Terrain surveying and mapping system and surveying and mapping method for ecological system monitoring and evaluation

Abstract

The invention discloses a topographic surveying and mapping system and surveying and mapping method for ecological system monitoring and evaluation, and belongs to the technical field of topographic surveying and mapping. The surveying and mapping method comprises the following steps of: acquiring multi-temporal high-resolution image data and three-dimensional point cloud data of a target area by using an aerial surveying and mapping technology, and preprocessing the multi-temporal high-resolution image data and the three-dimensional point cloud data; on the basis of the preprocessed image data, key ecological parameters are extracted to quantitatively represent regional ecological characteristics, and meanwhile, single tree parameters, canopy structure parameters and forest biomass information are automatically extracted through point cloud data; constructing a comprehensive ecological element library integrated with multi-source heterogeneous data; constructing an ecological process model based on the comprehensive ecological element library; the comprehensive ecological element library and the ecological process model are dynamically updated, and continuous monitoring and timely evaluation of the ecological system of the target area are achieved; analyzing the change trend and the potential risk of the ecological system of the target area; and a targeted ecological protection and restoration strategy is formulated.

Description

A topographic mapping system and method for ecosystem monitoring and assessment Technical Field

[0001] This application relates to the field of topographic mapping technology, and more specifically, to a topographic mapping system and method for ecosystem monitoring and assessment. Background Technology

[0002] In today's society, with the increasing prominence of global environmental problems, the effective monitoring and assessment of ecosystems has become more urgent and important. Traditional ecosystem monitoring methods often rely on ground surveys and local sampling data. While these methods can provide detailed local information, they cannot comprehensively reflect the large-scale and dynamically changing characteristics of ecosystems. To address this issue, aerial mapping technology is widely used in the field of ecosystem monitoring. Its advantage lies in its ability to quickly acquire large-scale, high-resolution data, enabling a comprehensive description and quantitative analysis of all elements of the ecosystem.

[0003] Existing aerial mapping technologies mainly focus on acquiring high-quality image data and basic 3D terrain information. However, they still face technical challenges and limitations in addressing complex ecosystem structures and functions, especially in the refined monitoring and assessment of complex ecological environments such as forests and wetlands. For example, traditional image classification and point cloud processing methods have limited capabilities for intelligent extraction and accurate quantification of ecological parameters, while multi-source data fusion and the construction of ecological process models also face challenges related to data consistency, model complexity, and real-time performance.

[0004] In conclusion, overcoming the limitations of traditional ecological monitoring methods and achieving more comprehensive, accurate, and dynamic monitoring and assessment of ecosystems has become an urgent problem to be solved. Summary of the Invention

[0005] To overcome a series of shortcomings in existing technologies, this application aims to provide a topographic mapping method for ecosystem monitoring and assessment, comprising the following steps: Step 1, acquiring multi-temporal high-resolution image data and 3D point cloud data of the target area using aerial surveying technology, and preprocessing it based on a unified spatiotemporal data standard; Step 2, extracting key ecological parameters based on the preprocessed image data to quantitatively characterize the regional ecological features, and simultaneously automatically extracting individual tree parameters, canopy structure parameters, and forest biomass information using the point cloud data; Step 3, fusing the data extracted in Step 2 with ground high-throughput sampling data to construct a comprehensive ecological element library integrating multi-source heterogeneous data; Step 4, constructing an ecological process model based on the comprehensive ecological element library; Step 5, conducting aerial surveying regularly to continuously acquire multi-temporal dynamic remote sensing data, dynamically updating the comprehensive ecological element library and ecological process model, and achieving continuous monitoring and timely assessment of the target area's ecosystem; Step 6, analyzing the changing trends and potential risks of the target area's ecosystem based on the continuous monitoring and assessment results; Step 7, formulating targeted ecological protection and restoration strategies based on the ecosystem's changing trends and potential risks.

[0006] Furthermore, step 1 includes the following steps: Based on the scope and topographic features of the target area, rationally plan the flight path, flight altitude, and side-view angle parameters, and formulate an aerial photography operation plan; use an airborne remote sensing platform to carry out systematic and repetitive aerial photography operations according to the planned route, simultaneously acquiring high-resolution image data and 3D point cloud data; perform radiometric calibration and atmospheric and ground feature correction on the image data to generate orthophotos that reflect the true reflectivity of ground features; perform stitching, registration, classification, filtering, and denoising on the point cloud data to distinguish ground feature types and generate a complete point cloud scene; perform coordinate transformation and mosaicking of all images and point cloud data according to a unified spatiotemporal coordinate framework to ensure that data from different time periods and different flights can be seamlessly stitched together to form a complete spatiotemporal dataset.

[0007] Furthermore, step 2 includes the following steps: classifying land cover types from image data using the maximum likelihood method to obtain land cover types, specifically using the formula: P(C k │x i )=P(x i │C k )·P(C k ) / P(x i ), where P(C k │x i ) is a given pixel spectral vector x i The posterior probability of belonging to class k; P(C k P(x) is the prior probability of category k, reflecting the relative frequency of each category in the target region; i ) is the pixel spectral vector x iThe marginal probability, P(C), is used as a normalization factor during classification and does not need to be explicitly calculated; k │x i ) is the pixel spectral vector x observed under category k. i The conditional probability is usually assumed to be normally distributed, and is determined by the class mean μ. k The covariance matrix Σ k Description: P(x) i │C k )=1 / ((2π) d / 2 │Σ k │ 1 / 2 )exp(-1 / 2(x i -μ k ) T Σ k -1 (x i -μ k ), where d is the spectral dimension, |Σ k | represents the determinant of the covariance matrix; during classification, for each pixel, the posterior probability of it belonging to all categories is calculated, and the category with the highest posterior probability is selected as the classification result for that pixel; vegetation type is extracted using the vegetation index algorithm, specifically calculated as: NDVI=(NIR-RED) / (NIR+RED), where NDVI is the vegetation index used to distinguish vegetation from other ground features, with a value range of [-1, 1]. Positive values ​​indicate the presence of vegetation cover, and larger values ​​indicate more abundant vegetation. NDVI values ​​are used to further classify vegetation types or estimate leaf area index; NIR is the reflectance in the near-infrared band; RED is the reflectance in the red band; based on the spectral decomposition method, the pixel spectral vector x i The spectral composition of the decomposed land cover components is given by the following formula: x i =Σ j=1 n a ij ·s j Where n is the pixel spectral vector x i The number of land cover components in s j It is the endmember of land feature component j, a ij It is the proportion of the surface component of component j in pixel i; solve for s j and a ij And according to a ij Surface reflectance index is obtained; individual tree parameters, including tree location, height, diameter at breast height (DBH), and crown size, are automatically extracted from point cloud data using a single-tree extraction algorithm for individual tree-level analysis. The tree height H is calculated using the formula: H = z top -z ground ,z top It is the height of the top of the tree canopy, z groundThe height of the tree's base is the ground level; the diameter at breast height (DBH) D is calculated as: D = 2√(A / π), where A is the area of ​​the trunk's cross-section, obtained by fitting cross-sectional point cloud data; the tree's location is represented by the geographic coordinates of the center point at the base of its trunk; the size of the crown is measured by defining the crown's boundary points; further, crown structure parameters, including crown shape, crown height distribution, gap ratio, and crown density information, are extracted from the point cloud data to describe the forest's three-dimensional structure and spatial distribution characteristics; based on individual tree parameters, crown structure parameters, and tree biomass models, forest biomass information is estimated, specifically calculated as: B = exp(a·ln(D / π)). 2 H)+b·ln(D)+c), where B is biomass, and a, b, c are model parameters that are adjusted according to different tree species and regions.

[0008] Furthermore, step 3 includes the following steps: Step 3.1, converting the format of the ecological parameters and ground high-throughput sampling data extracted in step 2 to ensure data format uniformity, and handling outliers and missing values ​​to ensure data quality; Step 3.2, reprojecting different source data onto unified geographic coordinates, and unifying data with different spatial resolutions to the same resolution; Step 3.3, identifying the correlations and influencing factors between data, and constructing logical relationships and mathematical models between data; Step 3.4, based on the constructed logical relationships and mathematical models, integrating and fusing multi-source data to form a comprehensive ecological element database.

[0009] Furthermore, step 4 includes the following steps: Step 4.1, analyze the data in the integrated ecological element database to reveal the main ecological patterns of the target area, including species distribution patterns, spatiotemporal changes in biodiversity, and the relationship between ecosystem structure and function; Step 4.2, select an appropriate ecological process model based on the identified ecological patterns; Step 4.3, use the data in the integrated ecological element database to determine the parameter values ​​required for the ecological process model; Step 4.4, use the integrated ecological elements as initial conditions and simulate the ecological dynamic changes of the target area at different time scales in the future using the ecological process model; Step 4.5, interpret the future ecological state predicted by the model, identify possible trends and key driving factors, and assess the potential impact of different management strategies on the ecosystem; Step 4.6, formulate ecological protection and management strategies for the target area based on the simulation results and assessment analysis.

[0010] Furthermore, step 5 includes the following steps: Step 5.1, determine the appropriate mapping frequency and specific time points based on the ecological change rate, key periods, and project requirements of the target area; Step 5.2, conduct aerial photography as planned to acquire new data, integrate it with the existing comprehensive ecological element database, replace old data, and supplement missing data to generate a new comprehensive ecological element database; Step 5.3, use the new comprehensive ecological element database to evaluate and revise the structure, parameters, or input variables of the ecological process model to improve the model's accuracy and predictive ability; Step 5.4, utilize the latest ecological element database and the updated ecological process model to continuously track the changing trends of the ecosystem in the target area and promptly identify potential environmental problems or ecological risks; Step 5.5, adjust the frequency and methods of aerial mapping based on monitoring results and feedback to better adapt to changes in the target area and project requirements.

[0011] Furthermore, step 5.2 includes the following steps: Converting and standardizing the newly acquired aerial data to conform to the data format and standards adopted by the integrated ecological element database; verifying the data structure, field definitions, and data quality of the existing integrated ecological element database to confirm whether adjustments are needed to accommodate the integration of new data, while ensuring that the spatial framework of the new data is completely consistent with that of the existing ecological element database; overlaying the new data with the corresponding layers in the existing ecological element database, and using GIS software to determine spatial relationships and identify changes in each ecological element within the coverage area of ​​the new data; automatically replacing old and new data that can be automatically identified and matched using GIS software; manually comparing and reviewing data that is difficult to match automatically to determine the validity of the new data and whether the old data needs to be replaced; identifying data in the existing ecological element database... For areas not yet covered or with missing data, information from the corresponding areas in the new aerial photography data will be added. For ecological elements that change over time, the new aerial photography data will be added as new time slices to the existing time series to form a continuous dynamic monitoring record. The updated data will be evaluated for accuracy, including spatial positioning accuracy, classification accuracy, and attribute data accuracy, to ensure that the data quality meets application requirements. The acquisition time, data source, and processing method of the new data will be recorded, and the metadata of the comprehensive ecological element database will be updated to maintain data traceability and transparency. The integrated new data will be imported into the comprehensive ecological element database to ensure the consistency of the data structure, update relevant indexes and relationships, and optimize data storage efficiency. The database before and after the update will be backed up, and historical versions of the data will be properly preserved for future trend analysis, model validation, or data recovery.

[0012] Furthermore, step 6 includes the following steps: Step 6.1, collect and analyze the results of continuous assessments to identify the changing trends of the ecosystem in the target area and explore the causes and mechanisms behind ecological changes; Step 6.2, based on the analysis results of step 6.1, identify the main ecological risks and problems faced by the target area and compare them with management objectives to determine priority issues to be addressed and possible intervention measures, and formulate targeted management strategies and decision-making recommendations; Step 6.3, transform the management strategies and decision-making recommendations into action plans, and understand whether the implemented measures have effectively solved ecological problems through continuous monitoring and evaluation; Step 6.4, based on the dynamic updates of the comprehensive ecological element database and ecological process model, set thresholds for key ecological indicators, and trigger early warnings when monitoring data exceeds or approaches these thresholds, prompting timely measures to prevent irreversible damage to the ecosystem; Step 6.5, using the dynamically updated ecological element database and ecological process model, quantitatively assess the ecosystem's service capacity in regulating climate, conserving water and soil, and maintaining biodiversity, providing a scientific basis for regional sustainable development.

[0013] Furthermore, identifying the main ecological risks and problems faced by the target area and comparing them with management objectives includes the following steps: refining the overall ecological management objectives of the target area into specific and quantifiable sub-objectives, and setting management target thresholds for each sub-objective; identifying the main ecological risks and problems existing in the target area based on data from a comprehensive ecological element database; selecting key indicators that reflect the severity of each risk and problem, analyzing their long-term trends, and revealing the dynamics of risk development; associating each risk and problem with its most directly affected management sub-objectives to clarify the potential threats of risks to the achievement of objectives; calculating the absolute gap between the current risk indicator value and the management target threshold; classifying each risk into levels based on the magnitude of the difference; prioritizing all identified ecological risks according to their risk levels; and proposing targeted prevention, mitigation, or adaptation measures for high-priority risks, based on the prediction results of ecological process models, to ensure consistency between management actions and objectives.

[0014] Furthermore, the formula for calculating the RiskScore is: RiskScore m =Σ p=1 N w p ·│R m -M p │, where RiskScore m represents the comprehensive score of the m-th ecological risk; N is the number of management objectives; w p The weight of the p-th management objective reflects its relative importance in the overall ecological management strategy, satisfying w p ≥0 and Σ p=1 Nw p =1; |R m -M p │ represents R m With M p The absolute difference between them; R m M represents the current risk indicator value of the m-th ecological risk; p This represents the threshold value for the p-th management objective.

[0015] Furthermore, setting thresholds for key ecological indicators includes the following steps: Based on research objectives and future forecasting needs, construct a series of simulation scenarios covering the magnitude and trends of changes in different driving factors; run ecological process models under each simulation scenario to generate dynamic change curves for key ecological indicators over a future period; conduct in-depth analysis of the simulation results to identify the trends, turning points, and fluctuation characteristics of key ecological indicators over time; based on the dynamic analysis results, identify points where key ecological indicator values ​​change significantly or transition from a stable to an unstable state; considering model uncertainty, natural variability, and data errors, set a threshold range; and test the robustness of the determined threshold by running more simulation scenarios. If the threshold remains stable under multiple scenarios, it is determined as the threshold for triggering an early warning.

[0016] Furthermore, step 7 includes the following steps: assessing the potential impact risks of ecological changes on biodiversity and ecosystem service functions, and identifying key protection targets and areas; based on the risk assessment results, formulating graded and classified protection and restoration strategies: for low-risk areas, adopting routine ecological monitoring and patrols; for medium-risk areas, formulating land use control and vegetation restoration measures; for high-risk areas, establishing strict protection red lines, restricting human activities, and implementing key ecological restoration projects; continuously tracking and evaluating the effectiveness of the strategy implementation, and dynamically adjusting and updating the strategy plan based on new ecological data.

[0017] A topographic mapping system for ecosystem monitoring and assessment, implementing the aforementioned topographic mapping methods, includes: a data acquisition module responsible for acquiring high-quality multi-temporal aerial imagery and point cloud data to lay the foundation for subsequent processing; a data interpretation and feature extraction module for intelligently extracting key ecological parameters from imagery and point cloud data to quantitatively describe regional ecological characteristics; a data fusion module for effectively integrating multi-source heterogeneous data to construct an integrated ecological feature library; an ecological process modeling module for constructing an ecological process model based on the ecological feature library, simulating future dynamics, and formulating management strategies; a continuous monitoring module for continuously collecting new data to update the ecological feature library and ecological process model, achieving continuous dynamic monitoring of the target area; and an analysis and decision-making module for analyzing monitoring results, identifying trends and risks, formulating scientific decisions, and conducting ecological early warning and service assessments.

[0018] Compared with existing technologies, the beneficial effects of this application are as follows: This application combines advanced aerial surveying technology and data processing methods, which solves the limitations of traditional ecological monitoring methods and provides technical support and solutions for comprehensive, accurate and dynamic monitoring and assessment of ecosystems. Attached Figure Description

[0019] Figure 1 is a flowchart of a topographic mapping method for ecosystem monitoring and assessment disclosed in an embodiment of this application.

[0020] Figure 2 is a structural diagram of a topographic mapping system for ecosystem monitoring and assessment disclosed in an embodiment of this application. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of the embodiments of this invention will be described in more detail below with reference to the accompanying drawings. In the drawings, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The described embodiments are some embodiments of this invention, but not all embodiments.

[0022] Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] The embodiments and directional terms described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0024] As shown in Figure 1, a topographic mapping method for ecosystem monitoring and assessment includes the following steps: Step 1, acquiring multi-temporal high-resolution image data and 3D point cloud data of the target area using aerial surveying technology, and preprocessing it based on a unified spatiotemporal data standard. This data, collected using aerial cameras, lidar, and other equipment, covers every corner of the target area, providing rich surface information. Preprocessing this data is crucial, including image radiometric calibration, atmospheric and ground feature correction, and point cloud data stitching, registration, classification, filtering, and denoising to ensure data quality and consistency, laying a reliable foundation for subsequent data analysis and processing.

[0025] Step 2: Based on the preprocessed image data, key ecological parameters are extracted to quantitatively characterize the regional ecological features. Simultaneously, point cloud data is used to automatically extract individual tree parameters, canopy structure parameters, and forest biomass information. Building upon the preprocessed image data, computer vision and image processing techniques are employed to extract key ecological parameters, such as vegetation cover, leaf area index (LAI), and chlorophyll content, to quantitatively characterize the regional ecological features. Furthermore, point cloud data can be used to automatically extract individual tree parameters, such as tree height, diameter at breast height (DBH), and crown shape, as well as canopy structure parameters and forest biomass information. These parameters provide crucial information for a deeper understanding of the target area's ecosystem.

[0026] Step 3 involves integrating the data extracted in Step 2 with high-throughput ground sampling data to construct a comprehensive ecological element library that integrates multi-source heterogeneous data. This comprehensive ecological element library is the core of the entire topographic mapping method, gathering various types of ecological data, including remote sensing data and ground sampling data, providing rich data resources for the establishment and analysis of ecological process models.

[0027] Step 4 involves constructing an ecological process model based on a comprehensive ecological element database. This is a crucial step in simulating and predicting the ecosystem of the target area. The ecological process model can simulate the dynamic changes in the ecosystem of the target area based on different ecological parameters and environmental conditions, helping to better understand the ecosystem's operational mechanisms and response patterns.

[0028] Step 5 involves regularly conducting aerial mapping to continuously acquire multi-temporal dynamic remote sensing data, dynamically updating the comprehensive ecological element database and ecological process model, and achieving continuous monitoring and timely assessment of the target area's ecosystem. This step ensures the continuous operation of the topographic mapping method, enabling timely detection and response to changes and risks in the target area's ecosystem through regular data updates and analysis.

[0029] Step 6: Based on continuous monitoring and assessment results, analyze the changing trends and potential risks of the target area's ecosystem. Using statistical and trend analysis methods, conduct an in-depth analysis of the changing trends of the target area's ecosystem, based on continuous monitoring and assessment results. This includes changes in vegetation cover, biomass increases and decreases, and changes in species diversity. Simultaneously, it also focuses on potential risks to the ecosystem, such as climate change and human disturbance. The analysis and assessment of these risks can provide important references for the formulation of ecological protection and restoration strategies.

[0030] Step 7: Based on ecosystem change trends and potential risks, develop targeted ecological protection and restoration strategies. These strategies include vegetation restoration, soil and water conservation, and biodiversity protection, among others. By implementing these strategies, the ecosystem status of the target area can be effectively improved and optimized, enhancing its ecosystem service functions and sustainability.

[0031] Furthermore, step 1 includes the following steps: Based on the scope and topographic features of the target area, rationally plan the flight path, flight altitude, and side-view angle parameters, and formulate an aerial photography operation plan. This step is the preliminary preparation work for topographic mapping methods, and it is necessary to fully consider factors such as the geomorphic features, distribution of features, and obstruction of the target area to determine the optimal flight path design in order to ensure the comprehensiveness and accuracy of the aerial photography data.

[0032] Using an aerial remote sensing platform, systematic and repetitive aerial photography operations are conducted along a planned route to simultaneously acquire high-resolution imagery and 3D point cloud data. Aerial remote sensing platforms can include drones, airships, helicopters, etc., with appropriate platforms and equipment selected based on the specific circumstances to ensure the acquisition of high-quality aerial data. Repetitive aerial photography operations guarantee the timeliness and consistency of the data, providing a reliable data foundation for subsequent dynamic monitoring.

[0033] Radiometric calibration and atmospheric and terrain feature correction are performed on the image data to generate orthophotos that reflect the true reflectance of terrain features. This step is a crucial part of data preprocessing. By correcting and processing the image data, the influence of atmospheric and topographical factors on the data is eliminated, ensuring the accuracy and comparability of the data and providing a reliable foundation for subsequent data analysis and processing.

[0034] Point cloud data is stitched, registered, classified, filtered, and denoised to distinguish land cover types and generate a complete point cloud scene. Point cloud data is an important data source for describing landforms and land cover features. By processing and analyzing point cloud data, three-dimensional terrain information of the target area can be obtained, providing key support for the implementation of topographic mapping methods.

[0035] All imagery and point cloud data are transformed and mosaicked according to a unified spatiotemporal coordinate framework to ensure seamless stitching of data from different time periods and flights, forming a complete spatiotemporal dataset. This step is a crucial link in data integration and fusion. By using a unified spatiotemporal coordinate framework, data from different sources and times are integrated into a complete dataset, providing a consistent and reliable data foundation for subsequent data analysis and model building.

[0036] Furthermore, step 2 includes the following steps: classifying land cover types from image data using the maximum likelihood method to obtain land cover types, specifically using the formula: P(C k │x i )=P(x i │C k )·P(C k ) / P(x i ), where P(C k │x i ) is a given pixel spectral vector xi The posterior probability of belonging to class k; P(C k P(x) is the prior probability of category k, reflecting the relative frequency of each category in the target region; i ) is the pixel spectral vector x i The marginal probability, P(C), is used as a normalization factor during classification and does not need to be explicitly calculated; k │x i ) is the pixel spectral vector x observed under category k. i The conditional probability is usually assumed to be normally distributed, and is determined by the class mean μ. k The covariance matrix Σ k Description: P(x) i │C k )=1 / ((2π) d / 2 │Σ k │ 1 / 2 )exp(-1 / 2(x i -μ k ) T Σ k -1 (x i -μ k ), where d is the spectral dimension, |Σ k | represents the determinant of the covariance matrix; during classification, for each pixel, the posterior probability of it belonging to all categories is calculated, and the category with the highest posterior probability is selected as the classification result for that pixel. This step can effectively classify pixels in image data into different land cover types, providing a foundation for subsequent ecological parameter extraction and analysis.

[0037] Vegetation types are extracted using a vegetation index algorithm. The specific calculation formula is: NDVI = (NIR - RED) / (NIR + RED). NDVI is the vegetation index used to distinguish vegetation from other land cover, with a value range of [-1, 1]. A positive value indicates the presence of vegetation cover, and a larger value indicates more abundant vegetation. NDVI values ​​are used to further classify vegetation types or estimate leaf area index. NIR is the reflectance in the near-infrared band, and RED is the reflectance in the red band. This step is of great significance for analyzing the distribution and coverage of vegetation, and helps to understand the vegetation status and ecological environment of the target area.

[0038] Based on the spectral decomposition method, the pixel spectral vector x i The spectral composition of the decomposed land cover components is given by the following formula: x i =Σ j=1 n a ij ·s j Where n is the pixel spectral vector x i The number of land cover components in s jIt is the endmember of land feature component j, a ij It is the proportion of the surface component of component j in pixel i; solve for s j and a ij And according to a ij Obtain the surface reflectance index. This step provides more detailed and accurate information about land features, supporting the refined extraction of ecological parameters.

[0039] A single-tree extraction algorithm is used to automatically extract individual tree parameters from point cloud data, including tree location, height, diameter at breast height (DBH), and crown size, for individual tree-level analysis. The formula for calculating tree height H is: H = z top -z ground ,z top It is the height of the top of the tree canopy, z ground The height of the tree is the point at the ground beneath it; the formula for calculating the diameter at breast height (D) is: D = 2√(A / π), where A is the area of ​​the trunk's cross-section, obtained by fitting cross-sectional point cloud data; the location is represented by the geographic coordinates of the center point at the base of the trunk; the size of the crown is usually measured by defining the crown's boundary points. This step helps to understand the distribution and growth status of trees within the target area, providing important reference for the management and protection of forest ecosystems.

[0040] Canopy structure parameters, including canopy shape, canopy height distribution, gap ratio, and canopy density, are further extracted from point cloud data to describe the three-dimensional structure and spatial distribution characteristics of the forest. Analyzing these canopy structure parameters provides insights into the spatial pattern and ecological functions of forest ecosystems, supporting the development of ecological process models and ecosystem management.

[0041] Based on individual tree parameters, canopy structure parameters, and tree biomass models, forest biomass information is estimated. The specific calculation formula is: B = exp(a·ln(D)). 2 The model is defined as H) + b·ln(D) + c), where B is biomass, and a, b, and c are model parameters adjusted according to different tree species and regions. The advantage of this model lies in considering the non-linear relationship between tree diameter at breast height (DBH), tree height, and biomass. Logarithmic transformation allows the data to better adapt to a linear relationship, while the exponential function ensures that the biomass estimation result is positive. The determination of model parameters a, b, and c typically requires fitting and adjusting based on field sampling data and tree biomass measurements to ensure that the model accurately reflects the characteristics of different tree species and regions.

[0042] Furthermore, step 3 includes the following steps: Step 3.1, converting the format of the ecological parameters and ground high-throughput sampling data extracted in step 2 to ensure data format uniformity, and handling outliers and missing values ​​to ensure data quality; Step 3.2, reprojecting different source data onto unified geographic coordinates, and unifying data with different spatial resolutions to the same resolution; Step 3.3, identifying the correlations and influencing factors between data, and constructing logical relationships and mathematical models between data; Step 3.4, based on the constructed logical relationships and mathematical models, integrating and fusing multi-source data to form a comprehensive ecological element database.

[0043] In step 3.1, the ecological parameters and ground high-throughput sampling data extracted in step 2 are first converted to a uniform format to ensure data consistency. This includes converting data from different data sources into the same format for subsequent processing and analysis. Simultaneously, outliers and missing values ​​are handled by using appropriate methods to fill or delete them, ensuring data quality and integrity.

[0044] In step 3.2, the different source data are reprojected onto a unified geographic coordinate system to ensure their geospatial consistency. Simultaneously, data with different spatial resolutions are unified to the same resolution for subsequent data integration and analysis. This includes processing the data such as interpolation or downsampling to ensure they have the same spatial resolution.

[0045] In step 3.3, the correlations and influencing factors between data are identified, and logical relationships and mathematical models between the data are constructed. By analyzing the relationships between data, potential connections and influencing factors can be discovered, providing a basis for subsequent data integration and analysis. Simultaneously, based on the constructed logical relationships and mathematical models, multi-source data can be integrated and fused to form a comprehensive ecological element database.

[0046] In step 3.4, based on the constructed logical relationships and mathematical models, multi-source data are integrated and fused to form a comprehensive ecological element database. This includes integrating data from different data sources to form a unified ecological element database, facilitating subsequent ecological process model building and analysis. Through data integration and fusion, a more comprehensive understanding of the ecosystem characteristics and dynamic changes in the target area can be achieved.

[0047] Furthermore, step 4 includes the following steps: Step 4.1, analyze the data in the integrated ecological element database to reveal the main ecological patterns of the target area, including species distribution patterns, spatiotemporal changes in biodiversity, and the relationship between ecosystem structure and function; Step 4.2, select an appropriate ecological process model based on the identified ecological patterns; Step 4.3, use the data in the integrated ecological element database to determine the parameter values ​​required for the ecological process model; Step 4.4, use the integrated ecological elements as initial conditions and simulate the ecological dynamic changes of the target area at different time scales in the future using the ecological process model; Step 4.5, interpret the future ecological state predicted by the model, identify possible trends and key driving factors, and assess the potential impact of different management strategies on the ecosystem; Step 4.6, formulate ecological protection and management strategies for the target area based on the simulation results and assessment analysis.

[0048] Step 4.1 analyzes and synthesizes data from the ecological element database to reveal the main ecological patterns in the target area. Statistical and spatial analysis of the data reveals species distribution patterns, spatiotemporal variations in biodiversity, and the relationship between ecosystem structure and function. This contributes to a deeper understanding of the ecosystem characteristics and dynamic changes in the target area and provides a foundation for subsequent ecological process modeling.

[0049] In step 4.2, based on the identified ecological patterns, appropriate ecological process models are selected. According to the characteristics of the target area and the research objectives, mathematical models suitable for describing the dynamic changes of the ecosystem are chosen. These models can cover all levels from species interactions to ecosystem functions, including energy flow, material cycling, and biological community dynamics.

[0050] In step 4.3, the parameter values ​​required for the ecological process model are determined using data from the comprehensive ecological element database. By analyzing and processing the data in the ecological element database, the parameter values ​​in the model are estimated, and the model parameters are calibrated and validated to ensure that the model accurately reflects the ecosystem characteristics and dynamic changes of the target area.

[0051] In step 4.4, integrated ecological elements are used as initial conditions, and an ecological process model is employed to simulate the dynamic changes in the ecological environment of the target area at different time scales in the future. By simulating the response and changes of the ecosystem under different management strategies, the possible development trends of the future ecological state can be predicted, and the effectiveness of different management strategies can be evaluated.

[0052] Step 4.5 interprets the model's predicted future ecological state, identifies potential trends and key drivers, and assesses the potential impacts of different management strategies on the ecosystem. The interpretation and evaluation of the simulation results provide a scientific basis for developing ecological protection and management strategies for the target area.

[0053] In step 4.6, based on the simulation results and assessment analysis, ecological protection and management strategies are formulated for the target area. Based on the understanding and prediction of ecosystem dynamics, specific management measures and policies are developed to promote the healthy development and sustainable management of the ecosystem.

[0054] Furthermore, step 5 includes the following steps: Step 5.1, determine the appropriate mapping frequency and specific time points based on the ecological change rate, key periods, and project requirements of the target area; Step 5.2, conduct aerial photography as planned to acquire new data, integrate it with the existing comprehensive ecological element database, replace old data, and supplement missing data to generate a new comprehensive ecological element database; Step 5.3, use the new comprehensive ecological element database to evaluate and revise the structure, parameters, or input variables of the ecological process model to improve the model's accuracy and predictive ability; Step 5.4, utilize the latest ecological element database and the updated ecological process model to continuously track the changing trends of the ecosystem in the target area and promptly identify potential environmental problems or ecological risks; Step 5.5, adjust the frequency and methods of aerial mapping based on monitoring results and feedback to better adapt to changes in the target area and project requirements.

[0055] Step 5.1 begins with a thorough understanding of the rate of ecological change in the target area. This requires considering various factors such as climate, topography, vegetation type, and human activities. Furthermore, critical periods and project needs are also important factors in determining the mapping frequency. For example, during periods sensitive to climate change or in areas with frequent human activity, it may be necessary to increase the mapping frequency to capture subtle ecological changes. Therefore, by considering these factors, a mapping plan that is both practical and feasible can be developed.

[0056] Step 5.2 is crucial in ensuring the compatibility of the new and old data in terms of format, resolution, and coordinate system. This step generates a more comprehensive and accurate new integrated ecological element database, providing strong data support for subsequent ecological process model assessments.

[0057] Step 5.3 will use a new integrated ecological element library to evaluate and revise the structure, parameters, or input variables of the ecological process model. This includes checking the model's accuracy, predictive ability, and for any biases. Through continuous evaluation and revision, the model's accuracy and predictive ability can be improved, providing a more reliable basis for subsequent ecological monitoring and assessment.

[0058] In step 5.3, after the evaluation and revision of the ecological process model, the latest ecological element database and the updated ecological process model will be used to continuously track the changing trends of the target area's ecosystem. This includes monitoring multiple aspects such as vegetation cover, water quality changes, and biodiversity. Through continuous tracking, potential environmental problems or ecological risks can be identified in a timely manner, providing a scientific basis for taking corresponding countermeasures.

[0059] In step 5.5, based on the monitoring results and feedback, the frequency and methods of aerial mapping need to be adjusted. This includes adjusting the mapping frequency according to the rate of ecological change and project requirements, and optimizing aerial photography techniques and data processing workflows based on actual applications. Through continuous adjustment and optimization, the continuity and effectiveness of ecological monitoring and assessment can be ensured, providing more comprehensive and accurate data support for ecological research.

[0060] Furthermore, step 5.2 includes the following steps: Converting and standardizing the newly acquired aerial data to conform to the data format and standards adopted by the comprehensive ecological element database. Converting and standardizing the newly acquired aerial data is a prerequisite for ensuring data consistency and comparability. Through format conversion, the new data can conform to the data format and standards adopted by the comprehensive ecological element database, laying the foundation for subsequent data fusion and analysis. During this process, attention needs to be paid to the potential differences in data formats generated by different aerial photography equipment, as well as the compatibility issues between different data standards, to ensure that the converted data meets the requirements of the comprehensive ecological element database.

[0061] The existing integrated ecological element database should be reviewed for its data structure, field definitions, and data quality to determine if adjustments are needed to accommodate the integration of new data, while ensuring complete spatial consistency between the new data and the existing database. This step not only helps understand the current state of the database but also provides a basis for subsequent data fusion. During the review, key areas of focus should be on the rationality of the data structure, the clarity of field definitions, and the reliability of data quality. If problems or deficiencies are found in the existing database, adjustments should be made promptly to meet the integration requirements of the new data. Ensuring complete spatial consistency between the new data and the existing ecological element database is crucial for guaranteeing data spatial consistency.

[0062] Overlaying new data with corresponding layers in the existing ecological element database and using GIS software to determine spatial relationships helps identify changes in various ecological elements within the coverage area of ​​the new data. This overlaying is a crucial method for revealing changes in ecological elements. Using GIS software to determine spatial relationships allows for the identification of changes in various ecological elements within the coverage area of ​​the new data, providing strong support for ecological monitoring and assessment. In this process, the spatial analysis function of GIS software plays a vital role, enabling it to quickly and accurately identify changes in ecological elements.

[0063] For new and old data that can be automatically identified and matched, using GIS software to automatically replace data can greatly improve data processing efficiency.

[0064] For data that is difficult to match automatically, manual comparison and review are required to determine the validity of the new data and whether the old data needs to be replaced. In practice, some data that is difficult to match automatically will inevitably be encountered. In this case, manual comparison and review are necessary to ensure the validity of the new data and whether the old data needs to be replaced. This step requires data processing personnel to have extensive professional knowledge and experience to ensure the accuracy and reliability of data processing.

[0065] By identifying areas not yet covered or lacking data in the existing ecological element database and supplementing them with information from new aerial photography data, the database's information coverage can be improved. This not only helps enhance the overall quality of the database but also provides more comprehensive and abundant data support for subsequent ecological research.

[0066] For ecological elements that change over time, new aerial photography data is added as new time slices to existing time series to form continuous dynamic monitoring records. This step helps to better understand the changing trends of ecological elements over time, providing a strong basis for ecological prediction and early warning. At the same time, this also places higher demands on data processing and analysis, requiring a high degree of rigor and accuracy in the processing.

[0067] The updated data undergoes an accuracy assessment, including spatial positioning accuracy, classification accuracy, and attribute data accuracy, to ensure data quality meets application requirements. Information regarding the acquisition time, data source, and processing methods of the new data is recorded, and the metadata of the comprehensive ecological element database is updated to maintain data traceability and transparency. After data integration, assessing the accuracy of the updated data is a crucial step in ensuring data quality meets application needs. A comprehensive assessment of spatial positioning accuracy, classification accuracy, and attribute data accuracy is required to ensure data accuracy and reliability. Furthermore, recording the acquisition time, data source, and processing methods of the new data is also essential, as this facilitates future data traceability and improved transparency.

[0068] The integrated new data is imported into the comprehensive ecological element database to ensure data structure consistency, update relevant indexes and relationships, and optimize data storage efficiency. Backups are made to the database before and after the update, and historical versions of data are properly preserved for future trend analysis, model validation, or data recovery. Importing the integrated new data into the comprehensive ecological element database and updating relevant indexes and relationships is a crucial step in ensuring data structure consistency and data storage efficiency. Simultaneously, backing up the database before and after the update and properly preserving historical versions of data demonstrates the standardization and forward-looking nature of data management, providing a solid foundation for future ecological research and practice.

[0069] Furthermore, step 6 includes the following steps: Step 6.1, collecting, organizing, and analyzing the results of the continuous assessment to identify trends in ecosystem change in the target area and explore the causes and mechanisms behind these ecological changes. The primary task of this step is to collect and organize the results of the continuous assessment, including biodiversity surveys, soil quality monitoring, water quality monitoring, and air quality monitoring. Then, this data is analyzed to identify trends in ecosystem change, such as decreased species richness and increased soil erosion. Simultaneously, it is necessary to explore the causes and mechanisms of these ecological changes, involving natural factors such as climate change and anthropogenic factors such as land use change. The purpose of this step is to provide a scientific basis for the formulation of subsequent management strategies.

[0070] Step 6.2, based on the analysis results of Step 6.1, identifies the main ecological risks and problems facing the target area and compares them with the management objectives to determine priority issues to be addressed and possible intervention measures, and formulates targeted management strategies and decision-making recommendations. In this step, the analysis results are compared with the established management objectives to identify the main ecological risks and problems facing the target area. Then, priority issues to be addressed are determined, and possible intervention measures are proposed, involving land restoration, biodiversity conservation, and water resource management. Finally, targeted management strategies and decision-making recommendations are formulated to address these ecological risks and problems and achieve the management objectives.

[0071] Step 6.3 translates management strategies and policy recommendations into action plans. Through continuous monitoring and evaluation, it determines whether the implemented measures have effectively addressed ecological issues. This step transforms the formulated management strategies and policy recommendations into concrete action plans and implements them in the target area. Simultaneously, through continuous monitoring and evaluation, it determines whether the implemented measures have effectively addressed ecological issues. If problems arise, the action plans are adjusted and improved promptly.

[0072] Step 6.4, based on the dynamic updates of the comprehensive ecological element database and ecological process model, sets thresholds for key ecological indicators. When monitoring data exceeds or approaches these thresholds, an early warning is triggered, prompting timely measures to prevent irreversible damage to the ecosystem. In this step, the thresholds for key ecological indicators are dynamically updated using the comprehensive ecological element database and ecological process model. When monitoring data exceeds or approaches these thresholds, an early warning is triggered, prompting timely measures to prevent irreversible damage to the ecosystem, which helps protect the health and stability of the ecosystem.

[0073] Step 6.5 utilizes a dynamically updated ecological element database and ecological process model to quantitatively assess the ecosystem's service capacity in regulating climate, conserving water and soil, and maintaining biodiversity, providing a scientific basis for regional sustainable development. This step uses a dynamically updated ecological element database and ecological process model to quantitatively assess the ecosystem's service capacity in regulating climate, conserving water and soil, and maintaining biodiversity. This provides a scientific basis for regional sustainable development and helps in formulating more effective ecological protection and management policies.

[0074] Furthermore, identifying the main ecological risks and problems facing the target area and comparing them with management objectives includes the following steps: refining the overall ecological management objectives of the target area into specific, quantifiable sub-objectives, and setting management target thresholds for each sub-objective. This step involves breaking down the overall objectives into more specific and actionable sub-objectives, such as improving water quality and increasing species diversity, and setting quantifiable indicators and thresholds for each sub-objective to monitor and evaluate the progress and effectiveness of management.

[0075] Based on data from the comprehensive ecological element database, the main ecological risks and problems existing in the target area are identified. This step relies on data from the comprehensive ecological element database, and by analyzing this data, the main ecological risks and problems in the target area are identified, such as biodiversity loss and water quality deterioration.

[0076] For each risk and problem, key indicators reflecting its severity are selected, and their long-term trends are analyzed to reveal the dynamics of risk development. For example, if the population of a species is declining rapidly, this could be a serious ecological risk. Close attention needs to be paid to changes in the species' population and the factors that may lead to such changes, such as environmental pollution and climate change.

[0077] By associating each risk with the problem and its most direct impact on the management sub-objectives, we can clearly identify the potential threat that the risk poses to achieving the objectives. This allows for a clearer understanding of which risks require priority and the importance of addressing them in achieving the management goals.

[0078] Calculate the absolute gap between the current risk indicator value and the management target threshold. This step helps to understand how far the current risk situation is from the management target and how much effort is required to achieve it.

[0079] Each risk is categorized into different levels based on the magnitude of the difference. This categorization allows for better resource allocation, prioritizing the risks that pose the greatest threat to achieving the objectives.

[0080] All identified ecological risks are prioritized according to their risk level. For high-priority risks, targeted prevention, mitigation, or adaptation measures are proposed based on the prediction results of ecological process models to ensure consistency between management actions and objectives. These measures should aim to ensure consistency between management actions and objectives to maximize the protection of ecosystem health and stability.

[0081] Furthermore, the formula for calculating the RiskScore is: RiskScore m =Σ p=1 N w p ·│R m -M p │, where RiskScore m represents the comprehensive score of the m-th ecological risk; N is the number of management objectives; w p The weight of the p-th management objective reflects its relative importance in the overall ecological management strategy, satisfying w p ≥0 and Σ p=1 N w p =1; |R m -M p │ represents R m With M p The absolute difference between them; R m M represents the current risk indicator value of the m-th ecological risk; p This represents the threshold value for the p-th management objective.

[0082] Furthermore, setting thresholds for key ecological indicators involves the following steps: Based on research objectives and future forecasting needs, construct a series of simulation scenarios covering the magnitude and trends of changes in different driving factors; these scenarios should encompass the magnitude and trends of changes in different driving factors, such as climate change and land use change. Each scenario represents a possible future development path; run ecological process models under each set simulation scenario to generate dynamic change curves for key ecological indicators over a future period; conduct in-depth analysis of the simulation results to identify the trends, turning points, and fluctuation characteristics of key ecological indicators over time; based on the dynamic analysis results, identify points where key ecological indicator values ​​change significantly or transition from a stable to an unstable state; considering model uncertainty, natural variability, and data errors, set a threshold range; test the robustness of the determined threshold by running more simulation scenarios; if the threshold remains stable under multiple scenarios, it is determined as the threshold for triggering an early warning.

[0083] Through the above steps, a threshold setting method based on simulated scenarios and dynamic analysis has been established, enabling timely detection of changes in ecosystem status and the implementation of corresponding management measures in future monitoring. This method considers uncertainty and variability, improving the accuracy and robustness of the threshold.

[0084] Furthermore, step 7 includes the following steps: assessing the potential risks of ecological change to biodiversity and ecosystem service functions, and identifying key conservation targets and areas. The purpose of this step is to understand the potential risks that ecological change may pose to biodiversity and ecosystem service functions through monitoring and analysis. Biodiversity refers to the richness of the variety of species on Earth, while ecosystem service functions refer to the various benefits that ecosystems provide to humans, such as food, water, and climate regulation. By assessing the risks, it is possible to identify which species, ecosystems, or geographical areas are most in need of protection, thus providing a basis for subsequent conservation and restoration strategies.

[0085] Based on the risk assessment results, a tiered and categorized protection and restoration strategy is formulated: for low-risk areas, routine ecological monitoring and patrols are implemented; for medium-risk areas, land use control and vegetation restoration measures are developed; and for high-risk areas, strict protection red lines are established, human activities are restricted, and key ecological restoration projects are implemented. The significance of this step lies in the fact that, based on the risk assessment results, targeted protection and restoration strategies can be developed. This tiered and categorized protection and restoration strategy can effectively protect the ecological environment while rationally utilizing resources, achieving a win-win situation for both the economy and the environment.

[0086] Continuously track and evaluate the effectiveness of the strategy implementation, and dynamically adjust and update the strategy based on new ecological data. The significance of this step lies in the fact that by continuously tracking and evaluating the effectiveness of the strategy, we can understand its impact and thus dynamically adjust and update the strategy based on new ecological data. This ensures the effectiveness of the strategy and makes ecological protection work more scientific and rational.

[0087] As shown in Figure 2, a topographic mapping system for ecosystem monitoring and assessment, implementing the aforementioned topographic mapping method, includes: a data acquisition module responsible for acquiring high-quality multi-temporal aerial imagery and point cloud data, laying the foundation for subsequent processing. The data acquisition module is the foundation of the entire system. It is responsible for acquiring high-quality multi-temporal aerial imagery and point cloud data, which are the raw materials for subsequent processing and analysis. Through advanced aerial mapping equipment and technology, high-precision, high-resolution imagery and point cloud data can be acquired, providing a solid foundation for subsequent processing work.

[0088] The data interpretation and feature extraction module intelligently extracts key ecological parameters from imagery and point cloud data, quantifying and describing the regional ecological characteristics. This module plays a crucial role, utilizing advanced image processing and artificial intelligence technologies to intelligently extract key ecological parameters from imagery and point cloud data. These parameters include, but are not limited to, topographic elevation, slope, aspect, vegetation cover, and biomass. They quantify the ecological characteristics of the region, providing important data for subsequent ecological process modeling and analysis.

[0089] The data fusion module effectively integrates multi-source, heterogeneous data to construct a comprehensive ecological element database. Within this module, the system effectively integrates multi-source, heterogeneous data with varying spatial resolutions and precisions. Through data fusion technology, this data can be consolidated into a comprehensive ecological element database, providing robust data support for subsequent ecological process modeling and analysis.

[0090] The ecological process modeling module constructs ecological process models based on an ecological element database, simulating future dynamics and formulating management strategies. These models can simulate regional ecological processes, including vegetation growth, hydrological cycles, and soil erosion, to predict future ecological change trends. Furthermore, based on the model results, corresponding management strategies can be developed, providing a basis for decision-making in ecological protection and sustainable development.

[0091] The continuous monitoring module is used to continuously collect new data to update the ecological element database and ecological process model, enabling continuous dynamic monitoring of the target area. The continuous monitoring module is a crucial component of the system. It is responsible for continuously collecting new data and updating the ecological element database and ecological process model. Through real-time monitoring and data analysis, ecological change trends and risks can be detected promptly, providing timely and accurate information for ecological early warning and service assessment.

[0092] The analysis and decision-making module is used to analyze monitoring results, identify trends and risks, formulate scientific decisions, and conduct ecological early warning and service assessments. This module is the core of the entire system. Based on monitoring results, it uses statistical analysis, machine learning, and other methods to deeply analyze ecological change trends and risks. By identifying key issues and formulating scientific decisions, it can provide strong support for ecological management and decision-making. Simultaneously, ecological early warning and service assessments can also help relevant departments respond to ecological problems in a timely manner, improving the efficiency of ecological protection and management.

[0093] In summary, this topographic mapping system for ecosystem monitoring and assessment, by integrating multiple functional modules, enables comprehensive and efficient mapping and analysis of regional ecosystems. It provides strong support for ecological management and decision-making, and helps promote ecological protection and sustainable development.

Claims

1. A topographic mapping method for ecosystem monitoring and assessment, characterized in that, The process includes the following steps: Step 1, acquiring multi-temporal high-resolution image data and 3D point cloud data of the target area using aerial surveying technology, and preprocessing them based on a unified spatiotemporal data standard; Step 2, extracting key ecological parameters based on the preprocessed image data to quantitatively characterize the regional ecological features, and automatically extracting individual tree parameters, canopy structure parameters, and forest biomass information using point cloud data; Step 3, integrating the data extracted in Step 2 with ground high-throughput sampling data to construct a comprehensive ecological element database integrating multi-source heterogeneous data; Step 4, constructing an ecological process model based on the comprehensive ecological element database, specifically including the following steps: Step 4.1, analyzing the data in the comprehensive ecological element database. According to the data, the main ecological patterns of the target area are revealed, including species distribution patterns, spatiotemporal changes in biodiversity, and the relationship between ecosystem structure and function; Step 4.2, based on the identified ecological patterns, a suitable ecological process model is selected; Step 4.3, using data from the integrated ecological element database, the parameter values ​​required for the ecological process model are determined; Step 4.4, using the integrated ecological elements as initial conditions, the ecological process model is used to simulate the ecological dynamic changes of the target area at different time scales in the future; Step 4.5, the future ecological state predicted by the model is interpreted, possible trends and key driving factors are identified, and the potential impact of different management strategies on the ecosystem is assessed; Step 4.6, based on... The simulation results and evaluation analysis are used to formulate ecological protection and management strategies for the target area; Step 5, regular aerial mapping is conducted to continuously acquire multi-temporal dynamic remote sensing data, dynamically update the comprehensive ecological element database and ecological process model, and achieve continuous monitoring and timely assessment of the target area's ecosystem. Specifically, this includes the following steps: Step 5.1, determining the appropriate mapping frequency and specific time points based on the ecological change rate, key periods, and project requirements of the target area; Step 5.2, conducting aerial photography according to the plan to acquire new data, integrating it with the existing comprehensive ecological element database, replacing old data and supplementing missing data to generate a new comprehensive ecological element database; Step 5.3, using the new comprehensive... The ecological element database is used to assess and revise the structure, parameters, or input variables of the ecological process model to improve the model's accuracy and predictive ability; Step 5.4: Using the latest ecological element database and the updated ecological process model, the changing trends of the target area's ecosystem are continuously tracked to promptly identify potential environmental problems or ecological risks; Step 5.5: Based on monitoring results and feedback, the frequency and methods of aerial mapping are adjusted to better adapt to changes in the target area and project needs; Step 6: Based on continuous monitoring and assessment results, the changing trends and potential risks of the target area's ecosystem are analyzed; Step 7: Based on the changing trends and potential risks of the ecosystem, targeted ecological protection and restoration strategies are formulated.

2. The topographic mapping method for ecosystem monitoring and assessment according to claim 1, characterized in that, Step 1 includes the following steps: Based on the scope and topographic features of the target area, rationally plan the flight path, flight altitude, and side-view angle parameters to formulate an aerial photography operation plan; use an airborne remote sensing platform to conduct systematic and repetitive aerial photography operations according to the planned route, simultaneously acquiring high-resolution image data and 3D point cloud data; perform radiometric calibration and atmospheric and ground feature correction on the image data to generate orthophotos that reflect the true reflectivity of ground features; perform stitching, registration, classification, filtering, and denoising on the point cloud data to distinguish ground feature types and generate a complete point cloud scene; perform coordinate transformation and mosaicking of all images and point cloud data according to a unified spatiotemporal coordinate framework to ensure seamless stitching of data from different time periods and different flights, forming a complete spatiotemporal dataset.

3. The topographic mapping method for ecosystem monitoring and assessment according to claim 1, characterized in that, Step 2 includes the following steps: classifying land cover types from image data using the maximum likelihood method, and obtaining the land cover type. The specific formula is: P(C k │x i )=P(x i │C k )·P(C k ) / P(x i ), where P(C k │x i ) is a given pixel spectral vector x i The posterior probability of belonging to class k; P(C k P(x) is the prior probability of category k, reflecting the relative frequency of each category in the target region; i ) is the pixel spectral vector x i The marginal probability, P(C), is used as a normalization factor during classification and does not need to be explicitly calculated; k │x i ) is the pixel spectral vector x observed under category k. i The conditional probability is given by the class mean μ. k The covariance matrix Σ k Description: P(x) i │C k )=1 / ((2π) d / 2 │Σ k │ 1 / 2 )exp(-1 / 2(x i -μ k ) T Σ k -1 (x i -μ k ), where d is the spectral dimension, |Σ k | is the determinant of the covariance matrix; during classification, for each pixel, calculate its posterior probability belonging to all categories, and select the category with the highest posterior probability as the classification result for that pixel: vegetation type is extracted using the vegetation index algorithm, the specific calculation formula is: NDVI=(NIR-RED) / (NIR+RED), NDVI is the vegetation index, used to distinguish vegetation from other ground features, its value range is [-1, 1], a positive value indicates the presence of vegetation cover, and the larger the value, the more lush the vegetation. NDVI value is used to further classify vegetation types or estimate leaf area index; NIR is the reflectance in the near-infrared band; RED is the reflectance in the red band; based on the spectral decomposition method, the pixel spectral vector x i The spectral composition of the decomposed land cover components is given by the following formula: x i =Σ j=1 n a ij ·s j Where n is the pixel spectral vector x i The number of land cover components in s j It is the endmember of land feature component j, a ij It is the proportion of the surface component of component j in pixel i; solve for s j and a ij And according to a ij Surface reflectance index is obtained; individual tree parameters, including tree location, height, diameter at breast height (DBH), and crown size, are automatically extracted from point cloud data using a single-tree extraction algorithm for individual tree-level analysis. The tree height H is calculated using the formula: H = z top -z ground ,z top It is the height of the top of the tree canopy, z ground The height of the tree's base is the ground level; the diameter at breast height (DBH) D is calculated as: D = 2√(A / π), where A is the area of ​​the trunk's cross-section, obtained by fitting cross-sectional point cloud data; the tree's location is represented by the geographic coordinates of the center point at the base of its trunk; the size of the crown is measured by defining the crown's boundary points; further, crown structure parameters, including crown shape, crown height distribution, gap ratio, and crown density information, are extracted from the point cloud data to describe the forest's three-dimensional structure and spatial distribution characteristics; based on individual tree parameters, crown structure parameters, and tree biomass models, forest biomass information is estimated, specifically calculated as: B = exp(a·ln(D / π)). 2 H)+b·ln(D)+c), where B is biomass, and a, b, c are model parameters that are adjusted according to different tree species and regions.

4. A topographic mapping method for ecosystem monitoring and assessment according to claim 1, characterized in that, Step 3 includes the following steps: Step 3.1, convert the format of the ecological parameters and ground high-throughput sampling data extracted in Step 2 to ensure data format uniformity, and handle outliers and missing values ​​to ensure data quality; Step 3.2, reproject the data from different sources to a unified geographic coordinate system, and unify data with different spatial resolutions to the same resolution; Step 3.3, identify the correlations and influencing factors between data, and construct logical relationships and mathematical models between data; Step 3.4, based on the constructed logical relationships and mathematical models, integrate and fuse the multi-source data to form a comprehensive ecological element database.

5. A topographic mapping method for ecosystem monitoring and assessment according to claim 1, characterized in that, Step 5.2 includes the following steps: Converting and standardizing the newly acquired aerial data to conform to the data format and standards adopted by the integrated ecological element database; verifying the data structure, field definitions, and data quality of the existing integrated ecological element database to confirm whether adjustments are needed to accommodate the integration of new data, while ensuring that the spatial framework of the new data is completely consistent with the existing ecological element database; overlaying the new data with the corresponding layers in the existing ecological element database and using GIS software to determine spatial relationships and identify changes in each ecological element within the coverage area of ​​the new data; automatically replacing old and new data that can be automatically identified and matched using GIS software; manually comparing and reviewing data that is difficult to match automatically to determine the validity of the new data and whether the old data needs to be replaced; identifying data in the existing ecological element database that has not yet been... For areas with coverage or missing data, information from the corresponding areas in the new aerial photography data will be added. For ecological elements that change over time, the new aerial photography data will be added as new time slices to the existing time series to form a continuous dynamic monitoring record. The updated data will be evaluated for accuracy, including spatial positioning accuracy, classification accuracy, and attribute data accuracy, to ensure that the data quality meets application requirements. The acquisition time, data source, and processing method of the new data will be recorded, and the metadata of the comprehensive ecological element database will be updated to maintain data traceability and transparency. The integrated new data will be imported into the comprehensive ecological element database to ensure the consistency of the data structure, update relevant indexes and relationships, and optimize data storage efficiency. The database before and after the update will be backed up, and historical versions of the data will be properly preserved for future trend analysis, model validation, or data recovery.

6. A topographic mapping method for ecosystem monitoring and assessment according to claim 1, characterized in that, Step 1 includes the following steps: Step 6.1: Collect, organize, and analyze the results of continuous assessments to identify the changing trends of the ecosystem in the target area and explore the causes and mechanisms behind ecological changes; Step 6.2: Based on the analysis results of Step 6.1, identify the main ecological risks and problems faced by the target area and compare them with management objectives to determine priority issues to be addressed and possible intervention measures, and formulate targeted management strategies and decision-making recommendations; Step 6.3: Transform management strategies and decision-making recommendations into action plans, and understand whether the implemented measures have effectively solved ecological problems through continuous monitoring and evaluation; Step 6.4: Based on the dynamic updates of the comprehensive ecological element database and ecological process model, set thresholds for key ecological indicators. When monitoring data exceeds or approaches these thresholds, an early warning is triggered, prompting timely measures to prevent irreversible damage to the ecosystem; Step 6.5: Using the dynamically updated ecological element database and ecological process model, quantitatively assess the ecosystem's service capacity in regulating climate, conserving water and soil, and maintaining biodiversity, providing a scientific basis for regional sustainable development.

7. A topographic mapping method for ecosystem monitoring and assessment according to claim 6, characterized in that, Identifying the main ecological risks and problems facing the target area and comparing them with management objectives includes the following steps: refining the overall ecological management objectives of the target area into specific, quantifiable sub-objectives, and setting management target thresholds for each sub-objective; identifying the main ecological risks and problems existing in the target area based on data from a comprehensive ecological element database; selecting key indicators that reflect the severity of each risk and problem, analyzing their long-term trends, and revealing the dynamics of risk development; associating each risk and problem with its most directly impacted management sub-objectives to clarify the potential threats of risks to the achievement of objectives; and calculating the absolute gap between the current risk indicator value and the management target threshold, where the risk indicator value is calculated using the formula: RiskScore. m =Σ p=1 N w p ·│R m -M p │, where RiskScore m represents the comprehensive score of the m-th ecological risk; N is the number of management objectives; w p The weight of the p-th management objective reflects its relative importance in the overall ecological management strategy, satisfying w p ≥0 and Σ p=1 N w p =1; |R m -M p │ represents R m With M p The absolute difference between them; R m M represents the current risk indicator value of the m-th ecological risk; p This represents the threshold for the p-th management objective; each risk is classified into levels based on the magnitude of the difference; all identified ecological risks are prioritized according to their risk levels; for high-priority risks, targeted prevention, mitigation, or adaptation measures are proposed based on the prediction results of the ecological process model to ensure consistency between management actions and objectives.

8. A topographic mapping method for ecosystem monitoring and assessment according to claim 6, characterized in that, Setting thresholds for key ecological indicators involves the following steps: Based on research objectives and future forecasting needs, construct a series of simulation scenarios covering the magnitude and trends of changes in different driving factors; run ecological process models under each simulation scenario to generate dynamic change curves for key ecological indicators over a future period; conduct in-depth analysis of the simulation results to identify the trends, turning points, and fluctuation characteristics of key ecological indicators over time; based on the dynamic analysis results, identify points where key ecological indicator values ​​change significantly or transition from a stable to an unstable state; considering model uncertainty, natural variability, and data errors, set a threshold range; and test the robustness of the determined threshold by running more simulation scenarios. If the threshold remains stable under multiple scenarios, it is determined as the threshold for triggering an early warning.

9. A topographic mapping method for ecosystem monitoring and assessment according to claim 1, characterized in that, Step 7 includes the following steps: assessing the potential impact risks of ecological changes on biodiversity and ecosystem service functions, and identifying key protection targets and areas; based on the risk assessment results, formulating graded and categorized protection and restoration strategies: for low-risk areas, implementing routine ecological monitoring and patrols; for medium-risk areas, formulating land use control and vegetation restoration measures; for high-risk areas, establishing strict protection red lines, restricting human activities, and implementing key ecological restoration projects; continuously tracking and evaluating the effectiveness of the strategy implementation, and dynamically adjusting and updating the strategy plan based on new ecological data.

10. A topographic mapping system for ecosystem monitoring and assessment, used to implement the topographic mapping method as described in any one of claims 1-9, characterized in that, It includes the following modules: a data acquisition module, which is responsible for acquiring high-quality multi-temporal aerial images and point cloud data to lay the foundation for subsequent processing; and a data interpretation and feature extraction module, which is used to intelligently extract key ecological parameters from the images and point cloud data to quantitatively describe the regional ecological characteristics. The data fusion module is used to effectively integrate multi-source heterogeneous data and build an integrated ecological element database; the ecological process modeling module builds ecological process models based on the ecological element database, simulates future dynamics, and formulates management strategies. The continuous monitoring module is used to continuously collect new data to update the ecological element database and ecological process model, so as to realize continuous dynamic monitoring of the target area. The analysis and decision-making module is used to analyze monitoring results, identify trends and risks, formulate scientific decisions, and conduct ecological early warning and service assessments.

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