Forest resource data analysis method and system based on forestry ecological data

Abstract

The invention relates to the technical field of forest resource analysis, in particular to a forest resource data analysis method and system based on forestry ecological data. The method comprises the following steps: performing altitude measurement on a target detection area to obtain an altitude measurement data set; performing landform classification on the target detection region according to the altitude measurement data set to obtain landform type data; performing ecological partition on the target detection region according to the landform type data to obtain ecological partition data; performing forestry ecological data acquisition on the target detection region according to the ecological partition data to obtain a forestry ecological data set; performing vegetation diversity determination on the target detection region based on the forestry ecological data set to obtain vegetation type distribution data; and performing land type-vegetation planting integrating degree determination on the target detection region according to the vegetation type distribution data to obtain a land-vegetation planting integrating degree set. According to the method, the efficiency and accuracy of forest resource data analysis are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of forest resource analysis, and in particular to a forest resource data analysis method and system based on forestry ecological data. Background Art

[0002] In current forestry ecology research and forest resource management, accurate and efficient data analysis methods and systems are crucial. However, traditional forest resource data analysis methods face many challenges. First, many existing methods rely on manual field measurements and data collection, which is not only time-consuming and labor-intensive, but also costly and difficult to adapt to large-scale forest resource survey needs. Secondly, traditional data analysis methods often lack accurate identification and classification of complex terrain and landforms, resulting in the inability to accurately locate and analyze forest resources of different landform types when collecting forestry ecology data.

[0003] In addition, existing forest resource data analysis systems often face the problem of low computational efficiency when processing large-scale data. These systems are usually unable to quickly respond to complex data analysis needs, such as the determination of vegetation diversity, the analysis of the compatibility of land types and vegetation planting, etc. This limits the decision-making efficiency of forestry managers when formulating forest protection and development strategies. Finally, when processing forestry ecological data, existing data analysis systems often lack the optimized layout of data collection points and comprehensive consideration of data collection items, resulting in incomplete and unbalanced data collection, affecting the accuracy and reliability of data analysis. Summary of the invention

[0004] Based on this, it is necessary for the present invention to provide a forest resource data analysis method and system based on forestry ecological data to solve at least one of the above technical problems.

[0005] To achieve the above purpose, a forest resource data analysis method based on forestry ecological data includes the following steps:

[0006] Step S1: measuring the altitude of the target detection area to obtain an altitude measurement data set; classifying the landforms of the target detection area according to the altitude measurement data set to obtain landform type data;

[0007] Step S2: ecologically partitioning the target detection area according to the landform type data to obtain ecological partitioning data;

[0008] Step S3: collecting forestry ecological data of the target detection area according to the ecological zoning data to obtain a forestry ecological data set; determining vegetation diversity of the target detection area based on the forestry ecological data set to obtain vegetation type distribution data;

[0009] Step S4: Determine the land type-vegetation planting compatibility of the target detection area according to the vegetation type distribution data, obtain a land-vegetation planting compatibility set, and use the vegetation type distribution data and the land-vegetation planting compatibility set as forest resource analysis data.

[0010] The present invention reduces the reliance on manual field measurement, reduces time and labor costs, and improves the speed and accuracy of data collection through automated altitude measurement and landform classification. This makes large-scale forest resource surveys more economical and efficient. By accurately classifying the landforms in the target detection area, forest resources of different landform types can be more accurately identified and analyzed. This helps forestry managers better understand the ecological characteristics of forests under different landforms. Through efficient ecological zoning and forestry ecological data collection, complex data analysis needs can be quickly responded to. This not only improves the calculation efficiency, but also makes the determination of vegetation diversity and the analysis of the fit between land type and vegetation planting more accurate, thereby improving the decision-making efficiency of forestry managers. By optimizing the layout of data collection points and comprehensively considering data collection items, the method ensures the comprehensiveness and balance of data collection. This helps to improve the accuracy and reliability of data analysis and provides solid data support for the rational use and protection of forestry resources. By combining vegetation type distribution data with land-vegetation planting fit set, the method provides forest resource analysis data. This helps to evaluate the current status of forest resources. The present invention significantly improves the efficiency and accuracy of forest resource data analysis.

[0011] Preferably, the present invention also provides a forest resource data analysis system based on forestry ecological data, which is used to execute the forest resource data analysis method based on forestry ecological data as described above. The forest resource data analysis system based on forestry ecological data comprises:

[0012] A landform classification module is used to measure the altitude of the target detection area to obtain an altitude measurement data set; and classify the landforms of the target detection area according to the altitude measurement data set to obtain landform type data;

[0013] The ecological zoning module is used to perform ecological zoning on the target detection area according to the landform type data to obtain ecological zoning data;

[0014] The data collection module is used to collect forestry ecological data of the target detection area according to the ecological zoning data to obtain a forestry ecological data set; based on the forestry ecological data set, the vegetation diversity of the target detection area is determined to obtain vegetation type distribution data;

[0015] The vegetation planting compatibility analysis module is used to determine the land type-vegetation planting compatibility of the target detection area according to the vegetation type distribution data, obtain the land-vegetation planting compatibility set, and use the vegetation type distribution data and the land-vegetation planting compatibility set as forest resource analysis data.

[0016] The present invention significantly improves the speed and accuracy of data processing through an automated landform classification module, reduces the need for manual operation, and reduces the possibility of human error. The precise landform classification function of the system can perform accurate altitude measurements and generate landform type data based on this, so as to more accurately understand the status of forest resources under different terrains and landforms. In addition, the efficient ecological zoning module of the system can quickly identify and manage forest resources in different ecological regions, while the comprehensive data acquisition module can generate detailed forestry ecological data sets, making it possible to obtain richer forest resource information. The system can also accurately determine vegetation diversity and generate vegetation type distribution data, which is crucial for understanding and protecting biodiversity in forest ecosystems. Finally, the vegetation planting compatibility analysis module can optimize vegetation planting strategies, improve the utilization efficiency of forest resources, and generate land type-vegetation planting compatibility sets. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Other features, objects and advantages of the present invention will become more apparent from the detailed description made with reference to the following drawings:

[0018] Figure 1 A schematic flow chart of the steps of a forest resource data analysis method based on forestry ecological data according to an embodiment is shown.

[0019] Figure 2 A detailed flow chart of step S28 of an embodiment is shown.

[0020] Figure 3 A detailed flow chart of step S4 of an embodiment is shown. DETAILED DESCRIPTION

[0021] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0022] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0023] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0024] To achieve this, please refer to Figures 1 to 3 The present invention provides a forest resource data analysis method based on forestry ecological data, comprising the following steps:

[0025] Step S1: measuring the altitude of the target detection area to obtain an altitude measurement data set; classifying the landforms of the target detection area according to the altitude measurement data set to obtain landform type data;

[0026] Step S2: ecologically partitioning the target detection area according to the landform type data to obtain ecological partitioning data;

[0027] Step S3: collecting forestry ecological data of the target detection area according to the ecological zoning data to obtain a forestry ecological data set; determining vegetation diversity of the target detection area based on the forestry ecological data set to obtain vegetation type distribution data;

[0028] Step S4: Determine the land type-vegetation planting compatibility of the target detection area according to the vegetation type distribution data, obtain a land-vegetation planting compatibility set, and use the vegetation type distribution data and the land-vegetation planting compatibility set as forest resource analysis data.

[0029] In the present embodiment, the target detection area is measured at altitude using a global positioning system (GPS) device and a handheld altimeter, and the geographical location and altitude of each measurement point are recorded to generate an altitude measurement data set. Next, the altitude measurement data set is imported using GIS software, and the data is processed by a spatial analysis tool, and the area is divided into different landform types, such as mountainous areas and plains, according to the altitude, slope and terrain undulation, to obtain landform type data. Then, according to the landform type data, remote sensing images and GIS software are used to perform ecological zoning, and the target detection area is further subdivided into areas with similar ecological characteristics, such as wetland ecological areas, forest ecological areas, etc., to obtain ecological zoning data. On the basis of ecological zoning, a forestry survey team is organized to enter the site, and standardized survey methods and data recording tables are used to collect forestry ecological data such as forest structure, plant species, growth conditions, and occurrence of pests and diseases in each ecological zone, to obtain a forestry ecological data set. Subsequently, the forestry ecological data set is analyzed by GIS software and biodiversity analysis tools to determine the distribution of different vegetation types and obtain vegetation type distribution data. Finally, combined with the vegetation type distribution data and land condition data such as soil and climate, the compatibility between the land type and vegetation planting in the target detection area is evaluated to obtain the land-vegetation planting compatibility set. This compatibility set will be used as forest resource analysis data.

[0030] Preferably, step S1 comprises the following steps:

[0031] Step S11: acquiring an electronic map of the target detection area to obtain an electronic map of the detection area;

[0032] Specifically, electronic maps can be obtained using geographic information system (GIS) software, which is a tool for map creation and editing. Online map services such as Google Earth, Baidu Maps, or geographic information services provided by the government are accessed through the interface of the GIS software. Taking Google Earth as an example, first open the Google Earth software and use its search function to locate the target detection area. After finding the area, use the software's built-in map download tool to select the appropriate zoom level and area range to download the electronic map of the area. The downloaded map is saved in KML or KMZ format, which can be recognized and processed by GIS software.

[0033] Step S12: extracting the map scale of the electronic map of the detection area to obtain the electronic map scale;

[0034] Specifically, the electronic map file obtained in step S11 can be opened and the map attributes can be viewed using GIS software. The map scale information will be included in the attribute table of the GIS software. If the scale information is not directly provided in the map file, the map calibration tool of the GIS software can be used to manually measure the distance on the map of a known distance and compare it with the actual distance to calculate the scale. For example, if it is known that the actual distance between two landmarks in the target detection area is 1000 meters, and the distance measured on the electronic map is 2.5 centimeters, then the scale can be calculated by the following formula: The scale of the electronic map is thus obtained and recorded in the attribute information of the map.

[0035] Step S13: Based on the electronic map of the detection area, the target detection area is spatially gridded according to the preset spatial grid unit area and the preset grid geometric shape according to the electronic map scale to obtain a regional spatial grid unit set, wherein the regional spatial grid unit set includes a plurality of regional spatial grid units, and the regional spatial grid unit includes a grid unit number, a grid unit upper left vertex coordinate and a grid unit lower right vertex coordinate, and the preset grid geometric shape is a rectangle;

[0036] Specifically, the electronic map obtained in step S11 can be opened in the GIS software, and the scale parameters of the map can be set according to the scale extracted in step S12. Next, the area of ​​the grid unit and the geometry of the grid are defined. For example, if the preset grid unit area is 1 square kilometer and the preset grid geometry is a rectangle, a "gridding" tool is used in the GIS software to create a grid. Enter the size of the grid in the "gridding" tool of the GIS software (for example, if the scale is 1:40000 and the actual area of ​​the grid is 1 square kilometer, then the size of each grid on the map may be 200 meters × 200 meters). Then, the software will automatically calculate and generate a grid covering the entire target detection area. The generated grid will automatically assign a number, and the upper left vertex coordinates and the lower right vertex coordinates of each grid unit will be recorded. The GIS software can be set so that the attribute table of each grid unit contains this information. In this way, a set of regional spatial grid units is obtained, and each grid unit has a unique identification and coordinate information.

[0037] Step S14: grid unit projection is performed on the electronic map of the detection area according to the set of regional spatial grid units, and the altitude measurement points of each regional grid unit are marked on the projected electronic map of the detection area to obtain a gridded electronic map of the detection area, wherein the altitude measurement points are the intersection points of the diagonals of the regional spatial grid units;

[0038] Specifically, you can use the "Spatial Analysis" tool in the GIS software, select the grid layer in the electronic map of the detection area, and perform the "Extract Polygon Center Point" operation to extract the center point of each grid cell. Since the preset grid geometry is a rectangle, the center point is the intersection of the diagonals, which is the altitude measurement point. Mark these center points on the electronic map as altitude measurement points. In the GIS software, this can be done with the "Create Point" tool. Just click on the center point of each grid, and the software will create a new point feature at the corresponding location, and you can add attribute information to it, such as the altitude measurement point. After completing the marking of the center points of all grid cells, you will get a new electronic map with the altitude measurement points of each grid cell clearly marked on it. This map is called a gridded detection area electronic map.

[0039] Step S15: marking altitude measurement points of the target detection area according to the gridded detection area electronic map, and performing altitude measurement to obtain an altitude measurement data set, wherein the altitude measurement data set includes a plurality of altitude measurement data, and the altitude measurement data includes an altitude measurement time, an altitude measurement height, and a corresponding grid unit number;

[0040] Specifically, please refer to the sub-steps of step S15 for the detailed implementation process of this embodiment.

[0041] Step S16: Classify the landforms of the target detection area according to the altitude measurement data set to obtain landform type data.

[0042] Specifically, please refer to the sub-steps of step S16 for the detailed implementation process of this embodiment.

[0043] The present invention ensures the accuracy of spatial gridding by obtaining electronic maps and extracting map scales. At the same time, by presetting the area and geometric shape of the grid cells, refined management of forest resources is achieved, so that each grid cell can be accurately identified and analyzed. In addition, by projecting grid cells on electronic maps and marking altitude measurement points, the data collection process is optimized and efficiency is improved. By marking the altitude measurement points and conducting systematic measurements, a detailed data set containing time, altitude and location information is generated. This not only enhances the scientific nature of landform classification, but also improves the adaptability of resource management, helps to better understand the status of forest resources under different terrains and landforms, and formulates resource management strategies adapted to the local environment.

[0044] Preferably, step S15 comprises the following steps:

[0045] Step S151: extracting the coordinates of altitude measurement points from the electronic map of the gridded detection area to obtain an altitude measurement point coordinate set, wherein the altitude measurement point coordinate set includes a plurality of altitude measurement point coordinates and their corresponding grid unit numbers;

[0046] Specifically, the electronic map of the gridded detection area can be opened in the GIS software, and the map contains the altitude measurement points marked in the previous step S14. Next, use the "attribute table" function of the GIS software to view the attribute information of each altitude measurement point, which should include the coordinates of the point and the corresponding grid cell number. Through the "export" function of the GIS software, the coordinates and grid cell numbers of these altitude measurement points are exported as a coordinate set file, such as CSV or Excel format. During the export process, you can choose to export only the columns containing X and Y coordinates and grid cell numbers. In this way, a set of altitude measurement point coordinates containing several altitude measurement point coordinates and their corresponding grid cell numbers is obtained.

[0047] Step S152: determining the terrain features of each altitude measurement point to obtain an altitude measurement point terrain feature set, wherein the altitude measurement point terrain feature set includes a plurality of altitude measurement point terrain features, the altitude measurement point terrain features include the altitude measurement point terrain and its corresponding grid unit number, the altitude measurement point terrain is any one of a hillside, a cliff, a steep wall, a lake, a river, and a plain;

[0048] Specifically, GIS software can be used to determine terrain features in combination with digital elevation model (DEM) data of the target detection area. DEM data can be obtained through aerial photogrammetry, satellite remote sensing or ground measurement, and contains elevation information of each point in the area. It is necessary to ensure that the DEM data layer is loaded in the GIS software. Use the "Slope Calculation" tool in the GIS software, which calculates the slope of each pixel (pixel) from the DEM data. Specifically, slope is a measure of the rate of change of surface elevation, expressed as an angle or percentage. Set appropriate calculation parameters, such as using a 3x3 or 5x5 window size to calculate the slope of the center point of each grid cell. After the calculation is completed, the slope layer is overlaid with the elevation measurement point layer to identify areas with larger slopes, which correspond to hillside terrain. Use the "Terrain Shadow" tool. This tool simulates the interaction of sunlight and the ground based on DEM data to generate a terrain shadow layer. Select the appropriate solar altitude angle and azimuth to highlight the shadow effect of the terrain. By analyzing the shadow layer, you can identify areas with steep changes in terrain, which are cliffs or cliffs. Use the "Extract Flow Direction" and "Calculate Flow Accumulation" tools in the GIS software to identify rivers and lakes. First, determine the flow direction of each grid cell using the "Extract Flow Direction" tool. Then, use the "Calculate Flow Accumulation" tool to identify areas where water flows converge, which correspond to rivers or lakes. Classify each elevation measurement point based on the DEM data and terrain classification standards. For example, set thresholds for elevation and slope to distinguish flat areas (such as plains) from areas with larger slopes (such as hillsides). In addition, existing topographic maps or field survey data can be referenced to assist in classification. After completing the above analysis, record the terrain features of each elevation measurement point in the attribute table to form an elevation measurement point terrain feature set. This feature set will contain the terrain type (such as hillside, cliff, cliff, lake, river, plain) of each measurement point and the corresponding grid cell number. For example, it is found that a certain measurement point is located in an area with a slope greater than 30%, and the area shows obvious shadow differences in the terrain shadow layer, so the point is judged to be a cliff. Record this information in the attribute table of the point. In this way, the terrain features of each elevation measurement point can be accurately determined.

[0049] Step S153: traverse the terrain feature set of the altitude measurement point, if the terrain feature of the altitude measurement point is a hillside or a plain, record the corresponding grid unit number into the stable grid unit set, otherwise, record the corresponding grid unit number into the adjusted grid unit set;

[0050] Specifically, you can open the layer containing the terrain features of the altitude measurement points in the GIS software, and make sure that the attribute table of each measurement point contains terrain feature information, such as hillsides, cliffs, plains, etc. Next, use the "Select" tool of the GIS software to query and filter according to the terrain feature field. For example, you can set the query condition to "terrain features" equal to "hillside" or "terrain features" equal to "plain" to select all measurement points located on hillsides or plains. The GIS software will highlight the measurement points that meet the conditions according to these conditions and update a list of selected features. Then, extract the grid cell numbers of these selected measurement points and record them in a new attribute table named the stable grid cell set. For other measurement points that do not meet the stability conditions, also extract their grid cell numbers and record them in another attribute table named the adjusted grid cell set.

[0051] Step S154: traversing the set of adjusted grid cells, determining the terrain of the entire grid cell area of ​​the adjusted grid cell based on the electronic map of the grid detection area, and if the entire grid cell area is located in any one of cliffs, steep walls, lakes, and rivers, removing the corresponding grid cell number from the set of adjusted grid cells to obtain a set of adjustable grid cells;

[0052] Specifically, the gridded detection area electronic map can be opened in the GIS software, and the "adjustment grid unit set" can be loaded. Then, the "fusion" tool of the GIS software is used to fuse the adjacent grid cells in the same set to generate a layer showing the entire grid unit area. Next, the "spatial query" function of the GIS software is used to query each fused grid unit area to check whether they are completely in a specific terrain such as cliffs, cliffs, lakes or rivers. This judgment can be made by superimposing the DEM data layer and combining the slope layer, hydrological analysis layer, etc. generated previously. For example, if the average slope of a grid unit area displayed in the slope layer is greater than a certain threshold (such as 45 degrees) and obvious cliff or cliff features are displayed in the terrain shadow layer, then it can be judged that the grid unit area is completely in the cliff or cliff. Similarly, if a grid unit area is identified as a lake or river in the hydrological analysis layer, a corresponding judgment can also be made. For the grid cells that meet the exclusion conditions, their numbers are recorded in a new list and these numbers are removed from the adjustment grid unit set. Finally, the remaining numbers constitute the "adjustable grid unit set".

[0053] Step S155: depicting and marking each terrain feature of each adjustable grid unit in the set of adjustable grid units in the grid detection area electronic map, and marking the terrain safety of the depicted grid detection area electronic map to obtain a marked grid detection area electronic map, wherein the terrain safety marking is specifically:

[0054] If the terrain feature of the adjustable grid cell is any one of cliffs, steep walls, lakes, and rivers, the area corresponding to the terrain feature is marked as a dangerous measurement area;

[0055] If the terrain feature of the adjustable grid cell is either a hillside or a plain, the area corresponding to the terrain feature is marked as a safe measurement area;

[0056] Specifically, the gridded detection area electronic map can be opened in the GIS software and the set of adjustable grid cells can be loaded. Next, the "symbolization" tool of the GIS software can be used to visually distinguish the grid cells according to the terrain feature field. For example, dangerous terrain such as cliffs and steep cliffs can be set to be highlighted in red, while safe terrain such as hillsides and plains can be displayed in green. Then, the "labeling" tool of the GIS software can be used to add text labels to each adjustable grid cell directly on the map to display its terrain features. The content and style of the label can be customized, such as font size, color and position. Next, terrain safety labeling is performed. Use the "drawing" tool of the GIS software, such as polygon or line tools, to draw the boundaries of dangerous and safe measurement areas on the map. For dangerous areas, a red boundary can be used and a skull symbol can be added; for safe areas, a green boundary can be used and a check symbol can be added. After completing the above steps, save the labeled gridded detection area electronic map to obtain a labeled gridded detection area electronic map, which clearly distinguishes dangerous and safe measurement areas.

[0057] Step S156: traversing the set of adjustable grid cells, for each adjustable grid cell, randomly selecting a coordinate in the safety measurement area of ​​the corresponding adjustable grid cell in the electronic map of the marked grid detection area as the adjusted altitude measurement coordinate of the corresponding adjustable grid cell, thereby obtaining an adjusted altitude measurement coordinate set, wherein the adjusted altitude measurement coordinate set includes a plurality of adjusted altitude measurement coordinates, and the adjusted altitude measurement coordinates include the adjusted altitude measurement coordinates and their corresponding grid cell numbers;

[0058] Specifically, the electronic map of the annotated gridded detection area can be opened in the GIS software, and the set of adjustable grid cells can be loaded. Then, the "Select" tool of the GIS software is used to select all the grid cells marked as safe measurement areas. Next, a point is randomly generated inside each selected safe measurement area using the "Random Point Generation" tool of the GIS software. The density and distribution of point generation can be set to ensure the representativeness and randomness of the points. The generated random points will automatically obtain their coordinates and the grid cell number where they are located. The coordinates and grid cell numbers of these points are recorded to form an adjusted altitude measurement coordinate set. For example, if a certain safe measurement area is located in a grid cell of plain terrain, a random point can be generated in the area, and the coordinates of the point (such as 118°23'15" east longitude, 32°10'30" north latitude) and the grid cell number (such as G1001) can be recorded. This information is added to the adjusted altitude measurement coordinate set. Through the above steps, a suitable adjusted altitude measurement coordinate can be determined for each adjustable grid cell.

[0059] Step S157: updating the altitude measurement point coordinate set according to the adjusted altitude measurement coordinate set to obtain an optimized altitude measurement coordinate set;

[0060] Specifically, you can open the original elevation measurement point coordinate set layer in the GIS software and check the attribute table to confirm that the grid cell number and other coordinate information of each measurement point are correct. Then, load the adjusted elevation measurement coordinate set and create a new attribute table to record the changes during the update process. Use the "Table" tool in the GIS software to extract the grid cell numbers of the adjusted elevation measurement coordinate set and create a numbered list. Next, do the following:

[0061] 1. Compare grid cell numbers: Use the "query" tool of the GIS software to traverse and query each grid cell number in the original altitude measurement point coordinate set.

[0062] 2. Update coordinate information: For each queried grid cell number, check whether it exists in the number list of the adjusted altitude measurement coordinate set. If it does, use the "Update" tool to replace the coordinate information of the original coordinate point with the corresponding new coordinate in the adjusted coordinate set.

[0063] 3. Delete redundant coordinates: If a grid cell number exists in the original elevation measurement point coordinate set but does not exist in the adjusted elevation measurement coordinate set, use the "Delete" tool to remove the coordinate point corresponding to the number from the original coordinate set.

[0064] For example, if the original elevation measurement point coordinate set has a coordinate point with grid cell number A, but the adjusted elevation measurement coordinate set does not have grid cell number A, the coordinate point with number A will be deleted using the GIS software. A script can be created to automate this process, which will automatically compare the two coordinate sets, update the existing numbers, and delete the non-existent numbers. This is usually achieved through SQL queries or the "field calculation" tool of the GIS software. After the update and delete operations are completed, the updated coordinate set is saved and a final check is performed to ensure that all updates are accurate and all coordinate points that are no longer needed have been deleted. Finally, this updated and cleaned coordinate set is exported to a new file, such as CSV or Shapefile format, which will become the final optimized elevation measurement coordinate set.

[0065] Step S158: marking the altitude measurement points of the target detection area according to the optimized altitude measurement coordinate set, and performing altitude measurement to obtain an altitude measurement data set, wherein the altitude measurement data set includes a plurality of altitude measurement data, and the altitude measurement data includes an altitude measurement time, an altitude measurement height, and a corresponding regional grid unit number.

[0066] Specifically, you can open the optimized altitude measurement coordinate set in the GIS software, and use the "Print" function or "Export to Map" function of the GIS software to generate a field measurement map containing all the optimized coordinate points. This map will serve as a guide for field measurement work. Then, carry the printed map and field measurement tools (such as handheld GPS devices) to the target detection area. At the location of each optimized coordinate point, use the GPS device to measure the real-time altitude of the current point, and record the measurement time and height. For example, at the coordinate point with the grid unit number G1001, after arriving at the point, the altitude is measured by the GPS device to be 150 meters, and the measurement time is 10:30 on May 20, 2024. Record this information in a data record table or a dedicated measurement APP. After completing the measurement of all optimized coordinate points, import the collected measurement data into the GIS software to create a new altitude measurement data set layer. Each measurement data includes the measurement time, altitude measurement height, and the corresponding regional grid unit number.

[0067] The present invention significantly improves the accuracy of data by extracting the coordinates of altitude measurement points in electronic maps. The present invention can accurately identify and classify key terrain features, such as hillsides and cliffs, so as to achieve more targeted analysis and management of forest resources in different terrains. In addition, by distinguishing between stable and adjusted grid cells, the selection of measurement points is optimized, and the efficiency and effect of measurement work are improved. At the same time, by identifying and marking dangerous and safe measurement areas, not only the safety of field measurement is improved, but also the safety of surveyors is ensured. By dynamically updating altitude data, terrain changes can be reflected in a timely manner, providing reliable data support for dynamic monitoring and evaluation of forest resources. In addition, by adaptive measurement of complex terrain, including difficult-to-reach areas such as cliffs and lakes, the comprehensiveness of measurement is improved. The optimized altitude measurement data set provides an accurate decision-making basis for landform classification and forest resource management, and helps to formulate effective resource management strategies. By marking dangerous areas, it helps to avoid unnecessary measurement activities in ecologically sensitive or dangerous areas, thereby promoting environmental protection. Finally, through automated and optimized processes, the workload of field measurement is reduced, and the overall work efficiency is significantly improved.

[0068] Preferably, step S16 comprises the following steps:

[0069] Step S161: extracting extreme values ​​from the altitude measurement data set to obtain the maximum altitude measurement height and the minimum altitude measurement height;

[0070] Specifically, you can open the layer containing the altitude measurement dataset in the GIS software and make sure that the altitude information is included in the attribute table of each measurement point. Then, use the "Statistical Analysis" tool of the GIS software and select the altitude field for analysis. For example, you can use the "Descriptive Statistics" function built into the GIS software to set query conditions to calculate the maximum and minimum values. In some GIS software, you can directly obtain the maximum and minimum values ​​through the "Summary Statistics" tool of the attribute table. Through this analysis, the maximum and minimum altitude values ​​of the altitude measurement are obtained.

[0071] Step S162: subtracting the maximum altitude measured from the minimum altitude measured to obtain altitude difference data;

[0072] Specifically, you can use simple subtraction to calculate the altitude difference. If you are doing the calculation in a GIS software, you can use the "Field Calculation" tool to create a new field, such as naming it altitude difference, and use the expression "maximum altitude - minimum altitude" to calculate. If you are using a database management tool, such as Microsoft Access or Excel, you can create a new column and then use a formula to calculate the altitude difference. For example, if the maximum altitude is 2500 meters and the minimum altitude is 500 meters, the altitude difference is 2000 meters.

[0073] Step S163: If the altitude difference data is within the preset plain altitude difference range, the plain is taken as the landform type data; otherwise, the mountain is taken as the landform type data.

[0074] Specifically, the range of altitude differences of the plain can be determined, and this range can be preset based on geographical knowledge, historical data or expert opinions. For example, assume that the preset range of altitude differences of the plain is within 100 meters. Then, compare the calculated altitude difference with the preset range of altitude differences of the plain. If the height difference is less than or equal to 100 meters, the area is judged to be a plain; if the height difference exceeds 100 meters, the area is judged to be a mountain. Record this judgment result in a new field, for example, create a field called "landform type" in the GIS attribute table, and fill in "plain" or "mountain" according to the comparison result.

[0075] The present invention significantly improves the automation level and efficiency of landform classification by automatically extracting the extreme values ​​of altitude measurement data, while ensuring the accuracy of classification, because the present invention determines the landform type based on actual measurement data. This not only optimizes resource allocation, allowing forestry activities to be more reasonably planned and managed according to landform characteristics, but also improves the scientific nature of decision-making, especially in land use planning and ecological protection. In addition, accurate landform classification supports environmental adaptability management, helps to distinguish different landform types such as plains and mountains, and implement more effective forestry management strategies. At the same time, it promotes ecological protection work and provides data support for their protection by identifying ecologically sensitive areas such as mountain water conservation areas and plain wetlands. The flexibility of the system has also been enhanced, and the landform classification can be dynamically adjusted according to the preset altitude difference range to ensure that data processing can adapt to different situations.

[0076] Preferably, step S2 comprises the following steps:

[0077] Step S21: obtaining an optimized altitude measurement coordinate set and an altitude measurement data set;

[0078] Specifically, you can open the optimized elevation measurement coordinate set layer generated in the previous step in the GIS software, and make sure that the attribute table of each coordinate point contains complete coordinate information and the corresponding grid unit number. At the same time, open the layer containing the original elevation measurement data, and check the attribute table to ensure that it contains the altitude information of each measurement point. Next, merge the two layers into a complete data set. You can use the "connect" or "associate" tool of the GIS software to associate the altitude information with the corresponding coordinate point according to the grid unit number to generate a new layer, which will serve as the final optimized elevation measurement data set. In addition, you can also use database management tools, such as SQL queries, to merge the coordinate information and altitude information into one table.

[0079] Step S22: collecting remote sensing images of grid cells in each region according to the optimized altitude measurement coordinate set to obtain a grid cell remote sensing image set, wherein the grid cell remote sensing image set includes a plurality of grid cell remote sensing images, and the grid cell remote sensing images include corresponding grid cell numbers;

[0080] Specifically, the optimized elevation measurement coordinate set can be opened in the GIS software, and the "Select" tool of the GIS software can be used to select the area where remote sensing images need to be collected according to the grid unit number. Next, use remote sensing image acquisition tools, such as satellite remote sensing data download software or drone aerial photography equipment, to collect images for each selected grid unit. If satellite remote sensing data is used, determine the appropriate satellite image source, such as Google Earth, Landsat or Sentinel, and download remote sensing images of corresponding resolution according to the coordinate points and grid range. You can also use a drone equipped with a high-definition camera to fly over each grid unit for aerial photography. The collected remote sensing images will contain detailed surface features of the grid unit, mark the grid unit number on the image, and record information such as the image shooting time, resolution, and shooting angle. These remote sensing images are organized into a grid unit remote sensing image set, each image file is named with the corresponding grid unit number, and stored in a unified folder. In addition, a metadata table of image information can be created to record detailed information for each image. In this way, a grid unit remote sensing image set containing rich surface feature information is successfully obtained.

[0081] Step S23: if the landform type data indicates that the target detection area is a plain, soil fertility data of each area grid unit is collected according to the optimized altitude measurement coordinate set to obtain a grid unit soil fertility data set, wherein the grid unit soil fertility data set includes a plurality of grid unit soil fertility data, and the grid unit soil fertility data includes corresponding grid unit numbers;

[0082] Specifically, if the landform type data indicates that the target detection area is a plain, use the "Select" tool of the GIS software to select the corresponding grid cell in the optimized elevation measurement coordinate set. Then, the locations of these grid cells will be visited in the field to collect soil fertility data. For field collection, soil drilling tools are used to collect soil samples, and soil fertility indicators such as pH value, organic matter content, nitrogen, phosphorus, and potassium content are measured by soil analysis equipment (such as soil nutrient rapid tester). The soil fertility data of each grid cell is recorded in the data table with the corresponding grid cell number. Finally, the soil fertility data of all grid cells collected are integrated into a grid cell soil fertility data set, and each data item contains a grid cell number and the corresponding soil fertility indicator value.

[0083] Step S24: collecting soil moisture data for grid cells in each region according to the optimized altitude measurement coordinate set to obtain a grid cell soil moisture data set, wherein the grid cell soil moisture data set includes a plurality of grid cell soil moisture data, and the grid cell soil moisture data includes corresponding grid cell numbers;

[0084] Specifically, the grid cells for which soil moisture data collection is required can be selected in the GIS software according to the optimized altitude measurement coordinate set. Then, these grid cells are visited in the field and soil moisture is measured using soil moisture measurement tools, such as soil moisture meters or capacitive soil moisture sensors. Soil moisture sensors are inserted at a specific depth of each grid cell (for example, 5 cm, 10 cm, and 20 cm below the surface), and the measured soil moisture values ​​are recorded. These measured values ​​are entered into a data table together with the grid cell number. The soil moisture data of all grid cells obtained by field measurement or remote sensing are integrated into a grid cell soil moisture data set, and each data item contains a grid cell number and a corresponding soil moisture value. In this way, a grid cell soil moisture data set containing the soil moisture data of each grid cell is obtained.

[0085] Step S25: stitching the grid unit remote sensing image set to obtain a global remote sensing image of the detection area;

[0086] Specifically, grid unit remote sensing image sets can be stitched by using professional image processing software, such as ERDASIMAGINE, PCI Geomatics or ArcGIS. First, import all grid unit remote sensing image sets into the image processing software, and make sure that these images are acquired under similar lighting and seasonal conditions to ensure the consistency of the stitched images. Next, use the "image registration" function of the software to select several images with wide coverage and high clarity as reference images, and then align other images with these reference images. Then, use the "image mosaic" tool to automatically or manually select the fusion line according to the overlapping area between the images, and set the fusion method (such as linear fusion, feather fusion, etc.), stitch and fuse the registered images to generate a seamless global remote sensing image (global remote sensing image of the detection area).

[0087] Step S26: performing vegetation recognition and mapping on the global remote sensing image of the detection area to obtain a vegetation distribution data set, wherein the vegetation distribution data set includes a plurality of vegetation distribution data, and the vegetation distribution data is used to indicate the spatial distribution of each vegetation;

[0088] Specifically, the global remote sensing image of the detection area can be opened in the remote sensing image interpretation software, and the threshold and classification system for vegetation identification can be set according to the spectral characteristics and terrain characteristics of the vegetation. Next, the remote sensing image is classified by vegetation using "automatic classification tools, such as maximum likelihood classification, support vector machine (SVM) or random forest classification algorithms. These algorithms can assign each pixel in the image to different vegetation types according to the spectral characteristics of the vegetation. Then, the classification results are manually checked and edited to correct the misclassified areas that appear in the automatic classification process. The "Sample Edit" tool can be used to select some known vegetation type areas as samples to train the classifier and reclassify. In addition, the "Object Recognition" tool can be used to identify and map vegetation objects in the image according to the characteristics of the vegetation, such as shape, size and texture. The classified and mapped vegetation distribution information is exported as a vegetation distribution dataset, and each data item contains information such as vegetation type, spatial distribution location and area. This dataset can be used to indicate the spatial distribution of each type of vegetation.

[0089] Step S27: Mapping the grid unit soil fertility dataset and the grid unit soil moisture dataset to the corresponding regional grid units in the vegetation distribution dataset to obtain the grid unit ecological dataset, and clustering the grid unit ecological dataset to obtain ecological zoning data, wherein the ecological zoning data is any one or more of an agricultural area, a grassland area, and a wetland area;

[0090] Specifically, the vegetation distribution dataset can be opened in the GIS software, and this dataset includes the vegetation distribution layer generated by step S26. Then, the grid unit soil fertility dataset and the grid unit soil moisture dataset are loaded into the GIS software in the form of a table. Use the "Join" or "Relate" function of the GIS software to associate the soil fertility data and the soil moisture data with the vegetation distribution layer according to the common grid unit number. This will cause the attribute table of each vegetation distribution layer to contain the corresponding soil fertility and moisture information. After the association is completed, the layer with enhanced attribute information is exported as a grid unit ecological dataset. Next, in order to obtain ecological zoning data, use the "Cluster Analysis" tool in the spatial analysis toolbox of the GIS software, such as K-means clustering or DBSCAN clustering. According to the properties such as soil fertility, soil moisture and vegetation type, set the parameters of the cluster analysis, such as the number of clusters or the distance threshold. The GIS software will automatically divide the grid cells into several ecological blocks according to these parameters. For example, regions are divided into agricultural areas, grassland areas, and wet areas according to soil fertility, soil moisture, and vegetation coverage. After cluster analysis, each block is assigned a corresponding ecological zoning label and an ecological zoning layer is generated. Finally, the ecological zoning layer is exported as the final ecological zoning data, which will contain the ecological zoning type of each regional grid unit.

[0091] Step S28: If the landform type data indicates that the target detection area is a mountainous area, ecological zoning is performed on the target detection area according to the altitude measurement data set to obtain ecological zoning data.

[0092] Specifically, please refer to the sub-steps of step S28 for the detailed implementation process of this embodiment.

[0093] The present invention significantly improves the comprehensiveness of data collection by using an optimized altitude measurement coordinate set to collect remote sensing images and soil data, covering key information such as topography, soil fertility and humidity. The global remote sensing image synthesized by image stitching technology enhances the macroscopic observation and analysis capabilities of the vegetation distribution in the entire detection area. The accuracy of vegetation monitoring is improved by vegetation identification and mapping technology, providing a scientific basis for vegetation protection and restoration. In addition, the effective integration of soil fertility and humidity data with vegetation distribution data can more comprehensively evaluate the ecological conditions of each grid unit. By performing cluster analysis on the grid unit ecological data set, the scientific nature of ecological zoning is optimized, laying the foundation for the implementation of precise ecological protection and management measures. By adopting corresponding data processing and ecological zoning strategies according to the landform type, the adaptability and flexibility of decision-making are improved.

[0094] Preferably, step S28 comprises the following steps:

[0095] Step S281: performing deduplication processing on the altitude measurement data set to obtain a deduplication altitude measurement data set;

[0096] Specifically, you can open an altitude measurement dataset in GIS software. This dataset may contain information such as altitude, location coordinates, and timestamps for multiple measurement points. In order to remove duplicates, you need to determine which field is the key field that identifies the duplicate data points, usually the location coordinates or the unique number of the measurement point. Next, use the "Duplicate Removal" tool provided by the GIS software to select the key field for deduplication. This tool will find records with the same key field value and only keep one of these records. For example, if the coordinates of two records are exactly the same, the "Duplicate Removal" tool will only keep one record and delete the other. Deduplication can be performed using database management software. For example, in Microsoft Excel, you can use the "Remove Duplicates" function under the "Data" tab. In more advanced database management systems, you can execute SQL queries and select specific fields for deduplication. After the deduplication operation is completed, you will get a deduplicated altitude measurement dataset, in which each measurement point record is unique. Then, save this deduplicated altitude measurement dataset as a new file.

[0097] Step S282: constructing an altitude ascending curve for the deduplicated altitude measurement data set to obtain an altitude ascending curve;

[0098] Specifically, you can open the deduplicated elevation measurement dataset in GIS software and sort the data by geographic location. Then, use the chart generation tool in the GIS software to select the altitude as the Y axis and the geographic location (for example, the location coordinates along a route or line) as the X axis to create a chart. You can select the "Line Chart" type through the chart tool of the GIS software and specify the data source of the X-axis and Y-axis in the chart settings. The software automatically calculates the altitude corresponding to each location coordinate and draws a line connecting all data points to form an elevation curve. In addition, you can also use professional statistical analysis software to construct an elevation curve. For example, in R language, you can use the graphics generation library to create a chart by specifying data points and drawing lines. After the chart is generated, review the elevation curve to ensure that it accurately reflects the ups and downs of the terrain. Then, save the chart as an image file or keep it as a layer in the software. This curve chart can help you intuitively understand the elevation changes of the terrain.

[0099] Step S283: Segmentally map the altitude rising curve according to the preset vertical ecological zoning gradient to obtain a vertical ecological zoning sequence, and record the vertical ecological zoning sequence as ecological zoning data, wherein the vertical ecological zoning sequence includes several vertical ecological zonings, and the vertical ecological zoning includes the name of the vertical ecological belt, the altitude range of the vertical ecological belt and the vertical ecological belt number. The name of the vertical ecological belt is any one or more of the low altitude zone, medium-low altitude zone, medium altitude zone, high altitude zone, subalpine zone and snow line zone.

[0100] Specifically, the deduplicated altitude measurement data set and the altitude gradient curve can be opened in the GIS software. Then, according to ecological principles and local natural environment characteristics, the gradient of vertical ecological zoning is determined. For example, the altitude range is divided into low altitude zone (0-500 meters), medium-low altitude zone (500-1000 meters), medium altitude zone (1000-1500 meters), high altitude zone (1500-2500 meters), subalpine area (2500-3500 meters) and snow line zone (above 3500 meters) according to experience or literature. Next, use the "segmentation" tool of the GIS software to segment the altitude gradient curve according to these preset altitude ranges. Assign a vertical ecological belt name to each segment, and record the altitude range and number of each ecological belt. For example, use the following steps:

[0101] 1. Determine the elevational boundaries of each ecoregion.

[0102] 2. Mark these limits on the altitude progression graph.

[0103] 3. Use the "regional division" tool in the GIS software to divide the curve map into different sections according to these boundaries.

[0104] 4. Assign a name to each section, such as “Low Altitude Area”, “Mid-Low Altitude Area”, etc., and give it a unique number.

[0105] You can also use statistical analysis software, such as R or Python, to process the elevation data and generate ecological zones. For example, in R, you can use the following method:

[0106] 1. Read the altitude measurement dataset.

[0107] 2. Use conditional statements or bin functions to separate the data into different ecoregions based on preset elevation boundaries.

[0108] 3. Assign a name and number to each ecoregion.

[0109] After completing the segment mapping, a sequence of several vertical ecological zones will be obtained, each with a clear name, altitude range and number. This vertical ecological zone sequence will be used as ecological zone data.

[0110] The present invention optimizes the quality of altitude measurement data through deduplication processing, ensures the accuracy of analysis, and truly reflects terrain changes by constructing an accurate altitude gradient curve, laying a solid foundation for ecological zoning. The preset vertical ecological zoning gradient is used to achieve refined management of mountain ecology and identify ecological characteristics at different altitudes. In addition, accurately divided vertical ecological zoning helps to strengthen the protection of ecological diversity. At the same time, it optimizes the utilization strategy of forestry resources, improves resource utilization efficiency, supports scientific research and environmental monitoring, and enhances the understanding of the complexity of mountain ecosystems.

[0111] Preferably, step S3 comprises the following steps:

[0112] Step S31: constructing a three-dimensional terrain structure of the target detection area based on the altitude measurement data set to obtain a three-dimensional terrain model of the target area;

[0113] Specifically, you can open the layer containing the elevation measurement dataset in the GIS software and ensure that the attribute table of each measurement point contains accurate elevation information. Then, use the "3D Terrain Construction" tool of the GIS software to import these elevation data. GIS software usually provides functions such as "Terrain Analysis" or "Create Terrain", which can be used to create a smooth, continuous 3D terrain surface based on the hashed elevation points. This process involves gridding the data, that is, converting irregularly distributed measurement points into regular grid data through interpolation methods. Set the interpolation parameters, such as the interpolation method (e.g. Kriging interpolation, inverse distance weighted, etc.) and grid resolution, and the GIS software will automatically generate a 3D terrain model based on these parameters. The generated 3D terrain model can be saved in the form of a digital elevation model (DEM), which contains complete elevation information of the target detection area.

[0114] Step S32: performing ecological zoning mapping on the three-dimensional terrain model of the target area according to the ecological zoning data to obtain a three-dimensional ecological zoning model;

[0115] Specifically, the three-dimensional terrain model of the target area can be opened in the GIS software, and it is ensured that the ecological zoning data has been loaded. Then, the ecological zoning data is combined with the three-dimensional terrain model using the "Ecological Zoning Mapping" tool of the GIS software. Different ecological zoning types are set according to the ecological zoning data, such as the vertical ecological zoning sequence, and a unique visual style, such as color, pattern or transparency, is assigned to each type. The GIS software allows an ecological zoning type to be defined for each area on the DEM. Next, the ecological zoning data is mapped onto the three-dimensional terrain model using the "Visualization" tool of the GIS software. The three-dimensional model of the ecological zoning can be rotated, scaled, and observed from different angles to ensure that each ecological zoning is correctly mapped on the corresponding terrain. After the mapping is completed, a three-dimensional model of the ecological zoning is obtained, which clearly shows the distribution of different ecological zoning in three-dimensional space.

[0116] Step S33: Calculate the area of ​​each ecological zone based on the three-dimensional model of ecological zones to obtain an ecological zone area set;

[0117] Specifically, you can open the three-dimensional model of ecological zones in GIS software and ensure that each ecological zone has a unique identifier or number. Next, use the "Area Calculation" tool of the GIS software to select each ecological zone in the three-dimensional model of ecological zones and calculate the surface area of ​​each zone. GIS software usually provides "area statistics" or "regional attribute calculation" functions, which can be used to calculate the area of ​​each ecological zone. In some GIS software, you can also select specific calculation units, such as square meters, hectares, or square kilometers. Set the calculation parameters and execute the calculation command. The GIS software will automatically calculate the area of ​​each ecological zone and add the results to the attribute table. This area data can be viewed and exported to form an ecological zone area collection.

[0118] Step S34: Determine the data collection point layout for each ecological zone according to the ecological zone area set, and obtain a plurality of collection point sequences, wherein the collection point sequence includes a plurality of forestry ecological data collection points, and the forestry ecological data collection points include ecological zone numbers, longitude and latitude positions, and altitudes, and the one collection point sequence is the data collection point of the corresponding ecological zone;

[0119] Specifically, the set of ecological zone areas can be analyzed to determine the size and shape of each ecological zone. Then, based on the area and ecological characteristics of the ecological zone, decide how many data collection points to place inside each ecological zone. You can use the "spatial analysis" tools of GIS software, such as grid creation or point generation, to evenly distribute data collection points inside each ecological zone. Set the distance between points or the number of points in each zone, and the GIS software will automatically generate data collection points inside each ecological zone. Each generated data collection point will automatically obtain its location information in three-dimensional space, including longitude, latitude and altitude. Use the attribute table of GIS software to record the ecological zone number, longitude and latitude position and altitude of each collection point. Next, use the buffer analysis or proximity analysis tools of GIS software to ensure that each collection point represents the characteristics of its ecological zone and that the distance between collection points is sufficient to cover the entire ecological zone. Finally, integrate all the generated data collection point information into a collection point sequence, each collection point sequence corresponding to an ecological zone.

[0120] Step S35: determining the forestry ecological data collection items for the corresponding ecological partitions according to the collection point sequence to obtain a collection data item set, wherein the collection data item set at least includes forest structure data, plant species data, forest growth data, regional climate data and pest and disease occurrence data;

[0121] Specifically, the characteristics of each ecological zone can be analyzed on the spot, and the guiding principles of forestry surveys can be referred to to determine the type of data that needs to be collected. Usually, this includes but is not limited to forest structure data (such as tree density, tree species distribution, canopy density), plant species data (such as plant species, quantity, biomass), forest growth data (such as tree height, diameter at breast height, growth rate), regional climate data (such as temperature, precipitation, humidity) and pest and disease occurrence data (such as pest and disease type, degree of impact). Use forestry survey forms or data collection apps to record this information, and ensure that each collection item has clear indicators and measurement methods. For example, for forest structure data, the angle gauge point method or line distance method is used to measure tree density and distribution; for plant species data, sample plot surveys or species lists are used to record. Technological tools can also be used, such as handheld GPS devices to record positioning information, digital cameras to record on-site conditions, and data loggers to collect and store data. Finally, these collection items are integrated into a collection data item set.

[0122] Step S36: collecting forestry ecological data for each ecological zone according to the collected data item set to obtain a forestry ecological data set, wherein the forestry ecological data set includes a number of forestry ecological data, the forestry ecological data including forest structure data, plant species data, forest growth data, regional climate data and pest and disease occurrence data, and the forestry ecological data uniquely corresponds to the ecological zone;

[0123] Specifically, according to the data item set, corresponding data collection tools can be prepared, such as measuring tape, height meter, growth cone, handheld GPS, digital camera, data logger, etc. Then, go to the collection point of each ecological zone for field investigation. At each collection point, according to the guidance of the data item set, collect forest structure data one by one, such as using height meter to measure tree height and using growth cone to measure breast diameter; collect plant species data, such as recording plant species and quantity in the sample; collect forest growth data, such as evaluating the growth status of trees; collect regional climate data, such as using thermometer and hygrometer to record the climate conditions on site; collect pest and disease occurrence data, such as observing and recording the symptoms of pests and diseases of trees. Use handheld GPS to record the precise location of each collection point, and use digital camera to take photos of the site to assist in data recording and subsequent analysis. All collected data are recorded in the data logger or data collection APP and associated with the corresponding ecological zone number. After completing the data collection of all collection points, organize these data into a forestry ecological data set. Each dataset uniquely corresponds to a specific ecological region and contains detailed information on the forest structure, plant species, growth conditions, climate conditions, and pests and diseases in that area.

[0124] Step S37: Perform vegetation type statistics on the forestry ecological data set to obtain vegetation type distribution data, wherein the vegetation type distribution data includes a number of vegetation types, and the vegetation type includes a vegetation type name and vegetation type spatial distribution data.

[0125] Specifically, you can open the forestry ecology dataset in GIS software. This dataset contains data such as forest structure, plant species, and forest growth collected from various ecological zones. Then, use the attribute table function of the GIS software to view and analyze the vegetation-related information of each data point. Next, use the classification and statistical tools of the GIS software to group the data according to vegetation type. You can set the vegetation type as the basis for classification, such as coniferous forest, broad-leaved forest, mixed forest, etc., and calculate the distribution of each vegetation type in each ecological zone. You can also use the spatial analysis tools of GIS software, such as hot spot analysis or spatial distribution pattern analysis, to identify the spatial distribution characteristics of vegetation types. This can help understand the distribution patterns and ecological habits of different vegetation types in the study area. In addition, you can use statistical software (such as Excel, SPSS, R, etc.) to perform more in-depth statistical analysis on the exported vegetation data. For example, you can use the pivot table function of Excel to quickly count the number and distribution area of ​​different vegetation types; or use the "ggplot2" package of R to generate a spatial distribution map of vegetation types. After completing the statistical analysis, you will get the vegetation type distribution data, which contains the name and spatial distribution information of each vegetation type. These data can help you understand the distribution characteristics of different vegetation types. Organize these vegetation type distribution data into a layer or table. This layer or table clearly shows the spatial distribution of different vegetation types.

[0126] The present invention significantly enhances the visualization effect of the terrain by constructing a three-dimensional terrain model of the target area, making the terrain features more intuitive and easy to understand. At the same time, the ecological zoning data is mapped to the three-dimensional terrain model, realizing the refined management of the ecological zoning and ensuring that the ecological characteristics of each area are clearly defined. Accurate area calculation of the ecological zoning is performed. Through the optimized layout of data collection points, the representativeness and comprehensiveness of data collection are ensured based on the consideration of the ecological zoning area. Through comprehensive forestry ecological data collection, multiple aspects such as forest structure, plant species, growth data, climate and pest and disease conditions are covered, providing comprehensive data support for forest resource management and improving the relevance and accuracy of the data. Accurate statistics of vegetation distribution are helpful for vegetation protection and restoration, while detailed vegetation distribution data supports ecological research and environmental monitoring, and enhances the understanding of ecosystem changes. These measures provide scientific and detailed data support for forestry managers and decision makers, and improve the scientific nature of decision-making. Finally, the vegetation types and distribution of different ecological zones are clarified, which helps to formulate targeted protection measures and enhances the pertinence of environmental protection.

[0127] Preferably, step S4 comprises the following steps:

[0128] Step S41: performing vegetation growth time statistics on the forestry ecological data set to obtain a vegetation growth time data set, wherein the vegetation growth time data set includes a number of vegetation growth time data, and the vegetation growth time data includes vegetation type, initial vegetation growth year, current year and vegetation growth duration;

[0129] Specifically, you can open the forestry ecology dataset in the GIS software. This dataset contains records of different vegetation types, the initial growth year of vegetation, and the current year. Next, use the attribute table of the GIS software to add a new attribute field to each vegetation type record in the dataset, named vegetation growth duration. By viewing the initial growth year and current year of each vegetation type record, calculate the growth duration of each vegetation type manually or using the built-in calculation function of the software. The calculation method is very simple, which is to subtract the initial growth year from the current year. Fill the calculated duration into the corresponding vegetation growth duration field. After the calculation is completed, export the attribute table containing the vegetation type, the initial growth year of vegetation, the current year, and the vegetation growth duration into a new dataset, such as an Excel or CSV file. This dataset is called the vegetation growth time dataset.

[0130] Step S42: traversing the vegetation growth time data set, extracting the maximum value of the vegetation growth duration, and obtaining the maximum vegetation growth duration;

[0131] Specifically, the vegetation growth time dataset can be opened in the statistical analysis software. Then, use the data analysis function of the software to find the maximum value of the growth duration. For example, in Excel, you can sort the growth duration column in descending order by using the sort and filter function under the "Data" menu, and then directly read the first value of the sort column as the maximum growth duration. In the database management software, you can execute a query command to request the database to return the maximum value of the growth duration field. For example, you can construct an SQL query to request the database to return the maximum value of the growth duration. Record the maximum growth duration obtained from the query. This value represents the longest growth period of vegetation observed in the study area.

[0132] Step S43: determining the historical remote sensing image acquisition years for the target detection area according to the maximum growth duration of vegetation, and obtaining a remote sensing acquisition year list;

[0133] Specifically, the corresponding year can be traced back according to the maximum growth duration of vegetation. For example, if the current year is 2024 and the maximum growth duration is 30 years, it is necessary to trace back to the remote sensing image of 1994 (2024-30=1994). Using calendar calculations or simple subtraction, determine all the years between the starting year of the maximum growth duration and the current year. This list will include all the years that need to be investigated to obtain a complete history of vegetation growth. Record this list of years to form a list of remote sensing acquisition years.

[0134] Step S44: collecting historical remote sensing images of the target detection area according to the remote sensing collection year list to obtain a historical remote sensing image set of the detection area, wherein the historical remote sensing image set of the detection area includes a plurality of historical remote sensing images of the detection area, and the historical remote sensing images of the detection area include shooting time, image resolution and shooting geographic coordinates;

[0135] Specifically, the year range of historical remote sensing images that need to be collected can be determined based on the list of remote sensing acquisition years. Then, access online remote sensing image archive services, such as the Earth Resources Observation and Science Center (EROS) Center, Landsat Image Archive, or Google Earth. In these online services, use the geographic location information of the target detection area, such as longitude and latitude coordinates, to search for historical remote sensing images of the corresponding year. According to information such as the image shooting time, resolution, and geographic coordinates, select images that meet the requirements. Download these historical remote sensing images and organize them into a set of historical remote sensing images of the detection area. Each image file contains metadata of its shooting time, image resolution, and shooting geographic coordinates. After completing the image acquisition, the historical remote sensing image set of the detection area is archived.

[0136] Step S45: obtaining a three-dimensional terrain model of the target area;

[0137] Specifically, you can use GIS software to open the digital elevation model (DEM) data of the target area. DEM data is usually obtained by aerial photogrammetry, satellite remote sensing or ground measurement, and can reflect the elevation information of the area. Next, use the 3D visualization function of the GIS software to convert the DEM data into a 3D terrain model. Many GIS software, such as ArcGIS or QGIS, have built-in 3D terrain generation tools that can automatically generate a 3D terrain model of the target area by setting DEM data as input.

[0138] Step S46: traversing the historical remote sensing image set of the detection area, and sequentially performing texture mapping on the historical remote sensing images of the detection area and the three-dimensional terrain model of the target area to obtain an augmented reality terrain-vegetation model set;

[0139] Specifically, the three-dimensional terrain model of the target area can be opened using GIS software or three-dimensional modeling software, and then the historical remote sensing images collected in step S44 are opened one by one. For each remote sensing image, image processing software, such as AdobePhotoshop or ERDASIMAGINE, is used to map the texture information of the image to the corresponding position of the three-dimensional terrain model. This process involves the correction, scaling and positioning of the image to ensure that the image texture matches the height and position of the terrain model. The texture of the remote sensing image is applied to the surface of the three-dimensional terrain model using the texture mapping tool of the software. This can be achieved by setting the image as a texture layer and projecting it onto the surface of the three-dimensional model. After completing the texture mapping of all historical remote sensing images, a series of augmented reality terrain-vegetation models are obtained. These models not only contain the height information of the terrain, but also reflect the vegetation coverage at different time points. These augmented reality terrain-vegetation models are organized into a collection and saved.

[0140] Step S47: Based on the augmented reality terrain-vegetation model set, the land type-vegetation planting compatibility of the target detection area is determined according to the vegetation type distribution data to obtain a land-vegetation planting compatibility set, and the vegetation type distribution data and the land-vegetation planting compatibility set are used as forest resource analysis data.

[0141] Specifically, please refer to the sub-steps of step S47 for the detailed implementation process of this embodiment.

[0142] By accurately counting the vegetation growth time, the present invention can grasp the growth dynamics of different vegetation types in detail, including the starting growth year and growth duration, so as to accurately grasp the vegetation growth status. By determining the acquisition year of historical remote sensing images, the system can trace and analyze the long-term change trend of vegetation and land cover, and effectively utilize historical data. The augmented reality terrain-vegetation model created by combining historical remote sensing images with a three-dimensional terrain model provides an intuitive tool for analyzing and understanding vegetation distribution and land use conditions. In addition, the present invention can scientifically evaluate the fit between land and vegetation planting based on these models and vegetation distribution data, and deepen forest resource analysis. These measures have promoted vegetation restoration and protection work, and improved the efficiency and accuracy of resource monitoring.

[0143] Preferably, step S47 includes the following steps:

[0144] Step S471: performing spatiotemporal vegetation dynamic modeling on the augmented reality terrain-vegetation model set to obtain a spatiotemporal vegetation dynamic model;

[0145] Specifically, the augmented reality terrain-vegetation model set can be imported into the GIS software. These model sets contain vegetation conditions at different time points. Then, use the spatiotemporal analysis tools of the GIS software, such as ArcGIS's spatiotemporal big data analysis tools, to process these data. Each model in the model set is timestamped and combined with geographic location information to create a spatiotemporal dataset. Then, use the interpolation tools in the GIS software, such as Kriging interpolation or inverse distance weighted interpolation, to estimate the vegetation conditions at unobserved time points and areas. After completing the spatiotemporal vegetation dynamic modeling, the obtained spatiotemporal vegetation dynamic model is saved. This model can describe the changes in vegetation over time and space.

[0146] Step S472: traverse the vegetation type distribution data, and execute steps S473 to S477 for each vegetation type;

[0147] Specifically, the vegetation type distribution dataset can be opened in the GIS software, and the relevant information of different vegetation types recorded in the attribute table can be viewed. Next, the dataset is classified according to the vegetation type, and a separate layer is created for each vegetation type. Using the query tool of the GIS software, select a vegetation type and export its corresponding layer or data subset. Then, perform the analysis in steps S473-S477 on this subset. Repeat this process until the data of all vegetation types have been traversed and analyzed. This process allows for a separate analysis of the changes in each vegetation type, thereby obtaining a spatiotemporal distribution map and growth trend data for each vegetation type.

[0148] Step S473: performing spatial feature matching and positioning on the spatiotemporal vegetation dynamic model according to the vegetation type name and the spatial distribution data of the vegetation type to obtain the target vegetation spatiotemporal sub-model;

[0149] Specifically, the spatiotemporal vegetation dynamics model and vegetation type distribution data can be loaded into the GIS software. This data contains the name of each vegetation type and its spatial distribution on the map. Next, the query and filter tools of the GIS software are used to select the corresponding spatial distribution data based on the vegetation type name. For example, if the vegetation type of interest is coniferous forest, they will filter out all records marked as coniferous forest in the attribute table. Then, using the spatial analysis tools of the GIS software, such as the extraction or selection function, the corresponding area is located in the spatiotemporal vegetation dynamics model based on the filtered spatial distribution data. The matched spatial data is extracted to generate the target vegetation spatiotemporal sub-model. This sub-model is specifically targeted at a specific vegetation type and contains detailed information on the changes of that type over time.

[0150] Step S474: extracting the growth dynamics of the target vegetation spatiotemporal sub-model to obtain a vegetation growth time series data set;

[0151] Specifically, the target vegetation spatiotemporal submodel can be opened in the GIS software, and it is ensured that the model contains vegetation data at different time points. Next, the time series analysis tools of the GIS software, such as the multi-temporal change detection function, are used to analyze the changes over time in the target vegetation submodel. This process involves comparing remote sensing images at different time points to identify vegetation growth, decline, or other changes. The analysis results are exported as a new dataset, the vegetation growth time series dataset. This dataset contains the growth status of the target vegetation type at various time points, such as changes in coverage, changes in biomass, etc. In addition, image processing software, such as ERDASIMAGINE or eCognition, can be used to further analyze remote sensing images and extract more detailed vegetation growth information, such as vegetation indices (such as NDVI), vegetation height changes, etc. After the growth dynamics extraction is completed, the obtained vegetation growth time series dataset is collated and archived.

[0152] Step S475: performing time extrapolation on the vegetation growth time series data set to obtain a vegetation spatiotemporal distribution map;

[0153] Specifically, a vegetation growth time series data set can be exported from a database or Excel spreadsheet. Assume that the data set contains vegetation coverage data for the past 15 years, recorded once a year, with a total of 15 data points. A linear regression model is used for time extrapolation because preliminary analysis shows that the vegetation growth trend is approximately linear. Use statistical analysis software (such as SPSS, Stata, or R language) to import the data set and select linear regression as the analysis method. Construct a model with year as the independent variable (X-axis) and vegetation coverage as the dependent variable (Y-axis). The software will automatically fit a linear regression model based on historical data and give model parameters such as slope and intercept. For example, the model may be expressed as: vegetation coverage = 0.5 × year + 20 vegetation coverage = 0.5 × year + 20, where the slope of 0.5 indicates that the vegetation coverage increases by 0.5% each year, and the intercept of 20 indicates the coverage baseline at the beginning of the year. Use this model to predict the vegetation coverage in the next 5 years. Substitute future years (such as 2025, 2026, etc.) into the model to calculate the corresponding vegetation coverage prediction value. Export the prediction results as a new dataset, including the year and the predicted vegetation coverage. Import the prediction results into the GIS software and overlay them with the existing vegetation distribution map for analysis. In GIS, create a new layer for the prediction data, use the year as a different classification, and use gradient colors to represent the vegetation coverage of different years. Using the visualization tools of the GIS software, combine the historical data with the prediction data to generate a vegetation spatiotemporal distribution map. This map shows the changes in vegetation coverage from the past to the present and during the future prediction period. Through the above steps, a detailed vegetation spatiotemporal distribution map is obtained, which not only shows the changes in historical vegetation coverage, but also predicts the vegetation distribution in the next few years.

[0154] Step S476: performing growth trend identification on the vegetation spatiotemporal distribution map to obtain vegetation growth trend data, wherein the vegetation growth trend data is any one of a rapid growth trend, a stable growth trend, a periodic growth trend, a degradation trend and an intermittent growth trend;

[0155] Specifically, you can open the spatiotemporal distribution map of vegetation in GIS software, and use the classification and symbolization tools of GIS software to define a series of judgment conditions for vegetation adaptability. For example:

[0156] Rapid growth trend: annual growth rate >5%.

[0157] Stable growth trend: annual growth rate is between 2% and 4%.

[0158] Cyclic growth trend: Seasonal growth pattern is obvious, and the annual growth rate fluctuates between 1% and 3%.

[0159] Degeneration trend: annual growth rate <1%.

[0160] Intermittent growth trend: non-continuous growth, large fluctuations in annual growth rate, and no obvious pattern.

[0161] Vegetation distribution data are classified according to growth conditions, such as rapid growth, stable growth, periodic growth, degeneration, and intermittent growth. Then, statistical analysis software is used to perform trend analysis on the growth conditions of each classification. Trend analysis methods, such as the Mann-Kendall trend test or Sen's Slope estimation, can be used to determine the growth trend of each vegetation type.

[0162] Step S477: matching the vegetation growth trend data with a preset growth trend-compatibility matching table to obtain the land-vegetation planting compatibility;

[0163] Specifically, a vegetation growth trend dataset can be loaded into the decision support system, which contains the growth trends of various vegetation types, such as rapid growth, stable growth, periodic growth, degradation, and intermittent growth. A land-vegetation planting compatibility score is set for each growth trend:

[0164] Rapid growth trend: fit score = 9;

[0165] Stable growth trend: fit score = 7;

[0166] Cyclic growth trend: fit score = 6;

[0167] Degenerative trend: fit score = 3;

[0168] Intermittent growth trend: fit score = 4; a land-vegetation fit is generated, listing the name of each vegetation type and its corresponding fit score. Check the fit to ensure the rationality and accuracy of the score. For example, for a vegetation with an annual growth rate of 6%, the system-assigned fit score should be 9, indicating a high fit with the land conditions. Through the above steps, a detailed land-vegetation fit is obtained.

[0169] Using the built-in matching algorithm in the decision support system, the growth trend of each vegetation type is compared with the preset judgment criteria. The algorithm automatically assigns a corresponding fit score to each vegetation type.

[0170] Step S478: When all vegetation types in the vegetation type distribution data are traversed, a land-vegetation planting compatibility set is obtained, and the vegetation type distribution data and the land-vegetation planting compatibility set are used as forest resource analysis data.

[0171] Specifically, a new data layer can be created in the GIS software to store land-vegetation planting compatibility information. Then, the distribution data of each vegetation type is traversed, and the compatibility score obtained in step S477 is added to the corresponding vegetation distribution layer. Using the attribute table function of the GIS software, a new field, such as the compatibility score, is added to each vegetation distribution layer, and the score obtained in step S477 is filled into this field. When the scores of all vegetation types have been added, these layers are merged to form a comprehensive land-vegetation planting compatibility set. This set contains the distribution information of all vegetation types in the study area and their growth compatibility scores.

[0172] The present invention realizes a refined simulation of vegetation growth and change through spatiotemporal vegetation dynamic modeling, and can generate a model that reflects the actual vegetation dynamics. By accurately extracting the growth characteristics and time series data of different vegetation types, the growth of each type of vegetation can be analyzed in detail. Furthermore, by extrapolating these data over time, the spatiotemporal distribution and future growth trends of vegetation can be scientifically predicted, providing the possibility for accurate assessment of vegetation adaptability. In addition, by matching these growth trends with preset adaptability conditions, the adaptability of different vegetation to specific land conditions can be evaluated, thereby obtaining the land-vegetation planting fit, providing a scientific basis for forestry planting and management.

[0173] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0174] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A forest resource data analysis method based on forestry ecological data, characterized in that: The following steps are involved: Step S1: measuring the altitude of the target detection area to obtain an altitude measurement data set; classifying the landforms of the target detection area according to the altitude measurement data set to obtain landform type data; Step S2: ecologically partitioning the target detection area according to the landform type data to obtain ecological partitioning data; Step S3: collecting forestry ecological data of the target detection area according to the ecological zoning data to obtain a forestry ecological data set; determining vegetation diversity of the target detection area based on the forestry ecological data set to obtain vegetation type distribution data; Step S4: Determine the land type-vegetation planting compatibility of the target detection area according to the vegetation type distribution data, obtain a land-vegetation planting compatibility set, and use the vegetation type distribution data and the land-vegetation planting compatibility set as forest resource analysis data.

2. The forest resource data analysis method based on forestry ecological data according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: acquiring an electronic map of the target detection area to obtain an electronic map of the detection area; Step S12: extracting the map scale of the electronic map of the detection area to obtain the electronic map scale; Step S13: Based on the electronic map of the detection area, the target detection area is spatially gridded according to the preset spatial grid unit area and the preset grid geometric shape according to the electronic map scale to obtain a regional spatial grid unit set, wherein the regional spatial grid unit set includes a plurality of regional spatial grid units, and the regional spatial grid unit includes a grid unit number, a grid unit upper left vertex coordinate and a grid unit lower right vertex coordinate, and the preset grid geometric shape is a rectangle; Step S14: grid unit projection is performed on the electronic map of the detection area according to the set of regional spatial grid units, and the altitude measurement points of each regional grid unit are marked on the projected electronic map of the detection area to obtain a gridded electronic map of the detection area, wherein the altitude measurement points are the intersection points of the diagonals of the regional spatial grid units; Step S15: marking altitude measurement points of the target detection area according to the gridded detection area electronic map, and performing altitude measurement to obtain an altitude measurement data set, wherein the altitude measurement data set includes a plurality of altitude measurement data, and the altitude measurement data includes an altitude measurement time, an altitude measurement height, and a corresponding grid unit number; Step S16: Classify the landforms of the target detection area according to the altitude measurement data set to obtain landform type data.

3. The forest resource data analysis method based on forestry ecological data according to claim 2 is characterized in that: Step S15 includes the following steps: Step S151: extracting the altitude measurement point coordinates from the gridded detection area electronic map to obtain an altitude measurement point coordinate set, wherein the altitude measurement point coordinate set includes a plurality of altitude measurement point coordinates and their corresponding grid unit numbers; Step S152: determining the terrain features of each altitude measurement point to obtain an altitude measurement point terrain feature set, wherein the altitude measurement point terrain feature set includes a plurality of altitude measurement point terrain features, the altitude measurement point terrain features include the altitude measurement point terrain and its corresponding grid unit number, the altitude measurement point terrain is any one of a hillside, a cliff, a steep wall, a lake, a river, and a plain; Step S153: traverse the terrain feature set of the altitude measurement point, if the terrain feature of the altitude measurement point is a hillside or a plain, record the corresponding grid unit number into the stable grid unit set, otherwise, record the corresponding grid unit number into the adjusted grid unit set; Step S154: traversing the set of adjusted grid cells, determining the terrain of the entire grid cell area of ​​the adjusted grid cell based on the electronic map of the grid detection area, and if the entire grid cell area is located in any one of cliffs, steep walls, lakes, and rivers, removing the corresponding grid cell number from the set of adjusted grid cells to obtain a set of adjustable grid cells; Step S155: depicting and marking each terrain feature of each adjustable grid unit in the set of adjustable grid units in the grid detection area electronic map, and marking the terrain safety of the depicted grid detection area electronic map to obtain a marked grid detection area electronic map, wherein the terrain safety marking is specifically: If the terrain feature of the adjustable grid cell is any one of cliffs, steep walls, lakes, and rivers, the area corresponding to the terrain feature is marked as a dangerous measurement area; If the terrain feature of the adjustable grid cell is either a hillside or a plain, the area corresponding to the terrain feature is marked as a safe measurement area; Step S156: traversing the set of adjustable grid cells, for each adjustable grid cell, randomly selecting a coordinate in the safety measurement area of ​​the corresponding adjustable grid cell in the electronic map of the marked grid detection area as the adjusted altitude measurement coordinate of the corresponding adjustable grid cell, thereby obtaining an adjusted altitude measurement coordinate set, wherein the adjusted altitude measurement coordinate set includes a plurality of adjusted altitude measurement coordinates, and the adjusted altitude measurement coordinates include the adjusted altitude measurement coordinates and their corresponding grid cell numbers; Step S157: updating the altitude measurement point coordinate set according to the adjusted altitude measurement coordinate set to obtain an optimized altitude measurement coordinate set; Step S158: marking the altitude measurement points of the target detection area according to the optimized altitude measurement coordinate set, and performing altitude measurement to obtain an altitude measurement data set, wherein the altitude measurement data set includes a plurality of altitude measurement data, and the altitude measurement data includes an altitude measurement time, an altitude measurement height, and a corresponding regional grid unit number.

4. The forest resource data analysis method based on forestry ecological data according to claim 2 is characterized in that: Step S16 includes the following steps: Step S161: extracting extreme values ​​from the altitude measurement data set to obtain the maximum altitude measurement height and the minimum altitude measurement height; Step S162: subtracting the maximum altitude measured from the minimum altitude measured to obtain altitude difference data; Step S163: If the altitude difference data is within the preset plain altitude difference range, the plain is taken as the landform type data; otherwise, the mountain is taken as the landform type data.

5. The forest resource data analysis method based on forestry ecological data according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: obtaining an optimized altitude measurement coordinate set and an altitude measurement data set; Step S22: collecting remote sensing images of grid cells in each region according to the optimized altitude measurement coordinate set to obtain a grid cell remote sensing image set, wherein the grid cell remote sensing image set includes a plurality of grid cell remote sensing images, and the grid cell remote sensing images include corresponding grid cell numbers; Step S23: if the landform type data indicates that the target detection area is a plain, soil fertility data of each area grid unit is collected according to the optimized altitude measurement coordinate set to obtain a grid unit soil fertility data set, wherein the grid unit soil fertility data set includes a plurality of grid unit soil fertility data, and the grid unit soil fertility data includes corresponding grid unit numbers; Step S24: collecting soil moisture data for grid cells in each region according to the optimized altitude measurement coordinate set to obtain a grid cell soil moisture data set, wherein the grid cell soil moisture data set includes a plurality of grid cell soil moisture data, and the grid cell soil moisture data includes corresponding grid cell numbers; Step S25: stitching the grid unit remote sensing image set to obtain a global remote sensing image of the detection area; Step S26: performing vegetation recognition and mapping on the global remote sensing image of the detection area to obtain a vegetation distribution data set, wherein the vegetation distribution data set includes a plurality of vegetation distribution data, and the vegetation distribution data is used to indicate the spatial distribution of each vegetation; Step S27: Mapping the grid unit soil fertility dataset and the grid unit soil moisture dataset to the corresponding regional grid units in the vegetation distribution dataset to obtain the grid unit ecological dataset, and clustering the grid unit ecological dataset to obtain ecological zoning data, wherein the ecological zoning data is any one or more of an agricultural area, a grassland area, and a wetland area; Step S28: If the landform type data indicates that the target detection area is a mountainous area, ecological zoning is performed on the target detection area according to the altitude measurement data set to obtain ecological zoning data.

6. The forest resource data analysis method based on forestry ecological data according to claim 5 is characterized in that: Step S28 includes the following steps: Step S281: performing deduplication processing on the altitude measurement data set to obtain a deduplication altitude measurement data set; Step S282: constructing an altitude ascending curve for the deduplicated altitude measurement data set to obtain an altitude ascending curve; Step S283: Segmentally map the altitude rising curve according to the preset vertical ecological zoning gradient to obtain a vertical ecological zoning sequence, and record the vertical ecological zoning sequence as ecological zoning data, wherein the vertical ecological zoning sequence includes several vertical ecological zonings, and the vertical ecological zoning includes the name of the vertical ecological belt, the altitude range of the vertical ecological belt and the vertical ecological belt number. The name of the vertical ecological belt is any one or more of the low altitude zone, medium-low altitude zone, medium altitude zone, high altitude zone, subalpine zone and snow line zone.

7. The forest resource data analysis method based on forestry ecological data according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: constructing a three-dimensional terrain structure of the target detection area based on the altitude measurement data set to obtain a three-dimensional terrain model of the target area; Step S32: performing ecological zoning mapping on the three-dimensional terrain model of the target area according to the ecological zoning data to obtain a three-dimensional ecological zoning model; Step S33: Calculate the area of ​​each ecological zone based on the three-dimensional model of ecological zones to obtain an ecological zone area set; Step S34: Determine the data collection point layout for each ecological zone according to the ecological zone area set, and obtain a plurality of collection point sequences, wherein the collection point sequence includes a plurality of forestry ecological data collection points, and the forestry ecological data collection points include ecological zone numbers, longitude and latitude positions, and altitudes, and the one collection point sequence is the data collection point of the corresponding ecological zone; Step S35: determining the forestry ecological data collection items for the corresponding ecological partitions according to the collection point sequence to obtain a collection data item set, wherein the collection data item set at least includes forest structure data, plant species data, forest growth data, regional climate data and pest and disease occurrence data; Step S36: collecting forestry ecological data for each ecological zone according to the collected data item set to obtain a forestry ecological data set, wherein the forestry ecological data set includes a number of forestry ecological data, the forestry ecological data including forest structure data, plant species data, forest growth data, regional climate data and pest and disease occurrence data, and the forestry ecological data uniquely corresponds to the ecological zone; Step S37: Perform vegetation type statistics on the forestry ecological data set to obtain vegetation type distribution data, wherein the vegetation type distribution data includes a number of vegetation types, and the vegetation type includes a vegetation type name and vegetation type spatial distribution data.

8. The forest resource data analysis method based on forestry ecological data according to claim 1 is characterized in that: Step S4 includes the following steps: Step S41: performing vegetation growth time statistics on the forestry ecological data set to obtain a vegetation growth time data set, wherein the vegetation growth time data set includes a number of vegetation growth time data, and the vegetation growth time data includes vegetation type, initial vegetation growth year, current year and vegetation growth duration; Step S42: traverse the vegetation growth time data set, extract the maximum value of the vegetation growth time, and obtain the maximum vegetation growth time; Step S43: determining the historical remote sensing image acquisition years for the target detection area according to the maximum growth duration of vegetation, and obtaining a remote sensing acquisition year list; Step S44: collecting historical remote sensing images of the target detection area according to the remote sensing collection year list to obtain a historical remote sensing image set of the detection area, wherein the historical remote sensing image set of the detection area includes a plurality of historical remote sensing images of the detection area, and the historical remote sensing images of the detection area include shooting time, image resolution and shooting geographic coordinates; Step S45: obtaining a three-dimensional terrain model of the target area; Step S46: traversing the historical remote sensing image set of the detection area, and sequentially performing texture mapping on the historical remote sensing images of the detection area and the three-dimensional terrain model of the target area to obtain an augmented reality terrain-vegetation model set; Step S47: Based on the augmented reality terrain-vegetation model set, the land type-vegetation planting compatibility of the target detection area is determined according to the vegetation type distribution data to obtain a land-vegetation planting compatibility set, and the vegetation type distribution data and the land-vegetation planting compatibility set are used as forest resource analysis data.

9. The forest resource data analysis method based on forestry ecological data according to claim 8 is characterized in that: Step S47 includes the following steps: Step S471: performing spatiotemporal vegetation dynamic modeling on the augmented reality terrain-vegetation model set to obtain a spatiotemporal vegetation dynamic model; Step S472: traverse the vegetation type distribution data, and execute steps S473 to S477 for each vegetation type; Step S473: performing spatial feature matching and positioning on the spatiotemporal vegetation dynamic model according to the vegetation type name and the spatial distribution data of the vegetation type to obtain the target vegetation spatiotemporal sub-model; Step S474: extracting the growth dynamics of the target vegetation spatiotemporal sub-model to obtain a vegetation growth time series data set; Step S475: performing time extrapolation on the vegetation growth time series data set to obtain a vegetation spatiotemporal distribution map; Step S476: performing growth trend identification on the vegetation spatiotemporal distribution map to obtain vegetation growth trend data, wherein the vegetation growth trend data is any one of a rapid growth trend, a stable growth trend, a periodic growth trend, a degradation trend and an intermittent growth trend; Step S477: matching the vegetation growth trend data with a preset growth trend-compatibility matching table to obtain the land-vegetation planting compatibility; Step S478: When all vegetation types in the vegetation type distribution data are traversed, a land-vegetation planting compatibility set is obtained, and the vegetation type distribution data and the land-vegetation planting compatibility set are used as forest resource analysis data.

10. A forest resource data analysis system based on forestry ecological data, characterized in that: Used to execute the forest resource data analysis method based on forestry ecological data as claimed in claim 1, the forest resource data analysis system based on forestry ecological data comprises: A landform classification module is used to measure the altitude of the target detection area to obtain an altitude measurement data set; and classify the landforms of the target detection area according to the altitude measurement data set to obtain landform type data; The ecological zoning module is used to perform ecological zoning on the target detection area according to the landform type data to obtain ecological zoning data; The data collection module is used to collect forestry ecological data of the target detection area according to the ecological zoning data to obtain a forestry ecological data set; based on the forestry ecological data set, the vegetation diversity of the target detection area is determined to obtain vegetation type distribution data; The vegetation planting compatibility analysis module is used to determine the land type-vegetation planting compatibility of the target detection area according to the vegetation type distribution data, obtain the land-vegetation planting compatibility set, and use the vegetation type distribution data and the land-vegetation planting compatibility set as forest resource analysis data.

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