135 results about "Aboveground biomass" patented technology

The invention relates to the technical field of remote sensing space information, in particular to a large-scale forest green carbon inversion method and system based on multi-source remote sensing data, and the method comprises the steps: resolving remote sensing parameters, and obtaining the crown breadth and height of a single-plant crown of a sample plot; taking the crown breadth and the height of the single tree crown of the sample plot as independent variables, and establishing different-speed growth models of different tree species; performing inversion based on original echo signal waveform data of the satellite-borne laser radar to obtain forest structure parameters; according to the forest structure parameters, calculating laser spot scale biomass by using a different-speed growth model; and based on the laser spot scale biomass and the multi-source remote sensing image features, using a U-Net deep learning model to obtain large-scale biomass data with continuous space, and obtaining a large-scale forest above-ground biomass distribution map according to the large-scale biomass data. According to the method, high-precision inversion from single-plant crown parameters to large-scale forest above-ground biomass is realized, and the efficiency of forest biomass estimation is improved.

The invention belongs to the technical field of agriculture, and particularly provides a compound microbial agent and application thereof. The compound microbial agent is prepared from bacillus velezensis RKN1111, bacillus subtilis N-3 and paenibacillus mucilaginosus HX-02, and the compound microbial agent is prepared from bacillus velezensis RKN1111, bacillus subtilis N-3 and paenibacillus mucilaginosus HX-02, and the compound microbial agent is prepared from bacillus velezensis RKN1111, bacillus subtilis N-3 and paenibacillus mucilaginosus HX-02. The compound microbial agent can effectively improve the pH and EC of saline alkali soil, increase the content of alkali-hydrolyzable nitrogen, available phosphorus and quick-acting potassium in the soil, and play a significant role in increasing the plant height, stem diameter, chlorophyll content and aboveground biomass of corn plants in coastal saline-alkali soil.

The invention relates to the technical field of biomass monitoring, and provides a forest above-ground biomass estimation method and device based on stereo density, and the method comprises the steps: obtaining various canopy height information and quadrat AGB stereo density from ground survey data, and obtaining forest features from airborne laser radar data; carrying out regression analysis on at least one of the various canopy height information and the quadrat AGB stereo density and the forest features to obtain the AGB stereo density of the flight area; stepwise linear regression analysis based on the regional scale is carried out on the AGB stereo density and the target parameters of the flight area to obtain an AGB stereo density estimation result, and a regional scale AGB estimation result is determined according to the AGB stereo density estimation result, the various canopy height information and the corresponding pixel areas. According to the method, planar AGB density calculation is converted into three-dimensional AGB density estimation, and the accuracy of the forest above-ground biomass estimation result in a complex scene is improved.

Provided is the use of overexpression of the auxin transporter PtoPIN1a gene in increasing the biomass of Populus tomentosa. The nucleotide sequence of the auxin transporter PtoPIN1a gene is as shown in SEQ ID No. 1. By means of using a genetic engineering method, wild-type Populus tomentosa is transformed by means of Agrobacterium tumefaciens infection to obtain a tissue-specific PtoPIN1a overexpressing transgenic plant, and subsequently, a plant with a relatively high expression level is selected, by means of positive identification, for phenotype observation. Results show that compared with the wild type in the same period, the PtoPIN1a transgenic plant has an increased plant height, a thicker stem and a significantly increased aboveground biomass. By means of the provided technical cultivation method, a high-biomass high-quality poplar variety is cultivated. The method is of great significance for future molecular breeding to cultivate excellent poplar varieties.

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.

The invention relates to the technical field of grass and livestock management, and provides a grass and livestock balance regulation and control method and system based on a light energy utilization model inversion difference value, and the method comprises the steps: obtaining environment detection data, grassland ecological characteristic parameters and historical grazing data of a target grassland; according to the environment detection data, utilizing a pre-constructed light energy utilization model to calculate and obtain inversion biomass distribution of the target grassland; establishing a biomass estimation model according to the historical grazing data and pre-constructed actual measurement quadrat data, and obtaining actual aboveground biomass distribution of the target grassland according to the grassland ecological characteristic parameters and the biomass estimation model; calculating biomass difference distribution data; determining grassland grazing intensity distribution data of the target grassland according to the biomass difference distribution data; and based on the grassland grazing intensity distribution data, dividing the target grassland into a plurality of grazing sub-regions with different bearing capacity grades, and carrying out grass and livestock balance regulation and control. According to the invention, the accuracy of grassland grazing intensity evaluation can be improved, and precise regulation and control of grass and livestock balance can be realized.

The invention discloses a forest degeneration degree, degeneration type and degeneration process identification method, and relates to the field of forest resource monitoring and ecological environment evaluation.The method comprises the steps that multi-source data obtained in a research area is preprocessed, and the preprocessed multi-source data is determined; selecting multi-dimensional indexes from three aspects of forest degradation structure, composition and function, and constructing a multi-dimensional index system according to the preprocessed multi-source data; the multi-dimensional indexes comprise forest coverage rate, crushing degree, tree variety diversity, aboveground biomass and net primary productivity; generating a forest degradation index according to each index in the multi-dimensional index system; determining a forest degeneration degree according to the forest degeneration index, generating a forest degeneration type graph in combination with land coverage data to reveal degeneration differences of different forest types, analyzing a dynamic change track of a forest degeneration region in combination with a normalized combustion index time sequence, and identifying a forest degeneration process; according to the method, the multi-dimensional characteristics of degradation can be comprehensively revealed.

The invention discloses a method for estimating overground biomass of a fresh songarus apetalus plant through photography. The method comprises the steps that five-lens oblique photography equipment obtains images of the fresh songarus apetalus plant land in the front view direction and the front, back, left and right directions; based on an improved CatBoost algorithm and the geographic position, the diameter at breast height, the crown breadth and the tree height of each tree, a geographic position weighted tree growth parameter nonlinear mapping model is constructed, and the tree growth parameter nonlinear mapping model is used for obtaining the diameter at breast height according to different geographic positions, the tree heights and the crown breadth; acquiring an image of a to-be-estimated fresh songarus apetala plant field in front view and front, back, left and right directions; the predicted diameter at breast height and the corresponding tree height are obtained from the obtained image, and the above-ground biomass of the fresh songarus apetalus plant of the image is obtained through calculation of the predicted diameter at breast height and the corresponding tree height based on a different-speed growth equation. According to the method, the overground biomass of the fresh songarus apetala plants can be estimated through low-cost and high-efficiency photography.

The invention discloses an emergent vegetation management method for increasing a biological carbon sequestration function, and belongs to the technical field of ecological system management and restoration, and the method comprises the following steps: in the stable growth period of emergent vegetation, discharging part of wetland moisture, and carrying out first mowing on overground parts of plants above the water surface; after the emergent vegetation further grows, the water level is restored to the end of the growth season, finally water is drained, secondary mowing is carried out, and all the emergent vegetation on the ground is harvested. According to the method, the overground biomass is increased through the plant photosynthetic carbon sequestration function by adjusting the appropriate harvesting time and mode of the wetland emergent vegetation, and therefore the biological carbon sequestration amount is increased. Compared with a traditional method for harvesting wetland vegetation at a time, the method has the advantages that the plant growth rate is remarkably increased, the overground biomass of the plants and the accumulation of organic carbon in surface soil are promoted, and the improvement of the wetland carbon sequestration function is facilitated.

The invention provides a multi-tree forest aboveground biomass remote sensing estimation method and system and a storage medium, and the method comprises the steps: obtaining multi-source remote sensing data based on a target region, including airborne LiDAR point cloud, ground survey data and DEM data; constructing a terrain heterogeneity index DGTHI in combination with DEM data, and quantifying terrain complexity through weight fusion of standardized terrain factors; extracting forest characteristic parameters and calculating the overground biomass of the single tree; performing classification modeling on the sampling region based on DGTHI, respectively establishing multiple linear regression models for different tree species, and optimizing feature parameter selection; and evaluating the precision of the model through cross validation, and popularizing the model to a county scale to perform overground biomass space inversion and mapping. According to the method, terrain heterogeneity classification and tree species specificity modeling are fused, so that the estimation precision of the forest biomass in the complex terrain region is remarkably improved, and a technical support is provided for regional carbon sink monitoring and sustainable forestry management.

Provided is a method for quantitatively estimating potassium demand of regional corn plants during key growth stages. The method includes: S1: calculating critical potassium concentration values Kc for each growth stage of corn; S2: calculating potassium nutrition indexes (KNIs) for each growth stage of the corn; S3: constructing KNI inversion models for each growth stage of the corn; S4: obtaining leaf area index (LAI) data of the corn and calculating above-ground biomass W for each growth stage of the corn; S5: calculating potassium fertilizer utilization rates KAE for each growth stage of the corn based on above-ground potassium accumulations in each growth stage of the corn; S6: calculating relative dry biomass RDW for each growth stage of the corn, and optimal KNI values, denoted as KNItarget, for each growth stage of the corn; and S7: obtaining a plant potassium content absorption model Kabs for each growth stage of the corn.

The invention discloses a songarus apetalus aboveground biomass inversion method using multi-source remote sensing data. The method is realized through the multi-source remote sensing data. The method comprises the following steps: firstly, performing quadrat survey to obtain biomass measured data, and scanning by using an unmanned aerial vehicle laser radar to obtain tree height data; and then, combining a quadrat survey result, biomass measured data and tree height data to construct a tree height-biomass inversion model, and estimating biomass distribution of songarus apetalus in a laser radar scanning area. Then, collecting sentinel No.2 and Landsat series images, extracting spectral features and tree age data, performing regression analysis, and constructing a songarus apetalus biomass inversion model based on satellite remote sensing data; and finally, estimating the biomass of the songarus apetalus in a satellite remote sensing image range by utilizing the model and satellite remote sensing data. According to the method, high-efficiency and high-precision overground biomass inversion of the songarus apetala is realized.

The invention belongs to the field of degraded grassland restoration evaluation research, and discloses a degraded grassland restoration evaluation research method based on multivariate remote sensing data, and the method comprises the steps: carrying out the observation through employing a FragMap unmanned aerial vehicle aerial photography analysis system, and carrying out the preprocessing through employing Pixel Base Management Classifier software; collecting grassland above-ground biomass data; acquiring and preprocessing remote sensing data; constructing a grassland coverage and aboveground biomass remote sensing estimation model, and performing precision verification; analyzing grassland coverage and biomass space difference before and after restoration of the degraded grassland; and analyzing space-time dynamic changes of grassland coverage and biomass. According to the method, a high-temporal-spatial-resolution NDVI data set of a research area is constructed by combining a high-temporal-spatial-resolution data fusion algorithm, a grassland coverage and aboveground biomass remote sensing estimation model is constructed by combining ground survey and unmanned aerial vehicle aerial photography, and the grassland coverage and aboveground biomass change conditions before and after a degraded grassland restoration measure is implemented are systematically evaluated. And the restoration effects of different restoration measures on the degraded grassland are evaluated.

The invention provides an overground carbon reserve assessment method and device based on laser radar point cloud data. The method provided by the invention comprises the following steps: acquiring point cloud data of a target area; processing the point cloud data; constructing a digital elevation model and a digital surface model based on the processed point cloud data; determining a canopy height model based on the digital elevation model and the digital surface model; inverting the above-ground biomass based on the canopy height model and the biomass empirical equation, and generating an above-ground biomass distribution diagram; on the basis of the above-ground biomass distribution diagram, carbon content conversion is conducted on the above-ground biomass, an above-ground carbon reserve distribution diagram is generated, and the above-ground carbon reserve of the target area is evaluated on the basis of the above-ground carbon reserve distribution diagram. According to the method and the device provided by the invention, the carbon reserve difference of different vegetation types in the target area is considered, the problems of insufficient precision and poor spatial heterogeneity of an overground carbon reserve assessment method are solved, and the reliability and the adaptability of carbon reserve assessment are improved.

The invention discloses an application and a method of an overexpression transcription factor PtoLBD1.2 gene in promoting growth and development of populus tomentosa, a nucleotide sequence of the transcription factor PtoLBD1.2 gene is shown as SEQ ID NO.3. A transgenic plant is overexpressed through transgenic tissue specificity PtoLBD1.2, a plant with high expression quantity is screened out through subsequent positive identification for phenotypic observation, and a result shows that the transcription factor PtoLBD1.2 gene can be used as an overexpression transcription factor PtoLBD1.2 gene for promoting growth and development of populus tomentosa. Compared with the wild type in the same period, the transgenic plant of PtoLBD1.2 has the advantages that the plant height is increased, the stem is thickened, and the aboveground biomass is obviously improved. According to the cultivation method provided by the invention, the high-quality poplar variety with high growth speed and high biomass is cultivated. The method has great significance in molecular breeding and cultivation of excellent poplar in the future.

Embodiments of the present description provide a carbon sink assessment method and apparatus for a target forest region. The method comprises: on the basis of laser echo signals collected for a target forest region, predicting a first tree feature of the target forest region, wherein the first tree feature is used for indicating tree heights at sampling points in the target forest region; on the basis of optical images collected by a camera for a plurality of sampling areas in the target forest region, determining a second tree feature corresponding to each of the plurality of sampling areas, wherein the second tree feature is used for indicating the number, species, and diameter at breast height of trees included in the corresponding sampling area; on the basis of the first tree feature and the second tree feature corresponding to each of the plurality of sampling areas, predicting a first aboveground biomass of the target forest region; and on the basis of the first aboveground biomass, predicting a carbon sink stock of the target forest region.

The invention provides a soil mixing and sowing method of typical herbaceous plants and a method for forming a biological ridge of a newly-built terrace edge ridge, which comprises the following steps of: 1) selecting qualified trifolium repens, ryegrass, alfalfa and vicia villosa seeds according to process requirements; 2) seed pretreatment: soaking trifolium repens and alfalfa seeds with warm water at 30-40 DEG C for 2-4 hours; the method comprises the following steps: soaking ryegrass and vetch seeds in cold water for 1-2 hours; (3) respectively and uniformly mixing the four seeds with soil according to the proportion required by the process; (4) uniformly sowing the seed-soil mixture on a target land parcel; and 5) management after sowing. According to the herbaceous vegetation soil mixing and sowing method, high germination and survival rate of the herbaceous vegetation can be guaranteed, the plant height, the above-ground biomass and the vegetation coverage of all the herbaceous vegetation are continuously increased in the growing season, and the root system biomass is in the trend of firstly decreasing and then increasing; and all herbaceous vegetation improves the water retention performance of the soil.

The invention discloses an automatic crop nitrogen fertilizer accurate management method based on a remote sensing image, and relates to the technical field of accurate fertilization, the method comprises the following steps: obtaining ground measured data containing overground biomass and plant nitrogen concentration in a key growth period, and establishing a nitrogen dilution curve model through nonlinear least square fitting; uAV multispectral images are collected synchronously, a vegetation index matrix is calculated, a machine learning regression model is trained in combination with ground data, and an inversion model of overground biomass and plant nitrogen accumulation is obtained; acquiring a current-season image before topdressing, and performing pixel-by-pixel inversion to generate a biomass and nitrogen accumulation spatial distribution diagram; and calculating a critical nitrogen accumulation amount according to the nitrogen dilution curve so as to obtain a nitrogen nutrition index, calculating a recommended topdressing amount in combination with a preset basic topdressing amount and a nitrogen fertilizer utilization rate, and outputting a prescription map which can be directly imported into a variable fertilization machine. The method belongs to localized modeling and machine prescription map generation, the technical threshold is lowered, the nitrogen fertilizer utilization rate is increased, and environmental pollution is reduced.

The present invention provides a method for estimating forest aboveground biomass by combining multi-source remote sensing data and an integrated model, relating to the field of forest management technology. The method comprises: obtaining airborne LiDAR data, high-resolution orthophotos, and multi-source remote sensing data of a predetermined sample plot; using the high-resolution orthophotos as an aid, obtaining the forest aboveground biomass of the predetermined sample plot based on the airborne LiDAR data; performing feature optimization on the multi-source remote sensing data to obtain an optimal combination of remote sensing feature variables; constructing and stacking multiple models based on the optimal combination of remote sensing feature variables and the forest aboveground biomass of the predetermined sample plot using an extreme gradient boosting algorithm, an adaptive boosting algorithm, a random forest algorithm, and a gradient boosting decision tree algorithm; determining the model with the highest accuracy among all the models and using it to estimate the forest aboveground biomass of the predetermined study area to obtain an estimated result. The present invention solves the problem of low accuracy in forest biomass estimation in the prior art.

The invention relates to the technical field of forest fire prevention, in particular to a large-scale forest ground surface litter load calculation method. The method comprises the steps of obtaining a forest resource partition to which a target area belongs; determining the forest type of each forest class in the target area based on the national forest resource checking data; according to the forest resource subarea to which the target area belongs and the forest type of each forest class in the target area, establishing an above-ground biomass and stock regression model of each forest class in the target area; acquiring the aboveground biomass of each forest class in the target area according to the aboveground biomass and accumulation regression model; obtaining the land surface litter carrying capacity of each forest class in the target area by inquiring the proportion of the land surface litter carrying capacity in the aboveground biomass in the forest class of each forest type; and obtaining spatial distribution of the surface litter load of the target area according to the surface litter load of all forest classes in the target area. According to the method, the efficiency and accuracy of large-scale forest surface litter load calculation can be effectively improved.

The invention discloses a haloxylon ammodendron forest biomass estimation method based on unmanned aerial vehicle multi-source data collaborative inversion, and relates to the technical field of environment remote sensing and information, and the method comprises the steps: obtaining a training sample set; obtaining unmanned aerial vehicle multispectral image data and unmanned aerial vehicle laser radar point cloud data of each training sample plot; extracting a first type of feature set comprising vegetation index features and texture features; extracting a second type of feature set comprising a height feature and a density feature; fusing the two types of feature sets to form a joint feature set; screening the joint feature set to construct a target feature subset; and carrying out model training by utilizing a machine learning algorithm to obtain an above-ground biomass estimation model. According to the method, the technical problem that the existing single remote sensing data source is low in precision in estimating the biomass on the desert sparse haloxylon ammodendron forest land is solved, high-precision and automatic estimation from the sample land to the regional scale is realized, and technical support is provided for accurate accounting of the desert carbon reserve in the arid region.

The invention discloses a crop remote sensing yield estimation method and device fusing meteorological data and planting information, a medium and a product, and relates to the technical field of agricultural informationization, and the method comprises the steps: obtaining basic data of a to-be-estimated area; determining daily total primary productivity of the crops according to the meteorological data and the remote sensing image data; determining overground biomass of the crops according to the daily total primary productivity and the key growth period of the crops; according to the variety information and the remote sensing image data, constructing a filling period vegetation index attenuation model; the filling stage vegetation index attenuation model is used for dynamically determining a harvest index; according to the above-ground biomass and the harvest index of the crop, determining a per unit yield result of the crop; the accuracy of crop yield estimation can be improved.

The soybean aboveground biomass dynamic across growth period estimation method, system and program product relate to the crop yield technical field, fill the existing technology in the problem of combining research soybean AGB based on UAV remote sensing and machine learning relative blank. Through the unmanned aerial vehicle to soybean multiple growth stage data collection, and based on soybean growth stage data, respectively generate multispectral orthophoto and digital surface model; based on multispectral orthophoto and digital surface model, respectively extract the crown structure features, image texture features and multiple vegetation indexes related to aboveground biomass of crops; after improving the stacking model, using multiple data of soybean, crown structure features, image texture features and multiple vegetation indexes related to aboveground biomass of crops to train the improved stacking model, obtain the trained stacking model; based on the trained stacking model, complete the soybean aboveground biomass growth period estimation.

The invention relates to a remote sensing estimation method and system for grassland above-ground biomass and fixed carbon content thereof. The method comprises the following steps: acquiring a hyperspectral remote sensing image, and carrying out atmospheric correction to obtain a surface reflectance image; identifying grassland functional end members to obtain a localized grassland end member spectrum library; performing linear spectrum mixed decomposition in combination with the surface reflectance image and the localized grassland end member spectrum library to obtain a grassland plant functional abundance set; calculating a pixel-level dynamic specific carbon content in combination with time sequence multispectral image data to obtain a dynamic specific carbon content field; performing regression prediction calculation by combining the surface reflectance image, the radar data and the grassland plant functional abundance set to obtain a grassland above-ground biomass distribution map; and carrying out pixel-by-pixel multiplication calculation to obtain spatial distribution data of the overground carbon sequestration amount. By adopting the method, the problem of estimation deviation caused by using a fixed carbon content coefficient in the prior art can be solved, and the estimation precision is improved.

The present application relates to the field of agricultural ecological environment management and carbon sink monitoring technology, and more particularly to a tea tree lossless tea garden carbon fixation and sink increase method, comprising: pyrolyzing agricultural waste to obtain biochar and making the biochar into a specific standard biochar-based fertilizer; determining tea garden soil organic matter, cation exchange capacity and soil bulk density, calculating soil quality comprehensive coefficient by using improved Nemerow comprehensive index method and dividing soil quality grades according to the soil quality comprehensive coefficient; applying the biochar-based fertilizer in a differentiated application amount according to the soil quality grades; determining tea tree aboveground biomass after fertilization and calculating tea tree carbon storage according to a corresponding carbon storage model selected according to the age of the tea tree; simultaneously determining soil parameters to calculate tea garden soil carbon storage; and finally taking the tea tree carbon storage and the soil carbon storage as the total carbon sink of the tea garden. The present application realizes systematic, accurate and lossless monitoring of tea garden carbon fixation and sink increase technology.

The invention relates to an above-ground carbon reserve evaluation and prediction method and system based on forest change, and the method comprises the steps: constructing a corresponding carbon absorption model after forest interference according to a forest state data set and an above-ground biomass data set; tracking pixels with corresponding forest change states on a spatial scale by adopting a pixel-level discrimination technology; dividing a forest change state on a time scale, and setting a corresponding forest aboveground carbon reserve estimation mathematical model; acquiring an evaluation value of forest aboveground carbon reserves, and predicting future carbon reserve changes by using the prediction model; and obtaining an evaluation value and a prediction value of the forest aboveground carbon reserve corresponding to the target forest region according to the query request. The beneficial effects of the invention are that the method achieves the finer and more accurate estimation of the forest aboveground carbon reserve for different forest change activities, and carries out the prediction based on the prediction model.

The invention provides a method for recovering grassland between photovoltaic panels, and belongs to the technical field of grassland recovery. The recovery method comprises the steps that during soil preparation and deep scarification, crop waste is thrown, and an organic interlayer is constructed; meanwhile, base fertilizer is applied during soil preparation and deep scarification; the deep scarification depth ranges from 30 cm to 40 cm; before sowing, microbial fertilizer treatment is carried out, pasture mixed seeds are sown, and the pasture mixed seeds are northeast leymus chinensis, puccinellia koreana and elymus dahuricus. According to the method, through the synergistic effect of the northeast Chinese wildrye, the puccinellia koreana and the elymus dahuricus and the methods of organic interlayer salt resistance, fertilization, microbial fertilizer treatment and the like, the vegetation coverage, community density and overground biomass of the grassland between the photovoltaic panels are remarkably improved, the alkali spot area proportion of the grassland between the photovoltaic panels and the pH value of 0-20 cm surface soil are reduced, and compared with the current sowing year, the method has the advantages that the cost is reduced, and the yield is increased. The emergence rate of the pasture in the second year of sowing, the fresh weight of the overground plants and the dry weight of the overground plants are remarkably increased, and the recovery efficiency and ecological benefits of the grassland between the photovoltaic panels are improved.

The application discloses a temperate meadow steppe pasture grassland soil and vegetation synergic improvement method and application, and belongs to the technical field of grassland ecological restoration and degraded grassland improvement, and the method comprises the following steps: S1, pre-cutting roots to form narrow cracks; S2, forming an ice wedge, using winter natural cooling to make deep unfrozen water migrate to the cracks and expand by freezing, and the crack width is expanded; S3, hot melting and seeding, sowing the pasture grass seeds in the ice wedge cracks and applying a water-retaining agent when the surface soil is thawed in the next year; S4, spring root cutting, performing the root cutting operation when the soil layer thawing depth reaches 10-15 cm; and S5, applying a composite mixed fertilizer. Experiments show that the method can significantly improve the pasture grass community coverage, height and aboveground biomass, and simultaneously improve the contents of soil nutrient components, organic carbon and the like, and is suitable for the pasture grassland with the pasture grass as a dominant species, and has good ecological and economic benefits.

The present application provides a kind of grassland aboveground biomass physical inversion method and system, belong to ecological monitoring technical field, comprising: according to look-up table and the correction remote sensing image of measurement area, determine the actual vegetation parameter of each measurement point in measurement area;Look-up table includes multiple preset vegetation parameters and the preset vegetation spectral characteristics corresponding thereto;According to each actual vegetation parameter and the aboveground biomass measured value of corresponding measurement point, construct cost function, under the preset iteration termination condition, iteration solving grassland type corresponding optimization stem leaf ratio;According to optimization stem leaf ratio and each actual vegetation parameter, obtain the aboveground biomass optimization value of each measurement point;According to the aboveground biomass optimization value of all measurement points and the grassland type distribution diagram of all measurement area, generate the aboveground biomass distribution diagram of overall area.The present application is through look-up table and iteratively optimizes grassland type stem leaf ratio to realize the accuracy of improving the spatial distribution of overall area aboveground biomass by inverting measurement point vegetation parameter.

The present invention provides a strain of Pseudomonas chlororaphis, a composite bacterial agent and its application in the restoration of degraded grasslands, relating to the technical field of microorganisms. The Pseudomonas chlororaphis provided by the present invention is Pseudomonas chlororaphis PS08, and the preservation number is CGMCC No. 33616. This strain has the ability to decompose potassium and dissolve phosphorus, and also has the ability to produce siderophores, auxin and ACC deaminase, which can improve the seed germination rate, promote plant growth, increase plant biomass and promote plant root development, and thus be used for the restoration of degraded grasslands. The present invention uses the composite bacterial agent prepared from this strain to repair degraded grasslands. By constructing a multi-dimensional microhabitat of soil structure, nutrients and microorganisms, the density and vitality of underground propagules are enhanced, thereby improving the soil fertility. On this basis, Medicago falcata and wild native grass seeds are reseeded to achieve the synergistic restoration of vegetation and soil, increase the aboveground biomass and forage quality, and improve the soil fertility of grasslands.