

The invention discloses a space topology-based forest gap and forest growth cycle stage extraction method, and belongs to the technical field of forest gap and forest growth cycle stage extraction. In order to solve the problem that forest gaps are extracted by a spatial topology method based on brief information, the method comprises the following steps: acquiring forest information of a survey sample plot, setting a height threshold value, and reserving trees of which the tree heights are greater than the height threshold value to obtain a canopy tree set; a Delaunay triangulation network is established; the method comprises the following steps: constructing a position judgment method of edges in a Delaunay triangulation network, then dividing all the edges in the Delaunay triangulation network into a forest gap edge set and a forest crown edge set, and then dividing the forest gap edge set and the forest crown edge set into a forest gap triangle set and a forest crown triangle set; merging triangles with common forest gap edges in the forest gap triangle set to obtain an expanded forest gap set; and setting a height threshold gradient sequence based on the extracted different forest growth cycle stages to divide the forest growth cycle stages. The method can provide related theoretical support for forest ecological research.

Description

Technical Field

[0001] The present invention belongs to the technical field of extraction of forest gaps and forest growth cycle stages, and particularly relates to a method for extracting forest gaps and forest growth cycle stages based on spatial topology. Background Art

[0002] A forest gap refers to the gap formed in the originally closed forest canopy after the death of a single tree, a part of a tree (such as a branch), or multiple trees in a forest. Based on the classification method of forest gaps, forest gaps can be further divided into canopy gaps and extended gaps. Among them, an extended gap refers to the land area or space enclosed by the trunks of the surrounding trees of a canopy gap; a canopy gap refers to the part in an extended gap without canopy shading. The forest gap cycle, also known as the forest growth cycle or the internal cycle within a forest community, refers to the process of continuous repetition at a certain position in a forest stand, from the formation of a forest gap to its gradual filling, closing, and then to the next generation. Different forest gap development stages can be regarded as different forest growth cycle stages at the local scale. The forest growth cycle and forest gap dynamics are one of the important theories for explaining the maintenance and renewal of forest ecosystems. In a forest community, there is a series of local community successions at the medium and small scales based on forest gap dynamics. Pioneer tree species groups with strong ability to obtain and utilize resources quickly and faster growth rates can occupy advantages in forest gaps in the initial stage of forest gap development; while in the later stage of forest gap development, climax tree species groups with stronger shade tolerance and longer lifespan have greater advantages. This local succession recurs with the forest gap cycle, and at the scale of the entire forest stand, the forest gap cycles at different positions create the overall mobile mosaic of the forest stand; in a forest community at the climax of succession, the number of forest gaps at different development stages is relatively constant, that is, the mobile mosaic of the climax forest community is basically constant, with a mobile mosaic steady state. By analyzing the characteristics such as the number, area, shape, spatial distribution of forest gaps at different development stages in a forest stand and the tree species dominance in forest gaps, the understanding of the construction and assembly mechanisms of forest communities can be deepened. Mastering and reasonably applying these ecological mechanisms have certain significance for predicting forest stand succession and formulating forest management measures, etc.

[0003] From the formation and development process of forest gaps, it can be seen that different development stages of forest gaps reflect the dynamic changes in the spatial structure (horizontal and vertical distribution) of forest trees. The spatial distribution of forest trees and the data of diameter at breast height of each tree are important basic information reflecting the spatial structure of forest trees, and they are also the traditional data and the data with the largest stock in the industry. Therefore, how to use the brief spatial distribution data of forest trees and the data of diameter at breast height of each tree to extract the characteristics of forest gaps and closed canopies at different development stages is a scientific and technical problem worthy of exploration. Summary of the Invention

[0004] The problem to be solved by the present invention is to extract forest gaps by a spatial topology method based on brief information, and propose a method for extracting forest gaps and forest growth cycle stages based on spatial topology.

[0005] To achieve the above object, the present invention is realized through the following technical solutions:

[0006] A method for extracting forest gaps and forest growth cycle stages based on spatial topology, comprising the following steps:

[0007] S1. Obtain the tree information of the surveyed plot, including the set A of the spatial position coordinates of the trees t_c , the set A of the tree heights of the trees t_h , the set A of the crown widths of the trees t_r ;

[0008] S2. Set a height threshold, and filter the trees in the set A of the tree heights obtained in step S1 t_h with the height threshold to retain the trees with tree heights greater than the height threshold, and obtain the set A of canopy trees c_t ;

[0009] S3. Extract the spatial position coordinates of the trees in the set A of canopy trees obtained in step S2 c_t , establish a Delaunay triangulation, and construct the set A of the edges in the Delaunay triangulation s_l ;

[0010] S4. Construct a method for judging the positions of the edges in the Delaunay triangulation. Based on the tree crown width information of the trees obtained in step S1 and the side length information of the edges in the Delaunay triangulation obtained in step S3, judge the positions of all the edges in the Delaunay triangulation to be in the forest gap area or the forest canopy area, and then divide all the edges in the Delaunay triangulation into the set A of forest gap edges s_g and the set A of forest canopy edges s_c ;

[0011] S5. Based on the set A of forest gap edges s_g and the set A of forest canopy edges s_c obtained in step S4, classify each triangle in the Delaunay triangulation obtained in step S3 into the set A of forest gap triangles d_g and the set Ad of forest canopy triangles _c ;

[0012] S6. Based on the set A of forest gap triangles d_g obtained in step S5 and the set A of forest gap edges s_g obtained in step S4, merge the triangles in the set of forest gap triangles that have common forest gap edges until there are no forest gap triangles with common forest gap edges, and obtain the extended forest gap set A e_g ;

[0013] S7. Based on the set A of canopy trees c_tFor the set A of spatial position coordinates of the trees obtained in step S1 t_c and the set A of crown widths of the trees t_r are filtered to obtain the subset A of spatial coordinates of the canopy trees c_t_c and the subset A of crown widths of the canopy trees c_t_r ;

[0014] S8. The surveyed plot is rasterized and denoted as A gird , and each grid in the surveyed plot is classified one by one with the subset A of crown widths of the canopy trees obtained in step S7 c_t_r and the subset A of spatial coordinates of the canopy trees c_t_c ;

[0015] S9. Based on the extraction of different forest growth cycle stages, a height threshold gradient sequence H list =(H1, H2, …, H n ) is set, and then steps S1 to S8 are repeated to extract with the thresholds in H list to divide the forest growth cycle stages.

[0016] Furthermore, the method for obtaining the forest tree information in step S1 includes airborne radar echo point cloud acquisition, ground-based radar echo point cloud acquisition, and stand survey acquisition.

[0017] Furthermore, the method for judging the position of the edge in the Delaunay triangulation in step S4 is that L-(r1 + r2)>ε indicates the gap area, and vice versa indicates the canopy area, where L is the length of the edge in the Delaunay triangulation, r1 and r2 are respectively 1 / 2 of the crown widths of the two vertices of the edge in the Delaunay triangulation, and ε is the distance threshold.

[0018] Furthermore, the classification method in step S5 is as follows: Set the set E i of the 3 edges of the i-th triangle T ti in the Delaunay triangulation = {e ti1 , e ti2 , e ti3}, if T i contains an edge belonging to the gap, it is classified into the set of gap triangles; if T i does not contain an edge belonging to the gap, it is classified into the set of canopy triangles, and its mathematical expression is:

[0019]

[0020] where f(E ti ) is the discriminant function for the i-th triangle T i in the Delaunay triangulation belonging to the gap or the canopy, is an empty set;

[0021]

[0022] Furthermore, the specific implementation method of step S6 includes the following steps:

[0023] S6.1. First, let the set of gaps in the forest be A d_g and the triangle T i belong to the 0th gap set G0, and the expression is:

[0024] G0 = T i ;

[0025] Then, find the triangle T i connected to T j , and the expression is:

[0026]

[0027] where, T i ~T j means that the triangles T j and T i are connected to the same forest gap, and T j and T i belong to the 1st gap set G1;

[0028] Then, gradually perform recursion, and recursively define the nth gap set Gn n as:

[0029]

[0030] where, E Gn is the set of all sides of the triangles in Gn n , and when Gn n+1 = Gn n the recursion terminates;

[0031] S6.2. Traverse all the triangles in the set A d_g , classify them into different gap sets and form an extended gap set A e_g , and the expression is:

[0032] A e_g = {G one , G two , …, G N}

[0033] where, G one is the Delaunay triangle set belonging to the 1st gap, G two is the Delaunay triangle set belonging to the 2nd gap, G Nis the Delaunay triangle set belonging to the Nth forest gap.

[0034] Furthermore, the expression in the specific implementation method of step S7 is:

[0035] A c_t_c = {(x i , y i ) ∈ A t_c | i ∈ A c_t}

[0036] A c_t_r = {r i ∈ A t_r | i ∈ A c_t}

[0037] Among them, (x i , y i ) is the spatial distribution coordinate of the i-th tree, and r i is 1 / 2 of the crown width of the i-th tree.

[0038] Furthermore, the specific implementation method of step S8 is:

[0039] S8.1. Rasterize the investigation plot, denoted as A gird , and the expression is:

[0040] A gird = {G rid_1 , G rid_2 , …, G rid_j …, G rid_m}

[0041] Among them, G rid_j is the j-th grid, and m is the total number of grids;

[0042] S8.2. Calculate the distance dist(G rid_j , (x i , y i )) from each grid to the coordinates of each tree, and the expression is:

[0043]

[0044] Among them, x i is the x coordinate of the i-th canopy tree, y i is the y coordinate of the i-th canopy tree, n is the total number of canopy trees, x j is the x coordinate of the j-th grid, and y j is the y coordinate of the j-th grid;

[0045] S8.3. Classify the grids using the calculated distance from each grid to the coordinates of each tree, and the expression is:

[0046]

[0047] Among them, f(G rid_j ) is the raster classification function. When f(G rid_j ) = 1, the raster is the canopy coverage area. When f(G rid_j ) = 0, the raster is the gap area.

[0048] Furthermore, the specific implementation method of step S9 is as follows:

[0049] S9.1. Let G g_i be the gap raster extracted based on the i-th threshold in the height threshold sequence H list increasing by height. Then there is:

[0050] G g_i = f'(H i )

[0051] Among them, f'(H i ) is the function for extracting the gap range based on the i-th height threshold H list in the height threshold sequence H i . This function is the overall process from step S1 to step S8;

[0052] S9.2. Set the raster in the initial stage of the forest growth cycle as:

[0053] G fgc_1 = f'(H1);

[0054] Among them, G fgc_1 is the raster set in the first forest growth cycle stage extracted based on the first height threshold H1 in the height threshold sequence. f'(H1) is the function for extracting the gap range based on the first height threshold H1 in the height threshold sequence H list . It is also the function for extracting the first forest growth cycle stage. This function is the overall process from step S1 to step S8;

[0055] The non-first and non-final stages of the forest growth cycle are:

[0056] G fgc_i = f'(H i )\f'(H i-1 ) i≠1&i≠n;

[0057] Among them, G fgc_i is the raster set belonging to the i-th forest growth cycle stage, and n is the total number of height thresholds in H list ;

[0058] The final stage of the forest growth cycle is:

[0059] G fgc_max = A gird \f'(H n )

[0060] Wherein, G fgc_max is the raster set of the final stage of the forest growth cycle, A gird is the set of all rasters, and f'(H n ) is a function of the raster set of forest gaps extracted based on the maximum height threshold H list in the height threshold list H n .

[0061] Advantages of the present invention:

[0062] For the method for extracting forest gaps and forest growth cycle stages based on spatial topology according to the present invention, when extracting forest gaps, after screening canopy trees with a fixed height threshold, the spatial area within the forest stand is segmented based on the canopy trees with the Delaunay triangulation as the basis, and then adjacent forest gap triangles are merged based on the connectivity between Delaunay triangles, and finally different forest gap patches can be obtained. The method of spatially segmenting a determined forest stand based on the Delaunay triangulation is unique, which will endow a clear spatial subordination relationship to any position within the forest stand, that is, whether any position within the forest stand belongs to the canopy patch or the forest gap patch is determined by the trees at the 3 vertices of the Delaunay triangle where it is located; forest management and management measures can also be formulated separately for different triangles.

[0063] For the method for extracting forest gaps and forest growth cycle stages based on spatial topology according to the present invention, on the basis of extracting forest gaps based on a fixed height threshold, a method for extracting forest growth cycle stages based on a height threshold sequence is further proposed. The present invention can provide relevant theoretical support for forest ecological research. Description of the Drawings

[0064] Figure 1 is a flowchart of the method for extracting forest gaps and forest growth cycle stages based on spatial topology according to the present invention;

[0065] Figure 2 is the forest tree information collection diagram described in step S1 of the present invention;

[0066] Figure 3 is the Delaunay triangulation diagram established in step S3 of the present invention;

[0067] Figure 4 is the diagram that only retains non-forest gap edges by determining whether the edges in the triangulation belong to forest gap edges in step S4 of the present invention;

[0068] Figure 5It is the raster classification map described in step S8 of the present invention;

[0069] Figure 6 It is the forest gap raster map extracted with different height thresholds described in step S9 of the present invention;

[0070] Figure 7 It is the forest growth cycle stage division map described in step S9 of the present invention. Detailed implementation manners

[0071] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and specific implementation manners. It should be understood that the specific implementation manners described herein are only used to explain the present invention and are not used to limit the present invention, that is, the specific implementation manners described are only a part of the implementation manners of the present invention, rather than all of the specific implementation manners. The components of the specific implementation manners of the present invention usually described and shown in the drawings here can be arranged and designed in various different configurations, and the present invention can also have other implementation manners.

[0072] Therefore, the detailed description of the specific implementation manners of the present invention provided in the drawings below is not intended to limit the scope of the claimed present invention, but merely represents the selected specific implementation manners of the present invention. All other specific implementation manners obtained by those skilled in the art based on the specific implementation manners of the present invention without creative efforts belong to the scope of protection of the present invention.

[0073] To further understand the content, features and effects of the present invention, the following specific implementation manners are exemplified and combined with the attached Figure 1 - Attached Figure 7 The details are as follows:

[0074] Example 1:

[0075] A method for extracting forest gaps and forest growth cycle stages based on spatial topology includes the following steps:

[0076] S1. Obtain the tree information of the surveyed plot, including the set A of the spatial position coordinates of the trees t_c , the set A of the tree heights of the trees t_h , and the set A of the crown widths of the trees t_r ;

[0077] Furthermore, the method for obtaining the tree information of the surveyed plot in step S1 includes airborne radar echo point cloud collection, ground-based radar echo point cloud collection and forest stand survey collection;

[0078] Further, measure each tree in the surveyed sample plots and record the information of each tree measurement. When measuring each tree, the relative coordinates, tree height, and crown width of the tree in the sample plot need to be investigated, or the relative coordinates and diameter at breast height (DBH) of the tree need to be investigated, and the tree height and crown width of the tree are calculated based on the tree height and crown width equations with the DBH as the independent variable.

[0079] S2. Set a height threshold and for the set A of tree heights of the trees obtained in step S1 t_h Filter the trees with the height threshold, retain the trees with a tree height greater than the height threshold, and obtain the set A of canopy trees c_t ;

[0080] S3. Extract the spatial position coordinates of the trees in the set A of canopy trees obtained in step S2 c_t Establish a Delaunay triangulation and construct the set A of the edges in the Delaunay triangulation s_l ;

[0081] S4. Construct a method for judging the position of the edges in the Delaunay triangulation. Based on the tree crown width information of the trees obtained in step S1 and the side length information of the edges in the Delaunay triangulation obtained in step S3, judge the positions of all the edges in the Delaunay triangulation to be in the gap area or the canopy area, and then divide all the edges in the Delaunay triangulation into the set A of gap edges s_g and the set A of canopy edges s_c ;

[0082] Further, the method for judging the position of the edges in the Delaunay triangulation in step S4 is that L - (r1 + r2) > ε means it is in the gap area, otherwise it is in the canopy area, where L is the length of the edge in the Delaunay triangulation, r1 and r2 are respectively 1 / 2 of the crown widths of the trees at the two vertices of the edge in the Delaunay triangulation, and ε is the distance threshold;

[0083] S5. Based on the set A of gap edges s_g and the set A of canopy edges s_c obtained in step S4, classify each triangle in the Delaunay triangulation obtained in step S3 into the set A of gap triangles d_g and the set A of canopy triangles d_c ;

[0084] Further, the classification method in step S5 is as follows: Set the set E i of the 3 edges of the i-th triangle T ti in the Delaunay triangulation = {e ti1 , e ti2 , e ti3}, if T iIf it contains an edge belonging to a forest gap, it is classified into the forest gap triangle set; if T i does not contain an edge belonging to a forest gap, it is classified into the forest canopy triangle set, and its mathematical expression is:

[0085]

[0086] where f(E ti ) is the discriminant function for the i-th triangle T i in the Delaunay triangulation belonging to the forest gap or forest canopy, is an empty set;

[0087]

[0088] S6. Based on the forest gap triangle set A d_g obtained in step S5 and the forest gap edge set A s_g obtained in step S4, the triangles in the forest gap triangle set that have common forest gap edges are merged until there are no more forest gap triangles with common forest gap edges, and the extended forest gap set A e_g is obtained;

[0089] Furthermore, the specific implementation method of step S6 includes the following steps:

[0090] S6.1. First, let the triangle T d_g in the forest gap triangle set A i belong to the 0th forest gap set G0, and the expression is:

[0091] G0 = T i ;

[0092] Then, find the triangle T i connected to T j , and the expression is:

[0093]

[0094] where T i ~T j means that the triangles T j and T i are connected to the same forest gap, and T j and T i belong to the 1st forest gap set G1;

[0095] Then, perform recursive steps gradually. The recursive definition of the nth forest gap set G n is:

[0096]

[0097] where E Gn is Gn The set of the sides of all the triangles in G n+1 =G n The recursion terminates;

[0098] S6.2. Traverse all the triangles in A d_g classify them into different gap sets and form an extended gap set A e_g , the expression is:

[0099] A e_g ={G one ,G two ,…,G N}

[0100] where G one is the Delaunay triangle set belonging to the 1st gap, G two is the Delaunay triangle set belonging to the 2nd gap, G N is the Delaunay triangle set belonging to the Nth gap.

[0101] S7. Based on the canopy tree set A c_t obtained in step S2, filter the set A t_c of the spatial position coordinates of the trees and the set A t_r of the crown widths of the trees obtained in step S1 c_t_c to obtain the subset A c_t_r of the spatial coordinates of the canopy trees and the subset A

[0102] of the crown widths of the canopy trees;

[0103] A c_t_c ={(x i ,y i )∈A t_c |i∈A c_t}

[0104] A c_t_r ={r i ∈A t_r |i∈A c_t}

[0105] where (x i ,y i ) is the spatial distribution coordinate of the i-th tree, and r i is 1 / 2 of the crown width of the i-th tree.

[0106] S8. Rasterize the surveyed plot, denoted as A gird , and use the subset A c_t_r of the crown widths of the canopy trees obtained in step S7With the subset A of the spatial coordinates of the canopy trees c_t_c Classify each grid in the surveyed plot one by one;

[0107] Furthermore, the specific implementation method of step S8 is as follows:

[0108] S8.1. Gridify the surveyed plot, denoted as A gird , and the expression is:

[0109] A gird ={G rid_1 , G rid_2 ,…, G rid_j …, G rid_m}

[0110] where G rid_j is the j-th grid and m is the total number of grids;

[0111] S8.2. Calculate the distance dist(G rid_j , (x i , y i )) from each grid to the coordinates of each tree, and the expression is:

[0112]

[0113] where x i is the x-coordinate of the i-th canopy tree, y i is the y-coordinate of the i-th canopy tree, n is the total number of canopy trees, x j is the x-coordinate of the j-th grid, and y j is the y-coordinate of the j-th grid;

[0114] S8.3. Classify the grids using the calculated distances from each grid to the coordinates of each tree, and the expression is:

[0115]

[0116] where f(G rid_j ) is the grid classification function. When f(G rid_j ) = 1, the grid is the forest canopy coverage area; when f(G rid_j ) = 0, the grid is the forest gap area.

[0117] S9. Set the height threshold gradient sequence H list =(H1, H2,…, H n ) based on the extracted different forest growth cycle stages, and then repeat steps S1 to S8, and extract using the thresholds in H list to divide the forest growth cycle stages.

[0118] Further, the specific implementation method of step S9 is as follows:

[0119] S9.1. Let G g_i be the gap raster extracted based on the i-th threshold in the height threshold sequence H list increasing by height. Then, we have:

[0120] G g_i = f'(H i )

[0121] where f'(H i ) is the function for extracting the gap range based on the i-th height threshold H list in the height threshold sequence H i . This function is the overall process from step S1 to step S8;

[0122] S9.2. Set the raster at the initial stage of the forest growth cycle to:

[0123] G fgc_1 = f'(H1);

[0124] where G fgc_1 is the raster set at the first stage of the forest growth cycle extracted based on the first height threshold H1 in the height threshold sequence, and f'(H1) is the function for extracting the gap range based on the first height threshold H1 in the height threshold sequence H list . It is also the function for extracting the first stage of the forest growth cycle. This function is the overall process from step S1 to step S8;

[0125] For the non-first and non-final stages of the forest growth cycle:

[0126] G fgc_i = f'(H i ) \ f'(H i-1 ) for i ≠ 1 & i ≠ n;

[0127] where G fgc_i is the raster set belonging to the i-th stage of the forest growth cycle, and n is the total number of height thresholds in H list ;

[0128] For the final stage of the forest growth cycle:

[0129] G fgc_max = A gird \ f'(H n );

[0130] where G fgc_max is the raster set at the final stage of the forest growth cycle, A gird is the set of all rasters, and f'(H n)A function for extracting a set of forest gap grids based on the list of height thresholds H list The maximum height threshold H n in the list.

[0131] It should be noted that relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprising", "including" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the phrase "comprising a..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0132] Although the present application has been described above with reference to specific embodiments, various improvements can be made thereto and components therein can be replaced with equivalents without departing from the scope of the present application. In particular, as long as there is no structural conflict, the various features in the specific embodiments disclosed in the present application can be combined with each other in any way, and the exhaustive description of the situations of these combinations is omitted in this specification only for the sake of saving space and resources. Therefore, the present application is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A method for extracting forest gaps and forest growth cycle stages based on spatial topology, characterized in that, It includes the following steps: S1. Obtain the forest tree information of the surveyed plots, including the set A of the spatial position coordinates of the trees t_c , the set A of the tree heights of the trees t_h , the set A of the crown widths of the trees t_r ; S2. Set a height threshold and use it to filter the set A of tree heights of the trees obtained in step S1 t_h Filter the trees using the height threshold, and retain the trees with tree heights greater than the height threshold to obtain the set A of canopy trees c_t ; S3. Extract the spatial position coordinates of the trees in the canopy tree set A obtained in step S2, establish a Delaunay triangulation network, and construct a set A of the edges in the Delaunay triangulation network c_t ; s_l ; S4. Construct a method for judging the position of edges in the Delaunay triangulation. Based on the tree crown width information obtained in step S1 and the edge length information of the edges in the Delaunay triangulation obtained in step S3, judge the positions of all the edges in the Delaunay triangulation as being in the gap area or the crown area, and then divide all the edges in the Delaunay triangulation into a gap edge set A s_g and a crown edge set A s_c ; S5. Based on the set A of forest gap edges obtained in step S4 s_g and the set A of forest canopy edges s_c , classify each triangle in the Delaunay triangulation obtained in step S3 into a set A of forest gap triangles d_g and a set A of forest canopy triangles d_c ; S6. Based on the set A of gap triangles obtained in step S5 d_g and the set A of gap edges obtained in step S4 s_g , merge the triangles in the set of gap triangles that have common gap edges until there are no more gap triangles with common gap edges, obtaining the extended set A of gap triangles e_g ; S7. Based on the canopy tree set A obtained in step S2 c_t Filter the set A of spatial position coordinates of the trees obtained in step S1 t_c and the set A of tree crown widths t_r to obtain a subset A of the spatial coordinates of the canopy trees c_t_c and a subset A of the crown widths of the canopy trees c_t_r ; S8. Rasterize the surveyed plot and denote it as A gird and classify each grid in the surveyed plot one by one with the subset A c_t_r of the crown widths of the canopy trees and the subset A c_t_c of the spatial coordinates of the canopy trees obtained in step S7; S9. Set up a height threshold gradient sequence H based on extracting different forest growth cycle stages list =(H1, H2, …, H n ), and then repeat steps S1 to S8 to extract using the thresholds in H list and divide the forest growth cycle stages.

2. The extraction method of forest gaps and forest growth cycle stages based on spatial topology according to claim 1, characterized in that The method for obtaining forest tree information in step S1 includes airborne radar echo point cloud collection, ground-based radar echo point cloud collection, and stand survey collection.

3. The extraction method of forest gaps and forest growth cycle stages based on spatial topology according to claim 1 or 2, characterized in that, The method for determining the position of the edge in the Delaunay triangulation in step S4 is that when L - (r1 + r2) > ε, it is in the forest gap area, otherwise it is in the forest canopy area, where L is the length of the edge in the Delaunay triangulation, r1 and r2 are respectively 1 / 2 of the crown widths of the trees at the two vertices of the edge in the Delaunay triangulation, and ε is the distance threshold.

4. The extraction method of forest gaps and forest growth cycle stages based on spatial topology according to claim 3, characterized in that, The classification method in step S5 is as follows: Set the set E of the three sides of the i-th triangle T in the Delaunay triangulation i to be ti E = {e ti1 , e ti2 , e ti3}. If T i contains an edge belonging to the gap, then classify it into the set of gap triangles; if T i does not contain an edge belonging to the gap, then classify it into the set of canopy triangles. Its mathematical expression is: Among them, f(E ti ) is the discriminant function of the i-th triangle T i belonging to the forest gap or the forest canopy, is an empty set; 5. The extraction method of forest gaps and forest growth cycle stages based on spatial topology according to claim 4, characterized in that The specific implementation method of step S6 includes the following steps: S6.

1. First, set the set A of gaps triangles d_g where the triangle T i belongs to the 0th gap set G0, and the expression is: G0 = T i ; Then search for the triangle T i connected to j , and the expression is: Among them, T i ~T j represents triangle T j and T i are interconnected as the same forest gap, T j and T i belong to the first forest gap set G1; Then, perform recursion step by step, and recursively define the nth forest gap set G n as follows: where E Gn is the set of all the edges of the triangles in G n and the recursion terminates when G n+1 = G n ; S6.

2. Traverse all triangles in A d_g in the set, classify them into different gap sets and form an extended gap set A e_g , and the expression is: A e_g = {G one , G two , …, G N} Among them, G one is the Delaunay triangle set belonging to the first forest gap, and G two is the Delaunay triangle set belonging to the second forest gap, and G N is the Delaunay triangle set belonging to the Nth forest gap.

6. The extraction method of forest gaps and forest growth cycle stages based on spatial topology according to claim 5, characterized in that The expression in the specific implementation method of step S7 is: A c_t_c ={(x i ,y i ) ∈ A t_c | i ∈ A c_t} A c_t_r = {r i ∈ A t_r | i ∈ A c_t} Among them, (x i , y i ) is the spatial distribution coordinate of the i-th forest tree, and r i is 1 / 2 of the crown width of the i-th forest tree.

7. The extraction method of forest gap and forest growth cycle stage based on spatial topology according to claim 6, wherein The specific implementation method of step S8 is: S8.

1. Grid the investigation plots, denoted as A gird , and the expression is: A gird = {G rid_1 , G rid_2 , …, G rid_j …, G rid_m} Among them, G rid_j is the j-th grid, and m is the total number of grids; S8.

2. Calculate the distance dist(G from each grid to the coordinates of each tree rid_j , (x i , y i ), and the expression is: where x i is the x - coordinate of the i - th canopy tree, y i is the y - coordinate of the i - th canopy tree, n is the total number of canopy trees, x j is the x - coordinate of the j - th grid, y j is the y - coordinate of the j - th grid; S8.

3. Classify the grids using the calculated distances from each grid to the coordinates of each tree. The expression is: Among them, f(G rid_j ) is the raster classification function. When f(G rid_j ) = 1, the raster is the canopy coverage area. When f(G rid_j ) = 0, the raster is the gap area.

8. The extraction method of forest gap and forest growth cycle stage based on spatial topology according to claim 7, characterized in that, The specific implementation method of step S9 is: S9.

1. Let G g_i be the forest gap raster extracted based on the i-th threshold in the height threshold sequence H list increasing with height. Then, we have: G g_i = f'(H i ) Among them, f'(H i ) is a function for extracting the gap range based on the i-th height threshold H list in the height threshold sequence H i , and this function is the overall process from step S1 to step S8; S9.

2. Set the grids at the initial stage of the forest growth cycle as: G fgc_1 = f'(H1); Among them, G fgc_1 is the raster set of the first forest growth cycle stage extracted based on the first height threshold H1 in the height threshold sequence, and f'(H1) is based on the height threshold sequence H list The function for extracting the gap range based on the first height threshold H1 in, and is also the function for extracting the first forest growth cycle stage. This function is the overall process from step S1 to step S8; For the non-first and non-final stages of the forest growth cycle: G fgc_i = f'(H i ) \ f'(H i-1 ) i ≠ 1 & i ≠ n; Among them, G fgc_i is the raster set belonging to the i-th forest growth cycle stage, and n is the total number of height thresholds in H list ; For the final stage of the forest growth cycle: G fgc_max = A gird \f'(H n ) Among them, G fgc_max is the raster set of the final stage of the forest growth cycle, A gird is the set of all rasters, f'(H n ) is a function of the raster set of forest gaps extracted based on the maximum height threshold H list in the height threshold list H n .

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