Forest resource map modeling method and system

Abstract

The invention discloses a forest resource map modeling method and system, and belongs to the technical field of forestry information. The method comprises the following steps: capturing multi-source heterogeneous original observation data through a sensing unit group deployed in a forest region; performing space-time alignment and quality evaluation on the data by a map generation engine to generate an original observation sequence; calling and analyzing auxiliary geographic information in the environment context library to set prior configuration parameters of the atlas reckoning device; and finally, a map reckoning device is driven to perform fusion reckoning on the observation sequence, and a structured forest resource semantic network is output. The system correspondingly comprises a sensing unit group, an environment context library, an atlas generation engine and an atlas reckoning device. According to the method, full-chain intelligent management of forest resources from precise perception and intelligent cognition to prospective planning is realized through space-based collaborative intelligent perception, a depth generation model of historical knowledge injection and operation simulation based on space-time prediction, and the precision, efficiency and decision support capability of forest resource monitoring are greatly improved.

Description

Technical Field

[0001] This application relates to computer technology, to a forest resource map construction technology, and particularly to a forest resource map modeling method and system. Background Technology

[0002] Forest resources are a core component of terrestrial ecosystems, and accurate surveys and monitoring are crucial for maintaining ecological balance, ensuring timber supply, and addressing climate change. Traditional forest resource surveys primarily rely on manual field surveys and static remote sensing image interpretation. Manual survey methods are costly, inefficient, time-consuming, and difficult to cover large areas or terrainally complex forest regions. Traditional remote sensing interpretation methods, on the other hand, often depend on single data sources (such as optical images), are susceptible to weather conditions (such as cloud cover), lack three-dimensional structural information, and have limited ability to capture understory conditions, fine structure at the individual tree scale, and dynamic changes in the forest.

[0003] With technological advancements, new methods such as lidar and multispectral sensing have been introduced, enriching data types to some extent. However, existing technological systems still have significant shortcomings: First, multi-source data are often isolated or simply superimposed, lacking effective spatiotemporal registration and deep fusion mechanisms, making it difficult to form a unified and consistent understanding of forest ecosystems. Second, data analysis models are mostly discriminative, focusing on fitting patterns from observational data and failing to effectively incorporate long-accumulated prior knowledge in the forestry field (such as historical distribution patterns and ecological laws), resulting in weak model generalization ability and poor ecological rationality of prediction results when data quality is poor. Finally, most system functions stop at assessing the current state of resources, lacking the ability to simulate and predict the future dynamic evolution of forests and the long-term impacts of different human management strategies, thus failing to provide forward-looking quantitative decision-making basis for sustainable forest management. Summary of the Invention

[0004] In view of this, the present invention provides a method and system for mapping and modeling forest resources to solve the technical problems mentioned in the background.

[0005] The specific content includes: A method for mapping and modeling forest resources, characterized by comprising: Raw observation data is captured by a cluster of sensor units deployed in the forest area; The original observation data is received and processed by the map generation engine to obtain the original observation sequence; The map generation engine retrieves auxiliary geographic information from the environmental context library; The auxiliary geographic information is parsed by the map generation engine to set the prior configuration parameters of the map inference tool; and The map generation engine drives the map inferencer, which combines the prior configuration parameters to perform fusion inference on the original observation sequence and outputs a structured forest resource semantic network. The auxiliary geographic information includes at least one of the following: Forest digital elevation model, Multispectral vegetation index images, or Semantic network of historical forest resources.

[0006] Furthermore, the auxiliary geographic information includes a temporal multispectral image set of the forest area and multiple historical versions of the forest resource semantic network; The temporal multispectral image set is derived from satellite overhead data from different seasons; By performing change detection on the temporal multispectral image set, abnormal disturbance areas within the forest area are identified. Then, the map generation engine performs standing unit segmentation on the temporal multispectral image set to generate a standing location map, which includes the geographical locations of the segmented standing units. Furthermore, based on the multiple historical versions of the forest resource semantic network and combined with the abnormal disturbance areas, the map generation engine infers the dynamic displacement trajectory of the standing units, generates a standing displacement prediction field, and obtains the geographical locations of the abnormal disturbance morphology based on the standing displacement prediction field.

[0007] Furthermore, the original observation sequence is obtained in the following way: the task constraint set of historical data acquisition tasks and its corresponding benchmark data paradigm are obtained, wherein the task constraint set includes site environment factors or acquisition operation factors, and the benchmark data paradigm defines the inherent data output structure of the acquisition equipment; Based on the task constraint set and the benchmark data paradigm, a data paradigm mapper is trained to establish the correlation between the observed data and the benchmark data paradigm. Determine the target data paradigm for the current data acquisition task; Get the current task constraint set; Input the current observation data into the data paradigm mapper to obtain its output paradigm estimation result; Based on the paradigm inference results and the target data paradigm, target observation data are filtered from available data sources; and The map generation engine uses the prior configuration parameters to drive the map inferrer to process the target observation data and obtain the original observation sequence.

[0008] Furthermore, before performing spectral modeling using the target observation data, data cleansing and enhancement steps are also included: Identify the benchmark data paradigm and its quality rating corresponding to the target observation data; and Based on the identified benchmark data paradigm and quality rating, the corresponding data cleansing process is selected and executed from the preset algorithm library; Furthermore, the graph extrapolator is based on a Gaussian process regression model and sets prior configuration parameters based on the standing tree distribution estimation network, including: A first region set is determined, which contains geographical units whose entity presence probability in the standing tree distribution estimation network is higher than a threshold. Determine a second region set, which is the complement of the first region set; Set a first set of prior configuration parameters for the first region set; and Set a second set of prior configuration parameters for the second region set. The first set of prior configuration parameters and the second set of prior configuration parameters have a difference within a set range.

[0009] This invention also provides a forest resource mapping and modeling system, comprising: Sensor unit clusters are deployed in forest areas to capture raw observation data; An environmental context library is used to store auxiliary geographic information; The map generation engine connects the sensor unit group and the environment context library, and is configured as follows: The raw observation data is received and processed to obtain the raw observation sequence; Call the auxiliary geographic information; The auxiliary geographic information is analyzed to set the prior configuration parameters of the map extrapolator; and The map inferencer is driven to perform fusion inference on the original observation sequence in combination with the prior configuration parameters, and output a structured forest resource semantic network. The auxiliary geographic information includes at least one of the following: Forest digital elevation model, Multispectral vegetation index images, or Semantic network of historical forest resources.

[0010] Furthermore, the map extrapolator is based on a Gaussian process regression model, and the map generation engine is configured to set prior configuration parameters based on the standing tree distribution estimation network, including: Determine the first region set in the standing tree distribution estimation network where there are high-probability standing tree units; Determine the second region set in the standing tree distribution estimation network that either does not exist or has a low probability of containing standing tree units; Set a first set of prior configuration parameters for the first region set; and Set a second set of prior configuration parameters for the second region set. Wherein, the first set of prior configuration parameters and the second set of prior configuration parameters have a difference within a set range, and the difference is such that the first set of prior configuration parameters corresponds to a sparsity constraint with a set range greater than that of the second set of prior configuration parameters.

[0011] This invention establishes a graph inference tool based on a variational inference deep generative model and historical knowledge injection. The deep generative model adopted takes a "data generation" perspective, learning a complex mapping from latent space to observed data, essentially providing a deeper model of forest distribution patterns. Its core breakthrough lies in the way prior configuration parameters are set: instead of simple uninformative priors, it extracts global statistical features (such as standing density, clustering patterns, and tree species associations) from historical semantic networks using graph neural networks. These features are then encoded into the mean vector and covariance matrix of the latent variable prior Gaussian distribution using a flow model-based structured covariance learner. This process is equivalent to injecting reliable forest spatial pattern knowledge, validated by historical data, as a strong constraint into the training of the new model. This prior plays a powerful guiding role when the model processes observational data that may contain noise, cloud cover, or incomplete data. For example, in areas historically known as dense forests, even if current observations are incomplete due to cloud cover, the model tends to infer a large, continuous distribution of trees rather than scattered trees based on prior knowledge. It better understands the symbiotic or repulsive relationships between tree species, thus making inferences more consistent with ecological principles. This mechanism greatly enhances the model's ability to infer uncertainties, avoids absurd predictions, and ensures that the generated forest resource semantic network not only matches current data but also aligns with the ecological logic formed through long-term succession, achieving a leap from fitting data to understanding forests. Attached Figure Description

[0012] Figure 1 A flowchart of the method provided by the present invention; Figure 2 The flowchart of the method for providing the original observation sequence in this invention.

[0013] Figure 3 This is a schematic diagram of the system framework principle provided by the present invention. Detailed Implementation

[0014] Reference Figure 1 and Figure 3 This invention provides 1. a method for mapping and modeling forest resources, comprising: Raw observation data is captured by a cluster of sensor units deployed in the forest area; the raw observation data is received and processed by a map generation engine to obtain a raw observation sequence; the map generation engine calls auxiliary geographic information from an environmental context library; the map generation engine parses the auxiliary geographic information to set the prior configuration parameters of the map inferencer; and the map generation engine drives the map inferencer to perform fusion inference on the raw observation sequence in combination with the prior configuration parameters, outputting a structured forest resource semantic network; wherein the auxiliary geographic information includes at least one of the following: a forest stand digital elevation model, a multispectral vegetation index image, or a historical forest resource semantic network.

[0015] The sensor unit group includes a spaceborne multispectral imager, an airborne lidar scanner, a microclimate sensor node network deployed under the forest canopy, and a field survey terminal equipped with high-precision GPS. The raw observation data includes a time-series vegetation index matrix provided by the spaceborne multispectral imager, a three-dimensional point cloud dataset provided by the airborne lidar scanner, a time-series of temperature, humidity, and soil moisture provided by the microclimate sensor node network, and a record table of standing tree specimen locations and diameter at breast height provided by the field survey terminal.

[0016] The airborne lidar scanner and the spaceborne multispectral imager perform a collaborative observation task, which is scheduled by a space-based collaborative control logic module. This module dynamically generates targeted lidar fine-scanning tracks based on the wide-area vegetation index anomaly areas provided by the spaceborne imager. The lidar scanner uses dual-band scanning technology, simultaneously emitting fundamental and frequency-doubled lasers. The primary laser band is used for terrain and vegetation structure detection, and its echo signals are processed to generate high-precision digital elevation models and canopy height models. Due to the sensitivity of the laser band to chlorophyll fluorescence, its echo waveform is used to invert the photosynthetically active radiation absorption ratio of vegetation, thereby generating a three-dimensional distribution map of forest stand photosynthetic activity. The three-dimensional point cloud dataset is an enhanced point cloud dataset that integrates the above-mentioned dual-band point clouds and includes photosynthetic activity intensity attributes.

[0017] The microclimate sensing node network is organized into an adaptive heterogeneous topology, comprising a solar main gateway node deployed above the canopy, routing relay nodes deployed in the canopy layer, and sensing leaf nodes deployed on the forest surface. Each leaf node integrates a soil three-parameter composite sensor (simultaneously measuring volumetric water content, conductivity, and temperature) and a forest light intensity sensor. The network operates a dynamic routing protocol based on fuzzy logic, which dynamically optimizes data transmission paths by comprehensively considering node remaining energy, link quality, and data priority. During the uploading of temperature, humidity, and soil moisture time series data from the leaf nodes to the main gateway node, the routing relay nodes perform Kalman filtering-based data fusion and compression to eliminate redundant readings and reduce overall network energy consumption. Finally, the main gateway node outputs a standardized dataset that reflects the microenvironmental gradients at different vertical levels within the forest, after spatiotemporal calibration and quality control.

[0018] The map generation engine processes the raw observation data to obtain the raw observation sequence. Specifically, this includes: starting a data synchronization and registration coprocessor, which performs spatiotemporal alignment operations on the heterogeneous raw observation data based on a unified geographic grid coordinate system and UTC time reference. The spatiotemporal alignment operations include: performing coordinate transformation and reprojection of lidar point cloud data to a standard grid, performing orthorectification on multispectral images and pixel-level registration with the point cloud data, and performing timestamp normalization and spatial kriging interpolation on microclimate sensor time series data to generate the raw observation sequence that is strictly consistent in the spatiotemporal dimensions.

[0019] The pixel-level registration of LiDAR point clouds and multispectral images employs a cross-modal registration algorithm based on deep feature matching. This algorithm first uses a dual-branch deep convolutional neural network to extract high-dimensional feature maps from both the point cloud intensity image and the multispectral image. The point cloud branch takes the intensity image generated by projecting the 3D point cloud onto a 2D plane as input, while the multispectral branch directly inputs the multi-band image. Subsequently, a cross-modal attention module is used to calculate the correlation between the two feature maps and generate dense feature matching correspondences. Finally, based on these matching points, the robust estimator RANSAC is used to solve for accurate affine transformation parameters, completing sub-pixel-level registration. This process particularly addresses the image geometric distortion problem caused by the undulating terrain of forest areas.

[0020] After completing the spatiotemporal alignment operation, the data synchronization and registration coprocessor further initiates a multi-source data quality fusion assessment and labeling process. This process associates a multi-dimensional quality vector with each standard geographic grid cell in the original observation sequence. The dimensions of this vector include: a) data completeness score, calculated based on the coverage of valid observations within the cell; b) spatial confidence score, comprehensively evaluated based on registration residuals, point cloud density, and image resolution; c) temporal consistency score, detected by comparing the differences between the cell and adjacent temporal data to identify abrupt changes; and d) intermodal consistency score, evaluated by comparing the logical rationality of the canopy height extracted by lidar and the vegetation index inferred from multispectral images within the cell. The multi-dimensional quality vector will be embedded as metadata into the original observation sequence for use by the subsequent map extrapolator for uncertainty weighting during fusion extrapolation.

[0021] The map inference tool adopts a deep generative model architecture based on variational inference; the prior configuration parameters include the mean vector and covariance matrix of the Gaussian prior in the latent space of the model; parsing the auxiliary geographic information to set the prior configuration parameters means: extracting statistical features of the standing tree distribution pattern from the historical forest resource semantic network, and encoding the statistical features as the initial values ​​of the mean vector and covariance matrix, thereby injecting historical knowledge as strong guiding information into the training process of the deep generative model.

[0022] The extraction of statistical features of standing tree distribution patterns from the historical forest resource semantic network is specifically achieved through graph neural network technology: the historical semantic network is constructed as an attribute graph, where nodes represent standing tree entities, edges represent spatial proximity relationships, and node attributes include tree species and diameter at breast height (DBH); a graph convolutional network is used to perform message passing and node embedding learning on this attribute graph to aggregate structural information within multi-hop neighborhoods; then, graph-level pooling is performed on all learned node embedding vectors to output a fixed-dimensional global statistical feature vector that represents the spatial distribution and composition structure of standing trees in the entire historical region; this global statistical feature vector is the direct basis for encoding the latent space Gaussian prior mean vector.

[0023] The method of encoding global statistical feature vectors into a covariance matrix of Gaussian prior in latent space employs a structured covariance learner based on a flow model. This learner takes the global statistical feature vectors as conditional input and outputs a strictly positive definite covariance matrix with a low-rank and diagonal structure through a series of invertible affine transformation layers. The low-rank part captures the principal component patterns of standing tree distribution in the historical semantic network, representing the coordinated change pattern of large-scale aggregation and dispersion in standing tree communities. The diagonal part represents the independent prior uncertainty of each latent variable unit. This structured design, while ensuring the model's expressive power and introducing historical spatial correlation, avoids the parameter explosion and overfitting risks of the complete covariance matrix, achieving refined injection of prior knowledge at the uncertainty level.

[0024] In some embodiments, the auxiliary geographic information includes a temporal multispectral image set of the forest area and multiple historical versions of the forest resource semantic network; the temporal multispectral image set comes from satellite over-the-head data from different seasons; by performing change detection on the temporal multispectral image set, anomalous disturbance areas within the forest area are identified, and then the map generation engine performs standing unit segmentation on the temporal multispectral image set to generate a standing location map, wherein the standing location map includes the geographic locations of the segmented standing units; and the map generation engine, based on the multiple historical versions of the forest resource semantic network and combined with the anomalous disturbance areas, infers the dynamic displacement trajectory of the standing units to generate a standing displacement prediction field, and obtains the geographic locations of the anomalous disturbance morphology based on the standing displacement prediction field.

[0025] The change detection of the temporal multispectral image set specifically employs a change recognition module. This change recognition module takes two images of the same region at different time phases as input, extracts high-level features through an encoder, performs feature difference, and then outputs a pixel-level change probability map through a decoder. The abnormal disturbance region is a connected region determined by applying an adaptive threshold segmentation algorithm to the change probability map and supplementing it with morphological opening and closing operations to remove noise.

[0026] The change recognition model is trained using an incremental training strategy based on active learning. After initializing a seed model, the strategy deploys it in an actual monitoring scenario. The output change probability map is processed by an uncertainty quantification module, which identifies the most uncertain image blocks by predicting variance. These high-uncertainty blocks are automatically pushed to a human interpretation interface for precise annotation by forestry experts, forming an incremental training sample library. The system periodically uses this incremental sample library to fine-tune the model, thus forming a closed-loop learning system of "model prediction, uncertainty assessment, expert intervention, and model optimization." This enables the change recognition model to continuously adapt to emerging disturbance patterns and seasonal phenological changes in specific forest areas, achieving dynamic evolution of recognition capabilities.

[0027] The initial disturbance type judgment module is integrated after the change recognition model. This module takes the change probability map and the corresponding original multispectral image patch as input, and uses a pre-trained deep convolutional neural network for feature extraction and classification. It outputs a preliminary type label for the abnormal disturbance area, which includes, but is not limited to, "logging site", "fire burn area", "pest and disease infestation area", "construction land expansion" and "phenological change". The preliminary judgment result is stored together with the geographic boundary vector data of the abnormal disturbance area to form a structured disturbance event record. This provides a typological basis for the subsequent generation of a tree displacement prediction field and directly serves the preliminary decision support for forestry disaster assessment and law enforcement supervision.

[0028] The standing tree unit segmentation is implemented using an instance segmentation neural network model. This model takes a single-period multispectral image as input, and its backbone network adopts a feature pyramid structure that incorporates an attention mechanism to extract the morphological and spectral features of standing trees at multiple scales. The model outputs a pixel-level mask for each detected standing tree instance and the geographic coordinates of its center point. The set of all standing tree instances constitutes the standing tree location map.

[0029] The instance segmentation neural network model is optimized using a co-training paradigm based on contrastive learning. This paradigm includes a primary segmentation model and an auxiliary tree species classification model. During training, the tree species classification model attempts to identify the tree species of candidate standing tree instances segmented by the primary model and generates tree species labels. Subsequently, a contrastive loss function is introduced into the training of the primary segmentation model. This loss function encourages the primary model to project standing tree instances of the same tree species to closer positions in the feature space, while pushing standing tree instances of different tree species further away in the feature space. This co-training mechanism forces the feature representation learned by the primary segmentation model to not only include morphological information but also incorporate deep spectral and textural semantics related to tree species identity, thereby significantly improving the model's fine segmentation accuracy in distinguishing overlapping and adhering heterogeneous tree crowns in complex mixed forests.

[0030] The instance segmentation neural network model is deployed on an embedded edge computing device mounted on a drone platform that performs data acquisition. The model is compressed using knowledge distillation technology and trained by a large teacher model guiding a small student model. This reduces the computational load and number of parameters by an order of magnitude while ensuring that the segmentation accuracy loss is less than 3%. After capturing multispectral images, the edge computing device runs the lightweight model in real time, generates tree location maps online, and only transmits the compressed location map vector data and thumbnails of representative trees back to the ground station. This deployment mode moves the data processing tasks that would normally be performed in the cloud to the acquisition end, achieving "instant recognition" of tree information, completely eliminating the transmission bottleneck of massive amounts of raw image data, and meeting the extreme timeliness requirements of scenarios such as emergency surveys.

[0031] The process of inferring the dynamic displacement trajectory of standing trees specifically includes: constructing a spatiotemporal graph convolutional network model, treating the standing tree nodes in the multiple historical versions of the forest resource semantic network as nodes on the spatiotemporal graph, with node attributes including standing tree location, species, and diameter at breast height (DBH), and edge attributes representing the spatial proximity relationship between standing trees; learning the temporal evolution pattern of standing tree nodes through this network, and coupling the anomalous disturbance region as a spatial constraint, and predicting the displacement vector field of the standing tree unit in the next temporal sequence through forward inference, which is the standing tree displacement prediction field.

[0032] The spatiotemporal graph convolutional network model introduces a physical information constraint mechanism. This mechanism embeds the ecophysical equations of forest growth as soft constraints into the network's loss function. Specifically, during model training, in addition to the conventional prediction error loss, an extra physical consistency loss term is added. This term calculates the stand density gradient based on the predicted displacement vector field and uses the -3 / 2 self-thinning rule in ecology to verify its rationality, penalizing prediction results that deviate significantly from this rule. This mechanism mathematically injects prior ecological knowledge such as "in the competition for growth, high-density areas of forests should expand to low-density areas" into the data-driven deep learning model, ensuring that the predicted displacement trajectory not only conforms to the statistical laws of historical data but also follows basic ecophysical principles, thereby improving the scientific nature and extrapolation reliability of the prediction results.

[0033] In some embodiments, the original observation sequence is obtained as follows: a task constraint set for historical data acquisition tasks and its corresponding baseline data paradigm are obtained, wherein the task constraint set includes site environment factors or acquisition operation factors, and the baseline data paradigm defines the inherent data output structure of the acquisition device; a data paradigm mapper is trained based on the task constraint set and the baseline data paradigm to establish a correlation between the observation data and the baseline data paradigm; the target data paradigm for the current acquisition task is determined; the current task constraint set is obtained; the current observation data is input into the data paradigm mapper to obtain its output paradigm estimation result; target observation data is filtered from available data sources based on the paradigm estimation result and the target data paradigm; and wherein the map generation engine uses the prior configuration parameters to drive the map inferrer to process the target observation data to obtain the original observation sequence.

[0034] The data paradigm mapper is a multi-classification model based on gradient boosting decision trees; its input feature vector is obtained by feature engineering the task constraint set, including one-hot encoding of meteorological factors, binning of terrain factors, and standardization of equipment parameters; the output layer of the model adopts the Softmax function, each neuron corresponds to a candidate baseline data paradigm, and the output value is the probability that the paradigm is predicted.

[0035] The gradient boosting decision tree model employs a dynamic feature importance-aware incremental update mechanism. After model deployment, this mechanism continuously monitors the split gain of each input feature in historical predictions to quantify its feature importance score. When the importance score of a specific feature in a new data collection task in the deployment environment remains below a threshold for more than a preset period, the system automatically triggers a feature set optimization process, removing the feature from the current input feature vector and retraining a simplified gradient boosting decision tree model based on the remaining features. Simultaneously, the system initiates a feature exploration experiment, controllably reintroducing the removed feature or its derivative features in subsequent new tasks to evaluate its utility in changing environments. This ensures that the data paradigm mapper always makes decisions based on the most relevant and concise feature set, effectively preventing feature redundancy and the curse of dimensionality, and improving the model's generalization ability and long-term stability.

[0036] The output layer of the data paradigm mapper is extended into a hierarchical Softmax structure to handle the hierarchical classification system of the baseline data paradigm. This system constructs the data paradigm into a tree-like hierarchical structure according to "acquisition platform-sensor type-data level". The hierarchical Softmax output layer is organized accordingly according to this tree structure, where each non-leaf node is a binary logistic regression classifier used to determine whether the data paradigm belongs to its left or right subtree, until the leaf node corresponds to a specific baseline data paradigm. This structure decomposes a single multi-class problem into a series of simple binary classification problems, greatly reducing the computational load of the model output layer. It is especially suitable for scenarios with a large number of baseline data paradigm categories. At the same time, this hierarchical output naturally gives the model the ability to make coarse-grained paradigm inferences when information is incomplete, improving the robustness of decision-making.

[0037] The specific logic for selecting target observation data based on paradigm inference results and target data paradigm is as follows: a data source optimization decision table is constructed, which takes paradigm matching degree as the core indicator and integrates the spatiotemporal resolution of the data source, data freshness and access cost as auxiliary decision factors; a weighted scoring algorithm is used to calculate the priority score for each available data source, and finally the data source with the highest score is selected as the target observation data.

[0038] The data source selection decision table is implemented as a dynamically configurable multi-objective optimization strategy engine. This engine allows users or upper-layer applications to set dynamic weights for the three auxiliary decision factors—spatial-temporal resolution, data freshness, and access cost—and define complex constraints through strategy configuration files. The strategy configuration files support task-based scenario-based presets. For example, in the "emergency monitoring" scenario, the system automatically activates the "high timeliness" strategy, assigning the highest weight to data freshness and relaxing the restrictions on access cost. In the "routine census" scenario, the "cost-effectiveness" strategy is activated, prioritizing data sources with low access costs and meeting basic spatiotemporal resolution requirements. This multi-objective optimization strategy engine decouples business logic from algorithmic logic, enabling the data filtering process to flexibly adapt to diverse actual business needs and budget constraints.

[0039] The multi-objective optimization strategy engine is deeply integrated with a data source metadata service. This metadata service maintains a dynamically updated capability profile for each available data source, including not only basic parameters but also real-time data on its historical service level agreement (SLA) achievement rate, recent data quality report summaries, and expected network transmission latency. When calculating priority scores, the strategy engine incorporates dynamic operational indicators such as data source reliability and service performance into a weighted scoring system by calling this metadata service. Simultaneously, the engine introduces a risk control module. This module performs a final review of the highest-scoring candidate data source. If its recent data quality report has serious defects or its SLA achievement rate remains consistently low, a veto mechanism is automatically triggered, downgrading the selection to a less optimal but more reliable data source. This systematically avoids the chain reaction risks caused by temporary failures or quality declines of a single data source on the overall graph modeling process.

[0040] When training the data paradigm mapper, if the data sample size of the historical data collection task is lower than a preset threshold, the training data augmentation process is initiated: This process calls the forestry process simulator, generates simulated observation data that conforms to the principles of forestry ecology and its corresponding simulated baseline data paradigm based on the site environmental factors in the task constraint set, and adds this simulated data pair to the training set to improve the model's generalization ability in data-scarce scenarios.

[0041] The forestry process simulator employs an agent-based forest ecosystem modeling framework. In this framework, each standing tree is modeled as an independent agent, whose growth, reproduction, and death behaviors are driven by a built-in physiological and ecological model and are influenced by site environmental factors and competitive interactions with surrounding tree agents. During operation, the simulator initializes a virtual stand based on the input task constraint set and accelerates the simulation of its successional dynamics over several to several decades. During the simulation, the "observation" behavior of the sensor unit group on the virtual stand is simulated periodically, generating simulated observation data and its corresponding simulated baseline data paradigm that are completely consistent with the real-world data structure. This creates enhanced training samples rich in ecological logic and covering a wide range of site conditions and stand development stages.

[0042] The training data augmentation process employs an adversarial data generation and filtering strategy. This strategy introduces a discriminator network, which is trained synchronously with the data paradigm mapper. Its task is to distinguish whether the input data samples come from real historical data or simulated data generated by the forestry process simulator. The forestry process simulator aims to continuously optimize its simulation parameters, striving to generate simulated data that can "deceive" the discriminator, making it unable to distinguish between the two. In each round of augmentation training, only high-quality simulated data that successfully "deceives" the current discriminator are added to the training set of the data paradigm mapper. This adversarial mechanism forces the data generated by the simulator to statistically approximate the distribution of real data infinitely, thereby ensuring the "realism" of the augmented data and effectively avoiding the potential negative impact of low-quality simulated data on the training of the data paradigm mapper.

[0043] In some embodiments, before performing graph modeling using the target observation data, a data cleansing and enhancement step is included: identifying the benchmark data paradigm and its quality rating corresponding to the target observation data; and selecting and executing the corresponding data cleansing process from a preset algorithm library based on the identified benchmark data paradigm and quality rating.

[0044] The identification quality rating is performed by a quality assessment submodule, which has a built-in multi-paradigm quality assessment matrix: for satellite remote sensing paradigms, the assessment indicators include cloud cover, spatial resolution deviation, and radiometric calibration error; for ground sensor paradigms, the assessment indicators include signal loss rate, reading drift, and the proportion of values ​​exceeding limits. The submodule calls the corresponding assessment indicator set according to the benchmark data paradigm of the target observation data, and divides the quality rating into three levels: L1 (high), L2 (medium), and L3 (low) based on the weighted scores of each indicator.

[0045] The quality assessment submodule integrates a co-evolutionary framework for quality assessment models based on federated learning. Under this framework, multiple independent system instances deployed in different forest areas conduct quality assessments locally, while their quality assessment submodules periodically upload anonymized assessment features and results to a central aggregation server. This server aggregates the model updates from each node using a federated averaging algorithm, generates a globally optimized quality assessment model, and distributes it to all nodes. This mechanism enables the system in each forest area to learn from the diverse data quality problems encountered in other forest areas (such as typical cloud coverage patterns in different regions and systematic drift of specific sensor models), thereby significantly improving the quality assessment submodule's adaptation speed and assessment accuracy to new regions and new sensors, and realizing the continuous evolution of quality assessment capabilities driven by swarm intelligence.

[0046] The quality assessment submodule also includes a multimodal data consistency verification engine. This engine performs cross-validation on different paradigm observation data (such as optical imagery and lidar point clouds) for the same geographical area and time period. For example, it performs spatial correlation analysis between the canopy height model extracted from lidar point clouds and the leaf area index inverted from multispectral imagery through a radiative transfer model. If the two show a negative correlation or zero correlation at a significant level, which contradicts common ecological sense, it determines that at least one data source has a potential quality problem and automatically triggers a conservative quality downgrade for all data sources in the area, marking them as requiring priority manual verification. This consistency verification mechanism utilizes the inherent physical and logical connections between different modal data, effectively supplementing and correcting blind spots in the quality assessment within a single data source.

[0047] The preset algorithm library is a preset purification process for L3 (low) level lidar point cloud data, which includes: first, using a statistical outlier removal algorithm to filter noise; then, using a cloth simulation filtering algorithm to separate ground points from vegetation points.

[0048] The Euclidean distance-based clustering algorithm was replaced with a semantic segmentation algorithm based on graph neural networks for fine purification of vegetation point clouds. This algorithm first constructs a k-nearest neighbor graph of the vegetation point cloud, where nodes are points in the point cloud and edges represent spatial proximity relationships. Then, it uses a graph attention network to learn the deep features of each point, which integrate the point's geometric coordinates, reflection intensity, and the topology of its local neighborhood. Finally, it outputs semantic labels for each point, classifying it as belonging to "real standing tree canopy," "low shrubs and grasses," or "noise (such as birds)." Based on this, the system directly removes point clouds labeled as "noise" and can selectively separate "standing tree canopy" and "shrubs and grasses" point clouds. This process purifies noise while achieving fine differentiation of different vegetation layers, providing a purer and more semantically valuable data foundation for subsequent extraction of standing tree parameters.

[0049] The semantic segmentation algorithm based on graph neural networks employs an active learning-guided iterative training strategy. After initial model deployment, this strategy automatically submits point cloud blocks with low prediction confidence (typically blurred boundaries between "real tree canopy" and "tall shrubs and grasses") to a semi-automatic annotation platform. This platform utilizes 3D point cloud visualization tools and is supplemented by intelligent pre-annotation based on region growth, greatly reducing the burden of manual annotation. Forestry experts only need to perform minor corrections and confirmations on this platform to quickly generate high-quality training samples. These new samples are immediately used for incremental fine-tuning of the model. Through several iterations, the model quickly achieves expert-level semantic segmentation accuracy for vegetation types and noise characteristics in specific forest areas, realizing efficient adaptation of the purification algorithm from generalization to regional customization.

[0050] For L1 (high) and L2 (medium) multispectral image data, the corresponding data augmentation process includes: enabling a conditional generative adversarial network, which takes the original image and site environment factors as conditional inputs to generate augmented image samples that are consistent with the real data in spectral characteristics but have diversity in spatial texture, in order to expand the dataset required to train the spectral inferencer.

[0051] The generator in the conditional generative adversarial network employs a spectrum-aware generation structure. In addition to conventional RGB or near-infrared band generation, this structure includes a dedicated sub-network for red and near-infrared bands. This sub-network uses a physically guided loss function to strictly constrain the normalized vegetation index (NDI) values ​​of its generated images to be within a reasonable ecological range. Simultaneously, the discriminator is designed as a multi-scale discriminator, judging the authenticity of the generated images at the original resolution, 1 / 2 resolution, and 1 / 4 resolution, ensuring that the generated enhanced images are statistically indistinguishable from the real images at different scales, from local texture to global pattern. This design guarantees that the enhanced images are not only visually realistic but also ecologically sound in terms of key spectral indices and their spatial distribution patterns.

[0052] The data augmentation process also couples an augmentation sample validity evaluation and screening module. This module inputs the newly generated augmented images and the original real training set into a pre-trained image feature extractor, projecting all images into the same high-dimensional feature space. Subsequently, a density-based clustering algorithm is used to analyze the distribution of this feature space and calculate the feature space distance between each augmented sample and its K nearest real samples. It automatically filters out "outlier" augmented samples located in areas with extremely low density in the real data distribution, while prioritizing the retention of "high-value" augmented samples located at the boundaries or sparse areas of the real data distribution that can effectively expand the distribution range of the training set. This mechanism ensures that data augmentation, on the basis of increasing the "quantity," achieves optimization of the "quality" of the training set, that is, it selectively fills the gaps in the model's cognition, thereby achieving a greater improvement in the model's generalization performance with a smaller data increment.

[0053] The map extrapolator is based on a Gaussian process regression model and sets prior configuration parameters based on the standing tree distribution estimation network, including: determining a first region set, which contains geographical units in the standing tree distribution estimation network whose entity existence probability is higher than a threshold; determining a second region set, which is the complement of the first region set; setting a first set of prior configuration parameters for the first region set; and setting a second set of prior configuration parameters for the second region set, wherein the first set of prior configuration parameters and the second set of prior configuration parameters have a difference within a set range.

[0054] The first set of prior configuration parameters set for the first region set are specifically the amplitude parameter and length scale parameter of the radial basis function kernel in the Gaussian process regression model; wherein, the amplitude parameter is set to a large constant value to characterize the strong confidence of the presence of standing trees in the region; the length scale parameter is set to an empirical value equivalent to the average crown diameter of the stand to match the typical spatial correlation scale of the standing trees.

[0055] The amplitude and length scale parameters are not fixed constants, but are dynamically fine-tuned through a hyperparameter optimizer embedded in the map generation engine. This optimizer uses the negative log-likelihood between the prediction results of the forest resource semantic network calculated on the reserved validation set and the field verification data as the loss function, and adopts a Bayesian optimization strategy to jointly optimize the amplitude and length scale parameters within a search space constructed around the empirical values ​​of the larger constant values ​​and the average crown diameter of the forest stand. This Bayesian optimization process constructs a surrogate model of the objective function (such as a Gaussian process) and intelligently selects the next set of evaluation points based on the acquisition function (such as desired improvement) to find the optimal parameter combination that maximizes the prediction likelihood with as few iterations as possible, thereby achieving adaptive matching to the spatial pattern characteristics of a specific forest area and avoiding the suboptimal nature of empirical parameter settings.

[0056] The Bayesian optimization process of the hyperparameter optimizer is designed as a multi-fidelity optimization strategy. This strategy allows for rapid but approximate parameter evaluation using downsampled low spatial resolution observation data and a simplified Gaussian process regression model in the early stages of optimization, enabling rapid localization of potential parameter regions over a wide area. As the optimization iteration progresses, the system gradually switches to a higher spatial resolution complete dataset and a full-featured inference model for precise evaluation, allowing for a fine-grained search within the narrowed optimal parameter region. This multi-fidelity strategy reduces the total computation time for finding the highest priority configuration parameters by more than 60% through intelligent allocation of computational resources at different optimization stages. This makes the dynamic fine-tuning function applicable to forestry monitoring tasks with timeliness requirements, achieving a balance between optimization efficiency and final accuracy.

[0057] The second set of prior configuration parameters set for the second region set is to set the amplitude parameter of the radial basis function kernel to a very small positive value close to zero, while increasing the baseline noise parameter of the kernel function by an order of magnitude relative to the first region set. The mathematical meaning of this parameter combination is that the observations in the region are assumed to be mainly composed of noise, and the function value itself is also close to zero, thereby guiding the model to output a smooth occupancy probability prediction result close to zero in the region.

[0058] The baseline noise parameter in the second set of prior configuration parameters is modeled as a spatially location-dependent heteroscedastic noise field, rather than a global constant. The construction of this heteroscedastic noise field relies on the uncertainty information provided by the standing tree distribution estimation network: in the second region set, for cells adjacent to the boundary of the first region set (high probability zone), their noise parameter is set to a relatively low value to preserve the possibility of observing weak signals from the high probability zone outwards; while for cells far from the first region set and located on land cover categories where standing trees are known to be impossible (such as digitized water bodies, bare rocks, roads), their noise parameter is set to an extremely high value to suppress the influence of any spurious signals in that region to the greatest extent. This spatially adaptive noise setting enables differentiated prior control over "potential transition zones" and "absolutely non-forested areas," improving the model's spatial prediction accuracy in forest edge areas.

[0059] A post-processing verification and enhancement module for prediction results in a second region set is provided. This module performs automated spatial overlay analysis on the low occupancy probability results output by the map extrapolator in the second region set and a high-precision land use / cover classification map. For patches that are clearly identified as permanent non-vegetation types (such as water bodies, buildings, and paved roads) in the classification map and whose area is larger than the smallest mapping unit, if the predicted occupancy probability of all units within the patch is lower than a very low threshold, the module determines that the prediction for that area is correct and no further action is required. If continuous and significant predicted probability "noise" (i.e., probability values ​​abnormally higher than the surrounding background values) are found within such non-vegetation patches, the module automatically generates a binary mask and forces the occupancy status of these "noise" units to be corrected to "unoccupied" in the final output forest resource semantic network. This mechanism uses authoritative auxiliary geographic information to perform fallback correction on the model's weak points (false positives in low-probability areas), further improving the reliability of the final results.

[0060] The difference within the specified range is dynamically determined by a hyperparameter optimizer. This optimizer uses the weighted sum of the prediction accuracy and structured similarity index of the forest resource semantic network calculated on historical data as the loss function, and the ratio of each parameter in the first group and the second group of prior configuration parameters as the optimization variable. It searches through a Bayesian optimization framework to adaptively find the optimal parameter difference that maximizes the quality of the final map.

[0061] The "prediction accuracy" in the loss function is specifically measured using the F1 score based on field sample survey data. This score precisely quantifies the detection and recognition capabilities of the map at the standing tree unit scale. The "structured similarity index" uses the multi-scale MS-SSIM index, which simulates the human visual system's perception of image structure. It comprehensively evaluates the similarity between the predicted map and the reference map in terms of spatial structure, contrast, and brightness at multiple scales, from pixel neighborhoods and local regions to the entire image patch, paying particular attention to its ability to preserve key landscape structures such as forest gaps and forest edges. The specific weighting coefficients of the weighted sum can be flexibly configured by the user through an interactive interface according to the task emphasis (such as whether to value the accuracy of individual tree positioning or the fidelity of the overall pattern), so that the optimization objective is closely aligned with specific business needs.

[0062] The Bayesian optimization framework of the hyperparameter optimizer is extended into a meta-learning system with memory and transfer capabilities. This system maintains a historical database of optimal parameter differences for forest stands with different characteristics (such as those divided by dominant tree species, age group, and density). When faced with a new optimization task, the system first performs similarity matching in the historical database based on the key features of the current forest stand, finds the most similar past forest stands and their optimal parameter differences, and uses this difference as the starting point for the current Bayesian optimization. This "hot start" strategy uses historical experience to raise the starting point of Bayesian optimization from random or default settings to a region close to the optimum, thereby reducing the average number of iterations required to find the global optimum by about 50%, significantly improving the overall optimization efficiency and stability of the system when facing a series of diverse forest stands.

[0063] The present invention also provides a forest resource mapping modeling system, comprising: a group of sensor units deployed in forest areas for capturing raw observation data; and an environmental context library for storing auxiliary geographic information. The map generation engine connects the sensor unit group and the environmental context library, and is configured to: receive the raw observation data and process it to obtain the raw observation sequence; call the auxiliary geographic information; parse the auxiliary geographic information to set the prior configuration parameters of the map inferencer; and drive the map inferencer to perform fusion inference on the raw observation sequence in combination with the prior configuration parameters, and output a structured forest resource semantic network; wherein the auxiliary geographic information includes at least one of the following: forest stand digital elevation model, multispectral vegetation index image, or historical forest resource semantic network.

[0064] The sensor unit group includes a spaceborne multispectral imager, an airborne lidar scanner, a microclimate sensor node network deployed under the forest canopy, and a field survey terminal equipped with high-precision GPS. The raw observation data includes a time-series vegetation index matrix provided by the spaceborne multispectral imager, a three-dimensional point cloud dataset provided by the airborne lidar scanner, time-series temperature, humidity, and soil moisture data provided by the microclimate sensor node network, and a record table of standing tree specimen locations and diameter at breast height (DBH) provided by the field survey terminal. Furthermore, the airborne lidar scanner and the spaceborne multispectral imager perform a collaborative observation task, which is scheduled by a spaceborne collaborative control logic module. This module dynamically generates targeted lidar fine-scanning tracks based on the wide-area vegetation index anomaly areas provided by the spaceborne imager.

[0065] The workflow of the spaceborne collaborative control logic module is as follows: 1) This module continuously receives and processes time-series vegetation index data transmitted from the spaceborne multispectral imager, and identifies "anomaly areas" that show significant differences compared to historical data or surrounding areas using change detection algorithms (such as image difference analysis or machine learning classifiers). 2) Based on the spatial extent, shape, and degree of anomaly of these anomaly areas, the module dynamically plans one or more targeted lidar scanning tracks. The planning strategy comprehensively considers coverage integrity, flight efficiency, and safety constraints to ensure that the lidar can perform high-density, high-precision three-dimensional data acquisition of the anomaly areas. 3) The track command is issued to the UAV or manned aircraft platform equipped with the lidar for execution.

[0066] The lidar scanner employs dual-band scanning technology, simultaneously emitting fundamental frequency laser and frequency-doubled laser. The fundamental frequency laser band is primarily used for terrain and vegetation structure detection, and its echo signal is processed to generate a high-precision digital elevation model and canopy height model. Due to its sensitivity to chlorophyll fluorescence, the echo waveform of the frequency-doubled laser band is used to invert the photosynthetically active radiation absorption ratio of vegetation, thereby generating a three-dimensional distribution map of forest stand photosynthetic activity. The three-dimensional point cloud dataset is an enhanced point cloud dataset that integrates the aforementioned dual-band point clouds and includes photosynthetic activity intensity attributes.

[0067] The microclimate sensing node network is organized into an adaptive heterogeneous topology, comprising solar main gateway nodes deployed above the canopy, routing relay nodes deployed in the canopy layer, and sensing leaf nodes deployed on the forest surface. Each leaf node integrates a soil three-parameter composite sensor and a forest light intensity sensor. The network operates a dynamic routing protocol based on fuzzy logic, which comprehensively considers node remaining energy, link quality, and data priority to dynamically optimize data transmission paths. During the uploading of temperature, humidity, and soil moisture time series data from the leaf nodes to the main gateway node, the routing relay nodes perform Kalman filtering-based data fusion and compression to eliminate redundant readings and reduce overall network energy consumption.

[0068] The map generation engine processes the raw observation data to obtain the raw observation sequence. Specifically, this is achieved through a data synchronization and registration coprocessor. This coprocessor performs spatiotemporal alignment operations on the heterogeneous raw observation data based on a unified geographic grid coordinate system and UTC time reference. The spatiotemporal alignment operations include: performing coordinate transformation and reprojection of lidar point cloud data to a standard grid; performing orthorectification on multispectral imagery and pixel-level registration with the point cloud data; and performing timestamp normalization and spatial kriging interpolation on microclimate sensor time series data to generate the raw observation sequence that is strictly consistent in the spatiotemporal dimensions.

[0069] The pixel-level registration of the LiDAR point cloud and multispectral image employs a cross-modal registration algorithm based on deep feature matching. This algorithm first uses a dual-branch deep convolutional neural network to extract high-dimensional feature maps from the point cloud intensity image and the multispectral image, respectively. The point cloud branch generates an intensity image by projecting the 3D point cloud onto a 2D plane as input. Subsequently, a cross-modal attention module is used to calculate the correlation between the two feature maps and generate dense feature matching correspondences. Finally, based on these matching points, a robust estimator is used to solve for the accurate affine transformation parameters to complete the sub-pixel-level registration.

[0070] The cross-modal registration algorithm based on deep feature matching incorporates a two-branch deep convolutional neural network. One branch (image branch) takes multiple bands of multispectral imagery as input. The other branch (point cloud branch) first projects the 3D point cloud onto a 2D plane according to elevation or intensity values, generating a pseudo-color image or grayscale image as input. Both branches use convolutional neural network structures similar to ResNet or VGG, extracting high-level, abstract feature maps from the raw data through multiple layers of convolution and pooling operations. These features are no longer simple edges or corners, but rather features capable of representing semantic concepts such as "building corners," "tree canopy centers," and "linear road structures."

[0071] The cross-modal attention module receives feature maps from both branches and, by calculating attention weights, determines which locations in the point cloud feature map are semantically most relevant to a given location in the image feature map. For example, it learns that "red areas in the image (potentially corresponding to vegetation) should be associated with areas in the point cloud that have complex textures and a certain height." The module outputs a dense correspondence graph, meaning that for every point in the image feature map, the best-matching point can be found in the point cloud feature map.

[0072] Based on the large number of feature matching point pairs generated by the above steps, the RANSAC algorithm can be used to robustly estimate the transformation parameters. RANSAC can effectively eliminate erroneous matching points (outside points) and use only the correct interior points to compute the optimal affine transformation matrix (including rotation, translation, scaling, and shearing).

[0073] After completing the spatiotemporal alignment operation, the data synchronization and registration coprocessor further initiates a multi-source data quality fusion assessment and labeling process. This process associates a multi-dimensional quality vector with each standard geographic grid cell in the original observation sequence. The dimensions of this vector include: a) data completeness score, calculated based on the coverage of valid observations within the cell; b) spatial confidence score, comprehensively evaluated based on registration residuals, point cloud density, and image resolution; c) temporal consistency score, detected by comparing the differences between the cell and adjacent temporal data to identify abrupt changes; and d) intermodal consistency score, evaluated by comparing the logical rationality of the canopy height extracted by lidar and the vegetation index inferred from multispectral images within the cell. The multi-dimensional quality vector will be embedded as metadata into the original observation sequence for use by the subsequent map extrapolator for uncertainty weighting during fusion extrapolation.

[0074] The map inference tool employs a deep generative model architecture based on variational inference. The prior configuration parameters include the mean vector and covariance matrix of the Gaussian prior in the latent space of the model. Parsing the auxiliary geographic information to set the prior configuration parameters refers to extracting statistical features of the standing tree distribution pattern from the historical forest resource semantic network and encoding these statistical features as the initial values ​​of the mean vector and covariance matrix, thereby injecting historical knowledge as strong guiding information into the training process of the deep generative model. The specific process is as follows: Feature extraction: The historical semantic network is treated as a graph structure, and its overall statistical features are learned using techniques such as graph neural networks. For example, the average density of standing trees in the historical forest stand, the degree of aggregation (clustered or random distribution), and the spatial correlation between different tree species. These features are condensed into a fixed-length global statistical feature vector. Prior encoding: Using this statistical feature vector as a condition, a coding network is used to generate the parameters of the latent variable prior Gaussian distribution—the mean vector and covariance matrix. The mean vector encodes the overall expectation of the tree distribution, while the covariance matrix encodes the strength and pattern of spatial dependencies between trees. Model training: In subsequent variational inference training, the model aims to find a prior distribution that is as close as possible to this "knowledge-based" distribution, while also being able to well explain the latent variable posterior distribution of the current observation data.

[0075] The extraction of statistical features of standing tree distribution patterns from the historical forest resource semantic network is specifically achieved through graph neural network technology: the historical semantic network is constructed as an attribute graph, where nodes represent standing tree entities, edges represent spatial proximity relationships, and node attributes include tree species and diameter at breast height (DBH); a graph convolutional network is used to perform message passing and node embedding learning on this attribute graph to aggregate structural information within multi-hop neighborhoods; then, graph-level pooling is performed on all learned node embedding vectors to output a fixed-dimensional global statistical feature vector that represents the spatial distribution and composition structure of standing trees in the entire historical region; this global statistical feature vector is the direct basis for encoding the latent space Gaussian prior mean vector.

[0076] The method of encoding the global statistical feature vector into a covariance matrix of Gaussian prior in the latent space employs a structured covariance learner based on a flow model. This learner takes the global statistical feature vector as conditional input and outputs a strictly positive definite covariance matrix with a low-rank and diagonal structure through a series of invertible affine transformation layers. The low-rank part is used to capture the principal component patterns of standing tree distribution in the historical semantic network, representing the coordinated change law of large-scale aggregation and dispersion of standing tree communities. The diagonal part is used to represent the independent prior uncertainty of each latent variable unit.

[0077] The map extrapolator is based on a Gaussian process regression model, and the map generation engine is configured to set prior configuration parameters based on the standing tree distribution estimation network, including: determining a first set of regions in the standing tree distribution estimation network where there are high-probability standing tree units; determining a second set of regions in the standing tree distribution estimation network where there are no standing tree units or where there are low-probability standing tree units; setting a first set of prior configuration parameters for the first set of regions; and setting a second set of prior configuration parameters for the second set of regions, wherein the first set of prior configuration parameters and the second set of prior configuration parameters have a difference within a set range, and the difference is such that the first set of prior configuration parameters corresponds to a sparsity constraint within a set range than the second set of prior configuration parameters.

[0078] The amplitude parameter and length scale parameter are not fixed constants, but are dynamically fine-tuned by a hyperparameter optimizer embedded in the map generation engine. The optimizer uses the negative log-likelihood between the prediction results of the forest resource semantic network calculated on the reserved validation set and the field verification data as the loss function, and adopts a Bayesian optimization strategy to jointly optimize the amplitude parameter and length scale parameter within a search space constructed around the larger constant value and the empirical value of the average canopy diameter of the stand.

[0079] The Bayesian optimization process of the hyperparameter optimizer is designed as a multi-fidelity optimization strategy. This strategy allows for rapid but approximate parameter evaluation using downsampled low spatial resolution observation data and a simplified Gaussian process regression model in the early stages of optimization, in order to quickly locate potential parameter regions over a large area. As the optimization iteration progresses, the system gradually switches to a higher spatial resolution complete dataset and a full-featured inference model for precise evaluation, in order to perform a fine search within the narrowed optimal parameter region.

[0080] The second set of prior configuration parameters set for the second region set is to set the amplitude parameter of the radial basis function kernel to a very small positive value close to zero, while increasing the baseline noise parameter of the kernel function by an order of magnitude relative to the first region set. The mathematical meaning of this parameter combination is that the observations in the region are assumed to be mainly composed of noise, and the function value itself is also close to zero, thereby guiding the model to output a smooth occupancy probability prediction result close to zero in the region.

[0081] The baseline noise parameter in the second set of prior configuration parameters is modeled as a spatially dependent heteroscedastic noise field, rather than a global constant. The construction of this heteroscedastic noise field relies on the uncertainty information provided by the standing tree distribution estimation network: in the second region set, for cells adjacent to the boundary of the first region set (high probability zone), their noise parameter is set to a relatively low value to preserve the possibility of observing weak signals from the high probability zone outward; while for cells far from the first region set and located on land cover categories where standing trees are known to be impossible (such as digitized water bodies, bare rocks, roads), their noise parameter is set to an extremely high value to suppress the influence of any false signals in that region to the greatest extent.

[0082] A post-processing verification and enhancement module for the prediction results of a second region set; this module performs automated spatial overlay analysis on the low occupancy probability results output by the map inferencer for the second region set and a high-precision land use / cover classification map; for patches that are clearly identified as permanent non-vegetation types and have an area larger than the smallest cartographic unit in the classification map, if the predicted occupancy probability of all units within them is lower than a very low threshold, the module determines that the prediction for that region is correct and no further action is required; if continuous and significant predicted probability "noise" is found within such non-vegetation patches, the module automatically generates a binary mask and forces the occupancy status of these "noise" units to be corrected to "unoccupied" in the final output forest resource semantic network. Specific steps include: Data input: The module receives two inputs, one is the initial output forest resource semantic network from the map inferencer (each unit has an occupancy probability), and the other is a high-precision land use / cover classification map from an environmental context library. This classification map is typically produced by professional surveying departments and is highly authoritative and accurate in defining non-vegetated features (water bodies, buildings, roads, etc.). Spatial overlay analysis: The semantic network's predictions are precisely overlaid with the land use classification map. Regularized discrimination and correction: For contiguous areas (plots) clearly marked as "permanent non-vegetated types" (such as "water bodies," "construction land," "bare land") in the classification map, and whose area is larger than the smallest cartographic unit (to avoid harsh judgments on overly fragmented features), the module checks the semantic network's predictions within these plots. Case A (Correct Prediction): If the predicted occupancy probability of all units within the non-vegetated plot is below a very low threshold (e.g., 0.1), it indicates that the model has correctly identified the features, and the module does not perform any processing.

[0083] Case B (False Alarm Noise): If multiple consecutive units within a non-vegetated patch have a significantly higher predicted probability than the surrounding background value (i.e., forming "noise"), this is considered a false alarm by the model. The module automatically generates a binary mask based on the location of these noise points. In the final output semantic network, the occupancy status of all units within this mask is forcibly corrected to "unoccupied".

[0084] The difference within the specified range is dynamically determined by a hyperparameter optimizer. This optimizer uses the weighted sum of the prediction accuracy and structured similarity index of the forest resource semantic network calculated on historical data as the loss function, and the ratio of each parameter in the first group and the second group of prior configuration parameters as the optimization variable. It searches through a Bayesian optimization framework to adaptively find the optimal parameter difference that maximizes the quality of the final map.

Claims

1. A method for mapping and modeling forest resources, characterized in that, include: Raw observation data is captured by a cluster of sensor units deployed in the forest area; The original observation data is received and processed by the map generation engine to obtain the original observation sequence; The map generation engine retrieves auxiliary geographic information from the environmental context library; The auxiliary geographic information is parsed by the map generation engine to set the prior configuration parameters of the map inferencer; The map generation engine drives the map inference engine to perform fusion inference on the original observation sequence in combination with the prior configuration parameters, and outputs a structured forest resource semantic network. The auxiliary geographic information includes at least one of the following: Forest digital elevation model, Multispectral vegetation index images, or semantic networks of historical forest resources.

2. The forest resource mapping modeling method according to claim 1, characterized in that, The auxiliary geographic information includes a time-series multispectral image set of the forest area and multiple historical versions of the forest resource semantic network; The temporal multispectral image set is derived from satellite overhead data from different seasons; By performing change detection on the temporal multispectral image set, abnormal disturbance areas within the forest area are identified. Then, the map generation engine performs standing unit segmentation on the temporal multispectral image set to generate a standing location map, which includes the geographical locations of the segmented standing units. Furthermore, based on the multiple historical versions of the forest resource semantic network and combined with the abnormal disturbance areas, the map generation engine infers the dynamic displacement trajectory of the standing units, generates a standing displacement prediction field, and obtains the geographical locations of the abnormal disturbance morphology based on the standing displacement prediction field.

3. The forest resource mapping modeling method according to claim 1, characterized in that, The original observation sequence was obtained in the following way: Obtain the task constraint set of historical data acquisition tasks and its corresponding benchmark data paradigm. The task constraint set includes site environment factors or acquisition operation factors, and the benchmark data paradigm defines the inherent data output structure of the acquisition device. Based on the task constraint set and the benchmark data paradigm, a data paradigm mapper is trained to establish the correlation between the observed data and the benchmark data paradigm. Determine the target data paradigm for the current data acquisition task; Get the current task constraint set; Input the current observation data into the data paradigm mapper to obtain its output paradigm estimation result; Based on the paradigm inference results and the target data paradigm, target observation data are selected from available data sources; And therein, the map generation engine uses the prior configuration parameters to drive the map inferrer to process the target observation data and obtain the original observation sequence.

4. The forest resource mapping modeling method according to claim 3, characterized in that, Before using the target observation data for spectral modeling, data cleaning and enhancement steps are also included: Identify the benchmark data paradigm and its quality rating corresponding to the target observation data; Based on the identified benchmark data paradigms and quality ratings, the system selects and executes corresponding data cleansing processes from a pre-defined algorithm library.

5. The forest resource mapping modeling method according to claim 1, characterized in that, The graph extrapolator is based on a Gaussian process regression model and sets prior configuration parameters based on the standing tree distribution estimation network, including: A first region set is determined, which contains geographical units whose entity presence probability in the standing tree distribution estimation network is higher than a threshold. Determine a second region set, which is the complement of the first region set; A first set of prior configuration parameters is set for the first region set; and a second set of prior configuration parameters is set for the second region set. The first set of prior configuration parameters and the second set of prior configuration parameters have a difference within a set range.

6. A forest resource mapping and modeling system, characterized in that, include: Sensor unit clusters are deployed in forest areas to capture raw observation data; An environmental context library is used to store auxiliary geographic information; The map generation engine connects the sensor unit group and the environment context library, and is configured as follows: The raw observation data is received and processed to obtain the raw observation sequence; Call the auxiliary geographic information; The auxiliary geographic information is analyzed to set the prior configuration parameters of the map inferencer; and the map inferencer is driven to perform fusion inference on the original observation sequence in combination with the prior configuration parameters, and output a structured forest resource semantic network. The auxiliary geographic information includes at least one of the following: Forest digital elevation model, Multispectral vegetation index images, or Semantic network of historical forest resources.

7. The forest resource mapping and modeling system according to claim 6, characterized in that, The map extrapolator is based on a Gaussian process regression model, and the map generation engine is configured to set prior configuration parameters based on the standing tree distribution estimation network, including: Determine the first region set in the standing tree distribution estimation network where there are high-probability standing tree units; Determine the second region set in the standing tree distribution estimation network that either does not exist or has a low probability of containing standing tree units; A first set of prior configuration parameters is set for the first region set; and a second set of prior configuration parameters is set for the second region set. Wherein, the first set of prior configuration parameters and the second set of prior configuration parameters have a difference within a set range, and the difference is such that the first set of prior configuration parameters corresponds to a sparsity constraint with a set range greater than that of the second set of prior configuration parameters.

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