68 results about "Cover-abundance" patented technology

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 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 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.

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 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 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.

The invention relates to the technical field of remote sensing and ecological monitoring, and discloses a grassland ecology-oriented mousehole and vegetation coverage monitoring method, which integrates multi-temporal optical remote sensing, synthetic aperture radar and ground sensing data, realizes high-precision recognition of a mousehole by improving a U-shaped convolutional neural network, and improves the reliability of the mousehole. The accuracy is improved by combining interference coherence attenuation characteristics and microtopography verification; synchronously inverting the multi-exponential weighted vegetation coverage, constructing a time-space aligned double-variable time series data set, calculating a Granger causal coefficient by using a vector autoregression model, and quantifying the driving intensity of the mousehole density on vegetation degradation; and when the causal coefficient exceeds a threshold value and the vegetation coverage continuously decreases, triggering first-level ecological early warning. According to the method, accurate quantification and early warning of the rodent damage-vegetation causal chain are achieved, and scientificity and timeliness of grassland ecological monitoring are remarkably improved.

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

This invention discloses an intelligent monitoring method for vegetation in spoil heaps based on optimal layout, belonging to the field of open-pit mine ecological restoration monitoring technology. This invention employs spatial variogram and simulated annealing algorithms to achieve optimal monitoring point layout; it automatically acquires multi-angle images and videos using adjustable supports, and performs keyframe extraction, multi-angle registration, and adaptive denoising preprocessing; the images are input into a lightweight dual-stream feature fusion network, and vegetation areas are accurately segmented through RGB and NDVI dual-stream attention fusion and combined loss function optimization; based on the segmentation results and environmental factors, vegetation coverage, plant height, and tiller number are automatically calculated; the time series is decomposed using STL, and the logistic growth model is integrated into the LSTM network as a physical constraint to output a growth trend prediction that conforms to biological laws. This invention achieves fully automated monitoring from layout to prediction, significantly improving the accuracy and efficiency of monitoring reclaimed grassland in spoil heaps.

This invention relates to an automated monitoring method for the dynamic changes of plant diversity in mountainous forest areas, comprising: constructing a three-dimensional model of the target mountainous area and deploying a sensor network; collecting microclimate data and soil characteristic data to determine a spatial distribution matrix; acquiring historical plant distribution data of the target mountainous area, mapping it to the spatial distribution matrix to construct a comprehensive distribution map of environmental factors, and determining a correlation model between plant distribution and environmental factors; acquiring real-time satellite remote sensing image data of the target mountainous area, using the correlation model to analyze the characteristics of changes in plant cover and plant diversity distribution, and locating several key plant distribution areas; identifying plant species and counting their numbers in the key plant distribution areas, updating the comprehensive distribution map of environmental factors, and completing the automated monitoring of the dynamic changes of plant diversity in the target mountainous area. This invention integrates multi-source environmental data to achieve automated analysis of plant diversity distribution.

The invention discloses an ICESat-2 adaptive photon denoising method and an ICESat-2 adaptive photon denoising system based on vegetation layering. According to the method, a vegetation layering strategy is introduced, the photon data is divided into different gradient grades according to the vegetation coverage, and the problem that a traditional global denoising method is insufficient in adaptability in a complex vegetation environment is effectively solved. And the optimal denoising parameter of each vegetation level is adaptively determined in combination with the Bayesian optimization algorithm, so that the photon denoising precision and efficiency of the DBSCAN clustering in the vegetation region with uneven density are remarkably improved. According to the method, the advantages of multi-source remote sensing data are fully utilized, efficient screening and separation of signal photons and noise under different vegetation density conditions are achieved, and a higher-quality data basis is provided for subsequent forest structure parameter inversion and ground feature classification.

The invention discloses a narrow space exploration method based on multi-criterion decision and adaptive ray casting, and relates to the technical field of ecological restoration. The flooding time distribution of the river beach under the ultra-frequency flood scene is calculated through a Weibull formula; secondly, extracting information such as beach vegetation types and coverage based on a multi-source remote sensing image, and dividing suitable plant partitions in combination with flooding time; a habitat quality evaluation system is constructed from the dimensions of vegetation conditions, hydrological conditions, topographic features and the like, and habitat quality rating of each subarea is completed; finally, according to flooding time characteristics, plant suitability and habitat quality grades, ecological restoration partitions are proposed in a targeted mode. According to the method, accurate coupling of the flood hydrological process and the beach ecosystem is achieved, the problem that a traditional ecological restoration measure lacks a hydrological-ecological cooperation basis is solved, the scientificity and feasibility of a restoration scheme are improved, and the method is suitable for ecological restoration engineering around water areas such as river beaches, lake wetlands and the like in northern arid and semi-arid areas.

The present application relates to the technical field of ecological restoration, and more particularly to a caragana laying ecological restoration method based on remote sensing and intelligent monitoring. The method comprises the following steps: obtaining multispectral remote sensing images and ground survey data of the target grassland, and extracting vegetation coverage, soil erosion intensity and surface roughness; evaluating the degradation degree of the target grassland according to the vegetation coverage, soil erosion intensity and surface roughness, and determining the grassland degradation zoning map; determining the caragana laying parameters based on the grassland degradation zoning map; leveling and cleaning the target grassland; laying caragana on the pretreated target grassland surface according to the caragana laying parameters, and collecting monitoring images of the laying area in the target grassland by using a UAV at a preset period. The present application can accurately identify the degradation level and spatial difference of the grassland, thereby providing scientific and reasonable parameter settings for caragana laying, significantly improving the pertinence and effectiveness of the restoration measures, and optimizing the resource utilization efficiency.

The application discloses a kind of based on nest site preference and energy saving's habitat optimization method of relic gull breeding island, it is related to ecological protection technical field.It is surveyed lake island and is divided into historical nest area, potential nest area and non-nest area after relic gull breeding ends;In nest area, through simulating feeding trampling, selective mowing and artificial reseeding intervention, the comprehensive coverage of vegetation before nest building is controlled at 15%-40%, the average height is controlled at 5-25cm, the area of low green vegetation patch is expanded, and the bare sand or sand-dry grass mixed nest foundation area is retained not less than 30%.The method integrates the energy saving index of parent bird, determines the regulation threshold based on the quantitative research of relic gull nest site selection, is accurate and scientific;It can reduce the energy consumption of parent bird during incubation period, reduce egg weight loss, improve the quality of nestling, and improve the success rate of reproduction;And operation is simple, cost is low, can be popularized on a large scale, realizes the upgrade of relic gull protection from extensive to fine active management.

Computer-implemented method and system (100) for estimating vegetation coverage in a real-world environment. The system receives an RGB image (91) of a real-world scenery (1) with one or more plant elements (10) of one or more plant species. At least one channel of the RGB image (91) is provided to a semantic regression neural network (120) which is trained to estimate at least a near-infrared channel (NIR) from the RGB image. The system obtains an estimate of the near-infrared channel (NIR) by applying the semantic regression neural network (120) to the at least one RGB channel (91). A multi-channel image (92) comprising at least one of the R-, G-, B-channels (R, G, B) of the RGB image and the estimated near-infrared channel (NIR), is provided as test input (TI1) to a semantic segmentation neural network (130) trained with multi-channel images to segment the test input (TI1) into pixels associated with plant elements and pixels not associated with plant elements. The system segments the test input (TI1) using the semantic segmentation neural network (130) resulting in a vegetation coverage map (93) indicating pixels of the test input associated with plant elements (10) and indicating pixels of the test input not associated with plant elements.

The invention discloses an intelligent monitoring system for grassland vegetation, which relates to the technical field of ecological environment monitoring and is provided with a moving and positioning module to realize autonomous moving and positioning of the system in a grassland terrain. The vegetation quadrat information acquisition module frames a grassland vegetation sample area and acquires a top view image of the area to complete plant type identification and vegetation coverage calculation, and the vegetation three-dimensional structure measurement module acquires a vegetation multi-angle side view image in a quadrat and measures the average height, the maximum height and the number of plants. The central control and data processing module uniformly schedules all the modules to operate and transmits processed data to the cloud platform for storage; according to the method, the whole process automation of grassland vegetation monitoring from movement, sampling to analysis is realized, the monitoring efficiency is improved, subjective errors of manual measurement are eliminated through mechanical operation and algorithm analysis, data objectivity and accuracy are guaranteed, rich information dimensions are provided, and a comprehensive and three-dimensional basis is provided for grassland vegetation restoration effect evaluation.

The present application belongs to the technical field of smart city, and particularly relates to a smart city environment management method and system based on digital twinning. The method not only realizes high-precision collection of species distribution, vegetation coverage, ground temperature and wind speed data in the urban area, but also successfully simulates the dynamic changes of ground thermal radiation absorption and release caused by the increase and decrease of different species by constructing a dynamic spatial model containing species-environment interaction. Further, by calculating the hourly influence coefficient and iteratively deriving the microclimate evolution trend in a ten-year period, a priority ranking scheme for species protection or regulation is finally generated. This method greatly improves the understanding depth and response speed of urban managers on ecological environment changes, makes real-time calibration based on the latest monitoring data possible, and ensures the scientificity and foresight of urban management decisions.

The invention provides a method for restoring a degraded desert steppe soil seed bank, and belongs to the technical field of ecological restoration. The invention discloses a method for repairing a degraded desert steppe soil seed bank. The method comprises the following steps: dividing degraded desert steppes into three landscape types according to the high (greater than 0.3) vegetation coverage, the middle (0.15-0.3) vegetation coverage and the low (less than or equal to 0.15) vegetation coverage; the method comprises the following steps: respectively taking grassland sample plots of three landscape types as centers to make buffer areas with radiuses of 100-500m, and enclosing the buffer areas formed by the minimum radiuses and meeting the requirements that the aggregation degree index AI is greater than or equal to 80 and the average plaque area is greater than or equal to 2hm together with the grassland sample plots to form a soil seed remediation area; and after soil remediation is conducted on the soil seed remediation area, dominant species are introduced to increase the plant coverage degree. The method enriches the gene resources of the soil seed bank, further enhances the adaptability of plants to the ecological system, improves the survival rate of seeds, and promotes the restoration of the soil seed bank.