A vegetation monitoring system and method based on plot nested sampling

Abstract

The application discloses a vegetation monitoring system and method based on plot nesting sampling, and relates to the technical field of ecological evaluation. A unified corresponding relationship between a parent plot and nested subplots at all levels is established by using current monitoring records Cur, historical benchmark records Bas and a hierarchical correlation table Rel. Then, an anomaly trigger set Trg is used to identify anomalies at the scale of the parent plot, a positioning result Loc is used to shrink the anomaly range from the parent plot to the corresponding anomaly nested subplots, a hierarchical result Lay is used to continue shrinking the anomaly change to a target plant layer in the arbor layer, the shrub layer or the herb layer, and finally, a source result Src is used to implement the anomaly change to a specific hierarchical position and generate an interpretation result Res. Therefore, the problem that only the total number of species, the total coverage or the fluctuation of the dominant species ranking in the parent plot can be seen in the prior art, but it is impossible to further determine whether the anomaly comes from a local subplot or a plant layer, is solved.

Description

Technical Field

[0001] This invention relates to the field of ecological assessment technology, specifically to a vegetation monitoring system and method based on nested sampling of sample plots. Background Technology

[0002] Vegetation monitoring is continuously carried out in ecological restoration assessments, forest community surveys, grassland degradation identification, and long-term biodiversity observation. These monitoring tasks typically do not focus solely on species lists or canopy coverage results at a single point in time, but rather require continuous monitoring of changes at different spatial scales within the same monitoring area. Based on this, nested quadrat sampling has become a common organizational method in vegetation monitoring. This involves sequentially setting up subplots of different scales within a parent plot to record data in layers representing the tree, shrub, and herbaceous layers, thus taking into account both the overall pattern and local details.

[0003] Existing technologies can achieve long-term retesting through fixed quadrats and permanent monitoring points, and compare vegetation dynamics over different time periods by analyzing species type, quantity, and diversity indices. They can also establish 5m×5m shrub quadrats and 1m×1m herb quadrats within a 20m×20m tree quadrat to complete nested hierarchical surveys and examine the structural differences between the tree, shrub, and herb layers. However, when the overall indicators of the parent plot show abnormal fluctuations, such as a sudden drop in total cover, a sudden change in dominant species ranking, or an abnormal increase or decrease in the number of species over a certain period, only temporal comparison results at the quadrat level can be provided. It is impossible to further pinpoint which subquad, which vegetation layer, or which local change the anomaly originates from. Although nested hierarchical recording capabilities are available, the analysis focuses on structural differences between different layers and relationships between quadrats, without establishing a retrospective chain.

[0004] In actual monitoring, anomalies at the parent plot scale are often caused by changes in local spatial units, single vegetation layers, or short-term local disturbances. For example, edge disturbances, local shrub regeneration, seasonal replacement of the herbaceous layer, and the mixing of replanted and naturally restored areas may all occur first in local subplots before propagating to the parent plot's statistical results. When existing monitoring procedures lack cross-scale anomaly retrospective processes, monitoring personnel typically only see anomalies in the parent plot results but struggle to promptly identify the location, level, and type of the anomaly source. This can easily lead to three adverse consequences: First, anomalies are simply recorded as overall fluctuations, resulting in inaccurate subsequent restoration assessments and trend judgments; second, local anomalies are mixed with overall trends, easily misinterpreting local disturbances as overall community succession; third, verification work relies on large-scale field investigations again, increasing retesting costs and delaying the identification of anomaly causes. As long-term monitoring periods extend, these problems accumulate, ultimately weakening the interpretative and decision-support capabilities of nested plot sampling in long-term vegetation monitoring. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a vegetation monitoring system and method based on nested sampling of sample plots, which solves the problems mentioned in the background art.

[0006] This invention is achieved through the following technical solution: a vegetation monitoring method based on nested sampling of sample plots, comprising the following steps:

[0007] S1. Collect vegetation monitoring data of the parent plot and its nested sub-plots at all levels within the parent plot during the current monitoring period, and retrieve corresponding historical monitoring data. Based on the hierarchical and positional relationships between the parent plot and the nested sub-plots at all levels, generate the current monitoring record Cur, the historical baseline record Bas, and the hierarchical association table Rel; S2. Based on the current monitoring record Cur and the historical baseline record Bas, compare the differences in vegetation monitoring results at the parent plot scale. When the difference comparison results meet the preset anomaly triggering conditions, generate the corresponding anomaly trigger set Trag for the parent plot; S3. Based on the anomaly trigger set Trag... Using rg and the hierarchical association table Rel, the corresponding nested sub-quadrates of the abnormal parent plots are located level by level according to the nested hierarchy. The target plant layer corresponding to the abnormal change is determined by combining the current monitoring record Cur and the historical baseline record Bas, forming the location result Loc and the hierarchical result Lay. S4. Based on the location result Loc and the hierarchical result Lay, cross-scale source backtracking is performed on the abnormal change to determine the source of the abnormal change, and the source result Src is output. The interpretation result Res is generated according to the abnormal trigger set Trg, the location result Loc, the hierarchical result Lay and the source result Src.

[0008] Preferably, S1 includes S11; S11: Read the parent plot establishment record and the corresponding nested sub-quad plot establishment record; based on the parent plot number, four-corner fixed marker positions, boundary connection order, side length information, and azimuth baseline included in the parent plot establishment record, sequentially locate the four-corner fixed markers on site, and connect adjacent fixed markers according to the boundary connection order to determine the parent plot boundary; then, based on the previous level plot number, current level nested sub-quad plot number, nesting level, center position, boundary range, and layout order included in the nested sub-quad plot establishment record, determine the position and range of each first-level nested sub-quad plot within the parent plot whose boundary has been determined, determine the position and range of each second-level nested sub-quad plot within the first-level nested sub-quad plot whose position and range have been determined, and determine the position and range of each last-level nested sub-quad plot within the second-level nested sub-quad plot whose position and range have been determined. The location and scope were determined; then, the parent plot, first-level nested subquads, second-level nested subquads, and final-level nested subquads were respectively identified as survey units, and the parent plot number, nested subquad number, nesting level, center location, boundary range, and survey time were recorded for each survey unit. Within each survey unit, species name, species quantity, canopy, and height were collected according to the tree layer, shrub layer, and herb layer, respectively. When the tree layer was present, diameter at breast height (DBH) or crown width was collected; when the shrub layer was present, the number of trees or clumps was collected; and when the herb layer was present, the number of trees, clumps, or ground cover was collected. After the data collection of all survey units was completed, the data corresponding to each survey unit were organized according to the hierarchical order of parent plot, first-level nested subquad, second-level nested subquad, and final-level nested subquad to form the current monitoring record Cur.

[0009] Preferably, S1 further includes S12; S12: Read the parent plot number, nested sub-sample number, nesting level, center position, and boundary range corresponding to each survey object unit in the current monitoring record Cur, and retrieve the corresponding historical monitoring data from the historical monitoring archive according to the same parent plot number, the same nested sub-sample number, and the same nesting level; when multiple periods of historical monitoring data are retrieved, first read the historical monitoring data corresponding to the previous monitoring period, and compare the center position and boundary range corresponding to the previous monitoring period with the center position and boundary range in the current monitoring record Cur. If the center position and boundary range are the same, the historical monitoring data corresponding to the previous monitoring period is determined as the target historical monitoring data; if the historical monitoring data corresponding to the previous monitoring period does not exist, the center position is different, the boundary range is different, or the record is incomplete, continue to read more in reverse chronological order. Historical monitoring data from earlier monitoring periods are used, and the most recent complete and valid historical monitoring data with the same central location and boundary range are determined as the target historical monitoring data using the same comparison method. After the target historical monitoring data corresponding to all survey object units are determined, all target historical monitoring data are compiled into a historical baseline record (Bas). At the same time, the parent plot sampling record and nested sub-quadron sampling record are read. According to the inclusion relationship between the parent plot and the first-level nested sub-quadron, the inclusion relationship between the first-level nested sub-quadron and the second-level nested sub-quadron, the inclusion relationship between the second-level nested sub-quadron and the final-level nested sub-quadron, as well as the central location, boundary range and layout order of each level of nested sub-quadron within the corresponding upper-level plot, a hierarchical correspondence and positional correspondence relationship between the upper-level plot and the lower-level nested sub-quadron are established. The hierarchical correspondence and positional correspondence relationship are compiled into a hierarchical association table (Rel).

[0010] Preferably, S2 includes S21;

[0011] S21. Read the records with the same parent plot number in the current monitoring record Cur and the historical baseline record Bas, and merge and organize the corresponding records according to the parent plot number to form the current parent plot organization result Cut and the historical parent plot organization result His at the parent plot scale. Then, based on the current parent plot organization result Cut and the historical parent plot organization result His, perform comparisons respectively, including: performing comparisons according to the total number of species to form the species number difference result Spc; performing comparisons according to the total cover to form the cover difference result Cov; performing comparisons according to the dominant species ranking to form the dominant species difference result Dom; and performing comparisons according to the stratification distribution to form the stratification distribution difference result Lyr. Finally, summarize the species number difference result Spc, the cover difference result Cov, the dominant species difference result Dom, and the stratification distribution difference result Lyr according to the parent plot number to form the parent plot comparison result Cmp for the corresponding parent plot.

[0012] Preferably, S2 further includes S22;

[0013] S22. Read the comparison results Cmp of each parent plot and extract the species quantity difference results Spc, cover difference results Cov, dominant species difference results Dom, and stratum distribution difference results Lyr. Compare the species quantity difference results Spc, cover difference results Cov, dominant species difference results Dom, and stratum distribution difference results Lyr with the corresponding anomaly judgment conditions. When any difference result meets the corresponding anomaly judgment condition, generate the anomaly entry corresponding to the parent plot.

[0014] The abnormal entries include at least the parent plot number and the abnormal item name, which is an abnormality in species quantity, cover, dominant species, or stratum distribution. All abnormal entries are summarized by parent plot number to form an abnormality trigger set Trg.

[0015] Preferably, S3 includes S31;

[0016] S31. Read each anomaly entry in the anomaly trigger set Trg and read the hierarchical association table Rel that matches the corresponding parent plot number. Based on the hierarchical correspondence between the parent plot and the first-level nested sub-quads, the hierarchical correspondence between the first-level nested sub-quads and the second-level nested sub-quads, and the hierarchical correspondence between the second-level nested sub-quads and the last-level nested sub-quads in the hierarchical association table Rel, extract the first-level nested sub-quad numbers, second-level nested sub-quad numbers, and last-level nested sub-quad numbers under the corresponding parent plots level by level. Then, based on the extracted nested sub-quad numbers at each level, read the corresponding current monitoring data and historical monitoring data from the current monitoring record Cur and the historical baseline record Bas, respectively, and perform a comparison according to the anomaly item name of the corresponding anomaly entry. The nested sub-quad with the largest degree of change after comparison is determined as the corresponding anomaly nested sub-quad. Finally, organize the parent plot number, anomaly item name, corresponding anomaly nested sub-quad, and nesting level to form the location result Loc.

[0017] Preferably, S3 further includes S32;

[0018] S32. Read each location record in the location result Loc, and extract the tree layer record, shrub layer record, and herb layer record of the corresponding abnormal nested sub-quad from the current monitoring record Cur and the historical baseline record Bas, respectively; then, according to the abnormal item name in the corresponding location record, perform a comparison on the tree layer record, shrub layer record, and herb layer record, and summarize the comparison results according to the same corresponding abnormal nested sub-quad to form the plant layer comparison result Pla; then, compare the plant layer comparison result Pla with the abnormal item name in the corresponding location record to determine the plant layer with the greatest degree of change as the target plant layer; finally, organize the parent plot number, abnormal item name, corresponding abnormal nested sub-quad, and target plant layer to form the hierarchical result Lay.

[0019] Preferably, S4 includes S41;

[0020] S41. Read each location record in the location result Loc and read the target plant layer that matches the corresponding location record in the hierarchical result Lay. Then, according to the hierarchical association table Rel, extract the upper-level nested sub-quads and the lower-level nested sub-quads of the corresponding abnormal nested sub-quads. Extract the records corresponding to the target plant layer from the parent plot, the upper-level nested sub-quad, the corresponding abnormal nested sub-quad, and the lower-level nested sub-quad from the current monitoring record Cur and the historical baseline record Bas, respectively. Then, according to the abnormal item name in the corresponding location record, perform a backtracking comparison on the records corresponding to the above-mentioned hierarchical positions and determine the hierarchical position with the largest degree of change after comparison as the source of abnormal change. Finally, organize the parent plot number, abnormal item name, corresponding abnormal nested sub-quad, target plant layer, and source of abnormal change to form the source result Src.

[0021] Preferably, S4 further includes S42;

[0022] S42. Read the records with the same parent plot number and the same abnormal item name from the anomaly trigger set Trg, the location result Loc, the hierarchical result Lay, and the source result Src. Then, based on the parent plot number, the abnormal item name, and the corresponding anomaly nested subquad, summarize the anomaly entries, the corresponding anomaly nested subquads, the target vegetation layer, and the source of the anomaly change under the same parent plot. Finally, organize the summarized content into the interpretation record of the corresponding parent plot, and summarize it according to the parent plot number to form the interpretation result Res.

[0023] A vegetation monitoring system based on nested sampling plots includes a parent-child plot hierarchical data acquisition module, an anomaly triggering module, a location and hierarchical feedback module, and a summary decision module.

[0024] The parent-child plot hierarchical data acquisition module collects vegetation monitoring data of the parent plot and its nested subplots at all levels within the parent plot during the current monitoring period, and retrieves corresponding historical monitoring data. Based on the hierarchical and positional relationships between the parent plot and the nested subplots at all levels, it forms the current monitoring record Cur, the historical baseline record Bas, and the hierarchical association table Rel. The anomaly triggering module compares the vegetation monitoring results at the parent plot scale based on the current monitoring record Cur and the historical baseline record Bas. When the difference comparison results meet the preset anomaly triggering conditions, it generates the corresponding anomaly trigger set Trg for the parent plot.

[0025] The localization and hierarchical feedback module, based on the anomaly trigger set Trg and the hierarchical association table Rel, locates the corresponding nested sub-quadrats of the parent plots where anomalies occur, level by level according to the nested hierarchy. It also determines the target plant layer corresponding to the anomaly by combining the current monitoring record Cur and the historical baseline record Bas, forming the localization result Loc and the hierarchical result Lay. The summary decision module, based on the localization result Loc and the hierarchical result Lay, performs cross-scale source backtracking on the anomaly to determine the source of the anomaly, outputs the source result Src, and generates the interpretation result Res based on the anomaly trigger set Trg, the localization result Loc, the hierarchical result Lay, and the source result Src.

[0026] This invention provides a vegetation monitoring system and method based on nested plot sampling, which has the following beneficial effects:

[0027] (1) By using the current monitoring record Cur, historical baseline record Bas and hierarchical association table Rel to establish a unified correspondence between the parent plot and the nested subquads at all levels, the anomaly trigger set Trg first identifies the anomaly at the parent plot scale, then the location result Loc narrows the anomaly range from the parent plot to the corresponding anomaly nested subquad, then the hierarchical result Lay further narrows the anomaly change to the target plant layer in the tree layer, shrub layer or herb layer, and finally the source result Src places the anomaly change at a specific hierarchical position and generates the interpretation result Res; Therefore, the benefit achieved is not only to discover vegetation anomalies, but also to further interpret anomalies that can only remain at the overall statistical level of the parent plot as to which parent plot the anomaly occurred, which corresponding anomaly nested subquad caused the anomaly, which target plant layer dominated the anomaly, and at which hierarchical position the source of the anomaly change falls, thereby directly solving the problem in the existing technology that can only see fluctuations in the total number of species, total coverage or dominant species ranking of the parent plot, but cannot continue to determine whether the anomaly comes from a local subquad or a certain plant layer.

[0028] (2) By using the anomaly trigger set Trg and the hierarchical association table Rel, the anomaly entries already identified at the parent plot level are further located down to the corresponding anomaly nested subplots. Then, using the current monitoring record Cur, the historical baseline record Bas, and the plant layer comparison result Pla, the tree layer, shrub layer, and herb layer inside the corresponding anomaly nested subplot are further screened into a target plant layer. Therefore, the benefit achieved in this part is not to establish a homogeneous data basis as previously explained, nor to screen out anomalies at the parent plot scale, but to further narrow down the anomaly entries that were originally still at the parent plot level to which corresponding anomaly nested subplot has undergone a dominant change and which target plant layer has truly undertaken the anomaly change, thereby avoiding blind screening of all first-level nested subplots, second-level nested subplots, final-level nested subplots, and all plant layers.

[0029] (3) Based on the already obtained location result Loc and hierarchical result Lay, the abnormal change is further identified as a change in the target plant layer in the corresponding abnormal nested subplot. The abnormal change is further identified as a change in the target plant layer in the parent plot, the next level nested subplot, the corresponding abnormal nested subplot, or the next level nested subplot. Finally, the related content in the abnormal trigger set Trg, location result Loc, hierarchical result Lay, and source result Src is integrated into the interpretation result Res. Therefore, the benefit achieved in this part is not the establishment of a common source data basis as previously explained, nor is it screening out abnormalities at the parent plot scale or shrinking abnormalities to the target plant layer. Rather, it is the ability to further transform the abnormality that has been located to the target plant layer into a complete interpretation chain that can be directly used to determine the cause and take appropriate measures. Attached Figure Description

[0030] Figure 1 This is a schematic diagram illustrating the steps of a vegetation monitoring method based on nested sampling plots according to the present invention.

[0031] Figure 2 This is a schematic diagram of a vegetation monitoring system based on nested sampling plots according to the present invention.

[0032] Figure 3 A schematic diagram illustrating the basic construction of the hierarchical relational table Rel;

[0033] Figure 4 This is a schematic diagram for anomaly identification in the parent sample plot;

[0034] Figure 5 This is a schematic diagram illustrating the localization of abnormal nested subsamples.

[0035] Figure 6 A schematic diagram is provided to define the target plant layer. Detailed Implementation

[0036] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0037] Example 1

[0038] This invention provides a vegetation monitoring method based on nested plot sampling. Please refer to [link / reference]. Figure 1 This includes the following steps:

[0039] S1. Collect vegetation monitoring data of the parent plot and its nested sub-plots at all levels within the parent plot during the current monitoring period, and retrieve corresponding historical monitoring data. Based on the hierarchical and positional relationships between the parent plot and the nested sub-plots at all levels, generate the current monitoring record Cur, the historical baseline record Bas, and the hierarchical association table Rel; S2. Based on the current monitoring record Cur and the historical baseline record Bas, compare the differences in vegetation monitoring results at the parent plot scale. When the difference comparison results meet the preset anomaly triggering conditions, generate the corresponding anomaly trigger set Trag for the parent plot; S3. Based on the anomaly trigger set Trag... Using rg and the hierarchical association table Rel, the corresponding nested sub-quadrates of the abnormal parent plots are located level by level according to the nested hierarchy. The target plant layer corresponding to the abnormal change is determined by combining the current monitoring record Cur and the historical baseline record Bas, forming the location result Loc and the hierarchical result Lay. S4. Based on the location result Loc and the hierarchical result Lay, cross-scale source backtracking is performed on the abnormal change to determine the source of the abnormal change, and the source result Src is output. The interpretation result Res is generated according to the abnormal trigger set Trg, the location result Loc, the hierarchical result Lay and the source result Src.

[0040] In this embodiment, through the above steps, a unified correspondence between the parent plot and nested sub-quads at all levels can be established within the same monitoring period using the current monitoring record Cur, historical baseline record Bas, and hierarchical association table Rel. Then, the anomaly trigger set Trg first identifies anomalies at the parent plot scale. Next, the location result Loc narrows the anomaly range from the parent plot to the corresponding anomalous nested sub-quads. Then, the hierarchical result Lay further narrows the anomaly changes to the target plant layer within the tree, shrub, or herbaceous layer. Finally, the source result Src pinpoints the anomaly changes to a specific hierarchical location and generates the interpretation result Res. Therefore, the benefit of this scheme is not merely the discovery of vegetation anomalies, but the ability to further interpret anomalies that were previously only observed at the overall statistical level of the parent plot, identifying which parent plot experienced the anomaly, which corresponding anomalous nested sub-quad caused the anomaly, which target plant layer dominated the anomaly, and the hierarchical location of the source of the anomaly. This directly solves the problem in existing technologies where fluctuations in the total number of species, total cover, or dominant species ranking of the parent plot are only visible. However, it is impossible to further determine whether the anomaly originates from a local subplot or from a specific vegetation layer. For example, in the annual resurvey of a mountain ecological restoration area, a parent plot may show a decrease in total cover at the parent plot scale, triggering anomaly trigger set Trg. Existing methods can usually only yield a general conclusion of vegetation degradation in the parent plot. However, this scheme can further locate the corresponding nested anomaly subplot at the slope toe using the location result Loc, and then determine that the anomaly is mainly concentrated in the herbaceous layer using the hierarchical result Lay. Subsequently, the source result Src confirms that the source of the anomaly change is located in the target vegetation layer record of the next level nested subplot of the corresponding nested anomaly subplot. Thus, the implementers can further interpret the decrease in total cover of the parent plot as the decrease in herbaceous layer cover in a local area at the slope toe, leading to a decrease in the overall cover of the parent plot. This can avoid misjudging local disturbances as overall degradation of the entire community and provide direct evidence for subsequent replanting, enclosure, or investigation of human interference. This elevates the vegetation monitoring results from simple statistical results to interpretable results with spatial location, vegetation layer location, and source location capabilities.

[0041] Example 2

[0042] Please see Figure 3Specifically: S1 includes S11; S11: Read the parent plot establishment record and the corresponding nested sub-quad plot establishment record; based on the parent plot number, four-corner fixed marker positions, boundary connection order, side length information, and azimuth baseline included in the parent plot establishment record, sequentially locate the four-corner fixed markers on site, and connect adjacent fixed markers according to the boundary connection order to determine the parent plot boundary; then, based on the previous level plot number, current level nested sub-quad plot number, nesting level, center position, boundary range, and layout order included in the nested sub-quad plot establishment record, determine the position and range of each first-level nested sub-quad plot within the parent plot whose boundary has been determined, determine the position and range of each second-level nested sub-quad plot within the first-level nested sub-quad plot whose position and range have been determined, and determine the position and range of each final-level nested sub-quad plot within the second-level nested sub-quad plot whose position and range have been determined. Location and scope; then, the parent plot, first-level nested subquads, second-level nested subquads, and final-level nested subquads are respectively identified as survey units, and the parent plot number, nested subquad number, nesting level, center location, boundary range, and survey time are recorded for each survey unit; within each survey unit, species name, species quantity, canopy, and height are collected according to the tree layer, shrub layer, and herb layer, respectively. When the tree layer exists, diameter at breast height (DBH) or crown width is collected; when the shrub layer exists, the number of trees or clumps is collected; when the herb layer exists, the number of trees, clumps, or ground cover is collected; after completing the data collection for all survey units, the data corresponding to each survey unit are organized according to the hierarchical order of parent plot, first-level nested subquad, second-level nested subquad, and final-level nested subquad to form the current monitoring record Cur;

[0043] It should be noted that:

[0044] The parent plot establishment record refers to the original establishment data formed when the plot is first established. The parent plot establishment record usually includes fixed marker location record, side length measurement record, orientation record and numbering record. The personnel can restore the parent plot boundary based on the conventional plot establishment data, fixed boundary stake record, tape measure distance record and compass orientation record. The restored parent plot boundary can serve as a unified field benchmark for the current monitoring cycle.

[0045] Nested subplot establishment records refer to the original layout data generated when establishing nested subplots at various levels within a parent plot. Nested subplot establishment records typically include the parent plot number, the current nested subplot number, the center location record, the boundary range record, and the layout sequence record. Based on the nested subplot establishment records, implementers can reconstruct the positional relationship of the current nested subplots within the corresponding parent plot. The reconstructed positional relationship of nested subplots at various levels ensures that the current monitoring data remains spatially consistent with historical monitoring data.

[0046] The survey object unit is used to unify the recording granularity of the parent plot and nested sub-quads at all levels. After the implementers process the parent plot, first-level nested sub-quads, second-level nested sub-quads, and last-level nested sub-quads all as survey object units, subsequent historical retrieval, difference comparison, and hierarchical positioning can all be performed line by line at the same granularity, thereby avoiding the problem that the parent plot records and nested sub-quad records cannot be directly matched.

[0047] S1 further includes S12; S12: Read the parent plot number, nested sub-sample number, nesting level, center position, and boundary range corresponding to each survey object unit in the current monitoring record Cur, and retrieve the corresponding historical monitoring data from the historical monitoring archive according to the same parent plot number, the same nested sub-sample number, and the same nesting level; when multiple periods of historical monitoring data are retrieved, first read the historical monitoring data corresponding to the previous monitoring period, and compare the center position and boundary range corresponding to the previous monitoring period with the center position and boundary range in the current monitoring record Cur. If the center position and boundary range are the same, the historical monitoring data corresponding to the previous monitoring period is determined as the target historical monitoring data; if the historical monitoring data corresponding to the previous monitoring period does not exist, the center position is different, the boundary range is different, or the record is incomplete, continue to read earlier monitoring data in reverse chronological order. Historical monitoring data for the measurement period are used, and the most recent complete and valid historical monitoring data with the same center location and boundary range is determined as the target historical monitoring data according to the same comparison method. After the target historical monitoring data corresponding to all survey object units are determined, all target historical monitoring data are organized into a historical baseline record (Bas). At the same time, the parent plot sampling record and nested sub-quadron sampling record are read. According to the inclusion relationship between the parent plot and the first-level nested sub-quadron, the inclusion relationship between the first-level nested sub-quadron and the second-level nested sub-quadron, the inclusion relationship between the second-level nested sub-quadron and the final-level nested sub-quadron, as well as the center location, boundary range and layout order of each level of nested sub-quadron within the corresponding upper-level plot, the hierarchical correspondence and positional correspondence between the upper-level plot and the lower-level nested sub-quadron are established. The hierarchical correspondence and positional correspondence are organized into a hierarchical association table (Rel).

[0048] It should be noted that:

[0049] Historical monitoring data is used to identify a benchmark record from multiple historical monitoring data periods that can be directly used for subsequent comparisons. When implementing personnel conduct field re-surveys or organize data in the office, they first complete the initial screening by parent plot number, nested subplot number, and nesting level. Then, they complete the location consistency check by center location and boundary range. Finally, they complete the usability check by record completeness. The target historical monitoring data determined after the above three-level checks can avoid using data from different locations, different ranges, or missing records as the basis for comparison.

[0050] Historical baseline record (Bas) is a set of historical records formed by reorganizing the historical monitoring data of all survey target units according to the recording order of the current monitoring cycle. After the implementers form the historical baseline record (Bas), each subsequent survey target unit can directly find a unique historical record. Therefore, subsequent difference comparisons do not need to repeatedly search the historical archives, which can reduce data jumps and correspondence confusion when processing across steps.

[0051] The hierarchical association table Rel is used to solidify the hierarchical and positional relationships between the parent plot and each level of nested subplots into a directly callable association table. Implementers can use the hierarchical association table Rel to locate the parent plot level by level to the first-level nested subplot, the second-level nested subplot, and the final-level nested subplot, or trace back from any level of nested subplot to the corresponding parent plot, forming a stable hierarchical path. During subsequent anomaly location and backtracking, the hierarchical association table Rel ensures consistent location paths, avoiding omissions or mismatches caused by manual layer-by-layer searches.

[0052] In this embodiment, through steps S11 and S12, the boundaries of the parent plot and the positions and ranges of nested subquads at all levels can be restored within the current monitoring period using the parent plot sampling records and nested subquad sample records. Then, the parent plot, first-level nested subquads, second-level nested subquads, and final-level nested subquads are uniformly organized into survey object units. Furthermore, based on the parent plot number, nested subquad number, nesting level, center location, and boundary range, target historical monitoring data are selected from historical monitoring archives to form historical baseline records Bas and hierarchical association tables Rel. Therefore, the benefits achieved are not limited to data collection. It is not only complete, but also able to stably correspond one-to-one with the field collection results of the current monitoring cycle and the records with consistent spatial and hierarchical positions in the historical cycle, avoiding mistaking records of different locations, different levels or different deployment sequences under the same parent sample as the same comparison object. In this way, when performing difference comparison, anomaly location and source tracing using the current monitoring record Cur, the historical baseline record Bas and the hierarchical association table Rel, the front-end data foundation already has a unified spatial benchmark and a unified record granularity, which can significantly reduce misjudgments caused by re-measurement location drift, sample plot number confusion or historical record mismatch. Taking continuous monitoring of mountain enclosure and restoration areas as an example, the first-level nested sub-quads at the top of the slope and the first-level nested sub-quads at the bottom of the slope in the previous monitoring cycle often have different species composition and cover distribution. If the current monitoring location is not first mapped to the historical monitoring location through the parent plot sampling record, the nested sub-quad sampling record, the target historical monitoring data, and the hierarchical association table Rel, the implementers may easily mistake the current record at the bottom of the slope for the historical record at the top of the slope, and ultimately misjudge the originally normal spatial difference as an abnormal change. However, this solution can fix this positional correspondence first and then carry out subsequent processing, so that the subsequent anomaly trigger set Trg, the location result Loc, the hierarchical result Lay, and the source result Src are based on the same source, same location, and same level of data.

[0053] Example 3

[0054] Please see Figure 2 Specifically: S2 includes S21;

[0055] S21. Read the records with the same parent plot number in the current monitoring record Cur and the historical baseline record Bas, and merge and organize the corresponding records according to the parent plot number to form the current parent plot organization result Cut and the historical parent plot organization result His at the parent plot scale. Then, based on the current parent plot organization result Cut and the historical parent plot organization result His, perform comparisons respectively, including: comparing by total species number to form species number difference result Spc; comparing by total cover to form cover difference result Cov; comparing by dominant species order to form dominant species difference result Dom; and comparing by stratification to form stratification difference result Lyr. Finally, summarize the species number difference result Spc, cover difference result Cov, dominant species difference result Dom, and stratification difference result Lyr according to the parent plot number to form the parent plot comparison result Cmp for the corresponding parent plot.

[0056] It should be noted that:

[0057] The species abundance difference result (Spc) refers to the species abundance change result obtained by uniformly organizing the species composition records of the same parent plot under the current monitoring period and the historical baseline period.

[0058] During implementation, the implementers first extracted the species name records corresponding to all underground vegetation layers and all nested subplots of the same parent plot from the current processing results Cut of the parent plot. The same species name that appears repeatedly is only retained once, and the total number of species after deduplication is counted. Then, the species name records are extracted from the historical processing results His of the parent plot in the same way, and the total number of historical species is counted. Subsequently, the current total number of species is compared with the historical total number of species to obtain the result of increase, decrease or no change, and this result is determined as the species quantity difference result Spc.

[0059] After implementing the above method, the changes in species richness of the same parent plot between two monitoring periods can be directly obtained, thereby determining whether the parent plot has experienced species increase, species loss, or species composition contraction. This approach is based on the species catalog statistics in routine quadrat surveys, and has a clear data source and is repeatable.

[0060] Coverage difference results (Cov) refer to the coverage change results obtained after uniformly compiling the coverage records of the same parent plot under the current monitoring period and the historical baseline period.

[0061] During implementation, the coverage records corresponding to each survey object unit under the same parent plot are first read from the current processing results Cut of the parent plot, and summarized according to the same processing standard adopted in advance to obtain the current total coverage; then the coverage records are read and summarized from the historical processing results His of the parent plot according to the same processing standard to obtain the historical total coverage; subsequently, the current total coverage is compared with the historical total coverage to obtain the results of coverage increase, coverage decrease or coverage basically stable, and the result is determined as the coverage difference result Cov.

[0062] After executing the above method, the direction and degree of change in the overall vegetation cover status of the parent plot can be directly identified, thereby determining whether the parent plot has experienced vegetation expansion, vegetation degradation, or local expansion of bare areas. Since the cover record itself is a routine record item in vegetation surveys, the calculation and judgment of this result can be completed directly using existing field survey forms, historical ledgers, or electronic records.

[0063] Dominant species difference results (Dom) refer to the changes in dominant species obtained by comparing the ranking of dominant species in the same parent plot under the current monitoring period and the historical baseline period.

[0064] During implementation, the species quantity or coverage records corresponding to each species are first extracted from the current sorting results Cut of the parent plot, and arranged from high to low according to the same sorting rule to obtain the current dominant species ranking; then, the historical dominant species ranking is obtained by extracting and arranging them from the historical sorting results His of the parent plot according to the same sorting rule; subsequently, the current dominant species ranking is compared with the historical dominant species ranking one by one to identify whether there are cases of replacement of previous species, shift of ranking forward, shift of ranking backward, or unchanged dominant status, and the result is determined as the dominant species difference result Dom.

[0065] After implementing the above method, it is possible to directly determine whether the dominant vegetation in the mother plot has changed, such as the original dominant species being replaced by another species, or a certain species rising from a secondary position to a dominant position. This treatment method uses the species number ranking or coverage ranking method commonly used in vegetation community surveys.

[0066] The stratification difference result (Lyr) refers to the stratification change results obtained by comparing the spatial distribution and cover distribution of the tree layer, shrub layer and herb layer in the same parent plot under the current monitoring period and the historical baseline period.

[0067] During implementation, tree layer records, shrub layer records, and herb layer records are first extracted from the current data collection results (Cut) of the parent plot. The occurrence and coverage distribution of each plant layer in each survey unit are then compiled to obtain the current layer distribution. The historical layer distribution is then obtained from the historical data collection results (His) of the parent plot in the same way. Subsequently, the current layer distribution is compared with the historical layer distribution to identify whether there is any expansion, contraction, addition, or partial disappearance of a certain plant layer. This result is then identified as the layer distribution difference result (Lyr).

[0068] After performing the above procedure, it is possible to determine whether the spatial pattern of different plant layers within the parent plot has changed. For example, the shrub layer may expand towards the edge of the parent plot, the herbaceous layer may shrink in a local area, or the coverage of the tree layer may shift. This method can be implemented directly using routine survey records of the tree, shrub, and herbaceous layers, and can provide a clear basis for subsequent hierarchical location of nested subplots of anomalies and confirmation of the target plant layer.

[0069] S2 further includes S22;

[0070] S22. Read the comparison results Cmp of each parent plot and extract the species quantity difference results Spc, cover difference results Cov, dominant species difference results Dom, and stratum distribution difference results Lyr. Compare the species quantity difference results Spc, cover difference results Cov, dominant species difference results Dom, and stratum distribution difference results Lyr with the corresponding anomaly judgment conditions. When any difference result meets the corresponding anomaly judgment condition, generate the anomaly entry corresponding to the parent plot.

[0071] The abnormal entries include at least the parent plot number and the name of the abnormal item, which is an abnormality in species abundance, cover, dominant species, or strata distribution; all abnormal entries are summarized by parent plot number to form an abnormality trigger set Trg;

[0072] It should be noted that:

[0073] The comparison between the species quantity difference result Spc and the species quantity anomaly judgment criteria means that the difference between the current total number of species and the historical total number of species is first read from the species quantity difference result Spc, and then the difference is compared with the pre-set species quantity change threshold.

[0074] During implementation, the current total number of species and the historical total number of species under the same parent sample plot can be read directly. The change value is obtained by subtracting the historical total number of species from the current total number of species. Then, it is determined whether the change value reaches the threshold for increasing or decreasing the number of species. When the change value reaches the corresponding threshold, the parent sample plot is identified as a parent sample plot with abnormal species number, and an abnormal species number record is generated.

[0075] The comparison between the coverage difference result Cov and the coverage anomaly judgment condition means that the change value between the current total coverage and the historical total coverage is first read from the coverage difference result Cov, and then the change value is compared with the pre-set coverage change threshold.

[0076] During implementation, the current total cover and historical total cover of the same parent sample can be directly read. The change in cover value is obtained by subtracting the historical total cover from the current total cover. Then, it is determined whether the change in cover value reaches the cover increase threshold or the cover decrease threshold. When the change in cover value reaches the corresponding threshold, the parent sample is identified as a cover anomaly parent sample and a cover anomaly record is generated. The comparison between the dominant species difference results (Dom) and the dominant species anomaly judgment conditions refers to first reading the change between the current dominant species ranking and the historical dominant species ranking from the dominant species difference results (Dom), and then comparing the change with the pre-set dominant species change rules.

[0077] During implementation, the top species in the current dominant species ranking can be read first, and then the corresponding top species in the historical dominant species ranking can be read to determine whether there is a replacement of the first dominant species, a change in the order of the top dominant species, or a drop of the original top dominant species from the top. When any of the above changes reaches the pre-set range of dominant species anomalies, the mother plot is identified as a dominant species anomaly mother plot, and a dominant species anomaly record is generated.

[0078] The comparison between the layer distribution difference result Lyr and the layer distribution anomaly judgment conditions refers to first reading the changes between the current layer distribution and the historical layer distribution from the layer distribution difference result Lyr, and then comparing the changes with the pre-set layer distribution change rules.

[0079] During implementation, the occurrence range, coverage range, or distribution location of the tree layer, shrub layer, and herb layer in the current monitoring period and the historical baseline period can be read separately to determine whether there is a new appearance of a certain plant layer, a local disappearance of a certain plant layer, a significant expansion of a certain plant layer, or a significant shrinkage of a certain plant layer. When any of the above changes reaches the pre-set layer distribution anomaly range, the parent plot is identified as a layer distribution anomaly parent plot, and a layer distribution anomaly record is generated.

[0080] In this embodiment, through steps S21 and S22, the multi-layered records originally scattered across the parent plot, first-level nested subplots, second-level nested subplots, and final-level nested subplots in the current monitoring record Cur and the historical baseline record Bas are first uniformly organized into the current organized result Cut of the parent plot and the historical organized result His of the parent plot. Then, species abundance difference results Spc, cover difference results Cov, dominant species difference results Dom, and stratified distribution difference results Lyr are further formed, and finally summarized into the parent plot comparison result Cmp and the anomaly trigger set Trg. Therefore, the benefit achieved in this section is not, as mentioned earlier, ensuring that the current data and historical data are from the same source and location, nor is it simply explaining the anomaly all the way to its specific source. Rather, it is that at the parent plot scale, complex, scattered, and difficult-to-determine vegetation changes are compressed into a set of directly identifiable multi-dimensional anomaly screening results. This allows implementers to no longer rely on a single indicator or manual experience to roughly determine whether the parent plot is abnormal, but rather to simultaneously consider species richness... This method identifies anomalies from four perspectives: overall cover status, dominant species succession, and changes in the spatial pattern of tree, shrub, and herbaceous layers. The identification results are then standardized and written into the anomaly trigger set Trg. For example, in the annual resurvey of mine restoration areas or enclosure restoration areas, a parent plot may appear to have a total cover that is close to the historical total cover, seemingly without significant change. However, in reality, the original dominant herbaceous species under this parent plot has been replaced by another type of shrub seedling, and the herbaceous layer has shrunk significantly at the edge while the shrub layer has expanded inward. Traditional methods that only look at the cover can easily misjudge this community replacement as a stable state. However, this method can identify this situation where the total amount seems stable but the structure has changed by using the dominant species difference results (Dom) and the layer distribution difference results (Lyr). When the corresponding anomaly judgment conditions are met, anomaly entries are generated and written into the anomaly trigger set Trg, thereby screening out community succession signals that are easily overlooked in advance and providing a clear entry point for subsequent location of corresponding anomaly nested subplots and target plant layers.

[0081] Example 4

[0082] Please see Figure 5 Specifically: S3 includes S31;

[0083] S31. Read each anomaly entry in the anomaly trigger set Trg and read the hierarchical association table Rel that matches the corresponding parent plot number. Based on the hierarchical correspondence between the parent plot and the first-level nested sub-sample, between the first-level nested sub-sample and the second-level nested sub-sample, and between the second-level nested sub-sample and the last-level nested sub-sample in the hierarchical association table Rel, extract the first-level nested sub-sample number, second-level nested sub-sample number, and last-level nested sub-sample number under the corresponding parent plot level by level. Then, based on the extracted nested sub-sample numbers at each level, read the corresponding current monitoring data and historical monitoring data from the current monitoring record Cur and the historical baseline record Bas, respectively, and perform a comparison according to the anomaly item name of the corresponding anomaly entry. The nested sub-sample with the largest degree of change after comparison is determined as the corresponding anomaly nested sub-sample. Finally, organize the parent plot number, anomaly item name, corresponding anomaly nested sub-sample, and nesting level to form the location result Loc.

[0084] It should be noted that:

[0085] Consistent with the corresponding parent plot anomaly record means that the type, direction, and degree of change obtained from the comparison of the nested subsample records at each level are consistent with the anomaly record of the corresponding parent plot.

[0086] During implementation, the abnormal record types of the corresponding parent plot in the abnormal trigger set Trg are first read; when there are abnormal records of species quantity in the corresponding parent plot, the species quantity changes of the first-level nested subplots, second-level nested subplots, and last-level nested subplots are compared, and records of nested subplots at all levels whose species quantity change direction is consistent with that of the corresponding parent plot are screened out; when there are abnormal records of canopy in the corresponding parent plot, the canopy changes of the first-level nested subplots, second-level nested subplots, and last-level nested subplots are compared, and records of nested subplots at all levels whose canopy change direction is consistent with that of the corresponding parent plot are screened out.

[0087] When there are abnormal records of dominant species in the corresponding parent plot, the changes in the dominant species ranking of the first-level nested subplot, the second-level nested subplot, and the last-level nested subplot are compared, and records of each level of nested subplot whose dominant species ranking changes are consistent with those of the corresponding parent plot are screened out.

[0088] When there are records of abnormal layer distribution in the corresponding parent plot, the layer distribution changes of the first-level nested sub-quads, the second-level nested sub-quads and the last-level nested sub-quads are compared, and the records of each level of nested sub-quads whose layer distribution changes are consistent with the corresponding parent plot are screened out.

[0089] After completing the above screening, the selected nested subsample records at all levels are sorted according to the degree of change, and the nested subsample records at all levels with the greatest degree of change are identified as the corresponding abnormal nested subsamples; when there are two or more nested subsample records at all levels with the same degree of change, the two or more nested subsample records at all levels are identified as the corresponding abnormal nested subsamples.

[0090] After executing the above method, the anomaly records that were originally at the parent plot scale can be further identified at the specific first-level nested sub-quagmire, second-level nested sub-quagmire, or final-level nested sub-quagmire, thereby narrowing the anomaly range downward from the parent plot. After this processing, subsequent steps do not need to re-examine all levels of nested sub-quagmire, but only continue to judge the target vegetation layer around the already identified corresponding anomaly nested sub-quagmire, which can reduce the scope of subsequent comparisons and improve the location accuracy of cross-scale anomaly backtracking.

[0091] S3 further includes S32;

[0092] S32. Read each location record in the location result Loc, and extract the tree layer record, shrub layer record, and herb layer record of the corresponding abnormal nested sub-quad from the current monitoring record Cur and the historical baseline record Bas, respectively; then, according to the abnormal item name in the corresponding location record, perform a comparison on the tree layer record, shrub layer record, and herb layer record, and summarize the comparison results according to the same corresponding abnormal nested sub-quad to form the plant layer comparison result Pla; then, compare the plant layer comparison result Pla with the abnormal item name in the corresponding location record to determine the plant layer with the greatest degree of change as the target plant layer; finally, organize the parent plot number, abnormal item name, corresponding abnormal nested sub-quad, and target plant layer to form the hierarchical result Lay;

[0093] It should be noted that:

[0094] Then, in conjunction with the abnormal records of the corresponding parent plot in the abnormal trigger set Trg, comparisons are performed on the tree layer records, shrub layer records, and herb layer records respectively. The separate comparisons mentioned here mean that the implementer first reads the tree layer records, shrub layer records, and herb layer records corresponding to the same nested subplot of abnormality, then reads the abnormal record type of the corresponding parent plot in the abnormal trigger set Trg, and then performs fixed comparison actions according to the abnormal record type.

[0095] When the corresponding parent plot is a record of abnormal species abundance, the species names for the current monitoring period are extracted from the tree layer record, shrub layer record, and herb layer record, respectively, and deduplication is performed to obtain the current species abundance in the tree layer, shrub layer, and herb layer. Then, the species names for the historical baseline period are extracted from the corresponding records in the historical baseline record Bas, and deduplication is performed to obtain the historical species abundance in the tree layer, shrub layer, and herb layer. Finally, the current species abundance in the tree layer is subtracted from the historical species abundance in the tree layer, the current species abundance in the shrub layer is subtracted from the historical species abundance in the shrub layer, and the current species abundance in the herb layer is subtracted from the historical species abundance in the herb layer to obtain the species abundance comparison values ​​for each of the three plant layers.

[0096] When the corresponding parent plot is a record of abnormal canopy cover, the current monitoring period canopy cover values ​​in the tree layer record, shrub layer record, and herb layer record are read respectively. Then, the corresponding historical baseline period canopy cover values ​​are read respectively. Then, the current canopy cover value of the tree layer is subtracted from the historical canopy cover value of the tree layer, the current canopy cover value of the shrub layer is subtracted from the historical canopy cover value of the shrub layer, and the current canopy cover value of the herb layer is subtracted from the historical canopy cover value of the herb layer to obtain the canopy comparison values ​​of the three plant layers respectively.

[0097] When the corresponding parent plot contains an abnormal record of dominant species, the species names in the tree layer, shrub layer, and herb layer records are sorted from highest to lowest according to the number of species or coverage to obtain the current monitoring period sorting result; then, the corresponding historical baseline period records are sorted according to the same rules to obtain the historical baseline period sorting result; then, the current and historical first-ranked species names in the tree layer, the shrub layer, and the herb layer are read respectively; when the first-ranked species names are different, it is recorded that a dominant species replacement has occurred in that plant layer; when the first-ranked species names are the same, the number of species or coverage corresponding to the first-ranked species is read, and the current value is subtracted from the historical value to obtain the dominant species comparison value for each of the three plant layers.

[0098] When the corresponding parent plot records anomalies in stratification, the implementers read the occurrence and coverage values ​​of the tree layer, shrub layer, and herb layer records in the current monitoring period, and then read the occurrence and coverage values ​​in the corresponding historical baseline period. Occurrence is defined as the presence of the plant layer record with a coverage greater than zero, and absence of a record or a coverage value of zero. Then, the researchers compare whether the tree layer, shrub layer, and herb layer changed from occurrence to absence, or vice versa, in the two periods. When the occurrence changes, it is recorded as a change in stratification distribution for that plant layer. If the occurrence remains unchanged, the current coverage value is subtracted from the historical coverage value to obtain the comparative stratification values ​​for each of the three plant layers.

[0099] After completing the above comparisons, the comparison results of the tree layer, shrub layer, and herb layer corresponding to the same nested subplot of anomalies are written into the same record, forming the plant layer comparison result Pla. Following this procedure, the current and historical data of the three plant layers within the same nested subplot of anomalies can be compiled into a unified result. Subsequent judgments can be made directly by reading the plant layer comparison result Pla, without needing to separately retrieve the tree layer, shrub layer, and herb layer records.

[0100] The plant layer comparison result Pla is matched with the anomaly records of the corresponding parent plot to determine the target plant layer. The matching judgment mentioned here means that the implementer first reads the anomaly record type of the corresponding parent plot in the anomaly trigger set Trg, then reads the corresponding comparison values ​​of tree layer, shrub layer and herb layer from the plant layer comparison result Pla, and then directly selects a plant layer according to the anomaly record type.

[0101] When the corresponding parent plot is a record of abnormal species quantity, the species quantity comparison values ​​of the tree layer, shrub layer, and herb layer are read respectively, and the absolute values ​​of the three comparison values ​​are compared. The plant layer with the largest absolute value is determined as the target plant layer.

[0102] When the corresponding parent plot is a record of abnormal canopy cover, the canopy cover comparison values ​​of the tree layer, shrub layer, and herb layer are read respectively, and the absolute values ​​of the three comparison values ​​are compared. The plant layer with the largest absolute value is determined as the target plant layer.

[0103] When the corresponding parent plot is an abnormal record of dominant species, first determine which plant layer among the tree layer, shrub layer and herb layer has experienced dominant species replacement;

[0104] When only one plant layer undergoes dominant species replacement, that plant layer is directly designated as the target plant layer.

[0105] When two or more plant layers undergo dominant species replacement, the species number or coverage difference corresponding to the first species in the plant layer where the dominant species replacement occurred is read, and the plant layer with the largest absolute value of the difference is determined as the target plant layer.

[0106] When no dominant species replacement occurs in any of the three plant layers, the species quantity or coverage difference corresponding to the first species in each of the three plant layers is read, and the plant layer with the largest absolute value of the difference is determined as the target plant layer.

[0107] When the corresponding parent plot is a record of abnormal layer distribution, first determine which plant layer among the tree layer, shrub layer and herb layer has changed from appearing to not appearing or from not appearing to appearing;

[0108] When only one plant layer exhibits the above situation, that plant layer is directly identified as the target plant layer.

[0109] When the above situation occurs in two or more plant layers, the coverage comparison value of these plant layers is read separately, and the plant layer with the largest absolute value of the coverage comparison value is determined as the target plant layer.

[0110] When no state change occurs in any of the three plant layers, the coverage comparison value of each of the three plant layers is read, and the plant layer with the largest absolute value of the coverage comparison value is determined as the target plant layer.

[0111] After executing the above method, only one final vegetation layer can be identified from the three vegetation layers within the same nested subplot of anomalies, and the anomalies at the parent plot scale can be further narrowed down to the specific vegetation layer, providing a single and clear processing object for subsequent determination of the source of the anomalies.

[0112] In this embodiment, through steps S31 and S32, the abnormal entries already identified at the parent plot level can be further located down to the corresponding abnormal nested sub-quads based on the abnormal trigger set Trg and the hierarchical association table Rel. Then, using the current monitoring record Cur, the historical baseline record Bas, and the plant layer comparison result Pla, the tree layer, shrub layer, and herb layer inside the corresponding abnormal nested sub-quad are further screened into a target plant layer. Therefore, the benefit achieved in this part is not the establishment of a homologous data basis as previously explained, nor is it screening out abnormalities at the parent plot scale. Rather, it is the ability to further narrow down the abnormal entries that were originally still at the parent plot level to which corresponding abnormal nested sub-quad has undergone a dominant change and which target plant layer has truly undertaken the abnormal change. This avoids blindly screening all first-level nested sub-quads, second-level nested sub-quads, final-level nested sub-quads, and all plant layers in the future. Taking continuous monitoring of enclosure restoration areas as an example, when an anomaly entry appears in the anomaly trigger set Trg for a certain parent plot, traditional methods often only indicate that the overall cover of the parent plot has changed, and implementers still need to re-examine all nested subplots and all vegetation layers one by one. However, this solution can first narrow down the anomaly to the corresponding anomaly nested subplot in the slope foot area through the location result Loc, and then further confirm the herbaceous layer in the corresponding anomaly nested subplot as the target vegetation layer through the vegetation layer comparison result Pla and the hierarchical result Lay. In this way, implementers can directly focus subsequent verification on the herbaceous layer of the corresponding anomaly nested subplot, without having to repeatedly compare the tree layer, shrub layer or other nested subplots that have not changed significantly under the same parent plot. This not only improves the targeting of cross-scale anomaly location, but also makes the subsequent judgment of the source of anomaly change based on the clear object that has been narrowed down step by step.

[0113] Example 5

[0114] Specifically: S4 includes S41;

[0115] S41. Read each location record in the location result Loc and read the target plant layer that matches the corresponding location record in the hierarchical result Lay. Then, according to the hierarchical association table Rel, extract the upper-level nested sub-quads and the lower-level nested sub-quads of the corresponding abnormal nested sub-quads. Extract the records corresponding to the target plant layer from the parent plot, the upper-level nested sub-quad, the corresponding abnormal nested sub-quad, and the lower-level nested sub-quad from the current monitoring record Cur and the historical baseline record Bas, respectively. Then, according to the abnormal item name in the corresponding location record, perform a backtracking comparison on the records corresponding to the above-mentioned hierarchical positions and determine the hierarchical position with the largest degree of change after comparison as the source of abnormal change. Finally, organize the parent plot number, abnormal item name, corresponding abnormal nested sub-quad, target plant layer, and source of abnormal change to form the source result Src.

[0116] It should be noted that:

[0117] Extracting the corresponding records means first reading the parent plot number and the corresponding abnormal nested sub-quad in the location result Loc, then reading the target plant layer in the hierarchical result Lay; and then reading the upper-level nested sub-quad and the lower-level nested sub-quad connected to the corresponding abnormal nested sub-quad in the hierarchical association table Rel.

[0118] Subsequently, based on the same parent plot number, the same nested sub-quad plot number, and the same target plant layer, four types of current records were extracted from the current monitoring record Cur. The four types of current records include: the current record corresponding to the target plant layer in the parent plot, the current record corresponding to the target plant layer in the next higher nested sub-quad plot, the current record corresponding to the target plant layer in the corresponding abnormal nested sub-quad plot, and the current record corresponding to the target plant layer in the next lower nested sub-quad plot.

[0119] Then, in the same manner, extract the four types of historical records from the historical baseline record (Bas) that correspond one-to-one with the four types of current records mentioned above. After the implementers execute the above method, they can obtain the current and historical records of the same target vegetation layer at different levels within the same parent plot at once, and can then directly continue comparisons along the same level sequence.

[0120] Performing a backtracking comparison means first reading the abnormal record type corresponding to the parent sample in the abnormal trigger set Trg; then, in the order of parent sample → upper-level nested subsample → corresponding abnormal nested subsample → lower-level nested subsample, calculating the difference between the current value and the historical value for each of the extracted records.

[0121] When the abnormal record type is an abnormal record of species quantity, count the current species quantity of the target plant layer in the mother plot, the upper-level nested subplot, the corresponding abnormal nested subplot, and the lower-level nested subplot, respectively. Then count the corresponding historical species quantity, and then subtract the historical species quantity from the current species quantity.

[0122] When the abnormal record type is a cover abnormal record, read the current cover value and historical cover value of the target plant layer in the mother plot, the upper-level nested subplot, the corresponding abnormal nested subplot, and the lower-level nested subplot, respectively, and then subtract the historical cover value from the current cover value.

[0123] When the abnormal record type is a dominant species abnormal record, the species names of the target plant layer in the mother plot, the upper-level nested subplot, the corresponding abnormal nested subplot, and the lower-level nested subplot are sorted from high to low according to the number of species or the coverage. Then, the current first species name and the historical first species name are read for each position. When the first species name is different, it is recorded as a dominant species replacement at that position. When the first species name is the same, the current value and the historical value corresponding to the first species are read, and the historical value is subtracted from the current value.

[0124] When the abnormal record type is a layer distribution abnormal record, read the current record and historical record of the target plant layer in the parent plot, the upper-level nested subplot, the corresponding abnormal nested subplot, and the lower-level nested subplot, respectively;

[0125] When a record exists and its coverage is greater than zero, it is considered to have appeared.

[0126] If the current record does not exist or the current coverage is zero, it is recorded as not appearing.

[0127] When a historical record exists and its coverage is greater than zero, it is recorded as a historical occurrence.

[0128] When a historical record does not exist or the historical coverage is zero, it is recorded as if the history has not appeared.

[0129] Then, determine whether each position has changed from currently appearing to historically not appearing, or from currently not appearing to historically appearing;

[0130] If no state change occurs, subtract the historical coverage value from the current coverage value.

[0131] After executing the above method, continuous comparison values ​​of the same target plant layer at four levels can be obtained, which can then be used to determine which level the abnormal change first occurred at.

[0132] Determining the source of abnormal changes means checking the comparison values ​​obtained in the previous step in a fixed order: mother plot → upper-level nested sub-sample → corresponding abnormal nested sub-sample → lower-level nested sub-sample, and directly selecting the first position that meets the corresponding abnormal judgment condition according to the abnormal record type.

[0133] When the abnormal record type is a species quantity anomaly record, the species quantity difference at four locations is read sequentially. First, it is checked whether the species quantity difference of the parent plot meets the species quantity anomaly judgment criteria. If the species quantity difference of the parent plot does not meet the species quantity anomaly judgment criteria, then it is checked whether the species quantity difference of the next-level nested subplot meets the species quantity anomaly judgment criteria. Then, it is checked whether the species quantity difference of the corresponding abnormal nested subplot meets the species quantity anomaly judgment criteria. Finally, it is checked whether the species quantity difference of the next-level nested subplot meets the species quantity anomaly judgment criteria. The first location that meets the species quantity anomaly judgment criteria is identified as the source of the abnormal change.

[0134] When the abnormal record type is a coverage abnormal record, the coverage difference values ​​of the four positions are read in the same order, and it is checked which position first meets the coverage abnormality judgment condition; the position that first meets the coverage abnormality judgment condition is determined as the source of the abnormal change.

[0135] When the abnormal record type is a dominant species abnormal record, read the four positions in the same order to see if the first species name has changed; the first position where the first species name has changed is identified as the source of the abnormal change; when the first species name has not changed in any of the four positions, read the difference between the current value and the historical value corresponding to the first species in the four positions in the same order, and check which position first meets the dominant species abnormality judgment condition; the first position that meets the dominant species abnormality judgment condition is identified as the source of the abnormal change.

[0136] When the abnormal record type is a layered distribution abnormal record, read the four positions in the same order to see if they change from appearing to not appearing or from not appearing to appearing; the first position where the appearance status changes is determined as the source of the abnormal change; when none of the four positions have changed the appearance status, the implementer reads the coverage difference of the four positions in the same order and checks which position first meets the layered distribution abnormality judgment condition; the first position that meets the layered distribution abnormality judgment condition is determined as the source of the abnormal change.

[0137] After executing the above method, the abnormal changes that have been located in the target plant layer can be further identified at a unique hierarchical position, and this hierarchical position can be written into the source result Src, which can be directly called when generating the interpretation result Res later.

[0138] S4 also includes S42;

[0139] S42. Read the records with the same parent plot number and the same abnormal item name from the anomaly trigger set Trg, the location result Loc, the hierarchical result Lay, and the source result Src; then, based on the parent plot number, the abnormal item name, and the corresponding anomaly nested subquad, summarize the anomaly entries, the corresponding anomaly nested subquads, the target vegetation layer, and the source of the anomaly change under the same parent plot; finally, organize the summarized content into the interpretation records of the corresponding parent plot, and summarize them according to the parent plot number to form the interpretation result Res.

[0140] It should be noted that:

[0141] Then, the abnormal records, corresponding nested abnormal sub-quads, target vegetation layers, and sources of abnormal changes under the same parent sample plot are summarized. The summary here means that the implementers first read all abnormal records corresponding to the same parent sample plot number from the abnormal trigger set Trg, then read all corresponding nested abnormal sub-quads corresponding to the same parent sample plot number from the location result Loc, then read the target vegetation layer that is consistent with the corresponding nested abnormal sub-quad and the same parent sample plot number from the layer result Lay, and then read the sources of abnormal changes that are consistent with the corresponding nested abnormal sub-quads and the target vegetation layer and the same parent sample plot number from the source result Src.

[0142] Then, using the same parent plot number as the first corresponding condition, the corresponding nested anomalous quadrat as the second corresponding condition, and the target plant layer as the third corresponding condition, the above four parts are matched one by one; when an anomalous record in the anomalous trigger set Trg can find the corresponding nested anomalous quadrat under the same parent plot number in the location result Loc, and the corresponding nested anomalous quadrat can find the corresponding target plant layer in the hierarchical result Lay, and the target plant layer can find the corresponding source of anomalous change in the source result Src, this set of contents is written as the same summary content; when there are multiple anomalous records under the same parent plot, they are matched one by one and written one by one in the above manner, without mixing different anomalous records into the same summary content.

[0143] After all pairings are completed, each summary entry includes the parent plot number, anomaly record, corresponding nested anomaly subplot, target plant layer, and source of the anomaly.

[0144] After executing the above method, the related content originally scattered in the anomaly trigger set Trg, location result Loc, hierarchical result Lay and source result Src of the same parent sample can be organized into a single record. This allows subsequent interpretation records to be written directly based on a single summary content without having to search repeatedly across result sets, thus ensuring that the correspondence between interpretation results is clear and traceable.

[0145] In this embodiment, through steps S41 and S42, based on the already obtained location result Loc and hierarchical result Lay, the abnormal change is further identified as a change in the target plant layer in the corresponding abnormal nested subplot. This further clarifies whether the abnormal change originates from the parent plot, the next higher-level nested subplot, the corresponding abnormal nested subplot, or the next lower-level nested subplot. Finally, the related content in the anomaly trigger set Trg, location result Loc, hierarchical result Lay, and source result Src is integrated into the interpretation result Res. Therefore, the benefit achieved in this part is not the establishment of a common source data basis as previously explained, nor is it screening out anomalies at the parent plot scale or narrowing anomalies to the target plant layer. Rather, it is the ability to further transform anomalies that have been located to the target plant layer into a complete interpretation chain that can be directly used to determine the cause and take appropriate measures. Taking continuous monitoring of mountain restoration plots as an example, if an anomaly in the anomaly trigger set Trg is found in a parent plot, the location result Loc further locates the corresponding nested sub-quad in the slope foot area, and the hierarchical result Lay identifies the herbaceous layer as the target plant layer. Traditional methods usually only conclude that the herbaceous layer at the slope foot is abnormal at this point. However, this solution can continue to determine whether the anomaly comes from a change in the overall herbaceous layer pattern of the parent plot, or only from the herbaceous layer shrinkage in the edge area of ​​the upper-level nested sub-quad, or from the localized expansion of bareness within the lower-level nested sub-quad. Finally, the anomaly entry, the corresponding nested sub-quad, the target plant layer, and the source of the anomaly change are all written into the interpretation result Res. Thus, when the implementers receive the interpretation result Res, they not only know where the anomaly is, but also directly know which hierarchical path the anomaly propagates along and which specific hierarchical location it ultimately falls on. This transforms the subsequent handling in actual monitoring work from a general review of the entire plot to a targeted verification, replanting restoration, or interference investigation at the corresponding hierarchical location.

[0146] Example 6

[0147] A vegetation monitoring system based on nested plot sampling, please refer to... Figure 2 Specifically, it includes a parent-child hierarchical data acquisition module, an anomaly triggering module, a location and hierarchical feedback module, and a summary decision module;

[0148] The parent-child plot hierarchical data acquisition module collects vegetation monitoring data of the parent plot and its nested subplots at all levels within the parent plot during the current monitoring period, and retrieves corresponding historical monitoring data. Based on the hierarchical and positional relationships between the parent plot and the nested subplots at all levels, it forms the current monitoring record Cur, the historical baseline record Bas, and the hierarchical association table Rel. The anomaly triggering module compares the vegetation monitoring results at the parent plot scale based on the current monitoring record Cur and the historical baseline record Bas. When the difference comparison results meet the preset anomaly triggering conditions, it generates the corresponding anomaly trigger set Trg for the parent plot.

[0149] The localization and hierarchical feedback module, based on the anomaly trigger set Trg and the hierarchical association table Rel, locates the corresponding nested sub-quadrats of the parent plots where anomalies occur, level by level according to the nested hierarchy. It also determines the target plant layer corresponding to the anomaly by combining the current monitoring record Cur and the historical baseline record Bas, forming the localization result Loc and the hierarchical result Lay. The summary decision module, based on the localization result Loc and the hierarchical result Lay, performs cross-scale source backtracking on the anomaly to determine the source of the anomaly, outputs the source result Src, and generates the interpretation result Res based on the anomaly trigger set Trg, the localization result Loc, the hierarchical result Lay, and the source result Src.

[0150] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A vegetation monitoring method based on nested plot sampling, characterized in that: Includes the following steps: S1. Collect vegetation monitoring data of the parent plot and its nested sub-quads at all levels within the parent plot during the current monitoring period, and retrieve corresponding historical monitoring data. Based on the hierarchical and positional relationships between the parent plot and the nested sub-quads at all levels, form the current monitoring record Cur, the historical baseline record Bas, and the hierarchical association table Rel. S2. Based on the current monitoring record Cur and the historical baseline record Bas, compare the differences in vegetation monitoring results at the parent plot scale. When the difference comparison results meet the preset anomaly triggering conditions, generate the anomaly trigger set Trg for the corresponding parent plot. S3. Based on the anomaly trigger set Trg and the hierarchical association table Rel, locate the corresponding anomalous nested sub-quads in the parent plot where anomalies occur, step by step, according to the nesting hierarchy. Combine the current monitoring record Cur and the historical baseline record Bas to determine the target vegetation layer corresponding to the anomalous change, forming the location result Loc and the hierarchical result Lay. S4. Based on the location result Loc and the hierarchical result Lay, perform cross-scale source backtracking on the abnormal changes, determine the source of the abnormal changes, output the source result Src, and generate the interpretation result Res according to the abnormal trigger set Trg, the location result Loc, the hierarchical result Lay and the source result Src.

2. The vegetation monitoring method based on nested plot sampling according to claim 1, characterized in that: S1 includes S11; S11: Read the parent plot establishment record and the corresponding nested sub-quad plot establishment record; based on the parent plot number, four-corner fixed marker positions, boundary connection order, side length information, and azimuth baseline included in the parent plot establishment record, sequentially locate the four-corner fixed markers on site, and connect adjacent fixed markers according to the boundary connection order to determine the parent plot boundary; then, based on the previous level plot number, current level nested sub-quad plot number, nesting level, center position, boundary range, and layout order included in the nested sub-quad plot establishment record, determine the position and range of each first-level nested sub-quad plot within the parent plot whose boundary has been determined, determine the position and range of each second-level nested sub-quad plot within the first-level nested sub-quad plot whose position and range have been determined, and determine the position and range of each final-level nested sub-quad plot within the second-level nested sub-quad plot whose position and range have been determined. The scope was defined as follows: Then, the parent plot, first-level nested subquads, second-level nested subquads, and final-level nested subquads were identified as the survey units. For each survey unit, the parent plot number, nested subquad number, nesting level, center location, boundary range, and survey time were recorded. Within each survey unit, species names, species numbers, canopy coverage, and height were collected according to the tree layer, shrub layer, and herb layer, respectively. If the tree layer was present, diameter at breast height (DBH) or crown width was collected; if the shrub layer was present, the number of trees or clumps was collected; and if the herb layer was present, the number of trees, clumps, or ground cover was collected. After completing the data collection for all survey units, the data corresponding to each survey unit were organized according to the hierarchical order of parent plot, first-level nested subquad, second-level nested subquad, and final-level nested subquad to form the current monitoring record (Cur).

3. The vegetation monitoring method based on nested sampling plots according to claim 2, characterized in that: S1 further includes S12; S12: Read the parent plot number, nested sub-sample number, nesting level, center position, and boundary range corresponding to each survey object unit in the current monitoring record Cur, and retrieve the corresponding historical monitoring data from the historical monitoring archive according to the same parent plot number, the same nested sub-sample number, and the same nesting level; when multiple periods of historical monitoring data are retrieved, first read the historical monitoring data corresponding to the previous monitoring period, and compare the center position and boundary range corresponding to the previous monitoring period with the center position and boundary range in the current monitoring record Cur. If the center position and boundary range are the same, the historical monitoring data corresponding to the previous monitoring period is determined as the target historical monitoring data; if the historical monitoring data corresponding to the previous monitoring period does not exist, the center position is different, the boundary range is different, or the record is incomplete, continue to read earlier monitoring data in reverse chronological order. Historical monitoring data for the measurement period are used, and the most recent complete and valid historical monitoring data with the same center location and boundary range is determined as the target historical monitoring data according to the same comparison method. After the target historical monitoring data corresponding to all survey object units are determined, all target historical monitoring data are organized into a historical baseline record (Bas). At the same time, the parent plot sampling record and nested sub-quadron sampling record are read. According to the inclusion relationship between the parent plot and the first-level nested sub-quadron, the inclusion relationship between the first-level nested sub-quadron and the second-level nested sub-quadron, the inclusion relationship between the second-level nested sub-quadron and the final-level nested sub-quadron, as well as the center location, boundary range and layout order of each level of nested sub-quadron within the corresponding upper-level plot, the hierarchical correspondence and positional correspondence between the upper-level plot and the lower-level nested sub-quadron are established. The hierarchical correspondence and positional correspondence are organized into a hierarchical association table (Rel).

4. A vegetation monitoring method based on nested plot sampling according to claim 3, characterized in that: S2 includes S21; S21. Read the records with the same parent plot number in the current monitoring record Cur and the historical baseline record Bas, and merge and organize the corresponding records according to the parent plot number to form the current parent plot organization result Cut and the historical parent plot organization result His at the parent plot scale. Then, based on the current parent plot organization result Cut and the historical parent plot organization result His, perform comparisons respectively, including: performing comparisons according to the total number of species to form the species number difference result Spc; performing comparisons according to the total cover to form the cover difference result Cov; performing comparisons according to the dominant species ranking to form the dominant species difference result Dom; and performing comparisons according to the stratification distribution to form the stratification distribution difference result Lyr. Finally, summarize the species number difference result Spc, the cover difference result Cov, the dominant species difference result Dom, and the stratification distribution difference result Lyr according to the parent plot number to form the parent plot comparison result Cmp for the corresponding parent plot.

5. A vegetation monitoring method based on nested plot sampling according to claim 4, characterized in that: S2 further includes S22; S22. Read the comparison results Cmp of each parent plot and extract the species quantity difference results Spc, cover difference results Cov, dominant species difference results Dom, and stratum distribution difference results Lyr. Compare the species quantity difference results Spc, cover difference results Cov, dominant species difference results Dom, and stratum distribution difference results Lyr with the corresponding anomaly judgment conditions. When any difference result meets the corresponding anomaly judgment condition, generate the anomaly entry corresponding to the parent plot. The abnormal entries include at least the parent plot number and the abnormal item name, which is an abnormality in species quantity, cover, dominant species, or strata distribution. All abnormal entries are summarized by parent plot number to form an abnormality trigger set Trg.

6. A vegetation monitoring method based on nested plot sampling according to claim 5, characterized in that: S3 includes S31; S31. Read each anomaly entry in the anomaly trigger set Trg and read the hierarchical association table Rel that matches the corresponding parent plot number. Based on the hierarchical correspondence between the parent plot and the first-level nested sub-quads, the hierarchical correspondence between the first-level nested sub-quads and the second-level nested sub-quads, and the hierarchical correspondence between the second-level nested sub-quads and the last-level nested sub-quads in the hierarchical association table Rel, extract the first-level nested sub-quad numbers, second-level nested sub-quad numbers, and last-level nested sub-quad numbers under the corresponding parent plots level by level. Then, based on the extracted nested sub-quad numbers at each level, read the corresponding current monitoring data and historical monitoring data from the current monitoring record Cur and the historical baseline record Bas, respectively, and perform a comparison according to the anomaly item name of the corresponding anomaly entry. The nested sub-quad with the largest degree of change after comparison is determined as the corresponding anomaly nested sub-quad. Finally, organize the parent plot number, anomaly item name, corresponding anomaly nested sub-quad, and nesting level to form the location result Loc.

7. A vegetation monitoring method based on nested plot sampling according to claim 6, characterized in that: S3 further includes S32; S32. Read each location record in the location result Loc, and extract the tree layer record, shrub layer record, and herb layer record of the corresponding abnormal nested sub-quad from the current monitoring record Cur and the historical baseline record Bas, respectively; then, according to the abnormal item name in the corresponding location record, perform a comparison on the tree layer record, shrub layer record, and herb layer record, and summarize the comparison results according to the same corresponding abnormal nested sub-quad to form the plant layer comparison result Pla; then, compare the plant layer comparison result Pla with the abnormal item name in the corresponding location record to determine the plant layer with the greatest degree of change as the target plant layer; finally, organize the parent plot number, abnormal item name, corresponding abnormal nested sub-quad, and target plant layer to form the hierarchical result Lay.

8. A vegetation monitoring method based on nested plot sampling according to claim 7, characterized in that: S4 includes S41; S41. Read each location record in the location result Loc and read the target plant layer that matches the corresponding location record in the hierarchical result Lay. Then, according to the hierarchical association table Rel, extract the upper-level nested sub-quads and the lower-level nested sub-quads of the corresponding abnormal nested sub-quads. Extract the records corresponding to the target plant layer from the parent plot, the upper-level nested sub-quad, the corresponding abnormal nested sub-quad, and the lower-level nested sub-quad from the current monitoring record Cur and the historical baseline record Bas, respectively. Then, according to the abnormal item name in the corresponding location record, perform a backtracking comparison on the records corresponding to the above-mentioned hierarchical positions and determine the hierarchical position with the largest degree of change after comparison as the source of abnormal change. Finally, organize the parent plot number, abnormal item name, corresponding abnormal nested sub-quad, target plant layer, and source of abnormal change to form the source result Src.

9. A vegetation monitoring method based on nested plot sampling according to claim 8, characterized in that: S4 further includes S42; S42. Read the records with the same parent plot number and the same abnormal item name in the abnormal trigger set Trg, the location result Loc, the hierarchical result Lay and the source result Src; then, based on the parent plot number, the abnormal item name and the corresponding abnormal nested sub-quad, summarize the abnormal entries, the corresponding abnormal nested sub-quads, the target vegetation layer and the source of abnormal changes under the same parent plot. Then, the summarized content is organized into interpretation records for the corresponding mother plots, and summarized according to the mother plot number to form the interpretation results Res.

10. A vegetation monitoring system based on nested plot sampling, applied to the vegetation monitoring method based on nested plot sampling as described in any one of claims 1 to 9, characterized in that: It includes a parent-child hierarchical data acquisition module, an anomaly triggering module, a location and hierarchical feedback module, and a summary decision module; The parent-child plot hierarchical data acquisition module collects vegetation monitoring data of the parent plot and its nested subplots at all levels within the parent plot during the current monitoring period, and retrieves corresponding historical monitoring data. Based on the hierarchical and positional relationships between the parent plot and the nested subplots at all levels, it forms the current monitoring record Cur, the historical baseline record Bas, and the hierarchical association table Rel. The anomaly triggering module compares the vegetation monitoring results at the parent plot scale based on the current monitoring record Cur and the historical baseline record Bas. When the difference comparison results meet the preset anomaly triggering conditions, it generates the corresponding anomaly trigger set Trg for the parent plot. The localization and hierarchical feedback module, based on the anomaly trigger set Trg and the hierarchical association table Rel, locates the corresponding anomaly nested sub-samples of the parent sample plots that have anomalies according to the nested hierarchy, and determines the target plant layer corresponding to the anomaly change by combining the current monitoring record Cur and the historical baseline record Bas, thus forming the localization result Loc and the hierarchical result Lay. The aggregation decision module performs cross-scale source backtracking on abnormal changes based on the location result Loc and the hierarchical result Lay, determines the source of the abnormal changes, outputs the source result Src, and generates the explanation result Res based on the abnormal trigger set Trg, the location result Loc, the hierarchical result Lay, and the source result Src.

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