25 results about "Vegetation height" patented technology

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.

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 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 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.

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 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 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 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.

The application provides a kind of urban vegetation aboveground biomass accurate inversion method and system considering vegetation type, carry out research area vegetation aboveground biomass calculation, including corresponding biomass calculation after plot sampling according to different research areas;High-resolution optical data and spaceborne photon counting lidar data preprocessing, fine extraction of urban vegetation information, high-resolution remote sensing image data spectral feature extraction and optical-biomass inversion sub-model construction, vegetation height data extraction of spaceborne photon counting lidar data and spaceborne LiDAR-biomass inversion sub-model construction;Under the support of fine classification information of urban surface vegetation, integrate biomass inversion sub-model, obtain integrated inversion model by training urban vegetation biomass sample data, which fuses urban surface vegetation horizontal information and three-dimensional structure information, and carry out large-scale urban vegetation aboveground biomass accurate mapping.

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 application provides a high-cold grassland biomass acquisition method and system and a storage medium, and belongs to the technical field of data processing. The method comprises the following steps: acquiring the vegetation height, vegetation coverage and aboveground biomass of each sample region in each aerial image; obtaining the aboveground biomass prediction value of each sampling region of all aerial images according to the measured aboveground biomass and the spectral information of the pixel points in each sampling region; and obtaining the aboveground biomass of the region corresponding to the pixel points in the remote sensing image according to the aboveground biomass prediction value of all sampling regions in each remote sensing image and the spectral information of the pixel points corresponding to the sampling regions. The application aims to solve the problem that when the current aboveground biomass upscaling estimation model is established, the scale difference between the remote sensing satellite and the ground sampling plot is too large, and the saturation phenomenon occurs when a single vegetation index is used to fit the aboveground biomass, so that the difference between the prediction result and the actual result is too large.

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 utility model discloses an automatic lifting type planting frame for three-dimensional cultivation, which belongs to the technical field of planting frames and comprises a base, a mounting frame is fixedly connected to the top of the base, lifting adjusting structures are arranged on two sides of the mounting frame, and a dismounting support structure is arranged on the top of the base. The lifting adjusting structure comprises two mounting plates, the two mounting plates are symmetrically and fixedly connected to the two sides of the mounting frame, and driving motors are fixedly mounted at the tops of the mounting plates. According to the automatic lifting type planting frame for three-dimensional cultivation, through the arrangement of the lifting adjusting structure, under the action of a threaded rod and a threaded hole block, the lifting plate can be driven to move up and down; at the moment, the position of the lifting plate can be adjusted by controlling the lifting plate to move up and down, so that after the vegetation at the bottom is high, the lifting plate at the top can be adjusted to adapt to the vegetation, and the situation that the survival rate of the vegetation is reduced due to frequent transplanting is avoided.

The utility model discloses a portable grassland vegetation coverage and height measuring device belongs to vegetation coverage height measurement technical field, and this device includes support subassembly, and support subassembly contains at least three groups of support pole of main rod and top hinged, and main rod and support pole common support, support subassembly link telescopic cross bar, and its end portion connects coverage measurement subassembly, main rod sliding link sliding ring, and sliding ring rotation link height measuring board, and height measuring board can turn to horizontal height measurement. The device integrates coverage and height measurement function, need not to carry multiple equipment, and erects conveniently, sliding ring can adjust height measuring board position, adapts to different vegetation height, and limit component prevents sliding ring sliding and guarantees accuracy, and coverage measurement subassembly multi -component cooperation promotes measurement accuracy, and GPS position sensor records coordinates, and is suitable for grassland vegetation measurement.

A composite wetland water cycle simulation method and system based on digital twinning belong to the field of water conservancy informatization and digital twinning technology. In view of the problem of insufficient accuracy caused by ignoring the dynamic change of vegetation and lacking adaptive calibration in traditional wetland water cycle simulation, the method comprises the following steps: constructing a three-dimensional digital base of the wetland and generating an unstructured grid; deploying multispectral sensing equipment to obtain vegetation spectral characteristics, and inversing leaf area index and average vegetation height, and the collection frequency is dynamically triggered by the digital twinning model; establishing a segmented dynamic mapping of the submerged state of vegetation and the resistance of water flow, dynamically calculating the Manning roughness coefficient and the transpiration loss term; substituting into the shallow water equation set to perform water cycle simulation; comparing the simulated water level with the measured water level, and triggering adaptive calibration when the deviation shows statistical consistency, and using the sequential data assimilation algorithm to update the key weight coefficients of the mapping model online. The system corresponds to realize the above steps. The application improves the accuracy and adaptability of composite wetland water level and flow velocity simulation.

The invention discloses a northern Tibetan lake wetland ecological health monitoring model fused with multi-source high-resolution satellite data. The northern Tibetan lake wetland ecological health monitoring model comprises the following steps: multi-source data acquisition: acquiring high-resolution first satellite data, high-resolution second satellite data and high-resolution third satellite data covering a monitoring area and simple and convenient monitoring data; data preprocessing: performing radiation correction, atmospheric correction and de-noising processing on the satellite data; the simple and convenient monitoring data is subjected to standardized arrangement; core feature extraction: extracting water body area, vegetation coverage, soil water content and water body turbidity through satellite data; the water pH value, the vegetation height, the biomass and the water chlorophyll a concentration are simply and conveniently monitored; multi-source data fusion: integrating satellite data with simple and convenient monitoring data at corresponding positions; ecological health index calculation: fusing the data set to construct an ecological health index system, scoring to determine index weight, and calculating an ecological health comprehensive index; comprehensive evaluation of ecological health: comparing the comprehensive index with the standard, determining the ecological health condition of the wetland, and generating a monitoring report.