84 results about "Forest vegetation" patented technology

The invention relates to a mangrove forest vegetation carbon sink monitoring and metering method based on an unmanned aerial vehicle and an AI technology, and the method comprises the steps: constructing a mangrove forest recognition model of a U-Net improved model based on a PyTorch deep learning framework through the unmanned aerial vehicle AI technology, achieving the intelligent recognition and discrimination of various mangrove forest vegetations, and the area and number of regions thereof, and achieving the accuracy of 90% or above; through regular aerial photography monitoring of an unmanned aerial vehicle, in combination with growth parameters measured on site and variable parameters derived from aerial photography data of the unmanned aerial vehicle, an optimal fitting model of mangrove forest height crown breadth, overground biomass and unit leaf area based on aerial survey of the unmanned aerial vehicle is constructed, and the carbon reserve and carbon sequestration amount of mangrove forest region vegetation are accurately calculated. And comprehensively evaluating the mangrove forest carbon sequestration capability in the monitoring area. According to the application, intelligent, digital and comprehensive monitoring and evaluation of the mangrove forest wetland vegetation carbon sequestration capability are realized.

The invention relates to the technical field of satellite-borne hyperspectral image processing and analysis, and solves the technical problem that the huge spectral and spatial resolution difference between low-resolution hyperspectral data and high-resolution multispectral data cannot be fully considered in the existing method. The forest hyperspectral satellite image-oriented deep and shallow double-branch super-resolution method comprises the steps of constructing a double-branch network architecture, performing feature fusion reconstruction on output features of the double-branch network architecture, and obtaining high-resolution hyperspectral data with low spectral variation characteristics of forest vegetation in spaceborne hyperspectral image data. According to the method, the problem of modal difference between low-resolution hyperspectral data and high-resolution multispectral data is effectively solved by learning on different feature levels and scales, and the model is enabled to pay more attention to low-spectral variation characteristics of different forest vegetation in a satellite image through a plurality of feature attention mechanisms. And the requirements of subsequent fine monitoring tasks of various forest resources can be met.

The invention provides a vegetation surface biomass inversion method and device, electronic equipment and a storage medium. According to the invention, forest vegetation surface biomass field investigation is carried out by using data scanned by a knapsack laser radar and data measured manually, and actual measurement information of vegetation surface biomass in a sample plot, high-resolution panoramic image data in a sample plot range and high-precision knapsack three-dimensional laser radar point cloud data are obtained; scanning the sample plot by using an airborne laser radar, and extracting point cloud features capable of reflecting vegetation growth conditions from scanning data; then, the point cloud features serve as independent variables, total biomass obtained by processing data scanned by the backpack laser radar and actual measurement information serves as dependent variables, a biomass regression model is constructed, and the forest vegetation surface biomass is estimated through the model.

The invention relates to the technical field of environmental monitoring, in particular to a forest vegetation coverage growth monitoring system and method based on big data, and the method comprises the steps: extracting vegetation patch chlorophyll dispersion, texture direction and near-infrared uniformity to generate a microstructure feature set, comparing homogeneity intensity, a reference value and an adjacent condition marker to generate a connected state identifier, and carrying out the analysis of the connected state identifier; and according to the soil moisture capacity and the illumination radiation quantity, abnormal items of chlorophyll reflectivity deviation degree and near-infrared band concentration degree are removed. According to the method, by analyzing the chlorophyll ratio of the sub-pixels and the neighborhood texture, the near-infrared uniformity is quantified, and the heterogeneity precision is improved. And the multi-dimensional homogeneity strength is established by standardized parameters. And establishing a communication criterion by fusing edges, terrains and homogeneity. And performing dynamic screen spectrum abnormity early warning. And integrating the water retention, illumination and correction data of the soil to establish a dynamic model, and improving the prediction temporal-spatial resolution.

The invention discloses a forest vegetation carbon sink potential prediction system and method based on a forest growth rule, and belongs to the technical field of carbon sink potential prediction. The invention aims to solve the problem of dynamic response to forest carbon sink potential and long-term environmental climate change. The method comprises the steps of collecting data, establishing model construction, verifying a data set and a data set for model calculation and carbon sink potential evaluation, wherein the data set for model calculation and carbon sink potential evaluation comprises forest basic data and forest environment data; performing data processing; constructing a forest vegetation biomass model for predicting biomass growth of forest vegetation; evaluating the fitting result by adopting a root mean square error RMSE and a determination coefficient R2 to obtain a verified forest vegetation biomass model; and performing forest vegetation biomass growth prediction by using the verified forest vegetation biomass model, and performing forest vegetation carbon sink potential prediction according to an obtained forest vegetation biomass growth prediction result to obtain a forest vegetation carbon sink potential prediction result.

The invention discloses a large-area forest vegetation carbon reserve inversion method based on GEE platform multi-source data fusion. The method comprises the following steps: data acquisition and analysis: obtaining forest carbon storage literature analysis data and an index factor data set; performing correlation analysis, constructing a forest carbon reserve index system, and establishing a corresponding characteristic variable relationship between the independent variable index factor and the forest carbon reserve sample; a seq2seq deep learning model based on LSTM (Long Short Term Memory) is imported; precision analysis is carried out; and inverting a large-area forest vegetation carbon reserve data set by using the deep learning model, and analyzing main determinants of forest carbon reserves. According to the method, a Google Earth engine is introduced, factors such as waveband information, vegetation indexes and long-term change trend characteristics are extracted from the Google Earth engine through a huge remote sensing data file of the Google Earth engine and a strong parallel computing capability of the Google Earth engine, and the forest carbon reserve in the loess plateau region is inverted and main determinants of the forest carbon reserve are analyzed based on a seq2seq deep learning model of LSTM.

The invention specifically relates to a forest stock dynamic exploration system and method based on unmanned aerial vehicle multispectral fusion, and relates to the technical field of forest resource monitoring. A data preprocessing module; a feature extraction module; a model construction and estimation module; and a result output and visualization module. According to the method, a multi-dimensional data fusion and dynamic calibration mechanism changes the limitation of a traditional fixed soil regulation coefficient; when the soil regulation coefficient is obtained, four core factors of soil type, terrain illumination, soil humidity and texture partition distance are comprehensively considered; interference of different soil backgrounds, terrain illumination and humidity changes on vegetation index calculation is effectively eliminated, the enhanced vegetation index can reflect the coverage and health condition of forest vegetation more truly, a high-precision data basis is provided for subsequent forest stock estimation based on the vegetation index, and compared with a traditional method, the method has the advantage that the method is more accurate. And the data accuracy and reliability are improved.

The invention relates to the technical field of radar echo denoising, in particular to a radar echo signal filtering noise self-adaptive suppression method and a radar echo signal filtering noise self-adaptive suppression system. Acquiring radar station position and beam pointing data, and synchronously acquiring forest vegetation data and environment interference data of a beam pointing target area to form radar environment data; the radar environment data are identified through the multi-band decision model, and multi-modal waveform parameters are obtained; based on the multi-modal waveform parameters, radar equipment is controlled to perform periodic detection on the target area, and radar echo data are obtained; the method comprises the steps of identifying radar echo data and radar environment data, extracting dynamic spectrum characteristics in the radar echo data, performing clutter suppression processing on the dynamic spectrum characteristics by using the radar environment data, calculating the confidence probability that an early fire exists in a target area, and executing space-time consistency joint verification; according to the invention, fire early warning is carried out through the confidence probability of the fire.

The invention discloses a natural under-forest vegetation layer identification and biomass estimation method, belongs to the technical field of forest stand investigation, and aims to solve the problem that the accurate identification of under-forest vegetation layers is not facilitated due to the fact that the change of under-forest vegetation height is not considered when vegetation layer division is performed through a simple unified height threshold in the prior art. According to the method, a forest canopy point cloud profile is analyzed, a demarcation point is constructed based on a profile statistical function, and an under-forest vegetation layer is automatically identified; multiple groups of under-forest vegetation layer LiDAR point cloud features are extracted, feature variable optimization and modeling are carried out, and the under-forest vegetation layer biomass is estimated with high precision. According to the method, vertical and horizontal distribution variation of the under-forest vegetation is considered, so that the under-forest vegetation layer identification capability and precision are enhanced. The method can fundamentally improve the depiction capability of the canopy space structure of the point cloud data, and further improves the recognition precision of the under-forest vegetation layer and the estimation precision of the biomass of the under-forest vegetation layer.

The invention discloses an identification and treatment method for a water and soil loss priority treatment area of a small watershed slope in a hilly area. The method comprises the following steps: calculating a small watershed soil erosion modulus and a sediment connectivity index, and carrying out geospatial modeling to form a soil erosion and connectivity partition identification priority treatment area; determining the land utilization type and terrain status of water and soil loss, and dividing functional zones from the spatial angles of an upper slope, a middle slope and a lower slope; a water and soil conservation forest is built in a forest and grass vegetation area on the slope or an abandoned cultivated land above a reclamation prohibition slope and a water and soil loss treatment area on the barren mountain and slope according to the principle of suitable land and suitable trees, and water and soil loss is prevented and controlled through natural restoration; water and soil loss prevention and control measures of slope land reclamation-hydrological network optimization and node control-grassland soil fixation and fertility increasing are adopted in a slope water and soil loss treatment area below the reclamation forbidding gradient in the slope; the slope bottom is provided with a plant filter belt, a tail end grit chamber and an ecological ditch measure for desilting and pollution reduction; the problem that a water and soil conservation treatment system in a small watershed is low in treatment degree is solved.

The invention discloses a forest vegetation health index construction and diagnosis method based on multi-source remote sensing data, and the method achieves the fusion of a feature level and a decision level of data through selecting sample plots containing different health levels, collecting the multi-source remote sensing data of a satellite, an unmanned plane and a ground sensor, and constructing a multi-source data fusion network. Indexes reflecting forest health are extracted from the fused data, a hierarchical clustering method is adopted for screening and standardization processing, a health index model is constructed, and a machine learning algorithm is utilized for optimizing the weight. And finally, calculating a forest health index based on the optimized model, dividing health levels, and generating a forest health level distribution map. The method is suitable for large-area forest vegetation health diagnosis, has the characteristics of high efficiency, accuracy and comprehensiveness, can remarkably improve the scientificity and rationality of forest health monitoring, and can be expanded and applied to the fields of fire / insect pest early warning, carbon sink evaluation and the like.

The invention discloses a forest biomass and carbon library metering technology based on sample plot survey information, the technical scheme comprises a data acquisition system, a data processing and conversion module and a data website module, the data acquisition system is a basic part, and the data processing and conversion module is a core part. And the data website module provides data products and visual display for the user. Each tree is managed through a two-dimensional code, automatic quality control is performed based on historical survey data, forest vegetation biomass and carbon reserve situations of a quadrat and a sample plot are calculated by associating information of each tree of the quadrat and the sample plot, and a forest vegetation carbon sequestration rate and a carbon sequestration function are obtained by comparing previous survey data. According to the system, the investigation efficiency can be greatly improved, the forest vegetation carbon source function can be evaluated in real time, historical data can be used for automatic quality control of each re-investigation, investigation data can be uploaded in real time, and the working efficiency can be improved by more than 20 times through the application of the system.

The invention discloses a target identification method and device in a shielding environment, and belongs to the technical field of target identification. The method comprises the following steps: based on the imaging system, carrying out detection imaging on a target area in a shielded environment by adopting a single-point irradiation and single-point detection mode to obtain a target area image; performing background cancellation on the target area image to obtain a target image; comparing the target image with a standard image in a standard library one by one, and determining the type of the shielded target according to a comparison result; the standard library comprises a plurality of types of vehicle standard images; according to the imaging system, laser pulses are utilized to irradiate a target area, and the narrow laser pulses can penetrate through shelters such as forest vegetation and the like to obtain related information of a sheltered target, so that the information of the sheltered target can be fully expressed in an obtained target area image, and data support is provided for image processing and comparison; and the accuracy of target identification is improved.

The invention relates to the technical field of forest vegetation monitoring, in particular to a forest vegetation parameter real-time monitoring method based on unmanned aerial vehicle images, which comprises the following steps: starting an unmanned aerial vehicle, carrying out real-time image acquisition on a predetermined forest area through a camera, carrying out continuous image capture, synchronously calibrating the camera and setting matched differentiated illumination and depth-of-field conditions; and generating forest image acquisition data. According to the method, the chlorophyll concentration and the leaf area index can be accurately measured through analysis of different color wavelength reflectance, the understanding and tracking precision of the vegetation physiological state is improved, long-term vegetation changes can be carefully monitored through time sequence analysis, the prediction capacity of forest ecological behaviors is enhanced, and the method is suitable for popularization and application. Drought response and pest and disease damage signs are monitored in real time, the timeliness and accuracy of health state evaluation are enhanced, the real-time performance and continuity of data are ensured by dynamically updating a forest vegetation database, and the dynamic monitoring and decision support capacity of forest management is remarkably improved.

The invention discloses an under-forest autumn phenology quantification method and system based on an infrared camera, and belongs to the technical field of ecological detection, and the method comprises the steps: S1, under-forest vegetation image collection: employing the infrared camera to carry out the under-forest vegetation image shooting and collection at a phenology stage; s2, obtaining a daily-scale vegetation optimal image: carrying out image feature obtaining through an under-forest vegetation daily optimal image selection algorithm fusing multi-feature clustering and hierarchical screening; s3, checking the consistency of the meteorological data of the under-forest infrared camera and the data of the canopy meteorological station; s4, vegetation phenology dynamic extraction: adopting a dynamic quantile method to obtain a time sequence change curve, and determining a change rate curvature and a remote sensing phenology period of a dynamic threshold value based on a vegetation index; according to the method, forest under-forest vegetation dynamic monitoring is carried out by combining dynamic threshold identification and infrared camera imaging, redundant or low-quality images are effectively eliminated, the vegetation index extraction efficiency is improved, and the phenological period judgment precision is improved; and meanwhile, the algorithm is lightweight and efficient.

The invention discloses a forest community biomass stability monitoring device and method based on a foundation laser radar, and belongs to the technical field of forest community biomass stability research. The forest community biomass stability monitoring device comprises a mounting seat and supporting legs arranged on the mounting seat, and the mounting seat is provided with the laser radar and data processing equipment; the mounting seat comprises a storage box body and a supporting plate which is arranged in the storage box body in a sliding manner, the data processing equipment is arranged on the supporting plate, a fixed sample pile which is arranged on a fixed sample plot is detachably arranged on the storage box body, and a plurality of hanging plates which are used for marking vegetation on the fixed sample plot are arranged on the fixed sample pile in a sleeving manner; according to the invention, the LiDAR three-dimensional structure point cloud data of the forest vegetation of the fixed sample plot is obtained through the laser radar, and the data processing device processes the obtained LiDAR three-dimensional structure point cloud data to obtain the stability of the forest community biomass, so that the dependence on workers is reduced, and the accuracy of monitoring the stability of the forest community biomass is improved.

The invention belongs to the technical field of ecological hydrology, and particularly discloses a forest underlying surface earth surface evapotranspiration estimation and component segmentation method based on kNDVI. According to the method, based on a maximum entropy increase model, under the constraint of a surface energy balance equation, the surface state parameters of a forest vegetation-free area are used for resolving to obtain the soil evaporation capacity of the bare soil; on the basis of a maximum entropy increase model, under the constraint of a canopy energy balance equation, the vegetation transpiration amount is obtained through calculation according to the canopy state parameters of the forest vegetation coverage area; calculating the canopy interception evaporation capacity according to the leaf area index and the rainfall capacity of the forest vegetation coverage area; and finally, based on the kNDVI index, carrying out weighted summation on the bare soil evaporation amount, the vegetation evaporation amount and the canopy interception evaporation amount in the forest to obtain the total evapotranspiration of the underlying surface of the forest. Compared with an existing evapotranspiration estimation model, the forest underlying surface earth surface evapotranspiration estimation method is lower in complexity and higher in estimation precision.

The invention provides a regional forest vegetation carbon sequestration capability assessment method, and belongs to the technical field of forest vegetation resource monitoring and carbon sink assessment. According to the method, 13 representative core sample plots serve as hubs, unmanned aerial vehicle multi-source remote sensing and satellite images are fused, 20-meter-resolution carbon density mapping is achieved, and a space-time coupling carbon sequestration index is constructed to evaluate the carbon reserve stability and the carbon sequestration potential. Arranging 13 20-meter core sample plots in a typical subarea in a structured manner, and measuring and calculating the carbon density of arbor, shrub and grass; extracting optical and laser features of the unmanned aerial vehicle to establish a subarea-level carbon density inversion model; training a star-sky-ground fusion estimation model by taking the sub-region unmanned aerial vehicle carbon density grid as a supervision label, and expanding the model to a region; and calculating a space-time coupling carbon sequestration index according to the carbon density time sequence and interference monitoring information. The method gives consideration to sampling efficiency and estimation precision, and is suitable for large-area forest carbon sink monitoring and management.

The application discloses an under-forest goose breeding ecological circulation planting and breeding system, which comprises a first iron fence, fixing columns, double doors, a door frame, an opening and closing mechanism, a water storage tank, a dropper, a water receiving tank, a fixing plate, a fence, a second iron fence and a fixing shaft, the first iron fence and the second iron fence respectively enclose two planting and breeding circles, and a plurality of fixing columns are evenly arranged on the inner sides of the first iron fence and the second iron fence respectively. The application adopts the mode of breeding geese under fruit trees, uses goose excrement as fertilizer for fruit trees, and provides food for geese with the under-forest vegetation, which is beneficial to saving breeding cost, simultaneously producing fruits and geese, and improving breeding economic benefits. Meanwhile, the double-circle design is adopted, which avoids excessive damage to the vegetation caused by excessive grazing, and is beneficial to realizing sustainable breeding. Through the setting of the opening and closing mechanism, the double doors are automatically opened and closed to the two sides, and manual opening and closing of the doors is not needed, thereby improving work efficiency.

The invention belongs to the technical field of forest fire monitoring and early warning, and particularly relates to an extreme climate prediction method based on a large warm period weighted FWI index and application. According to the specific technical scheme, the method comprises the following steps: acquiring climate data of a great warm period, and calculating FWI index components in each time period according to the climate data of the great warm period; the method comprises the following steps: acquiring forest vegetation data and topographic data by utilizing satellite remote sensing, performing space matching on the forest vegetation data and the topographic data, and calculating a vegetation-topographic collaborative weight factor; calculating a weighted FWI index based on the vegetation-terrain collaborative weight factor; and analyzing the relationship between the weighted FWI index and the occurrence frequency, intensity and duration of the extreme climate event by adopting a time sequence analysis method and a space analysis method, and visualizing the analysis result. The application blank of vegetation and topographic factors in large warm period FWI calculation is filled, so that the scientificity and accuracy of extreme climate prediction are improved.

The invention discloses a litter leaf area index determination method, the proposed litter leaf area index has the same meaning as a vegetation leaf area index, can participate in related operation as the vegetation leaf area index, and can bring litters into a vegetation investigation system; the index is the number of covering layers of leaves on the earth surface, and the measuring and calculating method comprises the steps that the total spreading area of litter leaves on the unit area of the earth surface is established, and a litter leaf area index estimation model of forest lands such as needle-leaved, broad-leaved and needle-broad mixed forests is established by establishing the power exponent relation between the total spreading area and the dry weight of the litters in the unit area. Therefore, litter unit area dry weight index meaning conversion is achieved, and the method has better adaptive capacity in forest land vegetation investigation.

The present invention belongs to the technical field of laser radar remote sensing data processing, and specifically relates to a vegetation concentration index estimation method based on Poisson distribution model and point cloud clustering. The present invention uses laser radar to obtain three-dimensional point cloud data of forest vegetation canopy, and based on the principle that the ideal state of leaf distribution in the forest canopy satisfies the Poisson distribution, the point cloud is aligned, denoised, filtered and normalized; the degree of deviation between the cluster center and the Poisson point is quantitatively expressed, and the concentration index is calculated by the ratio between the number of cluster centers that have not been offset and the number of all Poisson points, and a laser radar vegetation concentration index estimation method based on Poisson distribution and point cloud clustering is established. The present invention overcomes the intermediate error caused by the prior art that the canopy gap rate must be inverted first, and at the same time avoids the defects of being unable to collect data around the clock due to limitations such as usage time and weather.

The invention discloses an image data acquisition method and device based on an unattended intelligent airport, and relates to the technical field of image data processing.The method comprises the steps that forest image data are acquired based on the unattended intelligent airport, the unattended intelligent airport selects a forest area meeting preset mountain forest conditions, and the selected forest area is selected as a forest area; the unattended intelligent airport is used for taking off, landing and parking a predetermined type of unmanned aerial vehicle, and the predetermined mountain forest conditions comprise that the height fall of a mountain land is greater than a preset fall threshold value, the forest vegetation coverage rate is higher than a preset coverage rate threshold value, and the number of roads in a forest is lower than a preset number threshold value; and analyzing the preprocessed forest image data by adopting a pre-trained machine learning model, identifying a forest resource state, and displaying the forest resource state on a terminal screen. The technical problems that image resource analysis is difficult to carry out on an unattended intelligent airport system in a severe environment and the operation efficiency is low in the prior art are solved.

The utility model discloses an under-forest multifunctional movable poultry breeding device, including the henhouse, and the henhouse includes the egg production area, the habitat area, the feeding area and the movable cage, and the henhouse bottom is provided with the bottom plate, and the bottom plate lower surface is provided with a plurality of moving wheels, and the bottom plate top is provided with the mesh, and the mesh and the bottom plate between sliding insertion have the excrement receiving tray, and the habitat area is provided with a plurality of habitat racks, and the habitat area top is provided with the supporting plate, and the supporting plate upper surface is provided with a plurality of egg production nests, and the mesh upper surface of feeding area is provided with the trough and the water tank respectively, and the feeding area one end surface is provided with the sliding door. The utility model discloses through setting up the pull rod and the moving wheel, can realize flexible movement, avoid poultry long -term concentration in the same area activity, can replace the site in time, protect under-forest vegetation and soil structure, maintain the ecological balance, set up the excrement receiving tray, and the excrement receiving tray lets the staff can daily unified collection excrement, avoid excrement pollution breeding environment, and the cleaning process need not frequently enter the henhouse, and the time and energy are saved.

The present application relates to the technical field of remote sensing, in particular to a method for estimating total primary productivity of pantropical forest vegetation depending on leaf age, which comprises obtaining a weather basic data set and a leaf age-based vegetation leaf area index data set, grouping current leaves according to leaf age, including new leaves, mature leaves and old leaves, obtaining the maximum light energy utilization rate of all leaves, and outputting the final productivity through a remote sensing estimation model of total primary productivity. The present application considers the regulation of leaf phenology on canopy photosynthesis, distinguishes the photosynthetic capacity difference between leaf age components, and constructs a remote sensing estimation method suitable for total primary productivity of pantropical forest vegetation, so as to improve the simulation accuracy of total primary productivity of pantropical forest vegetation, and help to accurately monitor the evolution trend of photosynthesis of pantropical forest under climate change.

The present invention relates to the field of remote sensing and telemetry identification technology, and more specifically, to a vegetation type identification and classification method, system, and medium based on remote sensing and telemetry data. The method provided by the present invention mainly includes determining the target area that needs to be identified by vegetation type through telemetry image data, judging it as a green vegetation area or a non-green vegetation area, and when the target area is a green vegetation area, further obtaining the average height of the vegetation in the unit to be identified through a three-dimensional model, and obtaining the basic parameters of the unit to be identified to establish a second identification model, and outputting the third judgment result of forest vegetation or grassland vegetation through the second identification model. The above method solves the problem of the difficulty in identifying forest vegetation or grassland vegetation to a certain extent, and provides a reference basis for relevant staff in preliminary vegetation survey work.

The invention relates to the technical field of image detection, in particular to a forest vegetation detection method and system based on an unmanned aerial vehicle, and the method comprises the steps: obtaining an RGB image of a forest region in real time through the unmanned aerial vehicle, and carrying out the preprocessing; constructing a comprehensive feature index of an image block in the image, wherein the comprehensive feature index is used for representing a comprehensive feature expression condition of the image block under different illumination influences; adaptively setting a contrast limit threshold value of each image block according to the comprehensive feature index, and performing local enhancement by adopting a CLAHE algorithm; rotten and dead wood detection model training is carried out on the enhanced RGB image so as to identify rotten and dead wood in the RGB image acquired by the unmanned aerial vehicle; and deploying the trained model to the unmanned aerial vehicle for real-time detection and position feedback. According to the method, the image is effectively enhanced according to the distinguishing features of the decayed dead wood in different blocks and the background surface soil in the image, and the accuracy of subsequent forest vegetation detection and recognition is improved.

Achieve greater efficiency and sophistication in forest management operations. [Solution] A program to be executed by a computer having a processor, which causes the processor to perform the following steps: acquiring status information indicating the state of the forest at a specified time from multiple information sources; organizing the acquired status information in chronological order using identification information that uniquely identifies specific areas within the forest geographically as a common key, and storing it in a database as historical information; analyzing time-series changes in the satellite data included in the historical information, and extracting areas of change where vegetation in the forest has changed; and comparing the extracted changed areas with ground record data included in the historical information, and calculating the priority of visiting the forest for on-site inspection based on whether or not there is ground record data corresponding to the changed areas.

The invention provides a man-made forest thinning device, which relates to the technical field of tree felling and comprises a load-carrying remote control car, a climbing mechanism, a multi-stage grabbing mechanism, a cutting mechanism and a remote controller, the load remote control car is provided with a butt joint part, and the climbing mechanism is detachably connected with the butt joint part; the multi-stage grabbing mechanism is arranged on the climbing mechanism; the cutting mechanism is arranged on the climbing mechanism; a controller is arranged on the load remote control car, and the controller is in communication connection with the load remote control car, the climbing mechanism, the multi-stage grabbing mechanism and the cutting mechanism. According to the device, the load-carrying remote control car carries out bearing movement in a man-made forest, damage to an under-forest vegetation structure in the moving process is reduced, through cooperation of the climbing mechanism, the multi-stage grabbing mechanism and the cutting mechanism, a target tree is gradually cut and decomposed, and the cut and decomposed trunks and branches are stably placed on the ground; damage to nearby reserved trees and under-forest vegetation caused by whole plant toppling is avoided, and high-quality thinning of the man-made forest is achieved.

This application relates to the field of computer vision technology and provides a method for detecting forest vegetation diseases and pests based on unmanned aerial vehicles (UAVs). The method includes: acquiring forest disease and pest images using a UAV; preprocessing the images to generate an initial input feature map; performing feature enhancement on the initial input feature map to obtain a first feature map; performing saliency gate calibration and feature encoding on the first feature map to generate a multi-scale fusion feature map; performing multi-level fusion on the multi-scale fusion feature map to obtain first feature maps of different scales; and performing reparameterized large kernel convolution on each first feature map to output a target feature map, which can locate small disease and pest areas. This application introduces targeted improvements to the YOLO11 model at each processing stage to generate an MDFR-YOLO model, thereby enhancing the network's ability to detect forest vegetation diseases and pests in complex backgrounds.