14 results about "Vegetation remote sensing" patented technology

The invention discloses a differential chlorophyll fluorescence inversion method, system and device and a storage medium, and is applied to the field of vegetation remote sensing and physiological ecological monitoring, and the method comprises the steps: obtaining vegetation state monitoring data of a target region; on the basis of vegetation state monitoring data, asynchronous leaf-changing type evergreen arbors and synchronous deciduous type arbors are identified; carrying out SIF simulation on the asynchronous leaf-changing type evergreen arbors and the synchronous deciduous type arbors by adopting corresponding models respectively; according to the phenological phase function and the stress response function, generating correction factors corresponding to the asynchronous leaf-changing evergreen arbors and the synchronous deciduous arbors; the obtained initial SIF value is optimized through a corresponding correction factor; fusing the optimized SIF values to generate an SIF time sequence product; according to the method, differential model configuration and physical mechanism driven online correction are coupled, so that the problems of model mismatching and physiological mechanism deficiency in heterogeneous vegetation SIF inversion are solved, and an SIF time sequence product which is high in precision and clear in physiological and ecological significance is generated.

This invention discloses a method and apparatus for fine extraction of aquatic vegetation and landscape connectivity assessment in rivers and lakes based on high-resolution remote sensing, belonging to the field of environmental remote sensing monitoring and water ecology assessment technology. It includes: acquiring and preprocessing high-resolution satellite imagery to obtain a standardized remote sensing image dataset; extracting water body masks and aquatic vegetation coverage areas through hierarchical threshold segmentation; constructing a dual-threshold discrimination rule based on the normalized vegetation index and soil-regulated vegetation index to finely divide the distribution areas of floating and submerged plants; constructing a binary raster map of vegetation distribution based on the distribution areas; combining a preset connectivity distance threshold; calculating the landscape connectivity index of vegetation patches based on graph theory; determining the ratio of equivalent connected area to effective connected area; and completing a comprehensive assessment of the ecological effectiveness of the aquatic vegetation community. This invention is mainly used for remote sensing monitoring of aquatic vegetation in urban rivers and lakes and for assessing the effectiveness of water ecology restoration, effectively mitigating spectral interference from complex water body sediment backgrounds.

The invention discloses a plant community structure inversion and ecological restoration strategy generation system and method, and relates to the technical field of ecological monitoring and restoration. The system comprises an unmanned aerial vehicle low-altitude remote sensing module, an image processing and feature extraction module, a machine learning inversion module, an ecological restoration strategy generation module and an image processing and feature extraction module which are connected in sequence and are used for carrying out preprocessing and feature extraction on a vegetation remote sensing image; the machine learning inversion module is internally provided with a trained plant community structure inversion model and is used for inverting species composition and space structure information of the plant community according to the extracted features; and the ecological restoration strategy generation module is used for generating an ecological restoration strategy scheme containing the varieties, the number and the spatial positions of the replanting plants according to the inverted species composition and spatial structure information in combination with a preset ecological restoration target. According to the method, the plant community structure can be quickly, accurately and automatically inverted, and a scientific and operable ecological restoration strategy is generated.

The invention discloses a steppe project disturbance and damage area degradation dominant factor spatialization method, and belongs to the field of ecological environment assessment. The method comprises the following steps of: firstly, acquiring sampling data of soil, vegetation, engineering disturbance and the like of a grassland engineering disturbance and damage area, a Sentinel-2 multispectral image and a meteorological and vegetation remote sensing product; the method comprises the following steps: standardizing and integrating sampled data, constructing a degradation index based on the sampled data, and extracting engineering disturbance related remote sensing features; utilizing correlation analysis, VIF test and recursive feature elimination to screen feature variables, constructing and verifying a machine learning model, and obtaining a degradation index based on remote sensing data; and further identifying the degradation dominant factors through a single-factor grid-by-grid disturbance experiment, grading the contribution degrees of the degradation dominant factors by adopting a quartile method, and finally generating a spatial distribution diagram of the degradation dominant factors and the contribution degrees of the degradation dominant factors in the grassland project disturbance and damage region. According to the method, identification and spatialization expression of the degradation dominant factors in the engineering disturbance and damage area are realized, and a scientific basis is provided for ecological restoration of the grassland.

The invention discloses a method and a system for quantifying a drought transfer retardation effect of soil moisture under drought stress, and the method comprises the following steps: obtaining meteorological data, soil humidity data and vegetation remote sensing observation data of a to-be-monitored region, and carrying out the standardization processing of the obtained data, obtaining a standardized rainfall evapotranspiration index, a standardized soil humidity index and a standardized vegetation index; constructing a three-dimensional joint distribution function based on the standardized rainfall evapotranspiration index, the standardized soil humidity index and the standardized vegetation index; calculating drought transfer time and a drought transfer probability based on the three-dimensional joint distribution function, and calculating a drought transfer index based on the drought transfer time and the drought transfer probability; and calculating the drought transfer retardation rate based on the drought transfer index to complete quantification of the drought transfer retardation effect.

This invention discloses a precision irrigation method for forestry, comprising the following steps: acquiring remote sensing data of vegetation in different target areas and transmitting it to an Internet of Things (IoT) cloud platform; acquiring real-time environmental data in different target areas and transmitting it to the IoT cloud platform; the IoT cloud platform processing the received data; acquiring meteorological data of different target areas through a meteorological station and optimizing the irrigation amount information of different target areas based on the meteorological data to obtain optimized irrigation amount information for different target areas; the IoT cloud platform generating corresponding execution control commands based on the optimized irrigation amount information and sending the commands to the irrigation execution module; the irrigation execution module generating corresponding equipment control commands to control valves and water pumps to perform irrigation operations. The method provided by this invention, by accurately assessing irrigation amounts, supplies the required water only to the areas requiring irrigation and during the required irrigation periods, avoiding the ineffective loss of water resources.

The invention discloses a forest carbon sink parameter time sequence product generation method, system and device and a storage medium, and is applied to the cross technical field of vegetation remote sensing and carbon cycle monitoring, and the method comprises the steps: obtaining multi-source data of a target region, and generating a vegetation index based on remote sensing data in the multi-source data; based on the vegetation index, constructing a nonlinear model for different forest types, and performing inversion to obtain a corresponding net primary productivity initial time sequence; performing type-oriented differential purification treatment on the initial time sequence of the net primary productivity; feature difference identification is carried out based on the purified time sequence, and the nonlinear model is optimized according to an identification result; generating a net primary productivity time sequence product through the optimized model; according to the method, the problems of mechanism mismatching and information loss when a traditional single model processes heterogeneous forest types are solved, and a high-credibility time sequence parameter product basis is provided for urban forest carbon sink accurate monitoring, operation and management policy making and carbon trading market support.

The invention relates to the technical field of vegetation remote sensing recognition and ecological monitoring, and particularly discloses a remote sensing recognition method for tamarix chinensis of the Yellow River delta based on an improved vegetation index. According to the method, remote sensing data acquisition and preprocessing are carried out by adopting a multispectral remote sensing image, targeted improvement is carried out on a traditional vegetation index structure according to spectral characteristics of tamarix chinensis in a red-edge wave band, tamarix chinensis / non-tamarix chinensis samples are constructed and a characteristic data set is generated, and automatic distinguishing of tamarix chinensis and non-tamarix chinensis areas is realized by combining a dichotomy model. And completing tamarix chinensis space distribution identification and result output. According to the method, the normalized difference item of the red-edge wave bands RE1 and RE2 is constructed in a combined mode according to the spectral characteristics of the tamarix chinensis, and the improved vegetation index is formed through the combined multiplication of the normalized difference item and the NDVI, so that the distinguishability and classification accuracy of the tamarix chinensis and the reed are enhanced, and the key technical problems that targets are not prominent, and classification is inaccurate are solved.

The application discloses a laser radar device structure for synchronously detecting vegetation reflection light and fluorescence signal, which is composed of the following parts: a laser emission unit for emitting monochromatic short-wave high-energy laser pulses to vegetation through a coated full reflection mirror; a return wave receiving unit and a spectrum splitting unit for acquiring and spectrally processing short-wave reflection light signals reflected by the vegetation target and wide-spectrum fluorescence signals generated by the vegetation under excitation; a spectrum data acquisition unit for performing signal conversion and recording on the received reflection light signals and fluorescence signals; a time sequence control unit connected with the laser emission unit and the spectrum data acquisition unit through a data transmission line; and an upper computer connected with the laser emission unit, the spectrum data acquisition unit and the time sequence control unit through connecting lines. The application realizes the integration of vegetation reflection light and fluorescence dual-spectrum signal imaging detection while realizing the acquisition of accurate spatial three-dimensional information of vegetation by laser radar technology, and better serves the vegetation remote sensing monitoring and practical application.

The invention provides a reservoir area water ecological space ecosystem evaluation method and system, and the method comprises the steps: obtaining a multiband spectral remote sensing image, and constructing a hyperspectral and vegetation remote sensing image; obtaining a ground feature category and spatial distribution through a spectrum detection network; modeling correlation between the vegetation index and the leaf area by using a Bayesian network, and constructing a leaf area network fusing a conditional probability structure and a deep neural network; screening a vegetation region based on ground feature distribution, and calculating a pixel-level leaf area index; and evaluating the structural integrity and health state of the ecological system by combining the leaf area index and the ground feature information. According to the method, the incidence relation between the vegetation indexes is quantified through the Bayesian network, the spatial features of the three-dimensional image are extracted in combination with the convolutional neural network, the vegetation growth state of the reservoir area and the spatial heterogeneity of the water ecosystem are effectively captured, and the method is suitable for the ecological space of the reservoir surface, the hydro-fluctuation area and the land area. And reliable technical support is provided for ecological environment assessment and dynamic monitoring and protection.

This invention relates to a method for removing interference from submerged emergent vegetation in remote sensing monitoring of submerged vegetation, belonging to the field of ecological and environmental remote sensing monitoring technology. This invention comprehensively utilizes the spectral recognition capabilities of multispectral remote sensing imagery and the surface moisture and structure detection capabilities of radar imagery, eliminating interference by leveraging the differences in the ecological responses of vegetation to continuous flooding. First, underwater vegetation areas containing both target and interfering vegetation are preliminarily identified based on multispectral imagery. Then, the cumulative flooding duration for each pixel is calculated using time-series radar imagery. Finally, by comparing the flooding duration with a locally determined emergent vegetation decay threshold, pixels that do not exceed the threshold are identified as submerged emergent vegetation and removed, thus obtaining an accurate distribution of submerged vegetation. This invention significantly improves the accuracy and reliability of remote sensing identification of submerged vegetation in waters with drastic seasonal water level changes, such as flooded lakes. It is suitable for large-scale, long-term dynamic monitoring and has the advantages of high automation, wide applicability, and low cost.

This invention discloses a method and apparatus for simultaneously inverting leaf reflectance and transmittance, comprising: decomposing canopy reflectance within the passive optical remote sensing band into primary scattering and multiple scattering; constructing a first relationship model between primary scattering and leaf reflectance and a second relationship model between multiple scattering and leaf scattering coefficient; obtaining an inversion model based on the first and second relationship models, wherein the inversion model expresses canopy reflectance as a binary function of leaf reflectance and leaf transmittance; and inputting multi-angle canopy reflectance observations into the inversion model to obtain the inversion results of leaf reflectance and leaf transmittance. This invention solves the problem of ill-conditioned inversion in the field of vegetation remote sensing.

To provide a method and system for quantifying the drought transfer inhibitory effect of soil moisture under drought stress. [Solution] This method includes the steps of: acquiring meteorological data, soil moisture data, and vegetation remote sensing observation data of a monitored area; standardizing the acquired data to obtain a standardized precipitation evapotranspiration index, a standardized soil moisture index, and a standardized vegetation index; constructing a three-dimensional simultaneous distribution function based on the standardized precipitation evapotranspiration index, a standardized soil moisture index, and a standardized vegetation index; calculating the drought transmission time and drought transmission probability based on the three-dimensional simultaneous distribution function; calculating the drought transmission index based on the drought transmission time and drought transmission probability; and calculating the drought transmission inhibition rate based on the drought transmission index to complete the quantification of the drought transmission inhibition effect.

The application discloses a lake aquatic vegetation remote sensing classification method fusing image space spectrum features, and comprises the following steps: acquiring multi-source remote sensing image data, calculating a spectrum index, the remote sensing image data is composed of a plurality of discontinuous optical bands, performing principal component analysis on the optical band image, calculating a gray level co-occurrence matrix according to the analysis result, and extracting texture features according to the gray level co-occurrence matrix; stacking the optical band, the spectrum index and the texture features in the dimension to form a model input tensor; building a space feature extraction branch and a spectrum feature extraction branch to construct a space spectrum double-branch cross attention network model; inputting the model input tensor into the space spectrum double-branch cross attention network model, and outputting a classification result by the space spectrum double-branch cross attention network model; and the space spectrum double-branch cross attention network model realizes deep collaborative modeling of the spectrum features and the space structure features by introducing a space spectrum cross attention mechanism.