12 results about "Forest plantation" patented technology

This invention relates to the field of forestry remote sensing monitoring technology and discloses a leaf SPAD prediction method based on UAV multispectral imaging. The method involves acquiring images of the target forest area through low-altitude multispectral aerial photography using a UAV, and extracting spectral data based on the locations of measured ground sampling points. Enhanced feature engineering is applied to the data to construct a multi-source feature set including original bands, basic vegetation indices, red-edge vegetation indices, and texture features. An early fusion strategy is used to construct a high-dimensional fusion feature vector, which is then input into an ensemble learning regression model based on ExtraTrees for training and prediction. Finally, a spatial distribution map of leaf SPAD values ​​in the forest area is generated. This invention effectively overcomes the shortcomings of conventional multispectral imaging, such as limited band size and saturation. By fusing the red-edge index and the ensemble learning algorithm, it significantly improves the accuracy and robustness of SPAD inversion, providing an effective technical means for precise management and rapid nutrient diagnosis of plantations.

This invention discloses a method and system for identifying planted and natural forests based on multi-source remote sensing and spatiotemporal joint optimization, belonging to the fields of remote sensing image processing, earth observation, and dynamic monitoring of forest ecology. Addressing the problems of high label noise and disordered temporal logic in existing technologies, this invention obtains high-purity training samples through multi-stage iterative purification by fusing CCDC mutation history and deep semantic features; obtains pixel-level confidence through independent initial classification by geographic partitioning; constructs an HMM model integrating ecological prior constraints and dynamic adaptive emission probabilities for Viterbi decoding; and performs constrained spatial restoration with cross-dimensional temporal security checks. This invention effectively suppresses temporal pseudo-variables and salt-and-pepper noise, achieving high-resolution thematic mapping year by year with no temporal logical contradictions and regular spatial patches, suitable for fine-grained forest classification and compliance monitoring of zero-deforestation laws in tropical and subtropical regions.

The application discloses a plantation remote sensing image segmentation and recognition method and system, relates to the technical field of remote sensing image processing, and comprises the following steps: acquiring multi-temporal remote sensing images and laser radar point cloud data and registering, extracting stand height information and crown structure information, using the crown structure information to constrain and reconstruct spectral characteristics to obtain structure spectral coupling characteristics; calculating spectral differences and texture changes, combining stand height time series changes to construct stand state evolution characteristics; fusing to obtain fusion characteristics; constructing a double-channel semantic segmentation model to train and perform pixel-level joint classification to obtain an initial classification result; based on the crown structure information, a matching constraint rule is established to modify the initial classification result to obtain a final classification result, and high-precision recognition of degraded forest land tree species is realized.

This invention discloses a method and system for inverting the nutrient content of eucalyptus canopy leaves based on multi-feature fusion, belonging to the field of UAV remote sensing and forestry information technology. The method includes: acquiring and preprocessing multispectral image data from a UAV; extracting vegetation index features and texture features of the eucalyptus canopy; filtering the fused features based on mutual information to construct a multi-feature fusion dataset; constructing an inversion model for the nitrogen, phosphorus, and potassium content of eucalyptus leaves using a random forest regression algorithm; and verifying and optimizing the accuracy of the inversion model. This invention effectively compensates for the information gaps of single features by fusing spectral and spatial structure information, significantly improving the inversion accuracy of eucalyptus canopy leaf nutrient content, and providing reliable technical support for precise nutrient management of eucalyptus plantations.

The application discloses a forest stand structure optimization method based on the relationship between forest density and carbon fixation rate, relates to the technical field of forest management, and comprises the following steps: S1, performing sample plot investigation; S2, determining the forest stand structure and annual average carbon fixation amount of each sample plot, and calculating the carbon fixation rate under the current forest stand structure; S3, determining the quantile regression relationship between the breast height cross-sectional area and the carbon fixation rate; S4, determining the proportion of a kind of tree species and two kinds of tree species in the forest stand structure in different sample plots, and determining the relationship between the forest stand structure and the carbon fixation rate; and S5, selecting at least one target area in each sample plot to perform logging under preset logging conditions, and forming an artificial forest window, so that the breast height cross-sectional area reaches the critical density. The method is used for guiding forest management activities and improving the carbon fixation and sink capacity of forests.

This invention relates to the field of puffball propagation technology in Yunnan and discloses a management system for promoting puffball propagation in Yunnan Pinus kesina plantations. The system includes a data acquisition module, a stand structure assessment module, a growth assessment module, and a propagation management module. The stand structure assessment module constructs the stand structure coefficient of the corresponding block of Pinus kesina plantation by reading the environmental data of the current Pinus kesina plantation, and simultaneously transmits the constructed stand structure coefficient to the propagation management module. This system simultaneously collects data on the aboveground stand environment and the underground soil environment, as well as all key parameters related to the host and symbiotic fungi. Through a forest block correction unit, it integrates sky visibility, litter conditions, and the dispersion of Pinus kesina diameter at breast height, ensuring the visualization and quantification of parameters during the cultivation of Yunnan puffball. It also promptly reminds planting staff to adjust the stand structure and correct growth deviations in puffball, improving seedling survival rate, stand stability, puffball yield, and quality.

This application relates to the field of image processing technology, specifically to a method for constructing a digital canopy height model (CHM) of plantations based on high-precision point cloud data. The method includes: selecting local RGB images corresponding to point cloud data points based on the registration results of the 3D point cloud and RGB images of the plantation area; setting an initial large-size grid and adaptively adjusting the initial grid size by combining the spatial characteristics of the point cloud within the grid and the corresponding local image features; extracting a seed terrain skeleton from the point cloud data; constructing an adaptive stiffness based on the current plantation environment characteristics using the seed terrain skeleton; optimizing the CSF algorithm; classifying the point cloud data using the improved CSF algorithm; and finally constructing a CHM model by combining ground point cloud data and non-ground point cloud data. The purpose of this application is to solve the problem of local distortion in UAV point cloud data caused by the sparse distribution of ground points in high-canopy-density plantations, thereby improving the accuracy of CHM construction.

The application provides a plantation thinning device and relates to the technical field of tree felling, which comprises a load remote control vehicle, a climbing mechanism, a multistage grabbing mechanism, a cutting mechanism and a remote controller. The load remote control vehicle is provided with a docking portion, and the climbing mechanism is detachably connected with the docking portion. The multistage grabbing mechanism is arranged on the climbing mechanism. The cutting mechanism is arranged on the climbing mechanism. The load remote control vehicle is provided with a controller, and the controller is in communication connection with the load remote control vehicle, the climbing mechanism, the multistage grabbing mechanism and the cutting mechanism. The load remote control vehicle is used for carrying and moving in the plantation, so that the damage to the undergrowth structure in the moving process is reduced. The target tree is gradually cut and decomposed through the cooperation of the climbing mechanism, the multistage grabbing mechanism and the cutting mechanism, and the trunk and branches after cutting and decomposition are stably placed on the ground, so that the damage to the nearby reserved wood and undergrowth caused by the whole tree falling is avoided, and high-quality thinning of the plantation is realized.

The utility model discloses a kind of artificial forest protection normality monitoring devices, it is related to artificial forest protection technical field, including intermediate pole, intermediate pole is as the installation positioning structure of monitoring device, the lower end of intermediate pole is welded with threaded rod, the surface of threaded rod is threadedly connected with ground stick, the top of ground stick is installed with displacement sensor, the bottom of displacement sensor is installed displacement probe through recess, the top of displacement sensor is connected with displacement data transmitter, the bottom of threaded rod is fixed with mounting pole.The utility model is connected with ground stick by screw, ground stick can keep the vertical state of intermediate pole, simultaneously ground stick is monitored by cooperation displacement sensor installed on its top, the thickness of artificial forest land soil layer can be continuously monitored, it is convenient for forest land protection personnel to record the thickness of soil layer, timely adjust soil layer thickness data, improve the accuracy of soil data record in artificial forest protection process, improve the protection efficacy of artificial forest land.

This invention discloses an integrated monitoring system and method for the structure and function of plantations based on multi-source data fusion in the field of plantation monitoring technology. The system includes: a movable ground monitoring box with a notch on one side for accommodating individual trees; a drone for multi-view observation of the tree canopy; and two landing platforms symmetrically distributed on the ground monitoring box on both sides of the notch for drone take-off and landing. The drone takes off from one of the landing platforms and flies around the individual tree along a preset flight path in the vertical plane to collect stand structure data of the bottom, top, and sides of the tree canopy. This system achieves multi-source data acquisition through the drone and ground monitoring components. A position conversion mechanism drives the landing platform and ground monitoring components to rotate synchronously and precisely, enabling multi-path canopy acquisition by the drone and omnidirectional measurement from the ground, significantly improving the integrity of canopy structure data and the accuracy of three-dimensional reconstruction.

The application discloses a natural propagation and artificial planting mangrove forest identification method and system, relates to the technical field of data processing, and comprises the following steps: extracting a comprehensive feature matrix of a research area in a target year; determining a plurality of initial sampling points in a known artificial forest patch; extracting an initial feature matrix of the initial sampling points from the comprehensive feature matrix; determining a target sampling point in a newly added mangrove forest area according to the initial feature matrix; extracting a target feature matrix of the target sampling point from the comprehensive feature matrix; inputting the target feature matrix into a pre-trained ensemble learning model to obtain a comprehensive probability distribution; and dividing the newly added mangrove forest area into at least one of natural propagation, artificial planting or natural expansion after human intervention according to the comprehensive probability distribution. The application can learn from the sampling points of the known artificial forest as a small sample, accurately divide the newly added mangrove forest area, and finally identify the type of the newly added mangrove forest area.

This invention discloses a method for cultivating seedlings in multi-layered mixed forests of Chinese fir and broadleaf trees, belonging to the field of forestry seedling technology. Addressing the technical problem of vicious competition and difficulty in forming a symbiotic relationship when Chinese fir and broadleaf trees are mixed, this invention employs a dual-cavity integrated symbiotic cultivation container with an internal biodegradable isolation membrane. Chinese fir and broadleaf trees are cultivated separately in dedicated functional substrates within the main and secondary cavities. Physical isolation occurs in the early stages of seedling cultivation, and the isolation membrane degrades later, guiding the roots to intertwine and form symbiotic root clusters. This method effectively solves interspecific competition, and the cultivated integrated symbiotic seedlings significantly improve the survival rate and growth of broadleaf trees.