1204 results about "Grassland" patented technology

A method and device for evaluating ecological cumulative effects of surface mining areas are provided. The method includes: constructing a surface mining areas eco-environmental evaluation index (SMAEEI) suitable for semi-arid grasslands; constructing a first eco-environmental quality condition adjustment coefficient R(Si,j,th) based on the actual land cover classification result and the SMAEEI; obtaining the actual ecosystem service value ESV per unit area of the area to be evaluated in several years through the unit area ecosystem service value coefficient VCif of the area to be evaluated and the first eco-environmental quality condition adjustment coefficient R(Si,j,th); based on the time-series trajectory integral value of the actual ecosystem service value ESV per unit area and the ideal ecosystem service value ESV′ per unit area undisturbed by human beings, performing residual analysis to obtain the ecosystem service value accumulation induced by anthropogenic factors (ESVA-AF).

The invention discloses a forest steppe fire risk assessment method and system, and belongs to the technical field of forest steppe fire prevention. The problem of high false alarm and missing report rate caused by lagging fire risk identification, weak multi-source data fusion and insufficient dynamic response in the prior art is solved. According to the technical scheme, the method comprises the following steps: deploying a ground sensing node array to obtain real-time environment data of a grassland region, fusing high-resolution satellite remote sensing and regional weather forecast data, and constructing a space-time aligned risk assessment data cube; establishing a grassland fire risk factor dynamic coupling model, and dynamically allocating factor weights in combination with the adaptive weight decision tree; generating a comprehensive risk index, carrying out nonlinear mapping to five risk levels, and linking a visual engine to generate a dynamic thermodynamic diagram and push the dynamic thermodynamic diagram to a command terminal; the system supports online updating of the model, and weight parameters are automatically optimized based on a new fire event. According to the invention, high-precision, real-time and spatialized evaluation is realized, the early warning capability and prevention and control decision efficiency are improved, and ecological and economic losses are reduced.

The invention relates to the technical field of strategy analysis, and discloses an alpine grassland rapid restoration strategy analysis method, system and device and a medium. The method comprises the following steps: performing quality evaluation and restoration on an alpine grassland multispectral image to obtain a standard image set; spectral texture features are extracted, degradation classification is carried out, and a feature set and a score table are obtained; evaluating the degradation degree in combination with multi-dimensional indexes, and generating a distribution matrix and an index set; forming a repair scheme library and a parameter list through case analysis and parameter optimization; analyzing a restoration effect in combination with environmental data to obtain an index matrix and an evaluation table; and optimizing the monitoring data to obtain a strategy scheme and an adjustment parameter set. According to the method and the device, the degradation conditions of alpine grassland of different types and different areas are accurately identified, the restoration strategy can be dynamically adjusted according to the real-time monitoring data, and the restoration effect is improved.

The invention discloses an ecological monitoring method and system for vegetation restoration of degraded grassland, and relates to the technical field of ecological monitoring, and the method comprises the steps: collecting environment data of the degraded grassland, extracting environment features, and calculating the contribution degree of the environment features; establishing a plant type list according to the contribution degree of the environmental characteristics, and optimally selecting plant types and planting proportions from the plant type list to form a plant planting scheme; planning a planting path based on the plant planting scheme for precise planting; and displaying and storing the plant planting scheme and the planting path in a database. According to the method, the relevance and adaptability between plant planting and the environment are greatly improved, the plant survival rate is improved, the plants are adapted by matching the degraded grassland planting area, the plant planting path is planned, and the plant planting efficiency is improved.

The invention discloses a construction method for governing and repairing desertification degraded grassland in an arid area, and relates to the technical field of sand governing. The method comprises the following steps: evaluating a native vegetation type, vegetation coverage, a soil degradation and desertification degree, a soil type and a water resource condition; performing classified measures according to natural conditions, and determining different types of dune (flowing, fixed or semi-fixed) treatment measures; construction preparation; grid sand barriers are laid on the windward sides of the moving dunes and the semi-fixed dunes, and sand stabilization operation is conducted in a mechanical and manual combined mode; digging pits to plant indigenous shrubs subjected to special treatment, and using rooting water retention bags to improve the survival rate; the granular organic fertilizer and the seeds are mixed and stirred according to a certain proportion and then sown into soil at a time, the operation frequency is reduced, and precise fertilization is achieved. Through effective sand prevention and control measures, the sand wind damage is reduced, the living environment of farmers and herdsmen is protected, and the living quality is improved.

The invention relates to the field of unmanned aerial vehicle multispectral remote sensing image analysis and inversion model construction based on a machine learning algorithm, in particular to a temperate zone typical grassland surface biomass estimation method. A multi-rotor unmanned aerial vehicle is used for carrying a multi-spectral sensor, multi-band spectral reflection information of a pasture is obtained from a near low altitude, a physical spectral reflection model is constructed to calculate real and accurate reflectivity data of a same-band spectrum, vegetation coverage, soil attributes and pasture canopy information of a sampling area are recorded, and the earth surface biomass is measured by cutting a sample; according to the method, a plurality of vegetation indexes and a 3D space model are combined with a multi-modal input structure, data enhancement and small sample learning strategies are adopted, a biomass inversion model is constructed through an MANBA machine learning framework, the precision of grassland biomass estimation is improved, and effective technical support is provided for grassland ecological monitoring and management.

The invention provides a natural resource dynamic monitoring method and system based on multi-source data fusion, a medium and equipment. The method comprises the following steps: acquiring satellite remote sensing data, aerial remote sensing data and ground monitoring data; performing data fusion on the satellite remote sensing data and the aerial remote sensing data to obtain fused data; integrating the fusion data with the ground monitoring data to obtain comprehensive monitoring data; performing terrain classification on the comprehensive monitoring data based on deep learning to obtain terrain classification results including cultivated land, construction land, water, forest land and grassland; and optimizing the ground feature classification result based on a space-time logic consistency algorithm to obtain an optimized classification result and ground feature change information. According to the method, through fusion of the multi-source data and classification optimization of the natural resources, in combination with deep learning and a spatial-temporal logic consistency algorithm, dynamic monitoring of the natural resources is realized, the monitoring precision is improved, and reliable data support is provided for natural resource management and environmental protection.

The invention provides an ecological restoration and rapid vegetation planting method suitable for an open grassland mining area, and provides a novel grass seed assembly structure, and according to regional climate characteristics and geographical general situations, in combination with actual planting conditions of a vegetation restoration area, and according to planting principles of local nature, species diversity, ecological complementation and the like. Based on the main biological ecology characteristics of different plant species and the collaborative succession and growth promotion co-fusion development characteristics of the suitable growth environment, multi-system, multi-dimension and multi-species technical assembly is carried out according to local conditions, and a cost-saving, efficient, function-harmonious and landscaping rapid vegetation recovery technical method is created.

The invention discloses a deep learning method for desertification grassland mouse hole recognition based on a neural network model, and belongs to the field of image recognition. The method solves the problems of low efficiency and poor accuracy of an existing mouse hole recognition method, constructs a mouse hole distribution image set through a high-resolution image obtained by an unmanned aerial vehicle, identifies a desertification grassland mouse hole sample according to the mouse hole distribution image set, can obtain a mouse hole feature data set, and achieves the recognition of a mouse hole based on a neural network model. The mouse hole feature data set is used for training the neural network model, mouse holes can be trained and recognized, then the test set is used for testing the trained neural network model, the performance of the neural network model can be evaluated, the steps are repeated, machine deep learning is carried out, and the accuracy of machine learning is improved. The neural network model parameters can be configured, the accuracy of the neural network model can be verified, the grassland mouse hole recognition deep learning method based on the neural network model is finally obtained, and the recognition efficiency and precision of mouse hole recognition can be improved based on the method.

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 invention discloses a method for improving pasture productivity by optimizing a mixed sowing proportion and a sowing mode, which comprises the following steps of: performing rotary tillage on soil and applying humus before freezing in winter, turning over the soil and applying a base fertilizer after reviving in spring, then selecting leguminous plants and gramineous plants according to a weight ratio of 3: 7, respectively and uniformly mixing seeds of two functional groups, the method comprises the following steps: ditching, ridging and sowing gramineous plant seeds in line, sowing leguminous plant seeds in holes between two rows of gramineous plants 20-30 days after the gramineous plant seeds emerge, carrying out topdressing and pest and weed damage management after sowing, mowing the overground part of the grassland at the last stage of plant growth, and controlling the stubble height of the leguminous plants and the gramineous plants. By accurately optimizing the mixed sowing proportion and sowing strategy of leguminous and gramineous pasture and combining the complementarity of plant functional groups in ecological niche (time and space), an innovative pasture mixed sowing method is provided, and theoretical support and technical guarantee are provided for efficient and sustainable development of animal husbandry production.

The invention discloses a method for reseeding and repairing alpine degraded grassland, which comprises the following steps: step 1, cutting: cutting native vegetation of a sample plot with the stubble height of 3-5cm; 2, sowing is conducted, before the rainy season comes, drilling is conducted on target grass seeds in a manual ditching mode, the target grass seeds are 2-3 grass seeds selected from each type of gramineae grass, leguminous grass and miscellaneous grass and mixed according to a certain proportion, and earthing is conducted after sowing is completed; 3, after-sowing management is conducted, agricultural measures such as impurity removal, fertilization and watering do not need to be conducted after sowing is completed, and grazing is forbidden for two years. According to the method, different functional types of plants and combinations suitable for the alpine region are screened out, the method suitable for reseeding restoration of the alpine degenerated grassland is formulated, the reseeding cost is controlled, meanwhile, plant communities high in yield, high in stability and diverse in function are built, and the problem of restoration of the degenerated grassland in the alpine region is solved.

The invention relates to an ecological system carbon sink evaluation method, which comprises the steps of generating a continuous climate distribution grid based on a global ecological system type and climate element data, extracting multi-dimensional characteristic parameters of temperature, rainfall and a carbon sink value through an improved weighted K-means clustering algorithm, quantifying climate transition zone boundary fuzziness in combination with a covariance matrix, and evaluating the ecological system carbon sink evaluation result. Dynamically adjusting the weight of the spatial variation coefficient; a simulated annealing algorithm is utilized to optimize correlation between the division scale and the carbon sink response, and a multi-scale regression model is constructed to generate a high-precision carbon sink distribution diagram; further fusing the remote sensing vegetation index and the ground actual measurement data, and eliminating the evaluation error caused by the conflict between the boundary fuzziness and heterogeneity of the transition zone; according to the method, the problems of insufficient climate-ecology interaction dynamic response modeling and low boundary division precision in the prior art can be solved, the reliability of carbon sink evaluation in complex areas such as forest-grassland interlaced areas and coastal wetlands is remarkably improved, and scientific data support is provided for ecological restoration project site selection and carbon trading markets.

The invention relates to the technical field of leaf nitrogen content estimation, in particular to a method for establishing a grassland plant nitrogen content monitoring model based on a remote sensing technology. The method comprises the following steps: controlling an unmanned aerial vehicle platform carrying a narrowband multispectral camera and a linear polarization camera, and collecting a spectral image sequence and a polarization image group at an observation window with a solar zenith angle of 30-40 degrees; extracting a reflectivity value of each wave band in the spectral image sequence, calculating a reflectivity first-order derivative and determining a red edge position offset; extracting horizontal and vertical polarization components in the polarization image group, calculating a polarization degree and constructing a polarization degree difference index; and identifying the center of the fecal spot by searching a pixel with a local maximum polarization degree difference index and red edge position offset blue shift exceeding a preset threshold value. According to the method, excrement spot recognition and remote sensing description of the nitrogen influence range thereof are achieved through double-index complementation of the red edge position offset and the polarization degree difference index.

The invention discloses a grassland nitrogen content inversion method and system, and relates to the technical field of grassland vegetation quantitative remote sensing. The method comprises the following steps: acquiring measured grassland hyperspectral data and environmental parameter data of a target area, and preprocessing the measured grassland hyperspectral data; according to the preprocessed grassland actually measured hyperspectral data, extracting mapping relation characteristics of hyperspectrum-leaf area indexes in vegetation; extracting a leaf area index-nitrogen content according to the environmental parameter data; and by taking the leaf area index as connection, coupling the extracted leaf area index-nitrogen content with the hyperspectrum-leaf area index, and carrying out inversion on the nitrogen content of the grassland. Through the method provided by the invention, the dynamic change of the nitrogen in the plant growth process can be better captured, and the method has higher stability and transportability so as to quickly and effectively invert the nitrogen content of the grassland, so that the nitrogen of the plant is stably and accurately extracted, and the generalization ability of the nitrogen extraction method is improved.

The invention discloses an evaluation method for dynamically monitoring carbon sink of a grassland and wetland ecosystem, which relates to the field of ecological environment monitoring and comprises the following steps: acquiring historical carbon flux observation data of a target area; dividing the target area into a plurality of space grid units, taking each grid unit as a node in the graph structure, and establishing graph edge connection between adjacent nodes; extracting ecological features for each node; calculating the edge weight of each edge in the graph structure according to the ecological characteristic difference between the adjacent nodes; inputting the graph structure containing the node features and the edge weights into a graph neural network model, and training the graph neural network model by using historical carbon flux observation data as labels; and predicting the carbon flux of an unobserved node or a future time period node by using the trained graph neural network model. The method solves the problem that a traditional model is large in estimation error of the carbon flux in the ecological boundary region, and can remarkably improve the carbon exchange prediction precision of the boundary region.

The invention relates to a southern grassland soil carbon reserve dynamic evaluation method and system fused with remote sensing monitoring, and relates to the technical field of soil carbon reserve evaluation, and the method comprises the steps: drawing a target region topographic map based on multi-source satellite remote sensing data, inputting an initial soil carbon reserve inversion model, and outputting an initial soil carbon reserve distribution diagram and a deterministic thermodynamic diagram; extracting an uncertain area according to a certainty degree threshold value, and configuring and labeling supplementary sampling points according to a division rule; acquiring parameter information of the detection robot, generating a digital twinborn model in combination with the topographic map, configuring a supplementary sampling scheme, and sampling supplementary sampling points to obtain supplementary soil carbon reserve data; and returning the supplemented soil carbon reserve data to the initial soil carbon reserve inversion model, generating a corrected soil carbon reserve distribution diagram and a corrected determinacy thermodynamic diagram, judging that the task is completed when determinacy reaches the upper limit of resource consumption, otherwise, continuing to supplement sampling correction. The problems that traditional soil carbon reserve assessment is inaccurate and low in efficiency are solved.

The invention discloses forest steppe fire extinguishing barrier foam and a preparation method thereof.The foam is prepared from 5%-20% of a material A, 0.5%-0.8% of a material B, 1.8%-3% of a coagulant and the balance water through a foam generation system; the material A is prepared from 1 to 10 parts of organic aggregate and 100 to 200 parts of inorganic aggregate; the material B is composed of 2-7 parts of a composite surfactant and 1-6 parts of a cross-linking agent; the coagulant is carbon dioxide gas. The inorganic aggregate is condensed under the action of hydrogen ions generated by dissolution of carbon dioxide to form colloidal particles, the colloidal particles are polymerized and crosslinked with the organic aggregate to form a gel skeleton of a three-dimensional network structure, the foam is converted into gel foam, and a heat-insulating and anti-burning barrier layer is formed on the surface of a combustible. The fire extinguishing agent is high in fire extinguishing efficiency, good in heat insulation effect and environment-friendly, and has wide application prospects in forest and grassland fire disasters.

The invention discloses a bacillus microbial agent and application thereof in artificial improvement of deteriorated grassland, the microbial agent can improve soil and improve plant productivity so as to promote vegetation recovery and deteriorated grassland restoration, especially restoration of deteriorated grassland in alpine regions, and has the characteristics of high efficiency, environmental protection, economy, practicability and the like.

The invention relates to the technical field of ecological remote sensing and image processing, and particularly discloses a method and system for monitoring the ecological restoration state of thinopyrum intermediary grassland in an alpine region. The method comprises the following steps: acquiring a multi-temporal remote sensing image and ground ecological parameters, and constructing a remote sensing-ground combined observation data set; utilizing a semantic segmentation model of a multi-scale attention mechanism to extract a thinopyrum intermedium block mask; vegetation indexes, texture features and ecological factors are extracted from the mask region to construct a multi-dimensional feature sequence; inputting the feature sequence and manual intervention information into a training factor interaction model in a graph neural network and causal modeling architecture, and outputting a repair level and evolution data according to the training factor interaction model; and generating a visual layer in combination with historical data, and updating a remote sensing feature extraction strategy based on model feedback. The alpine grassland ecological restoration state monitoring system realizes intelligent monitoring and dynamic feedback of the alpine grassland ecological restoration state, has strong timeliness, clear causality and adaptive optimization capability, and is suitable for continuous monitoring and management of a complex ecological system.

The invention relates to a grassland ecological degradation recovery effect dynamic evaluation method and device, equipment and a medium, and the method comprises the steps: obtaining interannual remote sensing data for a target region, carrying out the vegetation feature extraction of the interannual remote sensing data based on the sliding operation of a predetermined window on the interannual remote sensing data, and obtaining a reference time sequence; calculating the relative change rate of vegetation in the target area based on the reference time sequence; and according to the relative change rate and a preset threshold value, evaluating the degradation or recovery condition of the vegetation in the target area. According to the method, the optimal spatial analysis scale, namely the window, is determined according to spatial heterogeneity characteristics (semi-variable function range), adaptive conversion from the pixel to the landscape scale can be realized, subjective interference can be eliminated, a reference time sequence reflecting vegetation characteristics can be accurately determined on the basis of the window, and the method has the advantages of being simple in structure and convenient to operate. And the degradation or recovery condition of the target area can be accurately evaluated based on the reference time sequence.

The invention belongs to the technical field of remote sensing application, and provides a grassland biomass inversion method based on a radiation transfer model and machine learning, and the method comprises the following steps: obtaining and processing remote sensing data, DEM data and actually measured biomass data of a target region; performing terrain correction by adopting an SCS + C method; performing global sensitivity analysis on input parameters in the PROSAIL model, and calibrating the input parameters according to the global sensitivity analysis; introducing topographic factors into the model, and correcting the solar zenith angle and the observation zenith angle; generating simulated canopy reflectivity data by using the model, and constructing a data set; constructing a lookup table LUT by combining the simulated canopy reflectivity data and the above-ground biomass data, and performing machine learning modeling according to the canopy spectral data set; and verifying the inversion precision, and outputting a grassland above-ground biomass spatial distribution map. According to the inversion method, terrain factors are introduced on the basis of a traditional model, and the adaptability and estimation precision of the model to a complex terrain area are improved by combining a machine learning method.

The invention relates to the technical field of forest steppe fire emergency command, and discloses a forest steppe fire comprehensive monitoring and emergency command method and system. The method comprises the following steps: acquiring multi-source monitoring data of a satellite, an unmanned aerial vehicle and a ground sensor, and constructing a fire scene multi-dimensional feature vector through cooperative training and feature fusion; inputting the feature vectors into a physical simulation model to generate fire scene spreading speed and temperature gradient data; analyzing a terrain obstacle coefficient based on the spreading speed data and generating a helicopter no-fly zone coordinate and a passing attenuation factor; and solving and outputting a rescue unit deployment instruction through the scheduling matrix, and projecting a holographic image by using an augmented reality terminal to realize dynamic closed-loop feedback. Through deep fusion of a physical mechanism and reinforcement learning, the problem of separation of traditional fire prediction and resource scheduling is solved, intelligent decision-making of fire accurate prediction and emergency response is realized, and the accuracy and timeliness of fire prevention and control are remarkably improved.

The invention belongs to the technical field of genetic breeding, and particularly relates to application of a molecular marker for breast circumference character breeding of grassland short-tailed sheep, the molecular marker is at least one of the following (1)-(3): (1) a nucleotide sequence as shown in SEQ ID NO.1, nucleotide at the 101bp site is A or G; (2) a nucleotide sequence as shown in SEQ ID NO.3, wherein the nucleotide at the 101bp site is C or A; (3) a nucleotide sequence as shown in SEQ ID NO.5, wherein the nucleotide at the 101bp site is T or C; the application refers to any one of the following steps: identifying the chest circumference of the grassland short-tailed sheep; the chest circumference of the offspring of the grassland short-tailed sheep is improved. By detecting the genotype of the molecular marker and selecting a dominant genotype individual as a parent, the average chest circumference of offspring can be effectively improved, and a reliable tool is provided for realizing molecular assisted breeding and precise genetic improvement of the chest circumference character of the grassland short-tailed sheep.

The invention relates to the field of ecological environment restoration, in particular to a reseeding device for grassland ecological restoration, which comprises a vehicle body, a driving unit, a monitoring unit, a reseeding unit and a soil covering unit are mounted at the bottom of the vehicle body, and the driving unit comprises a first driving part; the reseeding unit comprises a plowing assembly and a seeding assembly, the plowing assembly comprises a shell, the shell is connected with a first driving piece, an opening is formed in the shell, a locking piece is installed at the opening, a plough claw is hinged to the shell, a sleeve is hinged to the plough claw through a connecting rod, and an eccentric roller is embedded in the sleeve; the sowing assembly is installed in the vehicle body, a sowing opening is formed in the bottom of the vehicle body and provided with a sealing cover, the sealing cover is provided with a power piece and a triggering assembly, the triggering assembly is in signal connection with a controller, and the monitoring unit, the first driving piece, the locking piece and the power piece are all in signal connection with the controller. By monitoring the ecological condition of the grassland and automatically turning up and sowing grass seeds according to the real-time monitoring data, the damaged grassland ecology is efficiently and accurately restored.

The invention relates to the technical field of biological improvement methods, and discloses a comprehensive biological improvement method for severe saline-alkali grassland. According to the method, S1, early evaluation and planning are conducted, S2, straw returning to the field and soil preliminary conditioning are conducted, S3, shallow ploughing and soil preparation operation is conducted, S4, microbial inoculation is conducted to strengthen soil activity, S5, pioneer plant sowing and planting are conducted, S6, precise irrigation and drainage regulation and control are conducted, and S7, long-term monitoring and later-period management and maintenance are conducted, through straw returning to the field and microbial inoculation, the pH value of soil can be reduced to 8.0-8.5 from the initial 8.5-10.5, and the salt content is reduced to 0.5%-1% from 1%-3%; compared with the prior art, the method has the advantages that the alkalinization degree is obviously reduced, meanwhile, the organic matter content is increased from lower than 1% to 2%-3%, the soil structure is improved, the porosity is increased, the air permeability and the water permeability are enhanced, a good soil environment is created for plant growth, for example, humus generated by straw decomposition can promote soil particle aggregation, a stable aggregate structure is formed, and the water and fertilizer retention capacity of soil is improved.

According to the basic grassland ecological data monitoring and sampling system disclosed by the invention, in the sampling operation, cutting rings can be automatically taken out and stored through rotation of a turntable, rotation of a butt-clamping mechanical claw and lifting of a butt-clamping sliding block, so that an unmanned vehicle can carry a plurality of cutting rings for sampling work, the sampling work efficiency is improved, and the sampling time is shortened. The cutting ring inserting work can be automatically completed through cooperation of the butt-clamping mechanical claw and the pressing head, soil around the cutting ring can be automatically excavated through the hollow drill, the labor intensity of an operator is relieved, the automation level is improved, the butt-clamping mechanical claw can enter an annular groove excavated by the hollow drill, the cutting ring is taken out by clamping the side face of the cutting ring, and the cutting ring inserting efficiency is improved. The cutting ring does not need to be additionally transformed, the universality of the cutting ring is improved, and the sheath is pressed outside the cutting ring before the soil around the cutting ring is dug out, so that the situation that the accuracy of an analysis result is reduced due to soil disturbance in the cutting ring caused by displacement of the cutting ring is avoided.

The invention provides an organic carrier in-root-rhizosphere growth-promoting microbial agent for ecological restoration of degraded grassland as well as a preparation method and application of the microbial agent, and belongs to the technical field of grassland ecological restoration and microbial agents. The organic carrier growth-promoting microbial agent disclosed by the invention is prepared from four kinds of growth-promoting bacteria (PGPE) in roots, four kinds of growth-promoting bacteria (PGPR) in roots and an organic carrier, the PGPE comprises terracotta warrior bacillus K4, bacillus 9-3-1, bacillus deserticola 11-5-4 and halomonas L31, the PGPR comprises serratia rubra 5, serratia rubra 23, cosinia cosinii 11 and enterobacter parabarium 24, and the organic carrier is a carbon-based organic fertilizer, a cattle and sheep manure organic fertilizer or biochar. When the organic carrier growth-promoting microbial agent is applied to the degraded grassland, the biomass and coverage of plant communities can be remarkably improved. The organic carrier growth-promoting microbial agent can be used for ecological restoration of degraded grassland.

The invention belongs to but not limited to the technical field of animal husbandry statistics, and particularly relates to an alpine meadow yak number calculation method and system based on an unmanned aerial vehicle image, and the method comprises the steps: S1, employing an unmanned aerial vehicle to shoot an orthoimage of a sample plot, and carrying out the statistics of the yak number; s2, splicing the shot images by using Pix4D software, screening a proper sample plot, and respectively counting the quantity of wet cow dung and the quantity of dry cow dung in a visual interpretation mode; and S3, carrying out linear fitting on the obtained data by using Origin 2024b, so as to obtain three relation curves. According to the method for estimating the number of the yaks through statistics of the number of the cow dung in the unmanned aerial vehicle image, the workload of data collection is greatly reduced, and the limitation that the number of the yaks cannot be obtained when the yaks are not grazed in the grassland is avoided. According to the invention, the data acquisition frequency of experimenters is effectively reduced, and the research cost is reduced.

The invention discloses an alpine meadow aboveground biomass monitoring method based on PROSAIL-BP, and belongs to the field of ecological remote sensing information processing. In order to overcome the defect that samples in the alpine region are insufficient and have high precision and high reliability, the method comprises the steps that an alpine meadow mask, remote sensing images and field biomass data in a target region are obtained, the remote sensing images are spliced, the wave band reflectivity is normalized, and the grassland region reflectivity is reserved in combination with mask cutting; predefining a PROSAIL model matched with the region features; performing Sobol global sensitivity analysis to obtain a first-order sensitivity index and a total-order sensitivity index of the parameter; screening a key wave band and four high-sensitivity parameters; uniformly sampling to generate a parameter group, simulating a hyperspectrum through PROSAIL, extracting the reflectivity of a key wave band, and constructing a data set by multiplying a leaf area index by a dry matter content as a target variable; and training a three-layer BP neural network, and processing the reflectivity output pixel biomass of the remote sensing key wave band. The method is applied to a remote sensing information processing system and has high precision and high reliability.