128 results about "Sample plot" patented technology

The invention discloses a pheromone binding protein derived peptide capable of effectively monitoring sex pheromones of fall webworms and a biosensor, and belongs to the field of biosensors. At present, methods such as sample plot survey and sex traps commonly used in fall webworm monitoring have the problems of poor timeliness, high labor cost, insufficient data continuity and the like. In order to solve the problem, the pheromone binding protein-derived peptide provided by the invention has an amino acid sequence as shown in SEQ ID NO: 2, the biosensor comprises a substrate and an interdigital electrode, a single-walled carbon nanotube is attached to the electrode, and the electrode is also connected with the pheromone binding protein-derived peptide. The sensor can specifically detect sex pheromones of fall webworms, has no response to 17 plant volatile matters, and can detect sex pheromones released by as low as five live female fall webworms under laboratory conditions. The method has application potential in the fields of early pest monitoring, prevention and control efficiency improvement, ecological safety guarantee and the like.

The invention relates to the technical field of forestry management, in particular to a forest and grass resource dynamic monitoring system which comprises the steps that a graph attention network is adopted to conduct high-low weight recognition processing on the structure difference value between node pairs, feature vectors are established through the canopy height difference and canopy density difference between adjacent nodes, and the canopy height difference and the canopy density difference between adjacent nodes are obtained; an edge weight scoring system is constructed in the form of segmented statistics and proportion weighting, node edge pairs with high influence relation strength are dynamically screened, ordered aggregation of spatial communication strength is realized, a graph neural network is introduced in a parameter fusion stage to construct a node parameter representation mechanism, and the spatial communication strength is improved. The tree species proportion, the grade of diameter at breast height and the community vertical structure in a regional sample plot are used as input features, unified mapping of node features of each region is completed in multiple rounds of iteration, cross-regional difference analysis is executed on model output in combination with biomass change frequency, parameter items with the difference proportion lower than a set threshold value are replaced with unified expression, and the model output is obtained. And parameter synchronization is realized to realize space nesting and feature integration.

The invention relates to the technical field of forest resource investigation and monitoring, and discloses a natural forest multi-source point cloud registration method and system based on point cloud segmentation optimization, and the method comprises the steps: laying a sample plot, collecting the diameter at breast height, the height and the crown breadth of a single tree, and obtaining the space coordinates of the single tree in combination with RTK; according to the method, unmanned aerial vehicle laser scanning and backpack laser scanning are adopted to collect and preprocess point cloud data of a research area, an ICP algorithm is adopted to align a source point cloud with a target point cloud through rotation and translation, point cloud registration is achieved through a minimization error function, point cloud registration precision and errors are quantified through evaluation indexes, and point cloud registration evaluation is carried out. Therefore, the point cloud registration precision is effectively improved through a segmented registration strategy, limitation caused by single sensor data can be overcome through fusion of BLS and ULS multi-source point cloud data, the extraction precision of tree structure parameters is remarkably improved, and the method is not only suitable for point cloud registration of natural forests, but also suitable for point cloud registration of natural forests. And reliable data support and technical support are provided for forest resource monitoring and ecological protection.

The invention discloses a carbon source and sink accounting method based on flux observation, an ecological system carbon reserve accounting system and a flux observation carbon source and sink accounting system are established, and the ecological system carbon reserve accounting system obtains carbon reserves of plants, soil, water and ground mud based on a sample point investigation method to estimate the carbon reserves of the ecological system. Estimating regional carbon reserves in combination with remote sensing survey; the flux observation carbon source sink accounting system is based on the constructed wetland carbon sink monitoring system and monitors data in real time; regional ecological system carbon reserve dynamic accounting is carried out by combining sample plot investigation with a carbon sink model and a remote sensing image technology, compared with the prior art, long-term fixed observation sample plots are set from multiple angles of lake wetland water, soil and gas generation, and Hongze lake wetland carbon sink observation data acquisition is carried out; and carrying out parameter adjustment and correction by utilizing a modern technical means and a large model technology, carrying out Hongze lake carbon sink model construction, and realizing Hongze lake carbon sink value accounting.

The invention relates to the technical field of grassland ecological evaluation, in particular to a dynamic reference index calculation method and system for grassland ecological quality evaluation. The method comprises the following steps: acquiring historical reference data of a non-degraded sample plot covered with different interference degrees in a target area, and constructing a reference system time sequence prediction model based on the historical reference data; based on pre-acquired historical monitoring data of a to-be-evaluated grassland, utilizing the reference system time sequence prediction model to obtain a dynamic reference index potential value; extracting an interference factor from the historical reference data, and setting an interference weight for the interference factor; and correcting the potential value of the dynamic reference index by using the interference weight based on the historical monitoring data and the historical reference data to obtain the dynamic reference index. The problem that in the prior art, a fixed reference index is difficult to dynamically respond to the change of the environment, so that the evaluation of the ecological condition of the grassland is inaccurate is solved, and the scientificity of reference index calculation is greatly improved.

The invention provides a forest sample plot monitoring system, the monitoring system comprises a three-dimensional live-action modeling subsystem, an Internet of Things monitoring perception subsystem and a data management analysis subsystem, the three-dimensional live-action modeling subsystem is used for collecting sky point cloud data and ground point cloud data, and constructing a forest sample plot three-dimensional model; the Internet of Things monitoring and sensing subsystem is used for collecting tree growth associated information collected by a sensor and gathering and transmitting the tree growth associated information to the data management and analysis subsystem; and the data management and analysis subsystem is used for combining the forest sample plot three-dimensional model with the tree growth associated information, and analyzing to obtain forest accumulation variation and tree growth data. Through high-precision, high-efficiency and automatic reconstruction of the three-dimensional real scene of the forest sample plot, automatic, real-time and continuous monitoring of key growth parameters such as diameters at breast height of all sample trees in the sample plot is realized, and the intelligent level and operation efficiency of forest monitoring are improved.

The invention provides a three-dimensional habitat visibility measurement method fusing ground-based and air-based laser radars, and the method comprises the steps: cutting ground-based laser radar point cloud data based on the sample plot size of a target forest region and the physiological morphological characteristics of a target animal; constructing a three-dimensional voxel model based on an occupancy grid algorithm and the clipped ground-based laser radar point cloud data, tracking a simulated animal sight line in the three-dimensional voxel model, calculating three-dimensional animal habitat visibility, calculating forest three-dimensional structure parameters based on the clipped air-based laser radar point cloud data, and calculating forest three-dimensional structure parameters based on the clipped ground-based laser radar point cloud data. A random forest model is constructed and trained based on the three-dimensional animal habitat visibility and the forest three-dimensional structure parameters, effective forest three-dimensional structure parameters are screened out, the precision of the random forest model is evaluated by adopting evaluation indexes obtained through taimen-taimen cross validation, and an optimal random forest model is obtained. And inputting the effective forest three-dimensional structure parameters into the optimal random forest model to predict the three-dimensional animal habitat visibility.

The invention discloses a method for extracting parameters of a (subtropical) eucalyptus man-made forest based on an unmanned aerial vehicle laser radar. The method comprises the following steps: investigating a sample plot, and measuring and calculating actual forest parameters such as average diameter, average height, sectional area, stand volume and the like; acquiring and preprocessing high-density unmanned aerial vehicle laser radar point cloud data, and extracting three types of feature variables of height, density and vertical structure; deducing a model structural formula on the basis of a stock different-speed growth equation, and screening variables according to rules; constructing a power model by using a regular exhaustion method; the optimal model is screened out through model parameter estimation, inspection, significance analysis and the like. According to the method, the variable synergistic effect is mined through the exhaustion method, the multi-dimensional test system is constructed, and the exclusive model is customized, so that the problems of one-sided variable screening, insufficient model reliability and poor specific forest stand adaptability in the prior art are solved, high-precision estimation of core forest parameters is realized, and the method is suitable for large-scale popularization and application. And scientific and effective technical support is provided for refined management and sustainable operation of eucalyptus man-made forest resources.

The application discloses a forest structure and intelligent coupling analysis and modeling method for absorbing photosynthetically active radiation, and belongs to the technical field of forest ecology remote sensing, structure analysis and computer simulation. The method focuses on fusing airborne and handheld laser radar information, accurately inverting forest structure indexes, covering single-tree level attributes, neighborhood spatial structure indexes, gap structure, stand density SD and slope SL; three-dimensional radiation transmission simulation is carried out; through the construction of a single-tree three-dimensional model library and a digital sample plot, high-precision three-dimensional forest reconstruction and hierarchical APAR simulation are realized, and photosynthetic spatial heterogeneity parameters are extracted; Spearman correlation analysis is carried out based on the structure and APAR parameters, and PCA dimension reduction and KMeans clustering are combined to identify the dominant structure mode and spatial type; based on the clustering result, a typed structure-APAR coupling integrated learning model is constructed, and the regulation mechanism of single-tree attributes and neighborhood structure on APAR is revealed.

The application discloses a prediction method of tree species structure of arbor forest in subtropical areas, and comprises the following steps: 1) dividing the tree species of arbor forest into three categories of pines, firs and broad-leaved trees, and establishing a prediction model according to volume proportion data of each tree species category obtained from fixed sample plots in previous forest resource surveys; 2) taking two-period survey data as modeling data, and fitting parameters by using STATA software; 3) substituting measured data of each sample plot into the prediction model for iterative calculation, obtaining volume proportion values of the future arbor forest in the two-period interval as prediction values; calculating average prediction values of the prediction values of the tree species proportions of all sample plots; and 4) comparing the prediction value of the volume proportion of the broad-leaved trees or the sum of the prediction values of the volume proportions of the pines and firs in the average prediction values of the tree species proportions with a critical value of coniferous and broad-leaved mixed forest, and determining whether artificial intervention needs to be applied in actual forestry management. The application can predict the tree species structure of arbor forest in future years, and has the function of guiding forestry production and operation activities.

To grasp a state of an environment where a plurality of trees such as forests exist while reducing time and cost required for work.SOLUTION: In the flight condition determination system 110 that determines a flight condition for performing forest measurement according to an embodiment, the processing device 111 acquires map information indicating a measurement target area 50 in which the unmanned aerial vehicle 1 is caused to fly and a plot area 50a that is a part of the measurement target area 50. The processing device 111 determines a flight condition of the unmanned aircraft 1 such that the total number of laser pulses 22 emitted per unit area is different between the plot area 50a and an off-plot area 50a that is an area other than the plot area 50b in the measurement target area 50.SELECTED DRAWING: Figure 13

The application discloses a canopy coverage considering aggregation index inversion method and correction method, relates to the technical field of remote sensing image processing and aggregation index inversion, collects remote sensing images of a vegetation canopy, extracts hot spot reflectivity and dark spot reflectivity respectively, calculates a normalized hot spot and dark spot index NDHD, calculates an initial value of an aggregation index CI according to a linear relationship between the NDHD and the CI, and corrects the initial value of the CI by using a canopy coverage Ccan, wherein an error between the initial value of the CI and a real value of the CI has a linear relationship with the Ccan. The application first establishes a CI correction model based on the Ccan, and solves the deviation problem of a traditional algorithm in a sample field with high or low Ccan. By improving a mainstream CI inversion algorithm based on the NDHD, the application significantly improves the precision of CI inversion, and provides more reliable technical support for forest ecological monitoring, carbon cycle research and remote sensing application.

This invention proposes a spatial mapping method and system for new bamboo shoot yield based on satellite-UAV fusion. Addressing the problem of remote sensing observation gaps caused by the underground growth of bamboo shoots, this invention constructs a multi-scale fusion framework of "satellite identification of potential high-yield areas + UAV inversion of old bamboo density." First, a new bamboo yield prediction model is constructed using old bamboo density and topographic factors as variables through ground-based sample plot surveys. Second, indices such as NDVI and LSWI are calculated using time-series imagery, and a decision tree model is used to identify potential new bamboo germination areas in high-yield bamboo forests. Then, based on high-resolution UAV imagery, a region growing algorithm is used to extract individual old bamboo plants and invert the spatial distribution of old bamboo density. Finally, using the potential areas as masks, old bamboo density and topographic factors are substituted to generate a spatial distribution map of new bamboo yield. This invention enables large-area, low-cost, and high-precision mapping of new bamboo shoot yield, providing core technical support for precise bamboo forest management.

The invention belongs to the technical field of carbon sink monitoring, and particularly discloses a scattered greenbelt carbon sink measuring and calculating device and method based on a box-type respiration method.The method comprises the steps that in the sample plot investigation stage, a gridding point distribution method is adopted to divide areas, and a quadrat covering different vegetation types and interference factors is randomly selected; and a dynamic acquisition and detection stage: setting time frequency parameters of dynamic acquisition, acquiring gas in an area according to the set time frequency parameters, simultaneously monitoring basic environment quality, vegetation coverage and man-made interference intensity in real time, simultaneously monitoring the running state of a sensor in real time, and if data transmission is abnormal, starting buzzing alarm by a data terminal so as to give an alarm. Abnormal time and abnormal types are automatically recorded; and a data correction calculation and output stage: calling and storing a corresponding correction model based on a microenvironment factor data type, calculating a carbon flux and a carbon sink amount according to a box-type method, and outputting the data according to two modes of real-time display and storage export.

This application relates to the field of topsoil quality testing technology, specifically to a method, system, device, and equipment for testing topsoil quality in power transmission and transformation projects. The method includes: collecting topsoil data from mixed soil samples at different depths in various research plots and standard topsoil data from standard mixed soil samples; obtaining comprehensive nutrient indices and comprehensive microbial diversity indices at the same depth for each research plot, as well as the average coupling characteristic value of the research plot; determining nutrient remediation factors and microbial remediation factors for the research plots, and constructing topsoil remediation factors; and determining the topsoil quality testing results for the power transmission and transformation projects in the research plots based on all topsoil data, average coupling characteristic values, and topsoil remediation factors. This application can improve the quality of topsoil quality testing in power transmission and transformation projects.

The present application relates to the field of botany and ecological measurement technology, and discloses a vegetation cover instrument calibration method and device, comprising: determining the position and area of the sample plot actually needed to be photographed, and taking a photo of the sample plot to obtain a sample plot photo; preprocessing the sample plot photo to obtain a processed photo; selecting several main vegetation colors in the processed photo as vegetation pixel colors, and selecting several main soil colors as soil pixel colors; setting a display screen; filling the display screen based on the selected vegetation pixel colors and soil pixel colors to form a training combination of the current color vegetation area ratio; comparing the similarity between the standardized vegetation map of the current color vegetation area ratio and the training combination to obtain a difference value; and calibrating the vegetation cover instrument based on the difference value. The present application can solve the problems of the existing vegetation cover instrument, such as the lack of measurement standards, large data deviation, and strong subjectivity of measurement results.

The application provides a kind of desert oil and gas field remote sensing monitoring result verification method, belongs to the technical field of desert oil and gas field ecology monitoring, effectively solves the problem of large labor cost and low efficiency of current desert oil and gas field remote sensing monitoring result verification method. The technical scheme reduces the labor cost and meets the requirements of long-term and positioning monitoring, sets up fixed monitoring sample plots, collects automatic ground data and samples, and then realizes the reliability and accuracy of verifying remote sensing monitoring results.

The invention discloses a method and system for constructing a comprehensive evaluation index of a soil water source conservation function, and relates to the technical field of soil water source conservation, and the method comprises the following steps: measuring a soil water retention performance index and a soil infiltration performance index; based on the measured indexes, determining the weight of each index by adopting a factor analysis method, and carrying out membership standardization processing on each index; and weighting the weight of each index and the standardized membership degree to obtain the comprehensive evaluation index of the soil water conservation function. According to the method, the soil water source conservation function can be quantitatively evaluated on the sample plot scale only through one-time or few-time soil index determination, dependence on a long-term hydrological sequence is avoided, and the data acquisition cost is remarkably reduced.

The invention discloses a forestry health information monitoring method based on multi-source heterogeneous remote sensing image feature fusion. According to the method, an unmanned aerial vehicle carrying a high-resolution multispectral camera and a laser radar is used for carrying out aerial photography on a target sample plot, remote sensing images are obtained, and then the images are processed to generate optical orthographic images and laser radar point cloud data; performing two-dimensional planarization on the point cloud data to generate a digital surface model (DSM), extracting multispectral data of the optical image, and performing feature fusion on the DSM and the multispectral data; and dividing a training set and a verification set based on the fused data, and training and optimizing a deep learning model. And finally, utilizing the trained model to classify withered canopies, healthy canopies and other categories in the remote sensing image to realize accurate monitoring of forestry health conditions. According to the method, through fusion of spectrum and structural features, the recognition precision of withered and healthy canopies is improved, and the limitation of single data feature application is overcome.

The present application relates to the technical field of grassland ecological evaluation, and particularly relates to a dynamic reference index calculation method and system for grassland ecological quality evaluation. The method comprises the following steps: obtaining historical baseline data of a target region covering different degrees of disturbance and undegraded sample plots, and constructing a reference system time series prediction model based on the historical baseline data; obtaining a dynamic reference index potential value by using the reference system time series prediction model based on historical monitoring data of a grassland to be evaluated; extracting a disturbance factor from the historical baseline data, and setting a disturbance weight for the disturbance factor; and correcting the dynamic reference index potential value by using the disturbance weight based on the historical monitoring data and the historical baseline data to obtain a dynamic reference index. The present application solves the problem that the fixed reference index in the prior art cannot dynamically respond to changes in the environment, thereby resulting in inaccurate evaluation of the ecological condition of the grassland. The present application greatly improves the scientificity of the calculation of the reference index.

The application discloses an information pheromone binding protein derivative peptide and a biosensor capable of effectively monitoring the sex pheromone of Hyphantria cunea, and belongs to the field of biosensors. At present, the monitoring of H. cunea is usually achieved by means of sample plot investigation, sex lures and other means, and problems such as poor timeliness, high labor cost and insufficient data continuity exist. In order to solve the above problems, the information pheromone binding protein derivative peptide provided by the application has an amino acid sequence as shown in SEQ ID NO: 2, the biosensor comprises a substrate and an interdigital electrode, single-walled carbon nanotubes are attached to the electrode, and the information pheromone binding protein derivative peptide is further connected. The sensor can specifically detect the sex pheromone of H. cunea and has no response to 17 kinds of plant volatiles, and can detect the sex pheromone released by as low as five live female H. cunea under laboratory conditions. The sensor has application potential in the fields of early-stage pest monitoring, improvement of prevention and control efficiency and guarantee of ecological safety.

This invention provides a method and system for mine ecological restoration, relating to the field of ecological environment restoration technology. The method includes: obtaining soil damage characteristics based on a pre-acquired ecological area to be restored; obtaining a mine geological composition based on the soil damage characteristics and pre-acquired restoration goals, combined with geological soil selection principles; obtaining a plant species composition based on sampling results from pre-acquired sample plots and restoration goals; and performing ecological restoration of the ecological area to be restored based on the mine geological composition and plant species composition. This invention accelerates the process of geological soil formation and vegetation restoration in abandoned open-pit mines, improves the tolerance of restored vegetation communities to climate fluctuations, and truly achieves precise and sustainable ecological restoration of open-pit mines.

The invention discloses a haloxylon ammodendron forest biomass estimation method based on unmanned aerial vehicle multi-source data collaborative inversion, and relates to the technical field of environment remote sensing and information, and the method comprises the steps: obtaining a training sample set; obtaining unmanned aerial vehicle multispectral image data and unmanned aerial vehicle laser radar point cloud data of each training sample plot; extracting a first type of feature set comprising vegetation index features and texture features; extracting a second type of feature set comprising a height feature and a density feature; fusing the two types of feature sets to form a joint feature set; screening the joint feature set to construct a target feature subset; and carrying out model training by utilizing a machine learning algorithm to obtain an above-ground biomass estimation model. According to the method, the technical problem that the existing single remote sensing data source is low in precision in estimating the biomass on the desert sparse haloxylon ammodendron forest land is solved, high-precision and automatic estimation from the sample land to the regional scale is realized, and technical support is provided for accurate accounting of the desert carbon reserve in the arid region.

The application discloses a forest growth parameter prediction method based on a cascade recurrent network, and comprises the following steps: collecting artificial measurement growth parameters of trees in a forest sample plot at multiple time nodes; acquiring the growth parameters of the trees through collected laser point cloud data; sorting the growth parameters according to data collection time to obtain a time sequence set of single-tree growth parameters; training a forest growth parameter prediction deep learning network by using the time sequence set of single-tree growth parameters; and predicting single-tree growth parameters corresponding to future time nodes by using the trained forest growth parameter prediction deep learning network. The application combines laser radar scanning and artificial sample plot measurement data to construct a training set, and compared with all artificial manual measurement data, the cost is lower and the efficiency is higher. Meanwhile, the future growth parameters of the trees can be effectively predicted, and the prediction accuracy is high.

The invention discloses a micro-plastic soil ecological risk assessment method and device, a medium and a product, and relates to the field of micro-plastic soil ecological effect assessment.The method comprises the steps that sampling is conducted in a to-be-assessed sample plot, and biological repeated samples are obtained; the method comprises the following steps: treating a biological repeated sample by using a co-extraction method to obtain total DNA of soil; performing high-throughput sequencing of amplicon genes on the total DNA of the soil to obtain original data; preprocessing the original data, and constructing a species abundance matrix; constructing a transboundary co-occurrence network according to the species abundance matrix; respectively calculating a first network topology index and a second network topology index; and according to the first network topology index and the second network topology index, determining a comprehensive ecological effect index of the to-be-evaluated sample plot by using a comprehensive ecological effect index determination model so as to evaluate the micro-plastic soil ecological risk. According to the invention, the integrity and accuracy of micro-plastic soil ecological risk assessment are improved.

The application discloses an odor receptor derivative peptide capable of effectively monitoring sex pheromone of Spodoptera frugiperda and a biosensor, and belongs to the field of biosensors. At present, the monitoring of Spodoptera frugiperda is usually performed by means of sample plot investigation, sex lures and the like, and problems of poor timeliness, high labor cost and insufficient data continuity exist. In order to solve the above problems, the pheromone binding protein derivative peptide provided by the application has an amino acid sequence as shown in SEQ ID NO: 5, the biosensor comprises a substrate and an interdigital electrode, single-walled carbon nanotubes are attached to the electrode, and the odor receptor derivative peptide is further connected. The sensor can specifically detect the sex pheromone of Spodoptera frugiperda and has no response to 17 kinds of plant volatiles, and can detect the sex pheromone released by as low as two live female Spodoptera frugiperda under laboratory conditions. The sensor has application potential in the fields of early-stage pest monitoring, improvement of prevention and control efficiency and guarantee of ecological safety.

The invention belongs to the technical field of forest carbon sink metering and monitoring, and particularly discloses a forestry carbon sink dynamic baseline investigation method based on a historical sample plot. The method disclosed by the invention comprises the following steps: collecting historical sample plot data in different periods, and integrating the historical sample plot data to a data platform for storage management; a project sample plot is arranged in the carbon sink project area and investigated, and project sample plot data is obtained; performing spatial analysis by using a geographic information system, and selecting a historical sample plot of which the relative error of tree species composition and stand density does not exceed 15% from a data platform as a contrast sample plot; fitting a growth curve of carbon reserves along with forest age or time by adopting a statistical model based on historical data of a contrast sample plot as a dynamic baseline; and calculating a difference value between the project carbon reserves and the dynamic baseline carbon reserves as the carbon sink quantity. According to the method, historical sample plot data can be fully utilized, the cost is reduced, and the accuracy, flexibility and adaptability of carbon sink calculation are improved. The method is suitable for forest carbon sink metering and carbon sink project monitoring.

This invention relates to a LiDAR-based non-destructive method for estimating stand biomass in complex forests, comprising: dividing the sample plot area to be tested into several small quadrats; locating the CGCS2000 geographic coordinates of the center point of each tree within each quadrat; calculating the maximum crown length of each tree; uniformly arranging several sampling points within each quadrat, scanning the trees within the quadrat at different angles using LiDAR, obtaining the relative coordinates (TLS) of the trees at each sampling point within each quadrat, and stitching them together; converting to the CGCS2000 coordinate system to obtain the complete tree point cloud of the sample plot; obtaining the point clouds of trees of different species within the sample plot using TLS and CORS-RTK; and estimating the stand volume and biomass of any single tree and all trees within the sample plot.

This disclosure relates to a data processing method, specifically a regional data processing method, apparatus, storage medium, and electronic device. The regional data processing method includes: responding to a user's input operation, acquiring regional data of a target region corresponding to the input operation; the regional data includes topographic data of the target region and soil data of sample plots within the target region; rasterizing the target region to obtain raster points, and determining factor data of the dominant micro-site features corresponding to the raster points based on the regional data; the dominant micro-site features include topographic factors and soil factors; determining the micro-site classification result corresponding to the raster points based on the factor data, and drawing a spatial distribution map of micro-site types in the target region based on the raster points and the micro-site classification result. The regional data processing method provided by this disclosure can conveniently, accurately, and intuitively obtain regional micro-site type classification results.

The utility model belongs to the field of ecological environment, and particularly relates to a device for vertically determining a quadrat center point in a riverside and lakeside zone, which comprises a support frame, a support rod and a laser transmitter, a fixing structure is arranged on the laser transmitter, the laser transmitter is connected on the support rod through the fixing structure, and the support rod is connected with the support frame; the supporting rods are provided with a plurality of sections, and the adjacent supporting rods are movably connected. The laser transmitter is arranged on the supporting rod, so that the problem that the error of the quadrat center point is large due to the fact that the vertical investigation quadrat center point needs to be determined through manual horizontal wire pulling in existing river and lakeside shoreline monitoring work is solved; by arranging the fixing structure on the laser transmitter, the purpose of flexibly adjusting the height of the laser transmitter can be achieved; and by arranging a plurality of sections of supporting rods which can be connected and arranging an adjuster on the supporting frame, the device is convenient to store and carry.