74 results about "Forest vegetation" patented technology

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 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 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 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 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 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.

The present application relates to the technical field of forest detection, and specifically discloses a forest vegetation detection method and system based on a UAV, which collects visible light images, spectral images and radar data of a forest area through a UAV carrying a visible light camera, a spectral camera and a radar, constructs a three-dimensional model of a tree based on the radar data, determines tree crown data and tree species of the tree based on the visible light images and the spectral images, and determines the volume, biomass and carbon storage of the tree based on the three-dimensional model, the tree species and the tree crown data, so as to calculate the total volume, total biomass and total carbon storage of all trees in the target forest area, without manually inspecting and sampling tree data, thereby shortening the time consumption of forest detection, improving the detection efficiency, and calculating the volume, biomass and carbon storage of each tree through single-tree modeling combined with visible light images and spectral images, and then summing up the total volume, total biomass and total carbon storage of the entire target forest area, so that the detection result is more accurate.

The present application relates to forest aboveground biomass measurement, specifically relates to a kind of forest aboveground biomass remote sensing measurement method based on improved Boruta algorithm, improved Boruta algorithm's forest aboveground biomass remote sensing measurement method, it is characterized in that, the method includes the following steps: (1) selecting the remote sensing image data of the region to be measured and data pretreatment;(2) design improved Boruta algorithm;(3) SVR model is established using improved Boruta algorithm;(4) the forest aboveground biomass of the region to be measured is measured using model, the precision of the present application is higher, the AGB distribution condition measured and the actual surveying research ground topography and forest vegetation distribution condition basically coincide, provide a new method for forest measurement.

The application discloses a kind of weakly supervised unmanned aerial vehicle multi-source hyperspectral point cloud semantic segmentation methods based on integrated prediction. It is based on unmanned aerial vehicle multi-source hyperspectral point cloud data as the research object, construct weakly supervised semantic segmentation framework based on integrated prediction, on the basis of incomplete supervision, embedding consistency constraint of integrated prediction, entropy regularization guided by integrated prediction result and self-adaptive soft pseudo label method, make full use of the encoding information of unlabeled data, provide various spatial and spectral constraints for weakly supervised training, enhance the semantic segmentation ability of network model. The application solves the problems of high cost of forestry vegetation data sampling, rare and difficult to obtain forest sample and time-consuming and laborious artificial high-precision label large scene forest vegetation data in the prior art; It has the advantages of guaranteeing the training efficiency of forest sample, increasing the supervision source for weakly supervised forestry information extraction network model and improving the forestry vegetation information extraction ability of learning model.

The present invention relates to a method for extracting forest coverage based on a hierarchical multiplication model and GF-1 data. The present invention includes accurately and efficiently extracting forest coverage from multi-spectral remote sensing data by using a hierarchical multiplication model. On the basis of preprocessing the remote sensing data, first, the land cover types are divided into three main levels according to the differences in the spectral characteristics of the ground objects. Secondly, the abundance of the ground objects is calculated by using a linear pixel decomposition model in each level. Finally, the abundance of forest vegetation in the first-level ground objects is calculated through a multiplication model. The present invention creates a pixel decomposition algorithm based on a multiplication model, improving the applicability and accuracy of extracting the abundance value of ground objects based on multi-spectral images. It solves the limitation of insufficient multi-spectral bands. For the problem that it is difficult to verify the pixel decomposition accuracy, it is proposed to use high-precision images for classification and calculate the abundance of each endmember at a large scale with the classification results to verify the pixel decomposition accuracy, improving the accuracy of pixel decomposition verification.

The invention discloses a comprehensive evaluation method for a service function of a forest vegetation ecosystem, and relates to the field of forest ecology, and the method comprises the steps: obtaining and preprocessing multi-source data of an evaluation region, and generating a standardized multi-source data cube; based on the standardized multi-source data cube, calculating the material quality of multiple ecological system service functions of each evaluation unit in the evaluation area, and generating an ecological system service function material quality data set; performing time sequence analysis on the ecosystem service function material quality data set, and calculating to obtain a service function toughness index of each evaluation unit for each ecosystem service function; and extracting each ecological system service function from the standardized multi-source data cube to form resource consumption data. According to the method, the conversion from static evaluation to sustainable comprehensive evaluation covering stability and efficiency is realized, and the problem that the long-term sustainability of an ecological system service function cannot be effectively quantified in the prior art is solved.

The invention provides a tree carbon reserve monitoring method and device based on a laser radar and a storage medium. The method comprises the following steps: scanning each sample plot in N sample plots sampled in a monitoring area corresponding to forest vegetation through a laser radar, and acquiring first point cloud data corresponding to a tree; preprocessing the first point cloud data to obtain second point cloud data of the trees in each sample plot; based on the second point cloud data of each sample plot, performing individual tree segmentation and parameter extraction to obtain a first individual tree number and a first feature parameter corresponding to the individual tree; correcting the first characteristic parameter based on a pre-established correction model to obtain a corrected first characteristic parameter; and based on the first individual tree number of each sample plot and the corrected first characteristic parameter, determining the tree carbon reserve of the monitoring area through a preset carbon measurement model. Therefore, the monitoring efficiency and the monitoring accuracy of the tree carbon reserves can be improved.

The application discloses a vegetation reflectance spectrum measurement method and system for forest monitoring, acquires original spectrum collection signals of a target area of a forest and carries out canopy penetration attenuation correction to establish a benchmark reflectance spectrum set, identifies a forest canopy projection area and atmospheric scattering interference of the benchmark reflectance spectrum set to form a ground true reflectance spectrum, carries out red edge feature positioning and band ratio analysis based on the ground true reflectance spectrum to form a vegetation reflectance spectrum index, carries out multi-time spectrum difference detection on the vegetation reflectance spectrum index and removes phenology interference to determine a change confidence interval, screens abnormal spectra based on the change confidence interval and carries out forest health grade labeling after correction by a spectrum evolution coefficient to output forest coverage change measurement results. The application can effectively eliminate the interference of forest canopy shadow and atmospheric scattering, distinguish normal phenology fluctuation from real health abnormality, and provide reliable spectrum data support for forest vegetation state monitoring.