367 results about "Tree species" patented technology

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 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 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 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 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 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 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 discloses a power transmission corridor tree classification method, system and device and a medium. The method comprises the following steps: acquiring a hyperspectral image of a power transmission corridor area; performing data preprocessing and data enhancement on the hyperspectral image; creating a deep learning model based on the preprocessed and enhanced hyperspectral image, and extracting multi-level spectral and spatial features; training the deep learning model by adopting an optimization loop technology to obtain an optimal model parameter; and inputting the new hyperspectral image data into the trained deep learning model to obtain a tree category result of the power transmission corridor area. According to the method, the defects of a simple model in hyperspectral data are overcome, and the classification accuracy is greatly improved especially in the environment that tree species are complex or uncertain factors such as illumination variation and interference exist.

The invention discloses a forest ecosystem carbon reserve determination method based on multi-source remote sensing data, and the method comprises the steps: constructing and analyzing a dominant tree species multi-temporal multi-feature classification data set according to Sentinel-1 and Sentinel-2 multi-source remote sensing data and research area forest resource checking vector data extraction; classifying the dominant tree species according to the multi-feature multi-temporal remote sensing data; and estimating the forest carbon reserves of different dominant tree species according to the optimal classification data set and the forest stock. According to the method, forest multi-feature and multi-temporal feature data are obtained by means of multi-source remote sensing data, and combined dimension reduction and separability analysis are performed, so that advantageous tree species spatial distribution is extracted, a forest stock and biomass linear model is fitted, and carbon reserve spatial distribution and features of a research area are estimated. And basic data and a practical basis are provided for further strengthening forest ecological system protection and implementing forest carbon reserve accurate improvement measures.

The invention relates to the technical field of power line monitoring, and discloses a tree growth prediction method, system and device based on a multi-temporal point cloud and a growth model, and a medium, and the method comprises the steps: collecting the multi-temporal data of a target region, carrying out the vegetation region recognition and single tree segmentation of a vegetation point cloud, extracting the structural features of each tree, and carrying out the prediction of the growth of each tree; generating a time sequence characteristic data set; constructing a tree growth model, taking the time sequence characteristic data set as model input, introducing a point cloud structure parameter as a dynamic constraint item, and performing parameter optimization on the model to obtain a prediction curve of a multi-dimensional growth index of the tree; and based on the prediction curve of the multi-dimensional growth indexes, performing dynamic geometric reconstruction through original point cloud data, and generating an interactive visual three-dimensional model. According to the method, on the basis of multi-source heterogeneous point cloud data, the single-tree-level growth model can be constructed, the time sequence structure change trend of the single-tree-level growth model is analyzed, and the space error and the time stability of a prediction result are quantified.

The invention relates to the technical field of tree species identification, in particular to a tree species identification method and system based on multi-source remote sensing data fusion, and the method comprises the steps: obtaining multi-source remote sensing data in a target identification region, carrying out the cross-domain feature decoupling processing of the multi-source remote sensing data, and obtaining the attribute features of tree species and the environmental interference features; inputting the tree species attribute features into a preset adaptive spatial-temporal feature library to obtain correction features; collecting current real-time environment parameters, and fusing the tree species attribute features, the environment interference features, the correction features and the real-time environment parameters to obtain multi-source coordination features; a tree species identification result is generated according to the multi-source coordination characteristics, tree species identification requirements in different geographical environments can be adaptively matched, the problem of precision attenuation caused by data distribution offset during cross-regional migration of a model system is effectively solved, and the tree species identification robustness in different ecological environments is remarkably improved.

The invention relates to the technical field of ecological restoration, in particular to a method for restoring a tropical degraded forest based on thinning and complementary planting of nitrogen fixation precious tree species. The method comprises the following steps: dividing a degenerated forest into three types, namely a mild type, a moderate type and a severe type according to canopy density and forest stand composition; for the light tropical degraded forest, a stand structure is built through target tree marking and interference tree felling, stand renewal is driven in cooperation with pruning work and selective felling, and a different-age double-layer forest is cultivated; for the moderate tropical degraded forest, a forest window updating unit is constructed, and survival rate and preservation rate dual monitoring is executed to maintain the forest window updating unit; for the severe tropical degraded forest, native vegetation is reserved through strip cleaning, seedlings are planted in a planting strip to construct a strip recovery substrate, synchronous complementary planting is carried out, and finally the strip succession process is stabilized in combination with continuous tending. According to the method, the problem of repairing the fragmented area is effectively solved, synergistic interaction of soil improvement and economic value improvement is achieved, and the technology generalizability is remarkably enhanced through a standardized operation system.

The invention discloses a tree growth prediction method and system based on a TCN time sequence convolution model, and the method comprises the steps: collecting the historical growth data of trees in a power transmission line corridor, and carrying out the data preprocessing; constructing a neural network model based on the TCN; dividing the preprocessed data into a training set and a test set, and training the TCN model by using the data of the training set; performing performance evaluation on the trained TCN model by using the test set; and deploying the trained TCN model in an actual application scene, and performing real-time growth prediction on the target tree species in the power transmission line corridor area. According to the method, through the model based on the time convolutional network (TCN), the growth condition of the tree species in the power transmission line corridor is accurately predicted. The TCN model captures the sequential relationship in the time sequence by using the convolution operation, thereby avoiding the problem of gradient disappearance or explosion easily occurring when the traditional recurrent neural network processes the long sequence, and improving the prediction accuracy.

The invention relates to the technical field of computer vision and laser scanning, in particular to a fine-grained city tree species classification method and device based on multi-modal data fusion, and the method comprises the steps: collecting the original point cloud of a street tree and a street view image of a target street; performing tree species information identification on the original point cloud of the border tree to construct a point cloud tree species classification data set; performing image cutting on the streetscape image according to the point cloud tree species classification data set to construct an image tree species data set; training a pre-constructed deep learning neural network by using the point cloud tree species classification data set and the image tree species data set until a preset requirement is met, and obtaining a final tree species classification model; and performing tree species classification on the to-be-classified urban street trees by using the final tree species classification model to obtain tree information of the to-be-classified urban street trees. Therefore, the problem that an existing image classification method based on deep learning is low in accuracy of automatically identifying the street tree species is solved.

The invention discloses a natural secondary forest tree species classification method based on multi-modal data fusion. The method comprises the following steps: S1, building a tree species space-spectrum feature extraction module; s2, building a tree canopy global context sensing fusion module; s3, building a tree perception detail optimization module; s4, building a tree species classification network based on multi-modal data fusion; s5, after the tree species classification network with multi-modal data fusion is built, corresponding hyperspectral and laser radar data sets are used for training and testing; according to the method, the problems of fuzzy crown boundary, difficulty in distinguishing tree species with similar structures and low classification precision caused by difficulty in considering long-distance global dependence modeling and fine-grained local detail reservation when an existing tree species classification technology is used for processing a complex forest stand environment are solved.

The invention relates to the technical field of artificial intelligence, can be applied to financial science and technology business scenes, and discloses a tree species distribution analysis method, device, equipment and medium based on a remote sensing image, comprising: acquiring a remote sensing image of a target area to generate an image set carrying geographic positioning information, loading a tree species identification model to configure an offline environment, performing image preprocessing to generate a preprocessed image sequence, inputting the preprocessed image sequence into the model to complete crown detection and category classification to generate a tree species labeling result, performing spatial splicing and duplicate removal on the labeling result to form a tree species instance set, and summarizing categories and spatial positions based on the instance set to generate a tree species quantity statistical table and spatial distribution data; and packaging the result into an output data packet and outputting the output data packet through a service interface. According to the method, an off-line automatic process is established, so that errors and workload caused by manual sampling are reduced, efficient and accurate statistics of tree species distribution and quantity in a large-range forest farm is realized, and reliable data support is provided for business decision making.

The invention belongs to the technical field of forestry ecological restoration, and discloses a method for transforming a pinus massoniana low-quality and low-efficiency water source forest into a coniferous and broad-leaved mixed forest, which comprises the following steps: (1) forest stand diagnosis and planning; (2) three-dimensional complementary planting; and (3) innovation of a mixed mode. The biological characteristics of different fireproof tree species are utilized, upper, middle and lower mixed spaces are matched, shade-tolerant and sun-loving tree species are matched, and the mutual promotion effect is achieved. The characteristics of various tree species are effectively utilized, matching of deep and shallow root system tree species is fully considered, the improvement of the water and soil conservation function of forest land soil is effectively ensured, and the water and soil conservation function of a water source forest region is particularly promoted.

The invention provides a forest community space construction method and system using AI model optimization, and the method comprises the steps: firstly obtaining a forest basic environment data set containing topographic features, soil attributes and climate condition information, carrying out the community suitability analysis processing of the forest basic environment data set, and obtaining the suitability evaluation results of different tree species; calling a pre-trained community space optimization model to carry out space configuration calculation, generating a forest community space configuration scheme including tree planting position distribution and planting density planning, and generating a community planting layout instruction including geographic coordinate marks and planting quantity distribution rules according to the forest community space configuration scheme, and the community planting layout instruction is sent to a forest planting execution system to trigger planting layout operation, so that the forest community can be scientifically and efficiently constructed, and the ecological and economic benefits of the forest are improved.

The invention discloses a forest fire danger early warning system and method based on meteorological and vegetation data analysis, and relates to the technical field of fire risk prediction and control. According to the forest fire danger early warning system and method based on meteorological and vegetation data analysis, fine grid division is carried out on a target forest area through a high-resolution DEM and a multispectral or hyperspectral image, and vegetation type identification is carried out on each monitoring subunit; calculating a wet-dry cycle index WDI, a monsoon effect index MFI and a dry thunderstorm risk factor DRF by combining hourly wind speed, wind direction, relative humidity, precipitation and vapor pressure difference meteorological data; through extracting canopy height, canopy density, under-forest shrub and grass layer thickness and high VOC tree species coverage rate, fuel continuity coefficient FCI and volatile matter flammability index VEI are calculated, and accurate quantification of continuity and flammability of vertical ladder fuel and horizontal canopy is realized. And a comprehensive fire danger index FFI is constructed and subjected to hierarchical management after judgment, so that the dynamism, the hierarchical property and the pertinence of fire danger early warning are realized, and the forest fire danger prevention and control precision and the patrol resource utilization efficiency are improved.

The invention discloses a multi-factor-driven grass, shrub and tree multi-level configuration method for a hydro-fluctuation belt in a high and cold reservoir area, and belongs to the technical field of plateau wetland ecological restoration and slope surface biological protection. The method comprises the following steps: S1, constructing a high and cold reservoir area bank slope three-dimensional terrain model, and obtaining the slope direction, gradient and elevation distribution information of each slope section; s2, establishing a zoning model; s3, carrying out ecological niche adaptability matching on each subarea, and selecting optimal herbaceous, shrub and arbor types and quantity ratios; s4, establishing a suitable plant database; s5, recovering automatic layout on the scheme graph; s6, adopting a combined construction mode of spray seeding, field planting and broadcast seeding to improve the survival rate; s7, establishing a recovery effect discrimination model, and forming a complementary planting feedback closed loop in combination with threshold control; a technical scheme which adapts to regional environment heterogeneity and automatically recommends a herbal, shrub and arbor configuration structure is constructed, and zoned planting, accurate recovery and structure optimization are respectively performed in three elevation zones A, B and C of the hydro-fluctuation belt.

The invention discloses an unmanned aerial vehicle aerial photography-based forest tree measurement automatic measurement method and system. The measurement method comprises the steps of obtaining a multispectral image set of a complex forest environment through unmanned aerial vehicle aerial photography; preprocessing the multispectral image set, and obtaining an image feature set by adopting a denoising and spectral correction method; determining a tree species distribution category according to the image feature set; based on the tree species distribution category, segmenting the overlapped tree crowns through a deep convolutional neural network to obtain an independent tree crown region set; obtaining crown breadth parameters according to the independent crown region set; extracting crown height information from the crown breadth parameters to obtain a tree height parameter set; estimating the diameter at breast height by applying a regression analysis model on the basis of the tree height parameter set in combination with near-infrared characteristics of the multispectral image to obtain a diameter at breast height parameter set; and generating a comprehensive forest resource data set by adopting a data fusion technology through tree species distribution categories, crown breadth parameters, a tree height parameter set and a diameter at breast height parameter set, and determining a final measurement result.

The invention discloses an oriented structural plate for a container bottom plate and a preparation method of the oriented structural plate. According to the method, fast-growing tree species wood is selected as a preparation raw material, and the preparation raw material is processed in a rotary cutting and slicing shaving mode; the wood shavings-shaped raw materials are dried; carrying out low-temperature plasma treatment on the dried shavings-shaped raw materials obtained by adopting the rotary cutting mode; selecting five layers of equal-proportion shaving-shaped raw materials, and carrying out sizing and directional paving to obtain shaving board blanks; the shaving board blank is subjected to three-section type long-time hot pressing treatment, and an oriented structural board with the thickness ranging from 15 mm to 18 mm is obtained; standing and storing the oriented structural plate subjected to hot pressing in a specified environment for specified time; according to the oriented structural plate for the container bottom plate and the preparation method of the oriented structural plate, the rotary-cut and sliced wood shaving layers are obtained in a wood shaving mode, the wood shaving layers are subjected to special drying, sizing and assembly structural design, and it is guaranteed that the plate has excellent bending resistance and is stable in size.

The invention discloses a tree species fine identification method and system, and relates to the technical field of remote sensing information processing and forestry resource management. The method comprises a multi-source data acquisition step, a feature extraction step, a feature fusion and enhancement step, a model training step and a classification step. The method is used for high-precision identification of coniferous forests, broad-leaved forests, shrubs and mixed forests in a complex terrain environment, and can be expanded to application scenes such as ecological monitoring, carbon sink evaluation and power corridor tree obstacle early warning.

The invention relates to the technical field of biological fireproof forests, and discloses a design method for a biological fireproof barrier forest belt, which comprises the following steps of: 1, designing a biological fireproof forest belt based on the canopy density of 0.2-0.5 of a broadleaf mixed forest by taking the existing forest stand type as the broadleaf mixed forest, screening suitable fireproof tree species by integrating site conditions, natural environment and hydrothermal conditions of the biological fireproof forest belt of the broadleaf mixed forest; 2, cleaning and soil preparation are conducted on a biological fireproof forest belt to form a hole belt-shaped area, comprehensive complementary planting is conducted on fireproof tree species, the complementary planting density of a broad-leaf mixed forest is 2520 plants per kilometer as tree planting points, the fireproof tree species are planted in the hole belt-shaped area through the tree planting points, and the fireproof tree species are planted in the hole belt-shaped area; after planting, a compound fertilizer is used for topdressing, so that the fireproof tree seeds absorb fertility to accelerate the growth speed.

The invention relates to the technical field of biology, in particular to a white birch vascular cambium specific expression promoter proBpWOX4 and application of the white birch vascular cambium specific expression promoter proBpWOX4. The promoter is derived from a genome region of a betula platyphylla vascular cambium specific expression gene BpWOX4, and can accurately drive specific expression of a target gene in vascular cambium tissue cells. Specifically, after a proBpWOX4 sequence is effectively connected with a required target gene to construct a recombinant plant expression vector, the recombinant plant expression vector is introduced into a plant body, so that the expression of the target gene only in a vascular cambium can be realized. The highly spatial specific control provides a powerful tool for accurately identifying the specific function of the gene in vascular development and secondary growth, effectively avoids the interference of non-target tissues, and overcomes the bottlenecks of insufficient expression intensity, poor space-time specificity and the like of the existing promoter in forest molecular design breeding. And a key technical support is provided for cultivating excellent tree species with accelerated growth, excellent wood quality and climate adaptability.

The invention discloses an ancient and famous tree identification method based on multi-source remote sensing data. According to the method, an ancient tree ecological niche model is introduced, the association degree of the actual habitat parameters of the regional ancient trees and the remote sensing features is quantified as the weight, and the feature selection process of the random forest is fused, so that the screened features have the capability of distinguishing different vegetation and meet the real living environment requirements of the ancient trees. According to the method, the problem of high distinction degree but irrelevance caused by the fact that a traditional method only depends on feature distinction degree screening is avoided, the key features directly related to the ancient tree can be more accurately captured, and the recognition pertinence of a subsequent model to the target tree species is improved; the ecological niche constrained feature screening mechanism ensures the strong relevance between the features of the input model and the ancient tree habitat from the source, the multi-modal feature fusion network is combined to comprehensively extract space, spectrum and structure information, the conditions of missing detection and false detection caused by habitat changes or new ancient trees are reduced, and the recognition effect is more stable and reliable in long-term application.

The present application relates to the technical field of land greening data management, in particular to a land greening system data information management method, comprising: arranging an ecological perception network composed of multiple source sensors in a target administrative region to collect vegetation physiology and soil environment data; carrying out denoising, interpolation preprocessing, standardization and coordinate system conversion in a data processing center to form a standardized basic ecological data set. Based on the data set, an attribute library containing multi-dimensional information such as tree species is constructed with a single plant or a reforestation plot as the smallest management unit, a dynamic three-dimensional green volume voxel model is generated in a geographic information system to reflect the spatial distribution of land greening resources. The model is used to calculate the carbon fixation and oxygen release potential values of different functional divisions, and a planning decision engine is called to generate suggestions for replanting or reforming in low potential value areas. The method effectively improves the data information processing efficiency, calls the planning decision engine to generate land greening implementation suggestions, and quickly and dynamically updates the land greening system data information.

This invention belongs to the field of remote sensing technology and relates to a method for predicting canopy combustible moisture content (CFMC) by combining remote sensing CFMC products and meteorological data. Specifically, through global two-stage sensitivity analysis, key input parameters for different tree species in the MEDFATE model are identified. Using these parameters, a cost function is constructed to measure the error between the CFMC simulated by the MEDFATE ecophysical process model and the CFMC measured in the field, achieving species-based localized correction of MEDFATE. Then, through four-dimensional variational data assimilation technology, the remote sensing CFMC product is combined with the localized MEDFATE. This achieves enhanced CFMC simulation, improving the accuracy of MEDFATE CFMC simulation while also increasing the temporal resolution of remote sensing data and the spatial resolution of the MEDFATE model. With accurate meteorological forecast data, effective daily CFMC prediction of vegetation can be achieved.

The invention discloses a tree species screening method for heavy metal lead contaminated soil remediation, and relates to the technical field of heavy metal lead contaminated ecological management. Common greening tree species such as cinnamomum camphora, willow, gingko, koelreuteria paniculata, photinia serrulata and osmanthus fragrans are selected, leaf and rhizosphere soil are collected in a point distribution mode, and the content of heavy metal Pb in a sample is measured; the heavy metal Pb content distribution characteristics of different tree species are quantified, the heavy metal Pb enrichment capacity difference of the greening tree species is explored, the heavy metal Pb enrichment and transfer efficiency of the greening tree species is comprehensively evaluated, and the greening tree species with the best heavy metal Pb purification effect is screened out. The method is a phytoremediation technology which has the advantages of being economical, simple, convenient, high in recognition degree, long in life cycle, free of damage to the soil structure, not prone to causing secondary pollution and the like.