24 results about "Broadleaf forest" patented technology

The present disclosure provides a method for identifying a pine wood nematode-infected discolored wood in a mixed coniferous and broadleaf forest, including inputting a pine forest image of the mixed coniferous and broadleaf forest to be identified into a trained identification model for the pine wood nematode-infected discolored wood to identify the pine wood nematode-infected discolored wood in the mixed coniferous and broadleaf forest, wherein the trained identification model is improved based on a you Only Look Once version 5 small (YOLOv5s) model by connecting a feature-filtering module after a Neck; constructing a feature-enhancing module to replace a C3 module in a Backbone; constructing a convolution-transformer module based on multi-head self-attention (MSHA) to connect after a last layer of residual units in the Backbone; constructing, by Group Shuffle Convolution (GSConv), a multi-scale feature fusion layer to replace an ordinary convolution in the Neck.

Disclosed is a method for extracting a boundary line of on-year and off-year moso bamboo forests based on Sentinel-2 remote sensing data, and relates to the technical field of forestry remote sensing. The method includes classifying remote sensing images to acquire spatial distribution of three land type results, the three land type results including several on-year moso bamboo forests, several off-year moso bamboo forests and other vegetations between the on-year and off-year moso bamboo forests; extracting an initial boundary line of the on-year and off-year moso bamboo forests according to spatial distribution of three land type results; building a buffer region of the initial boundary line, and acquiring intersecting pixels of the buffer region and the spatial distribution of three land type results; and calculating pixel thresholds by using the intersecting pixels, and acquiring a final boundary line of the on-year and off-year moso bamboo forests. Using the above method can accurately obtain the boundary line between on-year and off-year of moso bamboo forests.

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 discloses a method for extracting spring phenology of an evergreen broad-leaved forest, and relates to the technical field of ecological observation, and the method comprises the following steps: carrying out negative pixel correction processing and time interpolation processing on collected sunlight-induced chlorophyll fluorescence data; fusing the processed sunlight-induced chlorophyll fluorescence data with the temperature variable data, constructing a time sequence, and performing fitting processing to obtain a target time sequence; extracting spring phenology of the evergreen broad-leaved forest by adopting a plurality of preset vegetation phenology extraction methods based on the target time sequence; performing precision evaluation on the extracted spring phenology of the evergreen broad-leaved forest by using ground observation data; and based on a precision evaluation result, determining the optimal spring phenology of the evergreen broad-leaved forest from the extracted spring phenology of the evergreen broad-leaved forest. According to the method, through negative value pixel correction and time interpolation processing, sunlight-induced chlorophyll fluorescence data and temperature variable data are fused, and the combination of a multi-fitting method and a multi-extraction method is adopted, so that the accuracy of a result is improved.

The application discloses a kind of evergreen broad-leaved forest spring phenology extraction method, it is related to ecological observation technical field, the method comprises: to the collected sunlight-induced chlorophyll fluorescence data is handled with negative value pixel correction and time interpolation processing;The sunlight-induced chlorophyll fluorescence data after processing is fused with temperature variable data, constructs time series and is handled to obtain target time series;Based on target time series, a variety of preset vegetation phenology extraction methods are used to extract evergreen broad-leaved forest spring phenology respectively;The accuracy of the extracted evergreen broad-leaved forest spring phenology is evaluated using ground observation data;Based on the accuracy evaluation result, the optimal evergreen broad-leaved forest spring phenology is determined from the extracted evergreen broad-leaved forest spring phenology.The application improves the accuracy of the results by negative value pixel correction and time interpolation processing, the fusion of sunlight-induced chlorophyll fluorescence data and temperature variable data, the combination of multiple fitting methods and multiple extraction methods.

The invention provides a method for preparing a camellia oleifera forest soil conditioner and a soil organic fertilizer based on indigenous flora and application of the method, and belongs to the technical field of microorganism utilization. According to the method, indigenous microorganisms captured from soil without fertilization interference under old camellia oleifera forests and healthy camellia oleifera trees in a germplasm resource nursery of more than one hundred years and indigenous microorganisms captured from broad-leaved forest or bamboo forest soil in a natural ecological system are combined according to a specific proportion; the oil-tea camellia forest soil conditioner or the oil-tea camellia forest soil organic fertilizer obtained through propagation culture can quickly improve and restore the oil-tea camellia forest soil in hills and mountainous areas and remarkably improve the yield and quality of oil-tea camellia seeds, is a solution based on nature, and is economical, practical, efficient, green, environmentally friendly and sustainable.

The invention discloses a soil respiration monitoring method based on field gradient warming, which belongs to the technical field of soil respiration monitoring, and comprises the following steps: (1) selecting an old forest in a subtropical evergreen broad-leaved forest to construct an experimental sample plot, and setting 20 triangular quadrat in total; (2) carrying out gradient temperature increasing on the quadrats by adopting a triangular frame heating method, setting five gradient temperature increasing treatments, setting four quadrats for each treatment, and collecting the soil temperature and volumetric water content of the experimental sample plot; and (3) arranging a breathing ring seat which is not embedded into the soil and a breathing ring seat which is embedded into the soil by 50 cm in each quadrat, placing the static box on the breathing ring seats, extracting and storing gas in the static box, detecting the collected gas by using an FID (Flame Ionization Detector) of a gas chromatograph, obtaining the CO2 concentration, and then calculating soil respiration and component data thereof. According to the method, the soil respiration and the component data thereof under gradient temperature increase can be obtained, and technical support is provided for revealing the response rule and mechanism of the soil respiration to the gradient temperature increase in the subtropical forest.

The present application relates to the technical field of remote sensing, in particular to a forest point cloud registration method based on trunk position iteration. The present application makes full use of the natural complementarity of ULS and BLS data in obtaining vertical structure information of forest, converts the traditional discrete point set matching problem into the overlap optimization problem of continuous two-dimensional spatial distribution influence map; by introducing genetic algorithm to construct the spatial mapping relationship between non-homologous data and carry out global optimization, the interference of the horizontal offset of crown and trunk commonly existing in broad-leaved forest on the registration accuracy is fundamentally avoided. The present application can automatically obtain the global optimal solution and generate the homogeneous transformation matrix without laying artificial targets, finally realizes the high-precision rigid alignment of two sets of point clouds, has the advantages of high automation, strong noise-robustness and insensitivity to initial error, and can be widely applied in the fields of high-precision forest resource inventory, digital forestry construction and ecological environment monitoring.

The utility model relates to the technical field of pseudo-ginseng planting, and provides a mesh bag for planting pseudo-ginseng in broad-leaved forest, which comprises a connecting ring, a support device, a lodging prevention device and a feeding device. The connecting arm and the anchor rod are arranged, the anchor rod is rotated, the anchor rod moves downwards on the connecting arm, the planting mesh bag can be fixed in the land, the inclined anchor rod can provide a better anti-sliding effect, anchoring force is dispersed to a larger soil body range, local stress concentration is reduced, and the stability of the whole structure is improved; by arranging the supporting rings, the supporting rings can provide support for a soil layer outside the planting bag, and the situation that the external soil layer collapses inwards, and the stability of the planting mesh bag is affected is avoided; through the arrangement of the insertion holes, after pseudo-ginseng grows up, the supporting rods can be fixed through the insertion holes so as to fix pseudo-ginseng plants, through the arrangement of the connecting rods, the limiting rings and the supporting rods, the supporting rods are fixedly connected with the insertion holes through the connecting rods, and then the pseudo-ginseng plants are bound on the supporting rods, so that the pseudo-ginseng plants are prevented from lodging.

The invention belongs to the technical field of traditional Chinese medicine cultivation, and particularly relates to an under-forest rare traditional Chinese medicine bionic cultivation method based on a micro-sprinkling irrigation system, which comprises the following steps: step 1, site selection and pretreatment of an under-forest cultivation area: selecting a natural broad-leaved forest or a coniferous and broad-leaved mixed forest area with canopy density of 0.6-0.8 as a cultivation base, the method comprises the following steps: carrying out topographic survey on a selected region, marking a region with a gradient greater than 25 degrees, arranging a flood intercepting ditch, arranging a cultivation bed with a width of 1.2-1.5 m in a region with a gradient of 5-25 degrees by adopting a contour line reclamation mode, and carrying out cultivation on the region with the gradient greater than 25 degrees, so as to obtain a cultivation bed with a width of 1.2-1.5 m; and an interval of 0.8-1m is reserved between the cultivation beds to serve as an operation channel. By constructing the precise micro-spray irrigation regulation and control system and combining scientific cultivation management measures, precise simulation of the growth environment of the traditional Chinese medicinal materials is achieved, the survival rate and quality of the traditional Chinese medicinal materials are improved, and the landscape function is integrated.

The invention relates to the technical field of filter materials, in particular to cellulose-based biochar as well as a preparation method and application thereof. The method comprises the following steps: slicing broad-leaved forest wood along a direction perpendicular to the growth direction to obtain wood chips; the wood chips are immersed in a sodium chlorite solution for a lignin removal reaction, and lignin-removed sheets are obtained; the lignin-removed sheet is placed in an activating agent solution for activating and pore-forming, and an activated sheet is obtained; the activated sheet is carbonized, and the cellulose-based biochar is obtained. The cellulose-based biochar prepared by the preparation method disclosed by the invention can keep the smoked flavor of food while filtering BaP, does not block a flue in a smoking process, keeps stable smoke formation, and is suitable for online airflow filtration.

The invention discloses an evergreen broad-leaved forest vegetation zoning method and system based on data driving. The method comprises the following steps: acquiring remote sensing data of a target area; dividing the target area into a plurality of vegetation areas according to the remote sensing data; performing kernel density analysis on the density of the evergreen broad-leaved forest of each vegetation region to generate a kernel density surface field, and dividing a plurality of vegetation groups according to the kernel density surface field and the grouping species composition characteristics of the corresponding region; dividing each vegetation region into a plurality of vegetation districts according to the vegetation groups; and integrating the vegetation regions and the vegetation districts into a final zoning map. According to the method, through refined application of remote sensing data, specific ecological conditions and vegetation characteristics of a target area can be mapped according to vegetation horizontal zone characteristics reflected by heat and moisture conditions; the scientificity and accuracy of vegetation zoning are remarkably improved, and the method is an important supplement and improvement for a traditional vegetation zoning method.

This application provides a soil cultivation method and its application for lignin decomposition. Soil block A is selected from a broad-leaved forest area in a tropical or subtropical region with an undisturbed leaf decay layer of 5 cm or more. 10%-15% (by weight) of an ammonia absorption promoter is added to soil block A and mixed thoroughly. The soil is then cultivated for 2-6 weeks at a temperature of 25-33℃ and a humidity of 50%-70%. The soil prepared using this method can be used to decompose lignin. It is non-polluting to the soil, has low energy consumption, and decomposes lignin quickly. Compared to uncultivated soil, the cycle for lignin decomposition is shortened by approximately 10% after cultivation.

The present invention relates to the technical field of ecological environment assessment, and discloses a method for assessing the service value of a bamboo-broadleaf mixed forest ecosystem based on multiple indicators. The method comprises the following steps: step S101, collecting remote sensing data of the bamboo-broadleaf mixed forest; step S102, generating a feature vector; step S103, constructing spatiotemporal graph structure data; step S104, measuring carbon flux by using a carbon flux measurement model; step S105, measuring soil erosion by using a soil erosion measurement model; and step S106, calculating and obtaining a temperature adjustment factor and a humidity adjustment factor of the bamboo-broadleaf mixed forest. The method constructs remote sensing data of multiple sub-areas of the bamboo-broadleaf mixed forest into spatiotemporal graph structure data, performs spatiotemporal analysis on the spatiotemporal graph structure data by using the carbon flux measurement model, establishes a nonlinear mapping relationship between the remote sensing data and the carbon flux, thereby achieving large-area carbon flux measurement, and further provides a temperature adjustment factor and a humidity adjustment factor to implement a multi-indicator assessment method for the bamboo-broadleaf mixed forest.

This invention discloses a method for synergistic optimization of forest stand structure and biomass based on multi-platform LiDAR. Using airborne, vehicle-mounted, and backpack LiDAR point clouds and synchronously measured data, the method first performs denoising, registration, and ground filtering preprocessing. Voxel clustering and region growing algorithms are then used to extract trunk position, diameter at breast height (DBH), tree height, and crown width. Spatial structural units are divided based on a weighted Voronoi diagram of DBH-tree height-crown width grey relational degree. Combining parameter extraction algorithms and species-specific allometric growth equations, six parameters—angular scale (W), DBH-to-size ratio (U), aggregation index (R), openness (K), competition index (CI), and forest layer index (S)—are quantified, and the forest topography index (AGB) of the sample plots is estimated. A complete technical chain is constructed, from data acquisition—structural unit construction—parameter quantification—mechanism analysis—precision management, providing quantifiable support for the sustainable management and carbon sequestration enhancement of mixed coniferous and broadleaf forests.

The invention relates to a Dehua russule wine brewing process, which comprises the following steps: selecting wild russule which grows in a mountain forest zone with the altitude of 350-850 meters in Dehua region and has the annual rainfall of 1700-1900 millimeters, and removing roots and impurities; the pretreated russule is placed in an environment with the temperature controlled to be 50-70 DEG C to be baked for about 10 minutes until the russule is fragrant and crisp; grinding the baked russule into fine powder; soaking 0.5 kg of russule powder in 5 kg of pure-grain high-quality white spirit with the temperature of 50-55 DEG C, and sealing and soaking for 30-50 days; after soaking is finished, impurities such as russule residues are filtered out, and a Dehua russule wine finished product is obtained. According to the method, the Dehua russule grows in the mountain forest zone with the specific altitude and rainfall capacity, the russule is endowed with unique quality and nutritional ingredients under the natural environment conditions, the russule growing in the broad-leaved forest mixed zone with rich vegetation, low temperature and high relative humidity is selected, and it is ensured that the quality of the russule is high; and a solid raw material basis is provided for brewing high-quality russule wine.

The invention discloses an evergreen broad-leaved forest vegetation subtype classification and recognition method based on a hierarchical multi-label network, and the method comprises the steps: obtaining multi-source remote sensing data of a sample region, and extracting classification features from the multi-source remote sensing data; constructing a sample library of evergreen broad-leaved forests and subtypes of the evergreen broad-leaved forests in the sample region according to field survey data and an existing vegetation classification system; according to the sample library and the classification features, constructing multiple levels of classifiers which are arranged in sequence to form a hierarchical multi-label classification network; and classifying and identifying the evergreen broad-leaved forest vegetation in the target area through the hierarchical multi-label classification network. According to the method, a layered classification system of forests, evergreen forests, evergreen broad-leaved forests and slightly dry / slightly wet evergreen broad-leaved forests is constructed, and the defects of a traditional method in distinguishing similar vegetation types are overcome; and meanwhile, through a hierarchical error weighting mechanism, the influence of a low-level classification error on an overall result is reduced, and the accuracy and the reliability of classification of the evergreen broad-leaved forest with the partial dryness and the partial wetness are improved.

The invention discloses a ground feature classification method based on satellite-borne laser radar waveform data, and relates to the field of remote sensing technology and artificial intelligence, and the method comprises the following application steps: S1, obtaining laser echo waveform data of a to-be-classified region through a satellite-borne laser radar; s2, preprocessing the waveform data of the laser echo; the preprocessing comprises quality evaluation and standardization processing; s3, inputting the preprocessed laser echo waveform data into a pre-trained ground feature classification model, and outputting a ground feature classification result; the terrain classification result comprises one or more terrain types in a paddy field and a dry land and one or more terrain types in a broad-leaved forest and a coniferous forest; the ground feature classification model is a dense connection neural network model. The method has the advantages that the ground feature classification precision is improved, and ground feature types with similar spectral features and different structural features are effectively distinguished.

The present invention discloses a data-driven evergreen broad-leaved forest vegetation zoning method and system, the method comprising: obtaining remote sensing data of a target area; dividing the target area into multiple vegetation regions based on the remote sensing data; performing kernel density analysis on the evergreen broad-leaved forest density in each vegetation region to generate a kernel density surface field, and dividing multiple vegetation groups based on the kernel density surface field and the constituent species composition characteristics of the corresponding region; dividing each vegetation region into multiple vegetation plots based on the vegetation clusters; and integrating the vegetation regions and vegetation plots into a final zoning map. Through the refined application of remote sensing data, the present invention can map the unique ecological conditions and vegetation characteristics of the target region based on the horizontal zonal characteristics of vegetation reflected by heat and moisture conditions; significantly improving the scientific nature and accuracy of vegetation zoning, and is an important supplement and improvement to traditional vegetation zoning methods.

The invention discloses a method and system for estimating the total carbon reserve of a target ecological system carbon library, and relates to the technical field of forestry information, and the method comprises the following steps: carrying out the sample collection of a target ecological system; the vegetation type of the target ecological system is a subtropical evergreen broad-leaved forest, cypress, masson pine, alder and quercus acutissima are taken as main materials, the altitude is 182-954 m, the gradient is 0-45 degrees, the soil is yellow soil and purple soil, and the thickness of a dry branch and fallen leaf layer is 0-15 cm; the sample comprises at least one of a soil sample, a vegetation sample and a litter layer sample; carrying out analysis and calculation on the basis of the sample to obtain the total carbon reserve and carbon density of the target ecological system; the total carbon reserves and carbon density distribution characteristics of the target ecological system comprise the carbon density of a vegetation layer, the carbon reserves of the vegetation layer, the carbon density of a soil layer, the carbon reserves of the soil layer, the carbon density of a litter layer and the carbon reserves of the litter layer.

The application discloses a ground object classification method based on spaceborne laser radar waveform data, relates to the technical field of remote sensing and artificial intelligence, and comprises the following application steps: S1, acquiring laser echo waveform data of a region to be classified through a spaceborne laser radar; S2, pre-processing the laser echo waveform data; the pre-processing comprises quality evaluation and standardization processing; S3, inputting the pre-processed laser echo waveform data into a pre-trained ground object classification model, and outputting a ground object classification result; the ground object classification result comprises one or more ground object types in paddy fields and dry lands, one or more ground object types in broad-leaved forests and coniferous forests; and the ground object classification model is a densely connected neural network model. The application has the advantages of improving ground object classification precision and effectively distinguishing ground object types with similar spectral characteristics but different structural characteristics.

The invention relates to the technical field of forest ecology, and discloses a progressive transformation method for masson pine artificial pure forest to multi-layer broad-leaved forest, which comprises the following steps: (1) diagnosis and intermediate cutting; (2) naturally promoting and monitoring; (3) carrying out clear cutting and directional tending; the propagation of pine wood nematode diseases is efficiently blocked; and disease eradication is realized through staged clearing and full-chain source control. 35%-45% of sick and weak wood is accurately felling in the first year, and 80% or above of potential disease sources are removed; dynamically inspecting in the second year to the third year, and clearing up 125 new diseased trees in total to avoid diffusion; in the dormancy period of the fourth year, a transition layer is cut, all pine trunks are transported out, fine branches are smashed and returned to the field, residual hosts are eliminated, the disease occurrence rate is reduced by 90% or above after transformation is conducted for 9 years, and no new case occurs.

The present invention discloses a method for classifying and identifying evergreen broad-leaved forest vegetation subtypes based on a hierarchical multi-label network, comprising: obtaining multi-source remote sensing data of a sample area and extracting classification features from the multi-source remote sensing data; constructing a sample library of evergreen broad-leaved forests and their subtypes in the sample area based on field survey data and an existing vegetation classification system; constructing a hierarchical multi-label classification network based on the sample library and the classification features, using a multi-level, sequentially arranged classifier to form a hierarchical multi-label classification network; and classifying and identifying the evergreen broad-leaved forest vegetation in the target area using the hierarchical multi-label classification network. The present invention constructs a hierarchical classification system for forests, evergreen forests, evergreen broad-leaved forests, and dry / wet evergreen broad-leaved forests, addressing the shortcomings of traditional methods in distinguishing similar vegetation types. At the same time, through a hierarchical error weighting mechanism, the impact of low-level classification errors on the overall results is reduced, thereby improving the accuracy and reliability of the classification of dry and wet evergreen broad-leaved forests.

The device comprises a portable base and a backpack type power supply bag used for providing a power source, a groove is formed in the middle of the upper end of the outer wall of the portable base in a downwards-sunken mode, a ball head rod is rotationally connected into the groove in a matched mode, and an electric telescopic rod is fixedly arranged at the upper end of the ball head rod; a fixing sleeve is arranged at the upper end of the electric telescopic rod and detachably connected with a supporting rod, a rotating assembly is arranged at the upper end of the supporting rod, a device box is arranged on one side of the upper end of the rotating assembly, and a sliding block is slidably connected to the bottom of the inner wall of the device box; a first electric push rod is rotationally connected between the rear portion of the upper end of the sliding block and the upper portion of the rear end of the inner wall of the device box in a matched mode, a shearing assembly is arranged at the upper end of the sliding block, a notch is formed in the front end of the outer wall of the device box, and a control panel is installed on one side of the upper end of the portable base. The device is convenient to carry by a worker, is convenient to move under a broad-leaved forest, and is not limited by the ground surface under the forest during use, so that the pruning precision is improved.