40 results about "Vegetation height" patented technology

The invention discloses an ecological restoration area carbon sink increment real-time prediction method and system based on artificial intelligence, and the method comprises the following steps: multi-source data collection: obtaining a monthly vegetation coverage image through a satellite remote sensing platform, collecting the sensor data of soil temperature and humidity, air CO2 concentration and the like through a laid ground sensor network, and carrying out the real-time prediction of the carbon sink increment of an ecological restoration area; adopting an unmanned aerial vehicle laser radar to obtain vegetation canopy point cloud data according to a preset period, and collecting biomass actual measurement data of a restoration area in a historical preset age limit; preprocessing data, performing radiometric calibration, atmospheric correction and cutting splicing on a monthly vegetation coverage image acquired by a satellite remote sensing platform, and extracting a vegetation coverage and vegetation index time sequence; denoising, ground point separation and single tree canopy segmentation are carried out on vegetation canopy point cloud data acquired by the unmanned aerial vehicle laser radar, and the height and crown breadth of single plant vegetation are calculated. According to the invention, carbon sink increment prediction can be realized more accurately.

The invention relates to the technical field of power system safe operation safety and intelligent monitoring, and discloses a vegetation intrusion risk assessment method and system for a power transmission corridor and electronic equipment, and the method comprises the steps: obtaining multi-source remote sensing data and meteorological data of vegetation in a preset range of a target power transmission corridor; performing data fusion on the multi-source remote sensing data to obtain fused data; extracting a vegetation height change sequence based on the fused data, and performing prediction based on the vegetation height change sequence and the meteorological data to obtain a future time sequence height change curve; obtaining engineering parameters of a target power transmission corridor, and calculating a vegetation invasion risk value based on the engineering parameters and a future time sequence height change curve; and performing risk assessment based on the vegetation invasion risk value. The method breaks through the limitation of traditional static monitoring, integrates InSAR time sequence analysis and a machine learning algorithm, realizes vegetation growth dynamic modeling and future trend prediction, realizes risk level quantitative evaluation in combination with power transmission line structure parameters, and can provide technical support for power grid risk early warning.

The invention relates to an overhead line vegetation superelevation hidden danger early warning method, device and equipment and a storage medium. The method comprises the steps that the offset distance and the relative height between vegetation of an area where an overhead line is located and an overhead line conductor and a target detection point corresponding to the vegetation are obtained, and the offset distance represents the distance of the vegetation away from the vertical line to the ground of the overhead line conductor in the vertical section direction of a horizontal connecting line between adjacent overhead line towers; screening out a target vegetation superelevation hidden danger detection model matched with the position point from a plurality of constructed vegetation superelevation hidden danger detection models; inputting the offset distance and the relative height into a target vegetation superelevation hidden danger detection model, detecting whether the vegetation has a vegetation superelevation hidden danger or not, and obtaining a vegetation superelevation hidden danger risk detection result; and pushing early warning information under the condition that the vegetation superelevation hidden danger risk detection result represents that the vegetation superelevation hidden danger exists. The method is beneficial for improving the reliability of vegetation height exceeding hidden danger early warning of the power transmission line.

This invention discloses a method for inverting the height of low vegetation based on an adaptive polarization decomposition algorithm in the field of remote sensing technology. The method first preprocesses the polarimetric interferometric SAR image and constructs a cross-covariance matrix. Then, it extracts the volume scattering phase and complex coherence coefficient using a polarization decomposition algorithm with an adaptive weighting factor. Finally, it extracts the even-order scattering phase and complex coherence coefficient using Freeman decomposition. After phase unwrapping, the vegetation height is calculated using the phase difference method. This invention overcomes the bottleneck of traditional RVOG models in low vegetation scenarios where the scattering mechanism coupling is difficult to separate, effectively reducing the aliasing effect of volume scattering and surface scattering, and achieving high-precision inversion of low vegetation height.

The application provides a kind of geological disaster post-vegetation recovery monitoring method based on high-resolution remote sensing image, comprising: constructing fusion type "spectrum-vegetation index-texture" feature set;In sunny weather conditions, more evenly select n typical vegetation samples in the target area;Construct bagging type ensemble learning model;Analysis of the obvious differences of arbor, shrub and herbaceous vegetation in vegetation height, single plant vegetation horizontal projection coverage area, root depth and root extension range;By analyzing the vegetation change rate index and vegetation type change rate index of several continuous time phase remote sensing images according to the time phase change information, the vegetation recovery of the target area is quantitatively analyzed, so as to effectively improve the accuracy of pixel classification method, greatly improve the accuracy of quantitative analysis of vegetation in the target area, and greatly reduce the cost of long-term accurate monitoring.

The invention provides an automatic lifting height-adjustable vehicle drive axle. The automatic lifting height-adjustable vehicle drive axle comprises the steps that a specified area is scanned through the environment perception technology, vegetation growth characteristics in the specified area are constructed, and the height lifting range of a working vehicle in the specified area is determined; adjusting a lifting adaptation parameter of the working vehicle based on the height lifting range, drawing vegetation fluctuation information of the specified area according to the vegetation growth characteristics, and calling the corresponding lifting adaptation parameter according to the advancing position of the working vehicle to determine the adaptation height of the working vehicle, and controlling the speed of the working vehicle based on the height adjustment duration, adjusting the working vehicle to an adaptive height, obtaining vegetation height information through an environment perception technology, judging a height difference needing to be adjusted through algorithm calculation, and adjusting the height of the drive axle through mechanical control.

The invention discloses a GEDI point cloud and Sentinel-2 optical image combined intelligent identification method for corn and sorghum planting areas, and relates to the technical field of agricultural remote sensing, the technical scheme is characterized in that vegetation height features extracted by GEDI laser radar data are utilized to train a first machine learning model, rough classification is carried out on tall and dwarf crops, and a first machine learning model is established; generating a preliminary crop height label; on the basis of a Sentinel-2 time sequence image, calculating a novel sorghum-corn identification index (SCDI), and then training a second model by using an SCDI value and a preliminary label to realize fine distinguishing of sorghum and corn; and finally, a final classification model is trained in combination with multi-source features, and a high-precision automatic sorghum distribution diagram is generated. According to the method, the structure and spectral information are integrated, the recognition precision is remarkably improved, the whole process is highly automatic, large-area-scale efficient application can be realized by relying on cloud computing platforms such as a Google Earth Engine (GEE), and the method has strong cross-area application potential.

The present invention relates to a data processing method, device, and system in a wireless communication system, and relates to the field of wireless communication. The method includes obtaining a falling speed based on a temporary device; when the falling speed exceeds a preset anti-impact threshold, determining whether there is vegetation at the hovering position based on geographic information; when vegetation exists at the hovering position, determining the vegetation type based on the geographic information and the hovering position; determining the vegetation height based on the vegetation type, and obtaining the falling time based on the temporary device; determining the falling height based on the falling time and falling speed; and when the falling height is consistent with the vegetation height, controlling a preset temporary device to deploy a preset buffer device. The present application has the effect of improving the stability of wireless communication.

The invention relates to a data processing method, equipment and system in a wireless communication system, and relates to the field of wireless communication, and the method comprises the steps: obtaining a falling body speed based on a temporary device; when the falling body speed is higher than a preset anti-impact threshold value, whether vegetation exists at the hovering position or not is judged according to the geographic information; when the vegetation exists at the hovering position, determining the vegetation type according to the geographic information and the hovering position; determining the vegetation height according to the vegetation type, and obtaining the falling time based on the temporary device; determining the height of the falling body according to the falling body time and the falling body speed; and when the height of the falling body is consistent with the height of the vegetation, the preset temporary device is controlled to unfold the preset buffer device. The application has the effect of improving the stability of wireless communication.

The embodiment of the invention provides mobile monitoring equipment for outdoor environment testing, and relates to the field of garden equipment. Comprising a mounting seat, a connecting frame, a first lead screw, a lifting seat, a longitudinal measuring part, a transverse measuring part and a driving part, the number of the mounting seats is two, and the connecting frame is mounted between the two mounting seats; the first lead screw is rotationally mounted between the two mounting seats; the lifting seat is in threaded connection with the first lead screw; the longitudinal measuring part is mounted on the lifting seat and can be controlled to move in the vertical direction for measurement; the transverse measuring part is mounted on the lifting seat and can be controlled to move in the horizontal direction for measurement; the longitudinal measuring part is a first measuring arm; the transverse measuring part comprises a transverse frame, a second lead screw, a second motor and a second measuring arm. It can be guaranteed that vegetation height and width measurement is stable and accurate, and detection of vegetation of different specifications is adapted. And the system has the functions of collecting environment temperature and humidity and wind speed data and sampling soil.

The invention discloses a high-precision mountain area river course roughness extraction method based on a multi-source remote sensing image. Comprising the steps of S1, multi-source remote sensing image data acquisition and data preprocessing, S2, riverway center line extraction and riverway range determination based on remote sensing images, S3, vegetation coverage calculation and riverway vegetation region identification, 4, riverway vegetation height calculation, S5, hydraulic radius and water depth calculation, and S6, riverway roughness inversion based on vegetation-hydraulic parameters. The method is low in cost and high in timeliness, large-scale field actual measurement is not needed, multi-source remote sensing images are mainly adopted, and the data acquisition cost is obviously reduced; mountain area river channel parameter extraction can be realized, the period is shortened to 1-2 weeks, and the dynamic monitoring requirement is met; reliable parameters are provided for high-precision hydrological simulation; the method is wide in applicability and high in expansibility, and is suitable for mountainous river channels with different terrains and different vegetation types (herbaceous plants, shrubs and arbors); inSAR deformation data can be added subsequently, and a river channel erosion and deposition height change monitoring function is expanded.

The present invention provides a method for correcting grassland vegetation height using radar observations based on wind speed data. The method includes a three-dimensional laser scanning radar positioned above the vegetation canopy, a wind speed sensor positioned above the vegetation canopy height, and a data collector connected to the three-dimensional laser scanning radar and the wind speed sensor. The method uses real-time wind speed data to determine whether the grass height retrieved by the laser radar is subject to wind disturbances, and automatically corrects the grass height value retrieved by the laser radar based on the wind speed. Compared to methods that do not correct grass height retrieved by the laser radar based on wind speed data, this method can quickly and accurately correct abnormal grass height data that is subject to wind disturbances, reduce grass height deviations caused by wind disturbances, and improve the data quality and reliability of the final grass height retrieval. This method provides strong support for further precise detection and research on the application of laser radar for grass height retrieval in the ecological field.

The application discloses an ecological restoration area carbon sink increment real-time prediction method and system based on artificial intelligence, which comprises the following steps: multi-source data acquisition, obtaining monthly vegetation coverage images through a satellite remote sensing platform, collecting soil temperature and humidity, air CO2 concentration and other sensor data by using a laid ground sensor network, obtaining vegetation canopy point cloud data by using a UAV laser radar according to a preset period, and collecting biomass measured data of the restoration area in a preset time limit; data preprocessing, performing radiation calibration, atmospheric correction and cutting and splicing on the monthly vegetation coverage images obtained by the satellite remote sensing platform, and extracting vegetation coverage and vegetation index time series; carrying out denoising, ground point separation and single tree crown segmentation on the vegetation canopy point cloud data obtained by the UAV laser radar, and calculating single vegetation height and crown width. The application can more accurately realize carbon sink increment prediction.

The application provides a forest fire spreading control method, a control device and a forest fire prevention system. The method comprises the following steps: acquiring the maximum transverse wind volume, the crown angle range and the outside vegetation height of the fire prevention forest belt of a forest area, the crown angle range being the range of the angle between the crown of all the vegetation of the fire prevention forest belt and the ground, the outside vegetation height being the average height of the vegetation of the forest area, and the fire spreading direction being the direction of the fire spreading path of the fire approaching the forest area through the fire prevention forest belt; calculating the width of the fire prevention forest belt according to the crown angle range and the outside vegetation height; determining the target maximum fire spreading range according to the maximum transverse wind volume and the maximum fire spreading range table; and determining the width of the fire spreading path according to the target maximum fire spreading range, wherein the width of the fire spreading path is greater than the target maximum fire spreading range, thereby solving the problem that the forest fire prevention technology in the prior art is difficult to control the fire spreading of large-scale fires.

This invention provides a method, apparatus, and equipment for monitoring vegetation in power plants based on unmanned aerial vehicle (UAV) remote sensing. The method includes: acquiring distribution data of power generation equipment in a target area; dividing the target area into monitoring zones based on the power generation equipment distribution data; collecting data from the monitoring zones via UAV remote sensing according to a preset planned flight path to obtain initial multi-source data, which includes multispectral data and visible light data; preprocessing the initial multi-source data to obtain target multi-source data; obtaining vegetation growth indicators based on the target multi-source data, which include normalized vegetation index (NVI), vegetation cover, and vegetation height; and evaluating the vegetation in the power plant based on the vegetation growth indicators to obtain the power plant vegetation monitoring results. This invention can improve the accuracy, efficiency, and timeliness of power plant vegetation monitoring.

The application discloses a TECIS full waveform laser radar vegetation height extraction method, system and equipment, relates to the field of vegetation height calculation, and comprises the following steps: performing primary signal interception on the TECIS full waveform laser radar echo waveform of a target vegetation area to reduce data redundancy, and then performing secondary signal interception to preliminarily determine a signal search range; in combination with a preset multi-scale noise signal discrimination condition set and an adaptive noise coefficient selection method, an optimal signal threshold is adaptively determined, and then a final waveform effective signal range is determined, finally, a ground position and a crown position are determined, and after height difference calculation, the vegetation height is obtained. The adaptive threshold determination and waveform effective signal range extraction are realized based on the noise coefficient adaptive selection method, the limitation that an experienced noise coefficient selection depends on a large range of synchronous reference data can be eliminated, the applicability is improved, the vegetation height estimation precision is improved, and the production of a TECIS data large-range vegetation height product can be realized.

The application discloses an ecological garden greening design optimization method and relates to the technical field of garden greening design. By constructing an initial visual field characteristic vector FΩ, comprehensive extraction and integration of visual experience elements are realized. Further, by constructing an optimal vegetation height adjustment vector Hopt and combining a visual field obstacle matrix Um generated by fitting, not only can the key vegetation unit causing actual obstruction to the visual line of the visual point be accurately identified, but also the influence can be quantified as a viewing obstacle index U(k), and an optimized vegetation optimization density vector Pp is obtained accordingly, and finally a comprehensive visual field optimization index Q is calculated. The above processing process changes the traditional garden greening design relying on experience-based judgment into a systematic optimization process based on data driving and model reasoning, has obvious advantages in computability, structural expressiveness and visual smoothness, and significantly improves the scientificity and decision-making efficiency of the garden greening design scheme.

Methods, non-transitory computer-readable media, and property analysis systems are disclosed that extract vegetation location information from overhead and LIDAR images associated with a geographic location. The images are aligned based on the extracted vegetation information. Models are applied to the aligned images based on vegetation height information extracted from the LIDAR images. A graph is then generated based on a result of the application of the models. The graph represents a relationship between vegetation, one or more buildings, and one or more fire pathways associated with the geographic location. A wildfire risk score generated based on the graph is then output for the buildings via a GUI. Thus, the disclosed technology applies wildfire spread models and graphs to vegetation and buildings identified in aligned overhead and LIDAR imagery to provide relatively accurate wildfire risk assessment to insurers and homeowners and thereby facilitate informed decision-making and preventative measures.

The invention provides an ecological restoration method and system along a transmission and transformation project in a stony mountainous area, and the method comprises the steps: dividing a target region into a core protection region, a slope disturbance region and a restoration region along the project based on unmanned aerial vehicle aerial survey and ground actual measurement results, thereby providing a zoning basis for the targeted reinforcement of soil and the determination of vegetation types, and improving the reliability of the ecological restoration of the transmission and transformation project along the stony mountainous area. A soil strengthening strategy is determined based on area characteristics of a core protection area, a side slope disturbance area and a restoration area along a project, physicochemical properties of stone mountainous area soil can be remarkably improved, an adaptive environment is provided for follow-up vegetation rooting and growth, and after soil strengthening is completed, on the basis of monitoring data of a target area and in combination with a project line, a soil strengthening strategy is established. A directional planting strategy for the vegetation type of the target area is determined, the planting strategy is designed clearly in combination with engineering line requirements, the vegetation height and the root depth can be controlled in a targeted mode, and the core contradiction between ecological restoration and power transmission and transformation safety is solved from the source.

The invention relates to an urban crown distribution calculation method and device based on Gaofen-7 satellite images, and the method comprises the steps: obtaining the Gaofen-7 satellite images covering the same urban region in winter and summer, and carrying out the preprocessing of the Gaofen-7 satellite images, so as to obtain image data meeting a preset condition; generating an earth surface digital model grid file based on the image data, obtaining a vegetation height, and calculating a normalized vegetation index based on the image data to extract a vegetation range; based on the vegetation range and the vegetation height, trees and grass and shrubs are distinguished through a preset height threshold value, winter crown distribution and summer crown distribution are determined, and crown distribution of evergreen trees and deciduous trees is calculated. Therefore, the problems that in the related technology, due to the fact that existing open-source remote sensing data cannot meet the high-spatial-resolution monitoring requirement and an unmanned aerial vehicle scheme is difficult to achieve city scale application, city tree crown seasonal coverage changes are difficult to accurately quantify, and then city land coverage mapping and city thermal environment simulation accuracy are limited are solved.

The invention discloses a TECIS full-waveform laser radar vegetation height extraction method, system and device, and relates to the field of vegetation height calculation, and the method comprises the steps: carrying out the primary signal interception of a TECIS full-waveform laser radar echo waveform of a target vegetation region to reduce data redundancy, and carrying out the secondary signal interception to preliminarily determine a signal search range; and in combination with a preset multi-scale noise signal discrimination condition set and an adaptive noise coefficient selection method, adaptively determining an optimal signal threshold, further determining a final waveform effective signal range, finally determining a ground position and a crown position, and performing elevation difference calculation to obtain a vegetation height. According to the method, adaptive threshold determination and waveform effective signal range extraction are realized based on a noise coefficient adaptive selection method, the limitation that empirical noise coefficient selection depends on large-range synchronous reference data can be eliminated, the applicability is improved, the vegetation height estimation precision is improved, and production of TECIS data large-range vegetation height products is facilitated.

The invention provides an alpine grassland biomass obtaining method and system and a storage medium, and belongs to the technical field of data processing, and the method comprises the steps: obtaining the vegetation height, vegetation coverage and aboveground biomass of each sample region in each aerial image; according to the actually measured aboveground biomass and the spectral information of the pixel points in each sampling area, obtaining the aboveground biomass predicted value of each sampling area of all aerial images; and according to the above-ground biomass predicted values of all the sampling areas in each remote sensing image and the spectral information of the pixel points corresponding to the sampling areas, obtaining above-ground biomass of the areas corresponding to the pixel points in the remote sensing images. The objective of the invention is to solve the problem that the difference between a prediction result and an actual result is too large because the difference between the scale of a remote sensing satellite and the scale of a ground sampling quadrat is too large and a saturation phenomenon occurs when a single vegetation index is adopted to fit the above-ground biomass when an above-ground biomass upscaling estimation model is established at present.

The utility model relates to the technical field of vegetation monitoring, and discloses a vegetation growth condition monitoring device which comprises a stand column, a plug is fixedly connected to the bottom of the stand column, a photovoltaic panel is fixedly connected to the top of the stand column, a circular ring is fixedly connected to the lower middle portion of the outer wall of the stand column, and a plurality of fixing plates are fixedly connected to the outer wall of the circular ring at equal intervals. A sliding groove is formed in one side of each fixing plate, a sliding plate is slidably connected to the interior of each sliding groove, a plurality of adjusting holes are formed in one side of each fixing plate, and an adjusting bolt is rotatably connected to one side of one end of each sliding plate. According to the utility model, the plug is inserted into soil, the photovoltaic panel absorbs solar energy to supply power, the position of the sliding plate in the sliding groove is adjusted, and the threaded rod is rotated to drive the support column to lift, so that the monitoring device is stably installed, the adaptation to different terrains and vegetation heights is realized, and the stable monitoring work is ensured.

The present disclosure provides an accurate inversion method and system for aboveground biomass of urban vegetations considering vegetation types. The method includes: based on different research regions, performing corresponding biomass calculation after plot sampling; preprocessing high-resolution optical data and space-borne photon counting LiDAR data, performing fine extraction on urban vegetation information, extracting spectral features of high-resolution remote-sensing image data and constructing an optical-biomass inversion sub-model, extracting vegetation height data of the space-borne photon counting LiDAR data and constructing a space-borne LiDAR-biomass inversion sub-model; with the support of the information on fine classification on the urban vegetation types, performing integration on the biomass inversion sub-models; based on the sample plot biomass data of the urban vegetations, obtaining an integrated inversion model fusing the horizontal information and the three-dimensional structure information of the urban land surface vegetations by training to perform accurate mapping of the aboveground biomass of the urban vegetations.

The present invention relates to the technical field of moisture content measurement systems, and particularly relates to a dynamic measurement sensing system for the moisture content of forest combustibles, including a regional height measurement unit. The forest area to be measured for moisture content is denoted as the target area, and a drone is controlled to measure the ground height. There are multiple uniformly distributed height measurement points preset in the target area, and the height values of each height measurement point are analyzed and calculated; a contour construction unit constructs a three-dimensional contour model within the target area by using the Kriging method based on the ground height of each point on the flight trajectory of the drone and the height values of each height measurement point; a regional classification unit measures the vegetation height value during the flight of the drone and divides the target area into a surface combustible area and a crown combustible area; a dynamic measurement unit integrates the surface combustible areas based on the type of unit area to obtain a surface coverage area, and dynamically adjusts the installation position of the moisture content sensor within the surface coverage area.

The invention provides a virtual natural environment construction method, apparatus and device, a medium and a program. The method comprises the steps of obtaining a two-dimensional remote sensing image and a laser point cloud height program sequence of a natural environment; performing feature alignment and channel dimension fusion on the two-dimensional remote sensing image and the laser point cloud height program sequence to obtain an initial fusion feature; inputting the initial fusion features into a feature extraction network integrating a multi-head self-attention mechanism and a lightweight residual network to obtain deep features; performing multi-scale feature fusion on the deep features by adopting spatial pyramid pooling to obtain unified feature representation; and completing identification and segmentation of complex ground features in the natural environment based on unified feature representation, and generating a three-dimensional model of the virtual natural environment in combination with an identification result. According to the scheme, by fusing the elevation features of the laser point cloud, three-dimensional information such as vegetation height and water depth is supplemented, and the recognition precision of fuzzy ground features is improved.

The present invention discloses a method for extracting artificial forests remotely sensed based on the vertical structural complexity of satellite-borne laser waveforms. The method comprises the following steps: step 1, obtaining L1B geolocation waveforms, L2A spot-scale global surface height and vegetation height, and L2B spot-scale global crown cover and vertical profile index product data of a satellite-borne full-waveform laser radar (GEDI) in a forest area; step 2, performing a preprocessing operation on the acquired data to obtain high-quality satellite-borne laser radar spots in the area; step 3, performing feature extraction, calculating the information entropy of a single spot echo waveform and the waveform similarity of adjacent spots from the L1B geolocation waveform data, extracting canopy cover and leaf height diversity index from the L2B spot-scale global crown cover and vertical profile index, constructing a linear model of height percentiles in the L2A spot-scale global surface height and vegetation height data, and calculating the slope of the linear model as a feature; step 4, inputting the above five features and using a random forest model to classify and extract artificial forests and natural forests; and step 5, evaluating the extraction accuracy of artificial forests and the importance of features. The present invention obtains multiple vertical structural features of the forest through the waveform information of the satellite-borne laser radar, and combines it with the random forest model to extract the artificial forest stands. It can fully utilize the vertical structural characteristics of the satellite-borne laser waveform in the forest area to distinguish between artificial forests and natural forests.

The invention discloses an ecological landscaping design optimization method, relates to the technical field of landscaping design, and realizes comprehensive extraction and integration of factors influencing visual experience by constructing an initial view feature vector F [omega]. Furthermore, by constructing an optimal vegetation height adjustment vector Hopt and combining with a view barrier matrix Um of a view spot generated by fitting, not only can a key vegetation unit which causes actual shielding to a viewpoint sight be accurately identified, but also the influence can be quantified as a view barrier index U (k), and an optimized vegetation optimization density vector Pp is obtained accordingly, so that the optimal vegetation height adjustment vector Pp is obtained. And finally calculating a comprehensive view optimization index Q. According to the processing process, the landscaping design is converted into a system optimization process based on data driving and model reasoning from traditional empirical judgment, the method has obvious advantages in the aspects of calculability, structure expressivity and visual patency improvement, and the scientificity and decision-making efficiency of a landscaping design scheme are remarkably improved.

The invention discloses a vegetation pruning method and device, equipment, a storage medium and a product. The method comprises the steps of obtaining street lamp parameters and vegetation parameters of a target road section; wherein the street lamp parameters comprise street lamp height, and the vegetation parameters comprise leaf density and vegetation height; calculating a light attenuation factor by adopting a radiation transfer equation based on the street lamp parameters and the vegetation parameters; when the light attenuation factor is larger than a preset pruning threshold value, multi-round iteration is carried out on the vegetation pruning scheme, and the vegetation pruning scheme enabling the road surface lighting condition to be optimal is selected as the optimal vegetation pruning scheme so that vegetation pruning can be carried out on the target road section according to the optimal vegetation pruning scheme; wherein the road surface illumination condition and the light attenuation factor are in a negative correlation relationship. By considering the influence of vegetation on the lighting effect of the street lamp, the vegetation pruning scheme is optimized for guiding vegetation pruning, the lighting effect of the street lamp is improved, and therefore the energy utilization rate of the street lamp is effectively increased.

The application discloses a low-lying vegetation vertical height inversion method based on heavy tail features, and belongs to the field of vegetation height calculation. The method is as follows: based on the waveform peak point of the single-peak echo of a spaceborne laser radar, a single-peak waveform curve is segmented and fitted through a preset Gaussian-Cauchy model to obtain optimal fitting parameters of the single-peak waveform curve; according to a preset waveform feature index system, the optimal fitting parameters are mapped to a preset feature dimension to extract several heavy tail feature vectors of the single-peak waveform curve; a waveform spot provided by the echo signal is matched with a preset canopy height model in a coordinate system to obtain a real average height of low-lying vegetation, and by one-to-one correspondence between the real average height and the heavy tail feature vectors, an estimated result of the vertical height of the low-lying vegetation is obtained. Therefore, by implementing the application, the problem of large errors in the height calculation of low-lying vegetation in the prior art can be solved.