20 results about "Vegetation types" patented technology

This invention belongs to the field of ecological environment monitoring, specifically a dynamic monitoring method for carbon flux in wetland ecosystems that integrates eddy covariance (ECD) and satellite remote sensing. The method includes the following steps: S1: Monitoring station establishment: Based on the ECD method, flux monitoring stations are established in typical areas of lake wetlands. The selected typical areas should represent the main ecological characteristics and vegetation types of the wetlands to ensure the representativeness and reliability of the monitoring data. This invention combines ECD observation with satellite remote sensing technology to achieve real-time visual monitoring of carbon flux. It provides both data accumulation and visualization, improving the accuracy and reliability of wetland carbon source / sink assessment results. Simultaneously, the visualized spatial distribution of carbon sources / sinks facilitates the detection of changes in carbon sources / sinks in wetland areas, enabling timely detection and response to changes in wetland ecosystems, and scientifically increasing wetland carbon sinks through artificial regulation.

The present application relates to a kind of grassland vegetation coverage measuring device and measuring method, device includes connecting column, lifting mechanism, host computer and camera, host computer is connected with lifting mechanism and camera communication;Connecting column includes vertical connection column and cross bar, column one end is fixed on ground, host computer is installed on column;Lifting mechanism includes lifting block and telescopic machine, the output shaft of telescopic machine connects lifting block, the top of lifting block is equipped with guide shaft, the one end of cross bar is equipped with with guide shaft cooperation installation guide hole, camera is connected with lifting block;Camera changes observation height according to vegetation type.Compared with prior art, the height of camera is controlled by lifting mechanism in the present application, when using, camera hangs under cross bar to collect information, when height needs to be adjusted, telescopic machine can be started by host computer or remote signal control, drive camera to rise or fall, so as to change the effect of camera height control collection range, and the measuring effect is better.

The invention discloses a farmland tree remote sensing image optimization method, relates to the technical field of remote sensing image processing, and solves the problems that in an existing optical image processing technology, a data processing flow is too complex in an optical image processing process, and wrong division and missing division are likely to occur, so that farmland tree images in optical images are not clear, and the image quality is poor. Therefore, the application of optical images in agricultural operation activities is restricted. A farmland tree remote sensing image optimization method comprises a data acquisition stage, a vegetation and non-vegetation distinguishing stage, a vegetation type distinguishing stage, a farmland tree and non-farmland tree distinguishing stage and a farmland tree remote sensing image optimization stage. The method is suitable for the fields of farmland tree remote sensing monitoring, ecology, remote sensing technology, geographic information systems and the like.

This invention discloses a coupled simulation and prediction system for regional water and carbon cycle processes, relating to the field of watershed carbon cycle simulation. The system includes a data acquisition module that determines the watershed extent and identifies riverbank areas within a unified grid system. Simultaneously, vegetation type and soil organic carbon are mapped to riverbank grid cells, consistently expressing the spatial location of carbon sources in relation to hydrological watershed migration. This facilitates subsequent clarification of carbon source distribution. Based on a DEM (Digital Elevation Model), flow direction and confluence paths are constructed. Runoff in each grid is calculated under meteorological conditions and gradually converges into the river channel, establishing hydrodynamic transport paths with clear upstream and downstream relationships. This provides a physical carrier for carbon transport. Based on soil organic carbon and vegetation type in the riverbank grids, the system quantifies the differences in carbon supply capacity of different riverbank areas during regional water migration. Riverbank carbon sources are traced along the confluence paths. Spatial weights are constructed using distance attenuation and connectivity, mapping carbon sources to each grid cell. Carbon flux is then coupled with runoff to calculate and accumulate through gradual transport.

The application provides a carbon sink potential prediction method and device fusing remote sensing and model technology. The carbon sink potential prediction method fusing remote sensing and model technology comprises the following steps: inputting multi-temporal remote sensing data into a vegetation type identification model based on historical remote sensing data and historical vegetation type labels to obtain vegetation structure dynamic distribution data; inputting the vegetation structure dynamic distribution data into a regional carbon-nitrogen cycle model based on historical crop distribution data and vegetation and soil dynamic data to obtain a regional carbon-nitrogen cycle dynamic simulation result; and determining carbon sink data according to the regional carbon-nitrogen cycle dynamic simulation result. The application can break through the bottleneck of insufficient regional scale carbon accounting data and high uncertainty, and realize carbon sink maximization.

This invention provides a method, system, device, and medium for identifying vegetation lodging risk around power facilities, relating to the field of power facility risk identification. The method includes: extracting vegetation canopy data of the transmission corridor of the target power facility from satellite multispectral images; calculating the initial tilt angle and lodging direction; obtaining a list of potential lodging areas; acquiring visible light images of the transmission corridor using ground image acquisition equipment; extracting vegetation pixels and obtaining satellite normalized vegetation index values ​​for corresponding locations at multiple time phases to construct a seasonal fluctuation feature sequence; extracting texture features from corresponding locations in the ground visible light image; constructing a fused feature vector and inputting it into a convolutional neural network for vegetation type classification; and calculating the risk identification result for each vegetation pixel by combining the initial tilt angle and lodging direction. This invention improves the accuracy of vegetation lodging risk detection around power facilities.

This invention relates to a method for estimating surface dead combustible load in subtropical forests using multi-source remote sensing and machine learning. The method includes: acquiring and preprocessing multi-source remote sensing data and auxiliary data within a target area; acquiring preprocessed multi-source remote sensing data and auxiliary data; extracting features from the preprocessed multi-source remote sensing data and auxiliary data to obtain feature variables; selecting variables based on the feature variables and measured surface dead combustible load components; constructing a combustible load prediction model using multiple linear regression and machine learning models; evaluating the combustible load prediction model to obtain the optimal model; and using the optimal model to perform regional inversion of surface dead combustible load for all vegetation types within the target area to obtain spatial distribution data of surface dead combustible load. This invention combines multi-source remote sensing with machine learning to provide methodological support for fire risk assessment and precise combustible management in subtropical forests.

The application discloses a fly ash-based vegetation type modular wind-preventing and sand-fixing sand barrier component and belongs to the technical field of desertification prevention and ecological restoration engineering; the component is formed by splicing a plurality of lattice units; the unit comprises a hollow fly ash pipe and fly ash plates connected between the hollow fly ash pipe; and the modular assembly is realized through convex grooves and connecting rods; a seed bag containing soil, fertilizer and seeds is prearranged in the hollow fly ash pipe; and air-permeable and water-permeable holes are formed in the lower part of the pipe wall; the application takes fly ash as a main raw material, is low in cost and environment-friendly; the structure is modular, convenient to assemble and adaptable to topography; the application can effectively improve the survival rate of vegetation and has strong sand-fixing durability by combining physical sand fixation and ecological restoration.

This invention discloses a method for dynamic monitoring and inversion of vegetation moisture based on the GNSS reflection geometric response index, belonging to the field of GNSS remote sensing inversion and surface ecological parameter monitoring technology. This invention solves the problems of existing GNSS-R vegetation moisture indices, such as reliance on empirical normalization, sensitivity to observation geometry, and difficulty in long-term stable comparative analysis. This invention utilizes direct sunlight and surface reflection signals observed by a ground-based GNSS receiver during continuous observation. Based on the response characteristics of the reflection signals to changes in satellite geometry, a vegetation reflection geometric response index is established to characterize the changing features of vegetation moisture status. This invention uses the establishment of a reflection response function and the extraction of geometric sensitivity parameters and stable historical reference states to quantitatively characterize vegetation moisture changes. This reduces the impact of noise interference or abnormal observations, eliminates the need for inversion using external remote sensing products or ground-based measured data, and is suitable for long-term continuous vegetation moisture monitoring in various climatic zones and vegetation types.

The application discloses a method and device for retrieving aboveground vegetation carbon storage, equipment and medium, and relates to the field of ecological monitoring. The method comprises the following steps: obtaining hyperspectral images of a study area; classifying each pixel in the hyperspectral images by using a pre-trained classification model to determine the vegetation type of each pixel; extracting features from the hyperspectral images to determine the remote sensing features of each pixel; retrieving the biomass in the study area by using a retrieval model according to the remote sensing features of each pixel to obtain the biomass of each pixel; the retrieval model is obtained by training a training sample set in advance, the training sample set comprises the remote sensing features and real biomass of each pixel in multiple sample plots, and the real biomass is determined based on the hyperspectral images and three-dimensional structure parameters of the sample plots; and the aboveground vegetation carbon storage of the study area is determined according to the carbon conversion coefficient of each vegetation, the vegetation type and the biomass of each pixel. The application realizes accurate retrieval of the aboveground vegetation carbon storage.

The application discloses a kind of coupling optical and microwave remote sensing's daily scale canopy water content inversion method, belong to ecological environment remote sensing technical field, including: based on canopy radiation transfer model inversion optical image canopy water content data, based on zero-order microwave radiation transfer model and multi-channel collaborative inversion algorithm calculation vegetation optical thickness data;Obtain vegetation optical thickness data, vegetation type, vegetation index and meteorological data, after uniform resolution, construct sample data set;With optical image canopy water content data as true value label, combined with rainfall cumulative effect and vegetation optical thickness lag effect, construct extreme gradient boosting machine learning estimation model;The canopy water content of missing area of optical image is estimated using the model, and the global daily scale, spatiotemporal continuous vegetation canopy water content data set is obtained after filling up.The application uses the above method, realizes the complement of optical and microwave remote sensing advantage, provides reliable technical support for vegetation water condition monitoring.

The application provides a satellite vegetation index fusion method based on optimal interpolation and fast Fourier transform, comprising: obtaining a meteorological vegetation index product and a high-resolution vegetation index product; performing multi-level interpolation processing on the meteorological vegetation index product based on optimal interpolation and plane equation interpolation, taking a specified vegetation index product as a background field, to obtain a vegetation index interpolation; performing fast Fourier transform processing on the high-resolution vegetation index product to eliminate a seam area contained in the high-resolution vegetation index product, to obtain a new high-resolution vegetation index product; and fusing the vegetation index interpolation and texture features corresponding to the new high-resolution vegetation index product, to obtain a vegetation index fusion result, wherein the texture features are used to describe a vegetation index change between two adjacent pixels in the new high-resolution vegetation index product. The application can significantly improve the precision of the vegetation index fusion result in a complex terrain or a region where multiple vegetation types coexist.

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 application relates to the technical field of ecological slope protection engineering, and particularly relates to a method for calculating the cohesion of tree roots at a regional scale. According to the method, the target region is divided into effective units according to the vegetation type and the forest age distribution. For each effective unit, the forest age at the target year is dynamically predicted based on a tree mortality model and a Monte Carlo method. The forest age at the target year is used as the core driving force to calculate the number of roots, the root diameter and the root tensile strength. The root cohesion of each effective unit is calculated by integrating the root cohesion prediction model. Finally, the regional distribution information is obtained by summarizing the root cohesion of each effective unit. The present application builds a model for calculating the cohesion of tree roots, which can dynamically reflect the regional vegetation characteristics, the forest succession process and the spatial heterogeneity. The present application realizes the slope stability analysis at a regional scale and solves the problem of quantitatively evaluating the ecological reinforcement effect of a large-scale slope.

This invention discloses a method for retrieving diurnal canopy water content by coupling optical and microwave remote sensing, belonging to the field of ecological and environmental remote sensing technology. The method includes: retrieving canopy water content data from optical images based on a canopy radiative transfer model; calculating vegetation optical thickness data based on a zero-order microwave radiative transfer model and a multi-channel collaborative inversion algorithm; acquiring vegetation optical thickness data, vegetation type, vegetation index, and meteorological data, and constructing a sample dataset after unifying the resolution; using the canopy water content data from optical images as ground truth labels, and combining the cumulative effect of rainfall and the lag effect of vegetation optical thickness, constructing an extreme gradient boosting machine learning estimation model; using this model to estimate the canopy water content in areas missing from the optical images, and filling in the missing areas to obtain a global diurnal, spatiotemporally continuous vegetation canopy water content dataset. This invention, employing the above method, achieves the complementary advantages of optical and microwave remote sensing, providing reliable technical support for monitoring vegetation moisture status.

The application discloses a vegetation species identification method for strengthening AI computing power and time sequence tracing, comprising the following steps: collecting multi-source remote sensing data and performing pretreatment, constructing a vegetation classification dataset with spatiotemporal alignment and unified resolution; generating a vegetation mask based on the vegetation classification dataset, obtaining a standardized sample slice, and constructing a training dataset in combination with spectral characteristics; performing time-phase processing on the training dataset based on vegetation phenological characteristics, and outputting a preliminary vegetation classification result; performing object-level optimization on the preliminary vegetation classification result, and obtaining an optimized vegetation classification result; correcting the optimized vegetation classification result in combination with terrain data, generating a vegetation species classification map of a target year, and realizing vegetation dynamic change inversion in a specified time period through transfer learning. Therefore, the traditional resolution limit can be broken through, the classification error problem caused by independent use of multi-source data can be solved, the discrimination of complex vegetation types can be significantly improved, and historical vegetation dynamic backtracking analysis can be supported.

The application provides a kind of city small micro green space physical and mental health assessment and landscape element identification method, it is related to park health evaluation technical field, the method is by obtaining the landscape element data, sensory perception data and physical and mental health data of city small micro green space;Landscape element data includes green view rate, sky openness, sound environment data, microclimate parameters and facility configuration, vegetation type;Sensory perception data and physical and mental health data are associated and fused, input predetermined health benefit evaluation model, output key landscape element data and sensory perception that affect physical and mental health, the evaluation result is graphically visualized, shows the physical and mental health score and spatial distribution of each local area.The application solves the problem that the correlation between landscape elements and sensory perception cannot be established, making it difficult to identify key landscape elements affecting the physical and mental health of city small micro green space for targeted improvement.

This invention discloses an automatic generation method for coastal salt marsh vegetation samples based on phenological characteristics and outlier removal. The method includes: acquiring a multi-temporal remote sensing image time-series dataset of the study area; calculating time-series curves of various vegetation indices after preprocessing; constructing phenological characteristic rules for the target salt marsh vegetation based on the time-series curves of the vegetation indices; performing pixel-by-pixel judgment on each pixel of the study area using the phenological characteristic rules to select pixels that conform to the phenological characteristics of various vegetation types, forming initial candidate sample areas for each type of vegetation; constraining the initial candidate sample areas using a priori spatial mask to obtain purified candidate sample areas; generating a pure automated sample set after traversing all candidate samples; and performing a hierarchical classification process based on the generated pure automated sample set. This invention can combine temporal remote sensing characteristics with a hierarchical classification strategy to achieve high-precision identification and long-term temporal change monitoring of coastal salt marsh vegetation.

The present application belongs to the technical field of coastal salt marsh monitoring, and particularly relates to a method for estimating the coverage and biomass of coastal salt marsh herbaceous vegetation and application. The present application takes multispectral satellite images as the main data basis, adopts the normalized vegetation index threshold method to identify the salt marsh herbaceous vegetation coverage area, adopts the computer classification method to identify the salt marsh vegetation type, calculates the coverage of each type of vegetation on each pixel independently to generate a vegetation coverage result file, takes the coverage and biomass of the field investigation as the actual sample data to establish a quantitative inversion model of the coverage and biomass, and quantitatively inverts the biomass of the salt marsh herbaceous vegetation. The present application uses the differences in the vegetation spectral index under different coverages to invert the vegetation coverage from the high-resolution multispectral remote sensing image, and further uses the correlation between the coverage and the biomass to further invert the vegetation biomass, and performs the calculation or estimation pixel by pixel, which is suitable for the large-area and time-space synchronous coverage and biomass estimation of the salt marsh herbaceous vegetation.