35 results about "Forest type" patented technology

The embodiment of the invention provides a forest type remote sensing classification method and device, electronic equipment and a storage medium. The method comprises the following steps: determining a target geographic area and collecting multi-source remote sensing data; extracting canopy multi-dimensional space-time spectrum features based on multi-source remote sensing data, and screening geographical environment covariables capable of representing forest type differences; calculating the overall geographical environment similarity of the unknown point and the sample point by using a Gaussian similarity function; quantifying the individual representativeness of the sample points to the unknown point according to the overall geographical environment similarity, and selecting the forest type represented by the sample point with the highest individual representativeness as the forest type of the unknown point; drawing a forest type spatial distribution diagram and a classification uncertainty distribution diagram of the target geographic area based on classification results of forest types of all unknown points; and performing precision verification on the forest type spatial distribution map based on the classification uncertainty distribution map and a preset precision evaluation index.

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 a forest canopy height remote sensing estimation method based on multi-forest feature fusion and a bidirectional stacking model, and the method comprises the steps: obtaining multi-source remote sensing data of a target region, and employing a multi-stage feature selection method MBF-Control to screen an optimal feature subset for spatial distribution data of different forest types, the multi-source remote sensing data comprises GEDI data, Sentinel-1 / 2 data, Landsat-8 data and DEM (Digital Elevation Model) data; a bidirectional stacking model BS-MFTF of multi-forest type feature fusion is constructed; and inputting the optimal feature subset into the multi-forest feature fused bidirectional stacking model BS-MFTF, and outputting a canopy height estimated value of the target area. Structural diversity and spatial patterns are captured, and canopy height estimation and forest structure mapping are improved.

The invention relates to a forest carbon sequestration ecological function assessment method and device, a storage medium and electronic equipment. A forest carbon sequestration ecological function assessment method belongs to the technical field of ecological environment, and comprises the following steps: inputting meteorological data, soil data, vegetation data and geographic data of a target forest area into an FORCCHN model for setting tree physiological and ecological optimization parameters, obtaining forest net primary productivity of the unit area of the target forest area and forest soil heterotrophic respiration of the unit area of the target forest area; the FORCCHN model is set as tree physiological and ecological optimization parameters corresponding to the forest type of the target forest area; and determining the forest carbon sequestration amount of the unit area of the target forest region based on the forest net primary productivity of the unit area of the target forest region and the forest soil heterotrophic respiration of the unit area of the target forest region, thereby being beneficial to improving the carbon sequestration amount and carbon sequestration function estimation precision of different types of forests.

The invention discloses a basin hydrological simulation method and system embedded with a canopy interception mechanism, and the method comprises the steps: embedding a revised Gash canopy interception model module in an SWAT model, dividing a rainfall event into a wetting stage, a saturation stage and a drying stage according to the parameters of rainfall, a leaf area index and a tree height, dynamically calculating the canopy interception amount and the interception evaporation amount, and carrying out the calculation of the canopy interception amount and the interception evaporation amount. The effective rainfall capacity is used for replacing original rainfall input in the SWAT model, the adjusting effect of the forest canopy on the rainfall process is reflected more truly, and the basin hydrological simulation precision is improved. According to the method, the response and simulation capability of the SWAT model to the hydrological process of the underlying surface of the forest is effectively improved, the dynamic response to effective rainfall and evaporation terms is enhanced, the method is suitable for watershed hydrological process modeling of various forest types, and the reliability of flood forecasting and water conservation evaluation is improved.

The invention relates to the technical field of forest resource monitoring, in particular to a forest carbon reserve calculation system and method, and the method comprises the steps: obtaining target data corresponding to a target forest; obtaining forest stand features, climate features and external influence features corresponding to the target forest based on the target data; obtaining forest types, forest stand density, forest stand age groups and soil characteristics based on forest stand characteristics; based on the forest type and the stand density, vegetation carbon reserves are obtained; based on soil characteristics and forest stand age groups, obtaining soil carbon reserves; obtaining a carbon reserve calibration coefficient based on the climate characteristics and the external influence characteristics; and obtaining the forest carbon reserve based on the vegetation carbon reserve, the soil carbon reserve and the carbon reserve calibration coefficient. According to the invention, the calculation precision of the carbon reserves can be improved.

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 forest fire prevention, in particular to a large-scale forest ground surface litter load calculation method. The method comprises the steps of obtaining a forest resource partition to which a target area belongs; determining the forest type of each forest class in the target area based on the national forest resource checking data; according to the forest resource subarea to which the target area belongs and the forest type of each forest class in the target area, establishing an above-ground biomass and stock regression model of each forest class in the target area; acquiring the aboveground biomass of each forest class in the target area according to the aboveground biomass and accumulation regression model; obtaining the land surface litter carrying capacity of each forest class in the target area by inquiring the proportion of the land surface litter carrying capacity in the aboveground biomass in the forest class of each forest type; and obtaining spatial distribution of the surface litter load of the target area according to the surface litter load of all forest classes in the target area. According to the method, the efficiency and accuracy of large-scale forest surface litter load calculation can be effectively improved.

The invention relates to the technical field of forest multi-site registration, and discloses a forest multi-site point cloud registration method and related equipment, and aims to construct a feature grid map by fusing multiple classes of semantic tags, avoid registration failure caused by single feature deletion or poor quality, improve the adaptability in multi-forest, multi-season and multi-shielding scenes, and improve the registration accuracy. A high-stability registration area is selected by means of stability scores, self-adaptive adjustment of registration constraints is achieved in combination with a semantic weighting mechanism, the dependence of coarse registration on an initial pose and artificial experience is reduced, and wrong matching interference is reduced. Through semantic weighting ICP fine registration and global optimization, geometric stability differences of different semantic objects are distinguished, unstable structure interference is reduced, and registration precision is improved. Automatic stable registration can be achieved without manual target, the field operation cost and workload are reduced, and large-scale forest scene popularization is facilitated.

The application relates to the technical field of forest resource monitoring, in particular to a forest carbon storage calculation system and method, which comprises the following steps: obtaining target data corresponding to a target forest; obtaining stand characteristics, climate characteristics and external influence characteristics corresponding to the target forest based on the target data; obtaining forest types, stand densities, stand age groups and soil characteristics based on the stand characteristics; obtaining vegetation carbon storage based on the forest types and the stand densities; obtaining soil carbon storage based on the soil characteristics and the stand age groups; obtaining a carbon storage calibration coefficient based on the climate characteristics and the external influence characteristics; and obtaining forest carbon storage based on the vegetation carbon storage, the soil carbon storage and the carbon storage calibration coefficient. The application helps to improve the calculation accuracy of carbon storage.

The embodiment of the present application provides a method for determining bamboo cultivation target by using the canopy density of upper arbor vegetation type, relates to the technical field of forestry, and evaluates the relevant indexes of the growth of square bamboo planted under different vegetation types in different site conditions, determines the square bamboo cultivation target under the corresponding forest category, forest type and origin of forest in different site conditions according to the evaluation result and the range of the upper arbor canopy that can be adjusted to according to the relevant felling technical regulations, and can also determine the square bamboo cultivation target according to the determined square bamboo cultivation target, and assist in screening the cultivation site by combining factors such as the forest category, forest type and origin, so as to provide a scientific basis for the development planning of bamboo industry.

The invention belongs to the field of biomass high-valued utilization, and provides a method and system for estimating the carbon utilization efficiency of forest biomass high-valued utilization, and the method comprises the steps: determining the initial carbon component content based on the forest type; converting the woods according to a biomass high-valued conversion process, and determining the content of carbon components in the woods after each step of process is completed; constructing a carbon transfer matrix of each step of flow according to the carbon component contents of the input raw materials and the output products of each step of flow; determining the final carbon component content after the forest biomass is subjected to high-valued treatment by utilizing the initial carbon component content and the carbon transfer matrix of all the processes; and determining the high-valued carbon utilization efficiency of the forest biomass based on the ratio of the final carbon component content to the initial carbon component content after the forest biomass is subjected to the high-valued treatment. According to the method, the accuracy of estimating the high-value utilization carbon utilization efficiency of the biomass can be improved, and a reliable measurement index is provided for the technical improvement of the high-value utilization of the biomass.

The invention belongs to the technical field of forest ecological system carbon cycle monitoring, and relates to a regional scale forest net ecological system carbon exchange estimation method fusing ground vortex covariance observation and multi-source remote sensing data. The problems of insufficient representativeness of flux monitoring forest types and limited precision of pure remote sensing estimation during regional scale estimation of forest carbon sink in a traditional method are solved. A membership function matrix is innovatively constructed by using fuzzy mathematics, EC measured data and multi-source remote sensing data are matched, and effective fusion of point and surface data is realized. According to the method, a regional forest carbon sink measurement model based on remote sensing data and a flux observation network is constructed, high-precision regional carbon sink estimation is realized, and single-point limitation is overcome; the spatial pattern (presenting the gradient characteristic of high southeast and low northwest) of the temperate forest carbon sink and the seasonal change are disclosed; it is determined that carbon sink annual fluctuation is mainly influenced by meteorological conditions and vegetation state changes; and an effective tool is provided for accurate evaluation of regional forest carbon sink and carbon management decision.

The application discloses a severe degradation forest restoration force quantitative prediction method and system based on a landscape process, and belongs to the technical field of forest ecological restoration. In view of the problem that the prior art can only provide a qualitative tree species suggestion and ignores the spatial dependence relationship between patches, the application builds a same site condition stable domain quantitative restoration probability, introduces a patch inter-forest type dependence degree matrix to depict the landscape process, and combines a restoration force quantitative prediction model to output a restoration year limit interval of each grid, so that a leap from experience judgment to quantitative probability prediction driven by the landscape process is realized, problems such as inaccurate tree species screening and unreasonable planting density are effectively solved, and the success rate of large-area restoration planning and resource utilization efficiency are significantly improved.

The invention discloses a forest carbon sink parameter time sequence product generation method, system and device and a storage medium, and is applied to the cross technical field of vegetation remote sensing and carbon cycle monitoring, and the method comprises the steps: obtaining multi-source data of a target region, and generating a vegetation index based on remote sensing data in the multi-source data; based on the vegetation index, constructing a nonlinear model for different forest types, and performing inversion to obtain a corresponding net primary productivity initial time sequence; performing type-oriented differential purification treatment on the initial time sequence of the net primary productivity; feature difference identification is carried out based on the purified time sequence, and the nonlinear model is optimized according to an identification result; generating a net primary productivity time sequence product through the optimized model; according to the method, the problems of mechanism mismatching and information loss when a traditional single model processes heterogeneous forest types are solved, and a high-credibility time sequence parameter product basis is provided for urban forest carbon sink accurate monitoring, operation and management policy making and carbon trading market support.

The application provides a forest resource long-term fixed monitoring sample plot sampling and construction method.The forest resource long-term fixed monitoring sample plot sampling method comprises the following steps: step A1: a regional vegetation distribution map is made on a regional scale by taking a regional map and a boundary, and combining existing forest community distribution data; step A2: the regional map is divided into grids according to the number and density of the monitoring sample plots; and step A3: the number of the monitoring sample plots of each forest type is determined according to the regional vegetation distribution map made in step A1 and the actual needs of scientific research or production work.The forest resource long-term fixed monitoring sample plot sampling and construction method has the advantages that representative fixed monitoring sample plots can be arranged more conveniently, and the goal of accurate forest resource monitoring can be achieved.

The invention relates to the technical field of forest carbon reserve prediction and data screening, in particular to a forest carbon reserve estimation method fusing airborne hyperspectral and LiDAR data. The method comprises the following steps: firstly, determining a forest type, and obtaining a carbon reserve estimation model corresponding to each dominant tree species; aiming at various forests, extracting sample plot shapes, sizes and input data corresponding to the carbon reserve estimation models of the forests; the carbon reserve estimation model extracts key features from the input data of the sample plots, and estimates the carbon reserves of the corresponding sample plots based on the key features; and setting a sample plot with a shape and a size corresponding to the carbon reserve estimation model in each forest type, and obtaining a carbon reserve estimation value of the sample plot through the corresponding carbon reserve estimation model. The method can adapt to different forests and automatically adapt to sample plots, and the problems that when the forest carbon reserve is estimated through the remote sensing technology, a LiDAR single data source is relied on, the topographic relation is complex, and the precision is difficult to guarantee are solved.

The invention discloses a carbon sink measurement method and system for a coastal protection forest, and the method comprises the following steps: obtaining field data of a target coastal protection forest, and carrying out the preprocessing of the field data, and obtaining a three-dimensional point cloud model of the protection forest; based on the three-dimensional point cloud model of the protection forest, adaptive forest structure parameter extraction is carried out, and a vegetation type and a corresponding tree height are obtained; the adaptive forest tree structure parameter extraction comprises parameter adaptive single tree segmentation and segmentation-fitting mutual feedback optimization; and performing carbon sink index calculation based on the vegetation type and the corresponding tree height to obtain the carbon density of the protection forest. According to the invention, by introducing a parameter adaptive initialization and segmentation-fitting mutual feedback optimization mechanism driven by a forest type feature vector, internal self-checking and iterative optimization are realized, and an automatic high-precision carbon sink measurement method adaptively coping with changes of arbors and shrubs in a coastal area is realized.

1.The name of the present design product: gift box (forest type). 2.The use of the present design product: the present design product is used for packaging products. 3.The design points of the present design product: the combination of shape, pattern and color. 4.The picture or photo that best indicates the design points: set 1 open state reference drawing. 5.The design claimed contains color.

The present application relates to the field of intelligent planting of traditional Chinese medicinal materials and agricultural Internet of Things, and discloses a method for intelligent ecological planting of Morinda officinalis under forest canopy based on environmental monitoring, comprising: S1: determining the target canopy density range based on the forest type, and continuously acquiring the environmental parameters and leaf images of Morinda officinalis growth; S2: implementing ecological improvement of soil under forest canopy and deployment of water regulation infrastructure; S3: identifying and locating the disease spot based on the leaf images, segmenting the disease spot area, extracting the image features and fusing them with the corresponding environmental parameters to construct a multi-source feature set; using the improved NHWOA algorithm to optimize the risk assessment model and output the disease and pest risk grade; S4: making decision based on the real-time environmental parameters and the disease and pest risk grade, generating the regulation instruction and driving the corresponding equipment to execute, and adjusting the decision according to the execution feedback to form a closed loop. The present application realizes real-time sensing and accurate assessment of the microenvironment and disease and pest risk of Morinda officinalis under forest canopy.

The invention relates to the crossing field of traditional Chinese medicinal material intelligent planting and agricultural Internet of Things, and discloses an under-forest intelligent ecological planting method for Cirosa rugosa based on environmental monitoring, which comprises the following steps: S1, determining an adaptive target canopy density range based on a forest stand type, and continuously obtaining environmental parameters and leaf images of growth of Cirosa rugosa; s2, implementing under-forest soil ecological improvement and moisture regulation and control infrastructure deployment; s3, carrying out pest and disease damage identification based on the leaf image and positioning disease spots; segmenting a scab region, extracting image features of the scab region, fusing the image features with corresponding environment parameters, and constructing a multi-source feature set; optimizing the risk assessment model by using an improved NHWOA algorithm, and outputting a disease and pest risk level; and S4, performing decision judgment based on the real-time environment parameters and the disease and insect pest risk levels, generating a regulation and control instruction, driving corresponding equipment to execute, and adjusting a decision according to execution feedback to form a closed loop. According to the invention, real-time perception and accurate evaluation of microenvironment and pest and disease damage risks under the arrowhead forest are realized.

The application discloses a forest remote sensing image classification method and system based on a convolutional neural network, and relates to the technical field of remote sensing image processing. The method steps comprise the following steps: S1, a de-fogging algorithm is used to remove thin clouds and fog in a remote sensing image, and the brightness of the remote sensing image is enhanced; S2, the edge contour of a thick cloud layer shielding area in the remote sensing image is detected, and an initial mask of the thick cloud layer shielding area in the remote sensing image is obtained; S3, the thick cloud layer shielding area in the remote sensing image is replaced through global search matching, and the restoration of a ground object in the remote sensing image that is shielded by the thick cloud layer is realized; and S4, a convolutional neural network is used to extract features of the restored remote sensing image, and the detection and classification of forest types and pest conditions in the remote sensing image are realized. The application realizes the accurate detection of forest types and pest conditions by using high spatial resolution images in combination with a convolutional neural network.

The invention belongs to the technical field of forest remote sensing, and discloses a P-band SAR polarization characteristic-based forest type identification method, which comprises the following steps of: solving a coherence matrix and a covariance matrix on the basis of a pre-processed polarization scattering matrix, and carrying out wavelength dependence correction on basic radar statistical characteristics for a forest three-layer vertical superposition scattering structure formed by a P band; extracting feature subsets of a scattering mechanism related to a forest vertical structure, integrating the corrected basic features and adding polarization phase difference and scattering mechanism combination coefficient feature dimensions to form a complete polarization feature set covering multiple types of features, and extracting various terrain parameters of a target forest region based on DEM data to obtain a complete polarization feature set covering multiple types of features; performing terrain sensitivity analysis on the preliminarily screened feature subset and dividing sensitivity grades, constructing an adaptive terrain correction model in combination with P-band under-forest surface scattering proportion characteristics, and introducing a radar wave polarization state correction term to complete feature radiation normalization correction.

This invention discloses a collaborative inversion method for multiple attributes of individual trees based on transfer learning, relating to the field of forest ecological remote sensing monitoring technology. The method acquires remote sensing images and training samples, generates a canopy height model based on LiDAR point clouds, constructs and trains a single-tree segmentation model based on the U-Net++ architecture, and fine-tunes the encoder weights of the single-tree segmentation model by transferring them to a tree height prediction model and a forest type classification model. Finally, the remote sensing images are input into the three models to obtain the single-tree canopy boundary, predicted tree height, and forest type, and the integrated output is the multi-attribute inversion result for individual trees. This method addresses the problems of complex data dependencies, non-reusable features, low inversion accuracy and efficiency, and easy error accumulation inherent in traditional independent inversion of single-tree attributes.

The invention discloses a mountain area remote sensing image-based forest type identification method, and relates to the technical field of forest type identification. The method comprises the steps that a mountainous area remote sensing image to be recognized is cut to obtain a plurality of sub-image blocks, the space coordinate index of each sub-image block is recorded, and the cutting overlapping rate is larger than a preset value; respectively inputting each sub-image block into a trained mountain forest category identification model, and outputting a forest type classification result of each sub-image block; splicing the classification results based on the space coordinate index, and mapping the splicing results back to the mountain area remote sensing image space to be identified to obtain a forest type classification map with geographic reference; the model comprises a first classifier, and a multi-scale feature extraction unit, a feature fusion unit and a second classifier which are connected in sequence, and when the model is trained, the second classifier is assisted by the first classifier, so that loss values jointly participate in back propagation in a weighted sum mode. The method can improve the classification precision and classification efficiency of the forest types in the mountain area remote sensing image.

The invention belongs to the technical field of forestry planting equipment, and discloses a soil loosening device for forestry planting, which mainly comprises a walking bearing mechanism, a self-adaptive adjusting mechanism, a layered soil loosening mechanism and a central control system. The walking bearing mechanism provides a stable moving foundation; the self-adaptive adjusting mechanism integrates a soil parameter sensor and a forest type recognition camera and is used for sensing the working environment in real time. The layered soil loosening mechanism is composed of a twist drill and a shallow soil loosening roller which can be independently regulated and controlled, and collaborative operation is achieved. The device can dynamically adjust the soil loosening strategy and depth according to the parameters of soil hardness, humidity, forest type and the like, and the crossing from mechanization to intelligentization is realized. The beneficial effects are that the elastic soil loosening teeth can effectively prevent nursery stock root system damage, thus protecting soil structure; the operation quality and efficiency are improved through layered soil loosening; the solar power supply and remote monitoring function improves the environmental protection property and the modernization management level of the equipment.

The invention provides a forest type classification method, device, equipment and product, and relates to the technical field of IT support. The method comprises the following steps: acquiring a high-resolution multispectral image acquired by a remote sensing satellite; performing normalized difference vegetation index (NDVI) feature extraction on the high-resolution multispectral image to obtain an input image; obtaining a forest type classification map corresponding to the input image according to the input image; according to the scheme provided by the invention, the forest type classification map is obtained according to the input image obtained by extracting the high-resolution multispectral image acquired by the remote sensing satellite by adopting the normalized difference vegetation index NDVI feature, so that the problem of low forest type classification precision in the prior art is solved.

The invention provides a plateau mountain forest type remote sensing classification method and system based on collaborative joint learning, and belongs to the field of forest type identification. The method comprises the following steps: firstly, determining a cartographic geographic area and collecting data; extracting N-dimensional self-supervision features after preprocessing multi-source remote sensing data, and cutting the N-dimensional self-supervision features into standard image blocks after registration; carrying out image block feature fusion to form a self-supervised image block with an N-dimensional space and a self-supervised image block set; constructing first to third self-supervision signals based on the self-integrity and internal relevance of the self-supervision image block set and plateau mountain priori knowledge, and respectively inputting three branches of the self-supervision model to obtain general feature representation; constructing a forest type remote sensing classification model, performing training based on forest type field survey data, and adjusting the general feature representation to a corresponding classification task; and finally, inputting the self-supervised blocks into a classification model to obtain a forest type distribution thematic map, and evaluating the drawing precision. According to the invention, the classification accuracy and precision of forest types are improved.

The invention discloses an under-forest planting method for improving the yield and quality of aralia elata sprouts, and belongs to the technical field of under-forest economic plant cultivation. In order to solve the problem that in the prior art, due to the lack of multi-link integrated optimization of'provenance-habitat-harvesting ', the yield and quality of artificially planted aralia elata sprouts are difficult to synergistically improve and the like, the method comprises the following steps: investigating the yield of wild aralia elata in different regions, the saponin content of wild aralia elata in different forest types and different regions; the method comprises the following steps: selecting an optimal provenance for artificial planting of aralia elata, and preliminarily selecting a suitable forest type; then, the optimal provenance, forest type, canopy density, slope direction and harvesting strategy of artificial planting are determined by researching the influence of different forest types and different canopy densities on the growth and yield of the aralia elata planted artificially, the influence of different slope directions on the growth of the aralia elata and the influence of different harvesting strategies on the overwintering sprouting rate of the aralia elata in the next year; finally, the aralia elata under-forest planting method for simultaneously improving the yield and the quality of the bean sprouts is obtained.

The present application provides a kind of fern composite planting method based on birch forest, and belongs to the technical field of agricultural planting.The present application provides a kind of fern composite planting method based on birch forest in the low-quality birch forest area in northeast forest, solves the problem of low utilization rate of existing low-quality birch forest interspace and unbalanced supply and demand of fern market, and solves the problem of fern resource cultivation and artificial large-area planting difficulty.The present application solves the problem of low-quality birch forest space utilization difficulty by different forest type selection method and different canopy density fern planting method, realizes the one-year two-season harvesting mode of spring vegetable picking and autumn fruit picking by low canopy density plot black bud currant and fern intercropping, increases the utilization efficiency and multiple cropping index of interspace in forest, and provides a new mode for forest economy development and lays a foundation for forest fern industry development.