101 results about "Cover-abundance" patented technology

The invention relates to the technical field of hydrological process simulation, in particular to an ecological hydrological process simulation method and system based on dynamic vegetation and a medium. The method comprises the following steps: acquiring a vegetation physiological monitoring parameter set and a root system water absorption dynamic data set; performing time-space unified registration on the vegetation physiological monitoring parameter set and the root system water absorption dynamic data set to obtain a vegetation physiological dynamic parameter set; performing soil dry and wet process division on the target area based on the vegetation physiological dynamic parameter set to obtain a segmented soil moisture state sequence; dividing the vegetation physiological dynamic parameter set according to the segmented soil moisture state sequence to obtain a state classification vegetation physiological data set; and constructing a vegetation water stress adaptation strategy model based on the state classification vegetation physiological data set. According to the method, the simulation precision of vegetation coverage and soil moisture dynamic balance is improved, and the problem of simulation errors caused by vegetation parameter static processing in the prior art is solved.

The invention belongs to the field of remote sensing monitoring, and particularly relates to a land ecological condition monitoring system and method based on remote sensing data, and the method comprises the steps: obtaining a remote sensing image sequence through a preprocessing module, and obtaining initial cultivated land segmentation sub-regions and edge features; a feature extraction module extracts spatial features and fluctuation values such as normalized vegetation indexes and enhanced vegetation indexes based on the feature extraction module, and constructs a space-time coordinate system; the region determination module determines a real cultivated land area and a cultivated land area fluctuation value; the inversion module inverts the vegetation coverage, the moisture content and the soil fertility, and obtains an ecological degradation fluctuation value by combining with phenological period anomaly recognition; the early warning module performs real-time early warning based on the cultivated land area fluctuation value and the ecological degradation fluctuation value, and visually marks the position, the type and the severity of a change region through a GIS platform; according to the invention, precise monitoring and dynamic early warning of the ecological condition of the land are realized.

The invention discloses an alpine grassland no-tillage restoration suitability evaluation method based on a 3S technology, and particularly relates to the technical field of data processing. Through the combination of multi-temporal satellite images and global positioning system data, the spatial dynamic change of the frozen soil layer of the alpine grassland and the long-term influence of the spatial dynamic change on vegetation growth are accurately obtained, and a frozen soil layer depth and distribution dynamic monitoring model is constructed based on multi-source remote sensing data and historical weather and soil temperature and humidity data. Analyzing the influence of freezing and thawing alternation on plant root growth; establishing a vegetation recovery potential index and a soil moisture supply index by combining the vegetation coverage and the dynamic change of the frozen soil layer, and generating a suitability partition map through a GIS spatial analysis and analytic hierarchy process; through field sampling and long-term vegetation growth monitoring data verification and model optimization, the accuracy of no-tillage restoration suitability evaluation is remarkably improved, and scientific and accurate decision support is provided for ecological restoration of alpine grassland.

The present invention discloses a method for monitoring and early warning of wild plant distribution status based on image recognition, relating to the technical field of monitoring and early warning of wild plant distribution status. This method promotes the scientific assessment of ecological protection status of each sub-area by calculating coverage indexes Fgl and population complexity indexes When one of the coverage indexes or the population complexity indexes of a sub-area does not reach a preset threshold, an unqualified mark is marked accordingly. By calculating environmental pressure indexes HJyl, a first early warning instruction is automatically sent when one of the environmental pressure indexes HJyl exceeds a preset first pressure threshold, indicating that the environmental pollution status is unqualified.

The invention relates to the technical field of crop monitoring, in particular to a crop phenotypic parameter automatic calculation and extraction method based on a multi-source remote sensing image. Comprising the steps that all images are determined to be in the same coordinate system through geographical registration, point cloud file conversion and ground specific point matching, and image registration is achieved; performing automatic or semi-automatic segmentation on the images determined in the same coordinate system to obtain a multi-source crop remote sensing image cell segmentation map; extracting plant phenotypic parameters, physical parameters and chemical parameters from the multisource crop remote sensing image cell segmentation map by using an algorithm; the plant phenotype parameters comprise a vegetation index, a plant height, a surface area, a volume, a canopy coverage degree and a vegetation projection area; the physical parameters comprise a canopy average temperature value, a canopy temperature standard deviation and a canopy temperature variation coefficient; the chemical parameters comprise chemical elements such as nitrogen, phosphorus, potassium, calcium and magnesium in soil and vegetation. The method has the advantages that large-scale data processing and high-precision area prediction are realized, and the crop growth monitoring capability is enhanced.

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.

This disclosure relates to a method for estimating coverage of a plant in an image. The method comprises dividing the image into patches, applying a trained machine learning classifier to each of the multiple patches to classify each of the multiple patches into one of a first class or a second class, wherein the first class indicates presence of the plant, determining a sub-set of patches containing patches that are classified as the first class, determining a segmentation of each of the sub-set of patches, to determine areas of the sub-set of patches that contain the plant; and calculating the plant coverage of the plant in the image based on the segmentation.

The invention provides a mangrove forest degradation type and reason identification method and system based on remote sensing and a multi-factor index system. The method comprises the following steps: firstly, integrating a multi-temporal remote sensing image, ecological monitoring data and ground survey data, and constructing a systematic degradation factor index system covering natural, climate, biological and human factors; secondly, extracting typical degradation characteristics such as vegetation coverage decline and species composition change by using a remote sensing change detection technology and a principal component analysis method; then, through K-means clustering, an ecological classification algorithm based on rules and an ecological threshold standard, mangrove forest degradation types are identified and classified, and the types comprise a man-made interference type, a biological erosion type, a natural succession type and a multi-factor coupling type; then, adopting a supervised machine learning model and combining a feature importance evaluation method such as a Shapley value to identify a degradation cause and a weight thereof; and finally, verifying the precision of the model through an on-site monitoring quadrat and an expert interpretation result, and outputting visual results such as a degradation type distribution diagram, a factor contribution distribution diagram and the like.

A method for monitoring the grassland vegetation coverage based on continuous photography, comprising: determining an observation quadrat and continuously taking multiple vegetation photos of the vegetation within the observation quadrat throughout the entire growing season; cropping and geometrically correcting the multiple vegetation photos; obtaining the vegetation pixel ratio corresponding to the vegetation photos and comparing the vegetation pixel ratio with a preset second threshold; when the vegetation pixel ratio is greater than the second threshold, calculating the vegetation coverage corresponding to the vegetation photos by using the Otsu algorithm; otherwise, using the vegetation pixel ratio as the vegetation coverage; obtaining a dynamic change curve of the vegetation coverage based on the vegetation coverage corresponding to each vegetation photo. The present invention effectively improves the monitoring accuracy and efficiency of the dynamic change characteristics of the grassland vegetation coverage by combining continuous photography and the Otsu algorithm.

The invention provides a plant nitrogen content monitoring method and device, electronic equipment, a medium and a program product, and relates to the technical field of plant nitrogen content monitoring. The method comprises the following steps: acquiring a vegetation index and a vegetation coverage of a target plant; based on the vegetation coverage and the vegetation index, constructing a canopy structure correction index of the target plant; inputting the canopy structure correction index into a mapping relation between the canopy structure correction index and the plant nitrogen content to obtain the plant nitrogen content of the target plant; according to the method, the canopy structure correction index of the target plant can be obtained by obtaining the vegetation index and the vegetation coverage of the target plant, so that the influence of the plant canopy structures in different growth stages on the vegetation index is eliminated; further, the plant nitrogen content of the target plant is obtained according to the canopy structure correction index, and accurate monitoring of the plant nitrogen content of the target plant in different growth stages is achieved.

The invention discloses a mine ecological restoration effect evaluation method comprising the following steps: determining a mine ecological restoration area, obtaining a remote sensing image, and preprocessing the remote sensing image; calculating texture features of the remote sensing image based on wavelet decomposition; calculating the average value of the vegetation coverage change indexes; calculating the average gain of the biomass in the ecological restoration process according to the change of the vegetation biomass in the sampling area along with time; calculating the average purification benefit of the soil metal concentration in the ecological restoration process; and evaluating the ecological restoration effect of the mine according to the average purification benefit of the soil metal concentration, the average gain of the biomass and the average value of the vegetation coverage change indexes. The comprehensive influence of the mining area soil conditions on the vegetation biomass is accurately quantified, a scientific basis is provided for evaluation of the mine ecological restoration effect, and key evaluation parameters for evaluation of the mine ecological restoration effect are determined from ecological factors in an ecological system after mine ecological restoration.

The invention provides a technical method for performing vegetation coverage anomaly analysis based on a remote sensing image, which mainly comprises the following steps of: firstly, performing registration and preprocessing on a multi-temporal SAR (Synthetic Aperture Radar) image, and performing adaptive threshold coherence detection between adjacent time phases to quickly identify suspicious felling or severely sparse regions; secondly, dual-channel polarization InSAR height inversion is only carried out on the suspicious areas, and the height change of a forest canopy is accurately obtained so as to distinguish forest damage from other slight disturbances; and time sequence analysis is further performed on the multi-temporal image in combination with a small baseline subset InSAR technology, baseline optimization is realized in cooperation with vegetation coverage segmentation and coherence screening, and progressive or sudden vegetation coverage anomalies can be identified. And finally, according to comprehensive characteristics such as height change, coherence anomaly and phase jump, outputting the type and distribution of vegetation anomaly.

The invention discloses a microbial function synergistic degraded karst forest soil carbon accumulation regulation and control method, which comprises the following steps: measuring and collecting bacterial and fungal diversity sequence data of a degraded karst forest soil sample through high-throughput sequencing equipment; original sequencing data including diversity, community structures, functional abundance values and the like are stored in a computer readable storage medium, and a structured microbial community database is constructed; based on the database, the computer system performs core function microorganism screening influencing carbon storage; based on the screening result, the computer system performs processes comprising: calculating a functional abundance indicator of the core microorganism; determining a microbial function weight through multiple linear regression; establishing a carbon accumulation regulation coefficient calculation model; and preparing a microbial agent, generating a control instruction containing the application amount of the microbial agent and a vegetation coverage optimization scheme according to the calculated value of the carbon accumulation regulation coefficient and the soil organic carbon saturation deficit value, and transmitting the control instruction to field operation equipment.

The present application provides a kind of wind erosion sand transport rate vegetation correction factor dynamic optimization method, introduce the inherent cementation index of soil inorganic / organic carbon ratio, by quantifying carbon ratio influence coefficient, the inherent influence of soil carbon component structure on soil aggregate stability, sand critical condition is integrated into vegetation correction system, so that vegetation correction process is no longer separated from soil base attribute, substantially improve the physical mechanism rationality of wind erosion model. Meanwhile, through remote sensing per-pixel modeling and multi-factor spatial overlay operation, realize the dynamic adjustment of each pixel vegetation correction factor with the difference of soil carbon structure, vegetation coverage, vegetation type, solve the distortion problem caused by the parameter uniformization and staticization of traditional model.

The present application discloses a wetland vegetation carbon sequestration potential prediction method and system under the influence of future climate change, belongs to the technical field of wetland vegetation carbon sequestration potential prediction, and comprises the following steps: obtaining the net primary productivity of vegetation, the vegetation coverage, the monthly temperature, the precipitation and the atmospheric carbon dioxide concentration in the historical period and the future period; calculating the annual average net primary productivity of vegetation, the coverage and the multi-year average vegetation coverage; calculating the annual average temperature, the precipitation and the carbon dioxide concentration; extracting the wetland vegetation pixel distribution as the research area range; calculating the ratio of the annual average net primary productivity to the vegetation coverage to obtain the wetland vegetation carbon sequestration potential value in the historical period; adopting the multiple stepwise regression analysis method to build a per-pixel wetland vegetation carbon sequestration potential estimation model; and utilizing the annual temperature, the precipitation and the carbon dioxide concentration in the future period to realize the accurate prediction of the wetland vegetation carbon sequestration potential in the research area range in the future period.

The invention relates to a leaf area index time sequence processing method, which comprises the following steps of: inputting a time sequence remote sensing image and suburb forest and economic forest classification data of the same region; calculating a normalized differential vegetation index of the research area and carrying out time sequence sorting; calculating vegetation coverage data of the research area and carrying out time sequence sorting; synthesizing to obtain monthly FVC time sequence data of the suburb forest region and the economic forest region; calculating a suburb forest leaf area index LAI in the research area and performing time sequence sorting; calculating the economic forest leaf area index LAI of the research area and performing time sequence sorting; performing seasonal decomposition on the suburb forest time sequence data after adaptive filtering; performing seasonal decomposition on the economic forest time series data after adaptive filtering; merging the suburb forest time sequence data and the economic forest time sequence data; and outputting to obtain final optimized data. The invention further relates to a leaf area index time sequence processing system. According to the invention, a purification LAI time sequence product which clearly represents long-term trend, mutation and gradual change signals of respective vegetation canopy structures can be output.

The application provides a surface temperature-based surface ground temperature remote sensing measurement method, which comprises the following steps: expanding radiation terms of bare soil and vegetation at a target pixel into Taylor series at a remote sensing surface temperature respectively, establishing a remote sensing radiation equation of the remote sensing surface temperature by taking remote sensing vegetation coverage as a weight; solving remote sensing bare soil temperature and remote sensing vegetation temperature at the target pixel by combining the remote sensing radiation equation, a relationship equation of the remote sensing surface temperature and a remote sensing component temperature; expanding radiation terms of bare soil and vegetation at a simulation grid into Taylor series at a simulation surface temperature respectively, establishing a simulation radiation equation of the simulation surface temperature by taking simulation resolution vegetation coverage as a weight; solving simulation bare soil temperature and simulation vegetation temperature at the simulation grid by combining the simulation radiation equation, a relationship equation of the simulation surface temperature and a simulation component temperature; and estimating average surface ground temperature by using a correlation model between the simulation bare soil temperature and simulation surface ground temperature and the remote sensing bare soil temperature.

The application discloses a kind of transmission and transformation engineering area vegetation recovery evaluation method and system, applied to electric power engineering vegetation evaluation technical field, method includes obtaining the point cloud data and remote sensing image of target transmission and transformation engineering area;Point cloud data is processed to obtain canopy height model, each pixel in remote sensing image is identified using canopy height model, to obtain vegetation pixel set and soil pixel set;According to the reflectivity value of each pixel in vegetation pixel set and soil pixel set, the vegetation coverage of target transmission and transformation engineering area is calculated;The vegetation structure parameter and spectral vegetation index of each vegetation pixel are calculated, based on each vegetation structure parameter and each spectral vegetation index, to obtain vegetation biomass map;Using point cloud data and remote sensing image to calculate to obtain heterogeneity index map, based on vegetation coverage, vegetation biomass map and heterogeneity index map, to obtain vegetation recovery evaluation result, the accuracy of vegetation recovery condition evaluation is effectively improved by the method.

This invention discloses a method for assessing ecosystem service losses due to regional climate change. The region includes counties, cities, or important ecological function zones. The steps are as follows: S1. Collect remote sensing data and preprocess the data through band combination, geometric correction, and projection transformation. Select a standard color composite scheme, establish interpretation markers, and perform visual interpretation. S2. Calculate and statistically analyze the unit area yield value and equivalent factor of grain crops. The equivalent factor is 1 / 7 of the average market value of grain in the current year. S3. Statistically analyze land use types, calculate the magnitude of land use change, reflecting the changes in the total amount of different land use types and the overall trend of land use type changes. S4. Select vegetation cover as an indicator, and adjust the ecosystem service value based on two parameters: net primary productivity and vegetation cover. This invention is of great significance for formulating ecosystem management strategies under the background of future climate change.

The invention relates to the technical field of ecological assessment, in particular to a regional ecological restoration potential assessment method based on multi-dimensional data, which comprises the following steps: collecting pH value, moisture and nutrients; calculating microcosmic abnormal indexes and types, and determining microcosmic sub-regions; collecting the vegetation index and the coverage degree of the microscopic sub-region; calculating a mesoscopic anomaly index, and determining a mesoscopic sub-region; collecting rainfall and temperature of the mesoscopic sub-region; calculating a macroscopic adaptation index, and determining an index mode; calculating a potential index, and determining a macroscopic abnormal sub-region; calculating the aggregation degree of the macroscopic sub-regions, and adjusting the weight; and generating a recovery potential evaluation report. According to the invention, through step-by-step acquisition and analysis of multi-dimensional data, microscopic environment indexes, vegetation conditions and meteorological conditions are effectively integrated, a hierarchical evaluation system from microcosmic to macroscopic is formed, and the problem of low accuracy of evaluation of regional vegetation recovery potential caused by dependence on a single static index and a rough model is effectively solved.

The application discloses a rocky desertification rapid identification method and system, and the method comprises the following steps: inputting a ground surface image into a lightweight semantic segmentation model, and outputting bare rock area pixels, vegetation area pixels and other area pixels from the lightweight semantic segmentation model; determining a base rock exposure rate and a vegetation coverage degree according to the bare rock area pixels, the vegetation area pixels and the other area pixels; determining an interference intensity grade according to selection information; inputting a soil layer thickness measurement value, slope data, the base rock exposure rate, the vegetation coverage degree and the interference intensity grade into a multi-factor comprehensive evaluation model to calculate a rocky desertification comprehensive index; and calculating a rocky desertification grade according to a preset grade determination threshold and the rocky desertification comprehensive index, and displaying a calculation result. The application solves the problem that a single index cannot comprehensively reflect the rocky desertification degree, and unifies different factors into a comprehensive index, so that the evaluation result is scientific, the work efficiency of ground investigation is improved, and the work intensity of workers is reduced.

The invention provides a karst rocky desertification side slope ecological restoration method which comprises the following steps: selecting a rocky desertification side slope region, generating a spherical region range based on geographic coordinates, and recording center coordinates of a target side slope; the method comprises the following steps: acquiring vegetation coverage, height and type distribution data based on remote sensing data and field sampling, and acquiring wind speed, wind direction, soil element distribution, precipitation, runoff, vegetation seed bank quantity, seed bank soil humidity, seed particle size distribution and type distribution data at the same time; generating a wind power field function according to the wind speed and wind direction data, analyzing the soil element content, calculating the total rainwater amount and the total runoff amount, and generating the number of effective vegetation seeds; integrating the parameters to construct a carbon sequestration and slow release model, and outputting a carbon cycle model. According to the method, the carbon sequestration capability of the karst rocky desertification slope ecosystem can be accurately evaluated, the dynamic monitoring and optimization of the ecological restoration effect are realized, and the ecological restoration efficiency and the regional ecological stability are improved.

The invention discloses a sand vegetation coverage and hydrological parameter relationship determination system, which belongs to the technical field of ecological hydrological monitoring, and comprises a vegetation coverage measurement module, a soil moisture monitoring module, a rainfall infiltration monitoring module, a surface runoff monitoring module and a relationship analysis module, the vegetation coverage measurement module adopts a multispectral imaging technology to realize real-time automatic measurement, the soil moisture monitoring module adopts a distributed sensor array to realize multi-point multi-depth monitoring, the rainfall infiltration monitoring module determines infiltration rate based on dynamic change of soil moisture content, and the surface runoff monitoring module quantitatively measures runoff volume. The relation analysis module integrates data of all the modules to establish a vegetation-hydrological mutual feedback model, comprehensive continuous monitoring and quantitative analysis of the vegetation-hydrological process of the sand land are achieved, and a scientific basis is provided for ecological restoration effect evaluation and water and soil conservation engineering optimization.

This invention discloses a method for jointly regulating water resource utilization of desert riverbank vegetation based on ecological gate groups. The method includes: obtaining vegetation change patterns using a fitting method based on the kNDVI vegetation index; dividing the vegetation in the desert watershed into three vegetation distribution zones based on the vegetation change patterns and vegetation coverage; acquiring all ecological gates in the desert watershed and dividing them into multiple ecological gate groups; determining the weights of the three vegetation distribution zones for ecological protection objectives using the analytic hierarchy process (AHP), and calculating the comprehensive score of the regulation zone based on the proportion of ecological functional zones in the regulation zone; solving the joint model of the ecological gate groups using a single-objective genetic algorithm to obtain the ecological water demand and ecological water supply of each control zone when the total water shortage of all ecological gate groups is minimized; dividing the ecological gate groups into five levels based on the comprehensive score, and then activating the ecological gate groups to irrigate the irrigation area according to the ecological gate group level, ecological water demand, and the importance of the irrigation area.

The application provides a method and device for evaluating aboveground biomass of Spartina alterniflora based on coverage and height. The method provided by the application comprises the following steps: setting a first quadrat in a Spartina alterniflora growth area, collecting all plants in the first quadrat, measuring the height and dry weight of each plant, establishing a first allometric growth relationship model between the height and dry weight of each plant based on the height and dry weight of each plant, setting a second quadrat in the Spartina alterniflora growth area, recording the quadrat coverage, average height and plant number in the second quadrat, calculating the total dry weight of the quadrat and converting the total dry weight into aboveground biomass, establishing a second allometric growth relationship model between the coverage, height and aboveground biomass based on the quadrat coverage, average height and aboveground biomass, matching a corresponding target allometric growth relationship model based on the scale and data conditions of a region to be evaluated, inputting corresponding data of the region to be evaluated into the target allometric growth relationship model, and calculating the aboveground biomass of the region to be evaluated.

This invention relates to the field of remote sensing image processing technology, specifically to a method and system for wetland information extraction based on multi-source remote sensing data fusion. The method includes the following steps: acquiring and fusing wetland optical radar images and water level time-series data; extracting near-infrared and backscattering multi-source feature parameters to distinguish water body pixels; statistically aggregating spatial connectivity to generate water body patch boundaries; calculating a comprehensive model of area, perimeter, and shape index to classify wetland types; adjusting mismatched labels by combining vegetation cover and soil moisture; and forming a wetland information extraction result map. In this invention, the differences in water bodies are enhanced through joint reflection and scattering features; the differences between water bodies and their surroundings are enhanced through joint reflection and scattering characterization; boundary aliasing is suppressed through bidirectional threshold constraints; patch spatial integrity is restored through connectivity analysis; the sensitivity of type classification is enhanced through morphological parameter combinations; and the accuracy and type stability of wetland representation are improved through ecological attribute consistency correction.

The present invention relates to a method for reverse transformation of the habitat of Elodea in a reservoir area. Remote sensing images of the reservoir area are formed through drone aerial survey. UAV remote sensing has the characteristics of flexible data acquisition and high image resolution, and is very suitable for dynamic monitoring of different vegetation cover types and coverage of lakes, reservoirs and rivers at a landscape scale. The water depth threshold and flow rate threshold for reverse transformation of Elodea in each reservoir area are collected in real time, so that targeted reverse transformation can be carried out for Elodea in different growth environments in each reservoir area, thereby maximizing the transformation efficiency and reducing the transformation cost. After the threshold is collected in real time, hydrological conditions that are not conducive to the growth of Elodea can be created by increasing the water output Q of the upstream reservoir or opening the gates of the downstream reservoir to release water or carrying out local terrain transformation. The Elodea reproduction and growth are effectively suppressed in the early stages, thereby effectively inhibiting the reproduction and growth of the Elodea.

The application discloses a degraded grassland ecological restoration method and system based on ecological water demand inside and outside rivers and lakes, and comprises the following steps: conducting field investigation, analyzing the main characteristics of the degraded grassland to divide the degradation grade area, wherein the main characteristics include but are not limited to vegetation coverage, species diversity index and soil bulk density; carrying out ecological unit grid division based on terrain self-adaption; constructing a multi-objective function and constraint condition of the degraded grassland ecological restoration based on the ecological water demand inside and outside rivers and lakes according to water resource allocation, grassland restoration, grazing management and economic benefits; on the basis of the ecological water demand inside and outside rivers and lakes and in combination with the specific grassland degradation grade of the degraded area, the multi-objective function and the constraint condition are used to carry out restoration priority ranking, dynamic adjustment strategy and water resource dynamic adjustment, so that an optimal degraded grassland ecological restoration scheme is obtained; the application is a comprehensive ecological restoration scheme combining water resource utilization, ecological water demand, grazing management and economic benefits, and is helpful to promoting sustainable restoration of the grassland.

The invention provides a wind erosion model construction method for nesting non-photosynthetic vegetation parameters, and the method comprises the steps: obtaining the non-photosynthetic vegetation coverage of a sample plot based on the non-linear function relation between the constructed spectral information and the non-photosynthetic vegetation coverage in combination with the obtained remote sensing monitoring data of the sample plot. And calculating the total vegetation coverage of the region by combining the photosynthetic vegetation coverage obtained by the MODIS-NDVI. Correcting the quantitative relation between the vegetation coverage and the windward area index based on the actually measured vegetation parameters of the sample plot, and optimizing the roughness element correction function; and aerodynamics parameters containing non-photosynthetic vegetation information are obtained through inversion by using the total vegetation coverage. The aerodynamic parameters obtained through inversion and the optimized roughness element correction function are embedded into a traditional wind erosion model, and a wind erosion model with nested non-photosynthetic vegetation parameters is constructed. The wind erosion data is actually measured through a sample plot for verification, the result shows that the model can effectively represent the regulation effect of non-photosynthetic vegetation on the wind erosion process, and the accuracy of the prediction result of the wind erosion model is improved.

The invention provides a soybean single plant field phenotype continuous time-space dynamic analysis method and system. The problems that in field crop phenotype data extraction, group phenotype evaluation neglects single plant heterogeneity, canopy closure restricts single plant segmentation implementation, and unmanned aerial vehicle phenotype data accuracy defects exist are solved. The method comprises the following steps: carrying out whole-growth-period data acquisition through an unmanned aerial vehicle carrying multiple sensors, and carrying out multi-source space-time registration based on ground mark points; identifying a single plant from the seedling stage image and segmenting to obtain an independent single plant image; visible light, multispectral and laser radar data are fused, and the canopy height, the coverage degree and the normalized vegetation index of a single plant are extracted; and a multi-stage evaluation system is constructed by calculating a variable coefficient of phenotypic parameters of single plants in the group, so that the analysis from the heterogeneity of the single plants to the dynamic quantitative evaluation of the growth uniformity of the group is realized.