585 results about "Tree species" patented technology

The invention provides a tree identification method and system based on ground-air fusion three-dimensional laser point cloud, and belongs to the field of environment remote sensing data identification, and the method comprises the steps: obtaining laser radar point cloud data in a set forest region through employing an unmanned plane, and collecting the point cloud data in the set forest region through employing a laser scanning device; preprocessing the two groups of data to obtain preprocessed laser radar point clouds, and performing ground-air data seamless fusion on the preprocessed laser radar point clouds to obtain fused data; generating a canopy height model based on a watershed segmentation algorithm, and performing individual tree segmentation on the fused point cloud data by using the model; extracting a vector boundary after single tree segmentation, distributing a tree ID for each tree, and extracting a single tree point cloud according to the tree ID; the single tree point cloud is further processed to construct a multi-angle point cloud sample data set; and training a deep learning network model by using the multi-angle point cloud sample data set, and detecting the tree species to be detected by using the trained deep learning network model.

The invention relates to the technical field of remote sensing space information, in particular to a large-scale forest green carbon inversion method and system based on multi-source remote sensing data, and the method comprises the steps: resolving remote sensing parameters, and obtaining the crown breadth and height of a single-plant crown of a sample plot; taking the crown breadth and the height of the single tree crown of the sample plot as independent variables, and establishing different-speed growth models of different tree species; performing inversion based on original echo signal waveform data of the satellite-borne laser radar to obtain forest structure parameters; according to the forest structure parameters, calculating laser spot scale biomass by using a different-speed growth model; and based on the laser spot scale biomass and the multi-source remote sensing image features, using a U-Net deep learning model to obtain large-scale biomass data with continuous space, and obtaining a large-scale forest above-ground biomass distribution map according to the large-scale biomass data. According to the method, high-precision inversion from single-plant crown parameters to large-scale forest above-ground biomass is realized, and the efficiency of forest biomass estimation is improved.

The invention discloses a forest carbon reserve and carbon sink monitoring and evaluating method based on a multi-source remote sensing technology. The method comprises the following steps: multi-source remote sensing data acquisition: respectively acquiring optical remote sensing data and radar remote sensing data; tree species classification and identification: carrying out tree species classification on the forest in the research area to realize tree species scale space distribution information extraction; for different tree species, biomass model parameters obtained through field investigation are combined, a biomass estimation model based on the tree species is established, model input parameters are biomass model parameters, and vegetation biomass is output; calculating the carbon reserve: calculating the vegetation carbon reserve by utilizing the inverted vegetation biomass and combining the carbon content coefficients of different tree species; estimating the soil carbon reserve, and finally obtaining the total carbon reserve of the forest ecosystem: calculating the carbon reserve variation of the forest ecosystem at different time points based on the multi-source remote sensing data of the long-term sequence. According to the invention, large-range, high-precision and continuous forest carbon reserve and carbon sink monitoring and evaluation considering tree species difference is realized.

The invention relates to the technical field of ecological soil remediation, and discloses a moso bamboo ecological soil remediation directional regulation and control method and equipment which are used for solving the problems of insufficient light transmittance, low acidified soil remediation efficiency and the like caused by a fixed proportion of a traditional bamboo and broad-leaved mixed forest. A random forest algorithm is combined to dynamically optimize a mixing proportion and associated tree species configuration, an unmanned aerial vehicle is used for scanning to generate a three-dimensional crown model, intelligent equipment is guided to accurately intermediate cutting and complementary planting, and in cooperation with a pH response type shading film and an acid-resistant phosphate solubilizing bacterium agent, crown light transmittance improvement and acidified soil improvement are achieved. The equipment comprises an unmanned aerial vehicle carrying a laser radar, an intelligent transplanting robot and a solar drip irrigation system, and the concentration of a fungicide and an irrigation strategy can be dynamically regulated and controlled. The porous bamboo charcoal carrier is prepared by innovatively utilizing the moso bamboo waste, and the functional flora is loaded to replace the traditional peat raw material; according to the scheme, the problems of static configuration, manual dependence and resource waste are solved.

The invention relates to the technical field of multispectral image processing and analysis, solves the technical problems that a traditional tree species classification model cannot fully mine seasonal change characteristics and cannot effectively construct a tree species space-time spectrum constraint relation, and particularly relates to a dominant tree species identification method based on time-space-spectrum multi-domain perception and guidance. Comprising the following steps: acquiring multi-temporal satellite-borne multispectral image data; performing parallel feature extraction on the multispectral image data through a double-flow network architecture; inputting the extracted double-flow features into a time-space-spectrum feature fusion module, and performing cross-domain fusion on the multi-temporal dynamic features through a hierarchical time sequence spectrum integration mechanism and a dynamic space-time excitation optimization mechanism to obtain space-time spectrum features; and outputting an identification result of the forest dominant tree species based on the fused time-space spectrum features. Through the effective feature extraction and integration strategy, the consumption of computing resources is reduced, the efficiency of processing the large-scale satellite-borne multispectral image data is improved, and the high-efficiency requirement in practical application is met.

The invention relates to the technical field of agricultural fertilization control, in particular to an accurate fertilization flow control method and system for a water and fertilizer all-in-one machine for economic forest tree species, and the method comprises the steps: collecting water and fertilizer pressure data and water and fertilizer flow data in a water and fertilizer mixing pipeline in real time at a fertilization nozzle of each tree; analyzing the fluctuation condition of the water and fertilizer flow data of each tree within a preset duration before each collection moment, and determining the fluctuation trend influence coefficient of each tree at each collection moment; determining an upstream trend influence coefficient and a water and fertilizer flow deviation degree of each tree at each collection moment; determining the same-row fluctuation difference value of each tree at each collection moment; determining a proportionality coefficient adjustment value of each tree at each collection moment; and determining proportional parameters of a PID control algorithm, and controlling the fertilization flow of each tree. The invention aims to improve the control precision of the fertilization flow of each tree.

The invention discloses a power transmission channel vegetation carbon sink recovery potential assessment method and a system with the power transmission channel vegetation carbon sink recovery potential assessment method, and the power transmission channel vegetation carbon sink recovery potential assessment method comprises the following steps: collecting and fusing multi-aspect data of a power transmission channel planning target area; and establishing a detailed and highly-targeted base database for carbon sink recovery of the to-be-constructed target area of the power transmission channel. The method comprises the following steps: comprehensively considering the change of land use before and after power transmission channel construction, and formulating a typical tree species distribution pattern scheme according with a specific scene and corresponding accounting factors by combining land use types of areas to which land blocks belong; and a power grid safe operation constraint and a carbon sink metering method are combined. The comprehensive influence of power transmission channel construction on vegetation carbon sink can be accurately mastered, reasonable measures are taken to realize maximization of carbon sink benefits, and coordinated development of power transmission channel construction and ecological environment protection can be realized.

The invention provides a binary wood volume equation forest measurement method based on a ground-based laser radar. The method comprises the following steps: acquiring complete point cloud information scanned by the ground-based laser radar; processing the point cloud data to obtain tree structure parameters; fitting a plurality of cutting degree equation models through tree structure parameters to obtain a single-tree optimal cutting degree equation model; and a single-wood cutting degree-binary wood volume equation is constructed based on the single-wood optimal cutting degree equation, and the single-wood volume is calculated. According to the method, the traditional under-forest data acquisition work can be simplified, the tree species cutting degree equation can be updated for the sample plot forest more timely and accurately, the permanent damage to the tree caused by ground survey can be reduced, effective technical support is provided for the survey of the stand volume of a man-made forest and the estimation of the forest stand outturn percentage, and the method is suitable for popularization and application. Compared with a traditional method for estimating the volume and the stock of the single wood through a wood stock table, the method is higher in precision and better in flexibility, can be better used in the production practice process, and greatly saves manpower, material resources and financial resources.

The invention discloses a forest resource investigation method based on a laser radar and visible light synchronous photography technology, and belongs to the technical field of forestry. The method comprises the following steps: selecting a forest stand to be measured, and setting a scanning route; scanning the forest stand by using a laser radar and visible light synchronous photography technology; in the forest stand model, directly identifying tree species, and counting forest stand density; exporting point cloud data, performing individual tree segmentation according to a forest tree sequence, and generating DBH data and position coordinate data in batches; calculating the stand volume and tree species composition of the forest stand; determining a space structure of the forest stand according to the diameter at breast height data and the position coordinate data of the forest, and calculating a competition index of each tree; and linking the database with the forest stand model, establishing an augmented reality forest stand model, and realizing visualization of the forest resource data model. According to the method, the three-dimensional visual model of the forest stand can be established, the tree species can be directly identified, the forest resource survey data can be obtained through calculation, the survey efficiency is high, the cost is low, the data storage amount is small, and the later data processing difficulty is low.

The invention provides a rail transit environment vibration directional control method and system using a wedge-shaped tree array. According to the method, ground surface vertical vibration caused by passing of a train beside a certain ballast track is calculated by constructing a semi-analytical three-dimensional dynamic response model, simulation of the tree vibration reduction measure effect is achieved by coupling the wedge-shaped tree array into the model, and the tree vibration reduction measure effect is achieved by adjusting parameters of the wedge-shaped tree array. The wedge-shaped tree array with the expected vibration reduction effect is obtained, and then design parameters of wedge-shaped tree array vibration reduction measures are obtained. The wedge-shaped tree array is arranged between a track structure and a target vibration control area, and the selection and arrangement mode of tree characteristics such as tree species, tree height and tree diameter can be optimally designed according to a target vibration reduction frequency band and an expected vibration reduction effect. The invention overcomes the limitation of the traditional vibration isolation technology on cost and implementation, provides a wedge-shaped tree array vibration reduction measure which is low in cost and easy to arrange and meets the environmental protection requirement, and has good popularization and application potential.

The invention discloses an arbor community construction method suitable for urban landscaping, and relates to the technical field of urban planning, and the method comprises the steps: deploying an environment sensor in a target urban landscaping region, and collecting the environment data of the target region in real time through a wireless sensor network; based on the environment data, adopting a genetic algorithm to carry out adaptability evaluation on a plurality of candidate tree species, and selecting a tree species combination with the strongest adaptability to carry out preliminary community configuration; and a multi-objective optimization algorithm is adopted to carry out optimization configuration on the number, distribution and ecological functions of tree species in the initial community configuration, the performance of the community under different environmental changes is evaluated, and the overall ecological benefits of the community are maximized. According to the arbor community construction method suitable for urban landscaping provided by the invention, the configuration of the tree species is automatically adjusted based on multi-dimensional evaluation of environmental data, tree species adaptability and community ecological benefits, and the community ecological benefits are maximized.

The invention discloses a method for generating an aviation lifelike tree sample and identifying a tree species by using an image diffusion model. The method comprises the following steps: constructing a multi-modal data set; training an image-text comparison model and a Unet denoising network model; outputting predicted noise in the trained Unet denoising network model, and finally generating final image reconstruction which is in semantic alignment with the input text description; the obtained single tree crown images are synthesized; and constructing a YOLOv11 detection model, and training the YOLOv11 detection model by using the synthesized forest image to realize tree species identification. According to the method, language semantics and a diffusion model are combined to generate a vivid tree sample for capturing specific features of species and seasonal phenological changes, and the vivid tree sample is synthesized into a high-fidelity forest image, so that tree species identification in an aerial image is enhanced, and effective crown detection and accurate tree species identification are realized.

The invention discloses a secondary forest development stage division method based on forest stand state characteristics, and belongs to the technical field of forest development stage division. Comprising the following steps: selecting an initial index set, performing index forward and standardization processing on sample plot survey data, and forming a data set; classifying the data by adopting a system clustering analysis method, checking and analyzing a state index influencing a classification result, and selecting plt; the index of 0.05 constitutes a secondary forest development stage division index system; carrying out weight assignment, then correcting the weight by adopting an entropy evaluation method, and determining the comprehensive weight of the index by utilizing a Lagrange multiplier method; the sum of the dimension-removed value of the division index and the comprehensive weight value product is a development stage value; according to an equidistant method, the development stage of the secondary forest is divided into a forest gap stage, a renewal stage, a differentiation stage, a built-up stage and a stable stage. Depending on forest age is avoided, and the method is suitable for multi-tree mixed secondary forests; the indexes are easy to obtain, and the division result can accurately guide forest management.

The invention discloses an urban landscaping tree species classification method based on a Pleiades Neo ultrahigh resolution multispectral satellite image. According to the invention, based on the Pleiades Neo image, an NDVI threshold method is adopted to rapidly identify the urban vegetation area; using a Unet < 3 + > model to separate vegetation in the shadow area and the illumination area; a shadow region tree species classification integration model is constructed by combining a superpixel segmentation algorithm and introducing a weighted dictionary and an attention mechanism, so that the classification precision is effectively improved; and carrying out tree species classification on the illumination area by using a deep learning algorithm and making an overall tree species distribution diagram. Aiming at the shadow interference problem in a high-resolution image, a set of tree species classification scheme comprehensively considering vegetation spectrum characteristics of an illumination region and a shadow region is developed by combining a deep learning algorithm to deeply mine potential information, a more accurate and feasible tree species distribution acquisition approach is provided for fine management of urban landscaping, and the urban landscaping quality is improved. And important data support is provided for health assessment and planning of the urban ecosystem.

The invention provides a target tree species remote sensing recognition method based on space-air-ground cooperation, which comprises the following steps: performing adjacent superpixel combination on segmented pattern spots after superpixel segmentation, and performing feature factor-based model optimization on a current recognition model of a target tree species based on combined pattern spot distribution and field distribution data, so as to obtain a target tree species remote sensing recognition model. Target tree species recognition is executed based on the updated new recognition model, and correct recognition pattern spots are obtained; executing edge matching detection on the correctly recognized pattern spots, adjusting the current merging scale based on an edge matching detection result to obtain an optimal recognition model, and further obtaining a recognition result of the target tree species based on the optimal recognition model; according to the method, the accuracy of the pattern spot boundary is effectively improved while the recognition precision of the target tree species is ensured, so that the matching degree of the boundary of the segmented pattern spot and the actual boundary of the target tree species is effectively improved.

The invention discloses a construction method, a construction system, a recognition method and a recognition system for a fine tree species recognition model, belongs to the field of forest management and ecological protection, and solves the problems that the overall contribution of the whole group of time sequence features to model performance cannot be comprehensively examined, and due to the resolution limitation of remote sensing images and the randomness of forest species distribution space and quantity, the model performance cannot be comprehensively examined in the prior art. And the performance of the remote sensing tree species classification model is obviously reduced. The method comprises the steps of 1, constructing a high-dimensional multi-source feature data set; 2, performing feature optimization based on the high-dimensional multi-source feature data set to obtain an optimal classification feature set; 3, collecting actual measurement sample data, and constructing a deep learning sample library in combination with the optimal classification feature set; and step 4, presetting a fine tree species identification model, and inputting the deep learning sample library to train the preset fine tree species identification model to obtain the fine tree species identification model. The method is used for obtaining the accurate and newest tree species distribution map.

The invention discloses a tree species identification method based on a bark image and a lightweight deep learning model, and belongs to the crossing field of computer vision and forestry information technology, and the method comprises the steps: S1, collecting an image of a tree bark area; s2, preprocessing the collected data, and positioning and cutting a bark area based on an image segmentation technology; s3, using an improved lightweight deep learning model of a ConvNeXtV2 framework to carry out feature extraction on the preprocessed bark image; s4, after feature extraction is completed, features are integrated by using an adaptive feature fusion strategy, then tree species are classified, and a tree species identification result is output; s5, performing multi-dimensional verification and visual analysis on the model performance; according to the method, the calculation complexity is remarkably reduced, the generalization ability and stability of the model are kept, and reliable technical support is provided for practical application such as ecological monitoring, forest resource management and intelligent forestry.

The invention provides a method for identifying forest tree species by using laser point cloud data, and the method comprises the following steps: collecting three-dimensional laser point cloud data of a forest region, setting an elevation threshold, filtering ground points, and extracting a point cloud sample object; respectively extracting VFH, CVFH and ESF feature descriptors from the sample point cloud, and constructing three types of geometric feature vectors; performing supervised classification on the features by adopting a random forest and a support vector machine learning classifier; the output of each classifier is fused through strategies such as weighted voting, an average method or a stacking method, and a final tree species identification result is obtained; according to the method, three types of global or semi-global feature descriptors of VFH, CVFH and ESF are extracted for a point cloud sample object of a single tree, feature modeling is carried out on tree species from three dimensions of spatial attitude, local scale structure and global shape distribution, the advantage of real restoration of a target structure by using point cloud data is utilized, and the feature modeling efficiency is improved. And the problem of projection distortion of image features under multiple view angles is avoided.

The invention discloses a forest degeneration degree, degeneration type and degeneration process identification method, and relates to the field of forest resource monitoring and ecological environment evaluation.The method comprises the steps that multi-source data obtained in a research area is preprocessed, and the preprocessed multi-source data is determined; selecting multi-dimensional indexes from three aspects of forest degradation structure, composition and function, and constructing a multi-dimensional index system according to the preprocessed multi-source data; the multi-dimensional indexes comprise forest coverage rate, crushing degree, tree variety diversity, aboveground biomass and net primary productivity; generating a forest degradation index according to each index in the multi-dimensional index system; determining a forest degeneration degree according to the forest degeneration index, generating a forest degeneration type graph in combination with land coverage data to reveal degeneration differences of different forest types, analyzing a dynamic change track of a forest degeneration region in combination with a normalized combustion index time sequence, and identifying a forest degeneration process; according to the method, the multi-dimensional characteristics of degradation can be comprehensively revealed.

The invention discloses an intelligent forestry surveying and mapping method and system based on regional feature feedback, relates to the technical field of forest surveying and mapping, and provides the following scheme: extracting spectrum and texture features of a target forest image through satellite remote sensing data, realizing recognition and grading division of feature regions by using an intelligent segmentation algorithm, and obtaining a target forest image. A priority monitoring grid of the content evaluation indexes is generated; and scheduling the unmanned aerial vehicle to scan the priority monitoring grid for the first time, and integrating image data of the unmanned aerial vehicle and the satellite. According to the method, spectrum and texture features of a target forest region are extracted through satellite remote sensing data, a non-uniform priority monitoring grid containing a feature density index is generated, an unmanned aerial vehicle is scheduled to scan a high-priority grid for the first time, satellite multi-spectrum data, an unmanned aerial vehicle high-resolution image and LiDAR point cloud data are fused, and the target forest region is obtained. The tree species are identified, the health index thermodynamic diagram is generated, high-danger areas are rapidly identified, the scanning range of the unmanned aerial vehicle is remarkably compressed, and the monitoring efficiency is improved.

The invention relates to the technical field of forestry management, in particular to a forest and grass resource dynamic monitoring system which comprises the steps that a graph attention network is adopted to conduct high-low weight recognition processing on the structure difference value between node pairs, feature vectors are established through the canopy height difference and canopy density difference between adjacent nodes, and the canopy height difference and the canopy density difference between adjacent nodes are obtained; an edge weight scoring system is constructed in the form of segmented statistics and proportion weighting, node edge pairs with high influence relation strength are dynamically screened, ordered aggregation of spatial communication strength is realized, a graph neural network is introduced in a parameter fusion stage to construct a node parameter representation mechanism, and the spatial communication strength is improved. The tree species proportion, the grade of diameter at breast height and the community vertical structure in a regional sample plot are used as input features, unified mapping of node features of each region is completed in multiple rounds of iteration, cross-regional difference analysis is executed on model output in combination with biomass change frequency, parameter items with the difference proportion lower than a set threshold value are replaced with unified expression, and the model output is obtained. And parameter synchronization is realized to realize space nesting and feature integration.

The invention provides a fast-growing high-yield forest tree species afforestation area establishment method and system, and relates to the field of afforestation. According to the method, ecological environment and social economic two-dimensional information are fused, an improved maximum entropy reinforcement learning model is adopted to construct an ecological environment suitable area prediction model, social economic suitability is judged based on rule modeling, and a final suitable afforestation area is obtained through space superposition. According to the method, intellectualization and precision of afforestation site selection are achieved, scientificity and actual operability of suitability judgment are improved, and the method is suitable for afforestation planning and optimization under large-scale and multi-constraint conditions.

The invention provides an intelligent and rapid pre-examination and diagnosis method and system for tree health, and relates to the technical field of tree management. The method comprises the following steps: constructing a liquid flow database of a standard tree of a target tree species, carrying out model training on an established liquid flow calculation model by utilizing the liquid flow database, then collecting liquid flow data of a to-be-detected target tree in a preset detection time and meteorological and soil information of a growth environment, inputting an optimal liquid flow calculation model, and calculating a theoretical liquid flow value of the target tree, comparing with an actually monitored liquid flow value, calculating a liquid flow deviation degree, grading according to a daily maximum liquid flow deviation degree, and comprehensively evaluating a tree health grade in combination with phenotype information to obtain a diagnosis result; the rapid and intelligent pre-examination diagnosis of the tree health is realized, and the intervention timeliness is improved; and multi-dimensional data fusion is adopted, so that the diagnosis accuracy is improved, and automatic monitoring and real-time early warning are realized.

The invention discloses a method and a system for measuring carbon reserves from a single tree to a regional forest based on a hyperspectral image, which can effectively improve the precision and efficiency of forest carbon reserves, and the method comprises the following steps: obtaining hyperspectral data and field survey data; performing spectral feature analysis to determine a target tree species; extracting a crown breadth range of the crown based on a multi-scale segmentation algorithm of edge detection; based on the hyperspectral remote sensing data, classifying the target tree species by using a machine learning algorithm; selecting a tree species biomass equation based on the tree species category of the target area, and calculating the biomass of the overground part; based on the calculation result of the biomass equation and the carbon conversion coefficient, utilizing a deep learning algorithm to perform inversion of the aboveground carbon reserve of the target area; and on the basis of the above-ground carbon reserve inversion result, estimating the above-ground carbon reserve of the target area to obtain the above-ground carbon reserve spatial distribution of the vegetation in the target area.

The invention discloses a power transmission corridor tree species growth prediction method, system, device and medium, and belongs to the technical field of tree growth prediction.The method comprises the steps that tree species data and environment data in a power transmission corridor area are obtained, the tree species data and the environment data are preprocessed, and a training data set is obtained; training a cascade recurrent neural network model based on the training data set to obtain a trained cascade recurrent neural network model; predicting the growth trend of the tree species according to the trained cascade recurrent neural network model to obtain a prediction result, performing error analysis on the prediction result, and optimizing the trained cascade recurrent neural network model according to an error analysis result; performing growth trend analysis according to the prediction result, and determining the growth trend of the tree species; and carrying out early warning on the growth trend of the tree species through the prediction result in combination with a preset safety threshold. According to the method, the tree growth prediction precision is improved through a cascade structure.

The invention provides a large-scale forest individual tree instance segmentation method and device based on laser radar point clouds, and the method comprises the steps: carrying out the voxelization processing of a forest point cloud to be subjected to individual tree instance segmentation, and obtaining a plurality of target voxels; obtaining voxel features, offsets and semantic tags of the target voxels according to a pre-trained sparse convolutional neural network backbone model; constructing an offset space according to the offset; determining a rough instance mask corresponding to each individual tree instance in the forest point cloud to be subjected to individual tree instance segmentation according to each target voxel and a predetermined average voxel number corresponding to individual tree instances of different tree species; determining coordinates and features of initial query vectors corresponding to the individual tree instances according to the rough instance masks; determining super-points, super-point features and super-point space coordinates corresponding to the forest point cloud to be subjected to individual tree instance segmentation; and based on a pre-trained model, the super-point features, the super-point space coordinates and the initial query vectors, obtaining a segmentation result, and realizing high-quality segmentation.

The invention relates to the technical field of biology, in particular to a Korean pine control-based method for researching the resistance mechanism of larch to pine wood nematodes, which comprises the following steps: S1, extracting pine wood nematodes: separating to-be-tested nematodes from naturally infected larch by using a funnel; s2, pine wood nematode culture: utilizing botrytis cinerea to co-culture pine wood nematode as a test raw material; s3, selecting tree species: selecting twigs, boughs and trunks of healthy larch with tree ages of 1 year, 2 years and 3 years respectively, and selecting Korean pine with the same requirements as a contrast; s4, sampling tree species: sampling larch and Korean pine inoculated for 1 day, 3 days, 5 days, 7 days, 9 days, 14 days and 21 days; by analyzing the chemical components of the Korean pine and the larch in different stages before, during and after the bursaphelenchus xylophilus infection, the purpose of researching the pathogenesis of the Korean pine and the larch by local pine nematodes in Liaoning is achieved, so that the purpose of effectively preventing the bursaphelenchus xylophilus is achieved.

The embodiment of the invention provides a tree species identification method, device and equipment based on a dense time sequence image, and the method comprises the steps: obtaining a dense long time sequence multispectral remote sensing image and topographic data of a research area; carrying out image wave band fusion according to the dense long-time-sequence multispectral remote sensing image to construct 12 vegetation spectral indexes; performing land utilization classification on ground features in the dense long-time-sequence multispectral remote sensing image of the research area by utilizing a linear spectrum hybrid model in combination with topographic data, dividing the ground features into buildings, water bodies, other ground features and a forest land data set, and performing pre-classification based on the extracted forest land data set; dividing the forest land data set into an evergreen forest and a deciduous forest by combining an RVI threshold method with a linear spectrum hybrid model; according to the forest land data set, Savitzaky-Golay filtering transformation and spectral differential transformation are carried out, and an NDVI transformation image set is obtained; and performing combination according to the vegetation spectral index NDVI transformation image set as classification features, and discussing the classification effect of the tree species in the research area through a random forest model.

The invention provides a tree species screening method for high-altitude drought afforestation, an afforestation method and application, and belongs to the technical field of forest tree breeding and cultivation. According to the screening method, five physiological and biochemical indexes of soluble protein, malondialdehyde, proline, peroxidase and soluble sugar in the tree species are measured through a gradient drought stress experiment, and comprehensive scoring is performed by adopting a membership function, so that the drought-resistant tree species are scientifically screened. The tree species such as sophora moorcroftiana, rosa serrulata and amorpha fruticosa screened by the method are applied to afforestation in the high-altitude arid region, and the afforestation survival rate and preservation rate can be remarkably increased. The invention provides a reliable technical scheme for solving the long-term problem that the selection of tree species in the difficult site field lacks scientific basis.

The invention provides a multi-tree forest aboveground biomass remote sensing estimation method and system and a storage medium, and the method comprises the steps: obtaining multi-source remote sensing data based on a target region, including airborne LiDAR point cloud, ground survey data and DEM data; constructing a terrain heterogeneity index DGTHI in combination with DEM data, and quantifying terrain complexity through weight fusion of standardized terrain factors; extracting forest characteristic parameters and calculating the overground biomass of the single tree; performing classification modeling on the sampling region based on DGTHI, respectively establishing multiple linear regression models for different tree species, and optimizing feature parameter selection; and evaluating the precision of the model through cross validation, and popularizing the model to a county scale to perform overground biomass space inversion and mapping. According to the method, terrain heterogeneity classification and tree species specificity modeling are fused, so that the estimation precision of the forest biomass in the complex terrain region is remarkably improved, and a technical support is provided for regional carbon sink monitoring and sustainable forestry management.