164 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.

According to the unmanned aerial vehicle multi-source quantitative remote sensing rape growth state evaluation method, rape is divided into a seedling stage, a flowering stage and a legume stage according to growth nodes, ground hyperspectral and multi-spectral data are synchronously obtained, resolution is increased and decreased, the data are converted into all-dimensional, multi-scale and multi-temporal data, a growth state factor inversion model of the rape is established, and the rape growth state is evaluated. Evaluating the contribution of different growth state factors to the growth vigor of the rape, and evaluating the growth state of the rape based on comprehensive evaluation indexes of leaf area index, overground biomass and chlorophyll content. The method comprises the following steps: establishing a model based on multi-layer linear regression and partial least square optimization models of three types of growth factors LAI, AGB and CC, multi-mode prediction based on entropy aggregation and hierarchical analysis of entropy loading expert knowledge in combination with expert knowledge and agronomic knowledge, and setting a model with an optimal verification index in different growth periods as a growth state evaluation model in the period. The rape growth state evaluation period is short, the pertinence is strong, and the accuracy is good.

The invention belongs to the technical field of agriculture, and discloses a method for identifying and selecting drought-resistant and salt-tolerant wheat varieties in a growth period, which is used for identifying and selecting the drought-resistant and salt-tolerant wheat varieties in the growth period of different wheat varieties under the stress of salt with soil salt content of 0.3% and 0.6% as a correlation analysis result of membership function values and comprehensive evaluation values of all indexes in the whole growth period of the wheat. The above-ground biomass in the heading stage and the filling stage is preliminarily determined as a key stage for evaluating the drought resistance, and can be used as an identification index for screening salt-tolerant varieties. The salt tolerance comprehensive index established through the indexes can be used for clustering test varieties, a direct and simple basis is provided for variety salt tolerance identification and grading, and the salt tolerance of wheat varieties (lines) can be well evaluated by utilizing a whole-growth-period comprehensive model.

The invention discloses a natural grassland grazing duration prediction method and prediction device based on a remote sensing technology. The prediction method comprises the following steps: obtaining daily forage grass feed intake according to the existing above-ground biomass of the grassland and the potential above-ground biomass of the grassland; according to the daily forage grass feed intake and the total amount of edible forage grass in the future preset time period, the grazing duration is obtained. According to the method for predicting the grazing duration of the natural grassland based on the remote sensing technology, the grazing duration of the grassland can be effectively predicted by accurately calculating the daily forage grass feed intake of the natural grassland and the total amount of the edible forage grass in the future preset time period; through scientific evaluation of the existing above-ground biomass and the potential above-ground biomass of the grassland, reasonable utilization of pasture resources can be ensured, grassland degradation caused by excessive grazing is avoided, and the production potential of the grassland is maximized.

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 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 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 discloses a natural grassland forage grass demand remote sensing prediction method and a remote sensing prediction device. The prediction method comprises the following steps: obtaining daily forage grass feed intake according to the existing above-ground biomass of the grassland and the potential above-ground biomass of the grassland; according to the daily forage grass feed intake, the grassland potential above-ground biomass in the future preset time period and the natural grassland reasonable utilization rate, a forage grass demand prediction result in the future preset time period is obtained. According to the remote sensing prediction method for the forage grass demand quantity of the natural grassland provided by the invention, the forage grass demand quantity of the natural grassland can be effectively predicted by accurately calculating the daily forage grass feed intake of the natural grassland and the total amount of edible forage grass in the future preset time period; by dynamically and scientifically evaluating the grassland forage grass demand quantity, reasonable utilization of forage grass resources can be ensured, grassland degradation caused by excessive grazing is avoided, and the grassland production potential is maximized at the same time.

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 provides pseudomonas chlororaphis, a complex microbial inoculant and application of the complex microbial inoculant in restoration of degraded grassland, and relates to the technical field of microorganisms. The pseudomonas chlororaphis provided by the invention is pseudomonas chlororaphis PS08, and the preservation number of the pseudomonas chlororaphis PS08 is CGMCC (China General Microbiological Culture Collection Center) No.33616. The strain has the capacity of dissolving potassium and phosphorus, also has the capacity of producing siderophores, auxin and ACC deaminase, can improve the seed germination rate, promote plant growth, improve plant biomass and promote plant root development, and is further used for repairing degraded grassland. The complex microbial inoculant prepared from the strain is used for repairing degraded grassland, the soil fertility is improved by constructing a soil structure, nutrient and microorganism multi-dimensional microhabitat and enhancing the density and vitality of underground propagules, and on the basis, alfalfa and wild indigenous grass seeds are reseeded, so that synergistic repairing of vegetation and soil is achieved, and the soil fertility is improved. The above-ground biomass and the pasture quality are improved, and the grassland soil fertility is improved.

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 a point cloud tree biomass estimation method, which comprises the following steps of: firstly, clustering different branches in preprocessed point cloud data, and then, carrying out fine-grained voxelization on the point cloud of each branch to generate a dense voxel grid; the outer boundary of each branch is approximated by using a convex hull, and the number of points located in the convex hull is obtained based on the convex hull and a voxel network, that is, whether each voxel is filled by point cloud is determined by occupying a modeling technology, so that the space in the branch is filled. And finally, calculating the volume of each tree according to the total amount of the occupied voxels, and converting the volume into biomass in combination with parameters such as wood density and the like, thereby realizing accurate estimation of the aboveground biomass of the tree. According to the method, the complex three-dimensional structure of the tree can be captured more accurately, and estimation errors caused by structure simplification are reduced.

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.

The invention discloses a desert steppe shrub above-ground biomass satellite remote sensing estimation method, which is applied to desert steppe shrub above-ground biomass estimation and comprises the following steps: acquiring satellite remote sensing data of an unmanned aerial vehicle measurement area arranged in a desert steppe shrub area; performing gridding processing on an unmanned aerial vehicle sample plot arranged in the unmanned aerial vehicle measurement area to obtain a sample plot grid; acquiring remote sensing data corresponding to the sample plot grid according to the satellite remote sensing data of the unmanned aerial vehicle measurement area and the sample plot grid; and inputting remote sensing data corresponding to the sample plot grids into the trained relation model of the overground biomass of the shrubs in the unit area and the remote sensing data to obtain the overground biomass of the shrubs in the unit area. According to the method, through cooperative monitoring of the desert steppe shrub overground biomass from point to surface to domain, the monitoring scale limitation can be broken through, and rapid and accurate shrub overground biomass estimation can be realized in a region or a larger scale.

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 forest above-ground biomass estimation method and system based on open shared data, and relates to the technical field of forest carbon reserve measurement, and the method comprises the steps: obtaining the open shared data, carrying out the preprocessing, extracting a variable set from the open shared data, and taking the variable set as model input, then, variable roughing is carried out through a Kendall correlation coefficient analysis method, variable selection is carried out through a random forest change replacement method, and stable and reliable model variables with low redundancy are obtained; and respectively training and establishing above-ground biomass nonlinear estimation models of the coniferous forest, the broad-leaved forest and the coniferous and broad-leaved mixed forest by utilizing the optimized variables to obtain biomass estimation results of the coniferous forest, the broad-leaved forest and the mixed forest, and comprehensively calculating to obtain the above-ground total biomass of the forest. According to the method, nonlinear estimation models are innovatively established for a single tree species type and a mixed tree species type respectively, the precision of overground biomass estimation is improved, and the method can serve accurate estimation of carbon sources and carbon sinks of'region-country-global '.

The present invention relates to a mangrove vegetation carbon sink monitoring and measurement method based on drone and AI technology. A mangrove identification model based on the U‑Net improved model of the PyTorch deep learning framework is constructed through drone AI technology to realize intelligent identification and distinction of various types of mangrove vegetation and their regional area and quantity, with an accuracy rate of more than 90%. Through regular drone aerial photography monitoring, combined with field-measured growth parameters and variable parameters derived from drone aerial photography data, an optimal fitting model of mangrove tree crown width, aboveground biomass, and unit leaf area based on drone aerial survey is constructed to accurately calculate the carbon storage and carbon fixation of mangrove regional vegetation, and comprehensively evaluate the carbon sink capacity of mangroves in the monitoring area. This application realizes intelligent, digital, and comprehensive monitoring and evaluation of the carbon sink capacity of mangrove wetland vegetation.

The invention discloses a multi-scale forest above-ground biomass estimation method based on an individual tree detection technology, relates to the technical field of individual tree detection, and solves the problems that in an existing AGB estimation method, the overall accuracy of area AGB estimation is influenced by uncertainty of an individual tree method, and the overall accuracy of area AGB estimation is influenced due to the fact that a model adopts a complex mechanism. And therefore, the accuracy of the prediction is influenced by the random residual error in the actual predicted value, and the precision of the estimation result is reduced, and the like. According to the method, AGB estimation from the individual tree scale to the sample plot scale is carried out by adopting an individual tree method; based on the EIV model, constructing an AGB upscaling estimation model from the sample plot scale to the forest stand scale; amplifying a forest stand scale reference AGB sample by adopting an EIV model and canopy height extracted by ICESat-2 / ATL08; estimating a high-resolution forest AGB by using a random forest model, and the like. According to the method, multi-scale AGB high-precision estimation from a single tree to a region can be realized, and the estimation efficiency and precision of the forest biomass are greatly improved.

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.