208 results about "Vegetation types" patented technology

The invention discloses a forest prevention and control method based on disease and pest monitoring, and belongs to the technical field of forestry prevention and control, and the method comprises the steps: dividing a target forest into regions according to vegetation types, collecting historical multi-source data, filling the missing data, analyzing a time sequence and causal relationship to construct a time sequence causal diagram, and simulating a disease and pest spatio-temporal dynamic state in combination with current data. And determining a prevention and control risk level and a key induction factor of each region, executing a prevention and control strategy according to the level, and updating a causal diagram through reinforcement learning according to forest health response data. According to the method, a closed loop is formed from data processing to model construction, accurate prevention and control are achieved, efficiency and scientificity are improved, forest ecological changes can be dynamically optimized and adapted, and the prevention and control effect is guaranteed.

The invention discloses an intelligent forest fire monitoring method and device, electronic equipment and a medium, and the monitoring method employs a multi-mode deep learning model based on a double-attention mechanism to enhance the capturing capability of early flame thermal radiation characteristics and smoke form characteristics, and greatly improves the recognition sensitivity. In combination with triple verification and a confidence coefficient decision-making mechanism, errors caused by environmental interference are effectively avoided, and the recognition reliability is comprehensively improved; meanwhile, based on intelligent gridding three-dimensional monitoring of terrain complexity and vegetation types, the blind area coverage rate is greatly reduced, normalized inspection and post-disaster quick response are performed through the unmanned aerial vehicle platform, the monitoring coverage range is enlarged, and the response efficiency is improved.

The invention discloses a coastal zone carbon sink data acquisition management system and method, and particularly relates to the technical field of carbon sink evaluation, and the method comprises the following steps: dividing carbon flux parameter partitions based on vegetation types, sediment characteristics and historical carbon storage levels, and establishing initial reference values; plant biomass, soil organic carbon content and non-carbon dioxide greenhouse gas flux are regularly collected according to fixed sampling points, and a carbon sink observation data sequence is formed; when data exception triggers an evaluation process, generating a carbon sink credibility index and an ecological interference response index; inputting the two indexes into a prediction model to output an adjustment vector, generating an adjustment strategy in combination with a weight, and dynamically updating an initial carbon flux reference value of each partition; according to the method, regionalized management is realized by constructing carbon flux parameter partitions, a carbon sink observation data sequence is established, an evaluation index is dynamically generated, intelligent updating of a carbon flux reference value is completed in combination with a prediction model, and the precision, responsiveness and adaptive adjustment capability of carbon sink monitoring are improved.

The invention discloses a vegetation gross primary productivity monitoring method based on downscaling and multi-source remote sensing data, and belongs to the field of remote sensing image processing. In order to accurately monitor the GPP change of an engineering scale, the vegetation total primary productivity monitoring method comprises the following steps: acquiring first resolution multisource remote sensing data, downscaling by using CNN, inputting data containing DEM and LUCC in a layer, and dynamically adjusting background weight in a loss function through an LUCC binarization mask (a vegetation region is 1, and a non-vegetation region is 0); a CASA method is used for simulating GPP, a multi-year target area GPP data set is generated, and the maximum light energy utilization rate is determined by the vegetation type; analyzing GPP spatial and temporal change characteristics of the data set by using a Theil-Sen slope estimation method and the like, and extracting spatial distribution and a time change mode; extracting principal components of similar factors by using a principal component analysis method, calculating local correlation coefficients of the principal components and the GPP by using a partial correlation method, identifying dominant influence factors by using a contribution degree decomposition model, and extracting an environmental factor driven map. The method is applied to the field of ecological remote sensing monitoring.

The invention discloses an urban block green land planning method and device based on thermal environment big data, and the method comprises the steps: obtaining the thermal environment data, vegetation coverage data and a vegetation database of a target block; constructing a thermal environment map of the target block based on the thermal environment data, and obtaining a thermal environment feature vector of each sub-block according to the thermal environment map; acquiring a microclimate influence value of each vegetation type; performing correlation analysis on the thermal environment feature vector and the microclimate influence value of each sub-block to obtain the microclimate adjustment contribution degree of each vegetation type to each sub-block; and according to the microclimate adjustment contribution degree, determining a target vegetation type to be planted in each sub-block and a planting proportion of each target vegetation type, and obtaining a vegetation configuration scheme of the target block. The microclimate adjusting capability of the urban green land can be improved.

The embodiment of the invention relates to the technical field of artificial intelligence, and provides a coastal salt marsh vegetation carbon sink estimation method and system based on multi-temporal phenological characteristics. The method comprises the following steps: acquiring a remote sensing image of a target coastal salt marsh area in a key phenological period; extracting an initial vegetation contour of a vegetation coverage range in the target area from the remote sensing image by adopting an unsupervised classification method; constructing a phenological decision tree model by taking an NDVI threshold method as a core, and integrating a machine learning enhanced node division mechanism and a phenological period slope analysis method to enhance the phenological decision tree model; inputting the initial vegetation contour into a phenology decision tree model, and performing multi-level classification on vegetation types according to the initial vegetation contour through the phenology decision tree model to distinguish vegetation coverage ranges of various types of vegetation in the target coastal salt marsh area; and vegetation carbon density parameters are obtained, and the estimation of the total carbon sink amount of the target coastal salt marsh area is completed through spatial superposition calculation in combination with a vegetation coverage range, so that accurate classification of vegetation and efficient estimation of carbon sink are realized.

The invention relates to the technical field of geological engineering, in particular to a comprehensive evaluation method and system for a mine geological environment, and the method comprises the following steps: collecting mine surface geological data based on a target mine region, collecting vegetation types and vegetation coverage area data, evaluating the occurrence probability of landslide and land subsidence of a plurality of regions of a mine, and obtaining a comprehensive evaluation result; and obtaining a surface risk assessment result. According to the method, the accuracy and the reliability of risk assessment are improved by integrating the surface geological data and the vegetation coverage data and combining meteorological information and topographic analysis, the judgment for geological stability is enhanced by combining fixed-point assessment and stratigraphic analysis, so that early warning for a hidden danger area is more timely and specific, and the risk assessment accuracy and the risk assessment reliability are improved. By monitoring surface deformation in real time and combining long-term data analysis, a more dynamic and continuous geological environment change trend is provided, mining strategies and disaster prevention and reduction measures can be adjusted in time, and the environmental influence of mining activities is reduced.

The invention relates to the technical field of ecological restoration scene modeling, in particular to an ecological restoration scene modeling method and system platform applied to territorial space planning, and the method comprises the following steps: based on a successful ecological restoration technical scheme case and a to-be-restored land parcel unit. According to the method, normalization processing is carried out on multi-dimensional attributes between successful ecological restoration technical scheme cases and to-be-restored land parcel units, a source-target land parcel feature fingerprint set is constructed, and specific attributes such as annual average temperature, precipitation, soil types, pH values, altitudes, native vegetation types and labor cost are combined; modeling is realized on data source selection granularity and expression dimension. Furthermore, an importance coefficient is set according to the influence degree of each dimension in a historical repair success case, feature difference weight calculation is guided, and a technical migration adaptation degree score is obtained by combining normalized difference calculation, so that a quantifiable source-place adaptation capability evaluation index is established, and the limitation that judgment depends on experience in the prior art is broken through.

The invention discloses a highway area vegetation carbon sink plaque calculation method. The method comprises the following steps: step 1, acquiring multi-source remote sensing data; 2, data preprocessing; step 3, dividing vegetation types; step 4, vegetation feature extraction; 5, constructing a biomass estimation model; step 6, laboratory determination; step 7, spectral analysis; 8, calculating carbon reserves; step 9, carbon sink plaque identification; and step 10, carbon sink plaque feature analysis. The invention provides the carbon sink measuring and calculating method specially aiming at the highway area vegetation, the carbon sink quantity of the highway area vegetation is quickly obtained by comprehensively applying multi-source data and various technical means, and the carbon sink plaque characteristics of the highway area vegetation are deeply analyzed, so that a scientific and comprehensive basis is provided for ecological protection and carbon sink management.

The invention discloses a forest fire risk prediction method based on multi-source data, and belongs to the technical field of forest fire prevention. Aiming at the problem of low prediction precision caused by one-sided information of a single data source and insufficient multi-source data fusion in the prior art, the method is realized by the following steps: acquiring micrometeorological data including temperature and humidity, wind speed and air pressure, and image data including an infrared image and a visible light image; the data of the mountain fire-prone area comprises historical fire frequency, vegetation type and topographic information; uTC + 8 time synchronization and WGS84 coordinate system space calibration are carried out on the data, and missing values and abnormal values are processed; carrying out feature layer fusion by adopting an attention mechanism, and extracting core features such as a temperature and humidity coupling index and vegetation dryness; spatial correlation features are captured through CNN, a time sequence trend is captured through LSTM, a mountain fire occurrence probability is output by using a Sigmoid function after decision-making layer fusion, and a result is calibrated in combination with sub-region features. Through multi-source data deep fusion and spatial-temporal feature collaborative analysis, the accuracy and timeliness of forest fire risk prediction are improved, a new data source can be expanded and accessed, and the method is suitable for a complex forest fire prevention scene.

The invention provides a plant classification labeling method. The method comprises the following steps: S1, acquiring an orthoimage of a target area through an unmanned aerial vehicle, and segmenting to generate plant pattern spots; s2, superposing the orthoimage and the base map of the electronic map, and establishing a composite coordinate system; s3, displaying the superposed map on the mobile terminal, and receiving a marking instruction of a user on the pattern spots; when the user cannot identify the plant type, calling a plant identification API to classify the plant image uploaded by the user, and associating the result to the corresponding plant pattern spot; s4, storing the labeled plant data to a database, and supporting statistics and management of the vegetation types, number and distribution of the target area through a geofence function; s5, a visual interface is provided, a user is allowed to check detailed information of the plants by clicking map marks, the detailed information comprises names, photos and statistical results, through the plant classification marking method, the types, the number and the distribution of the vegetation in the statistical area can be systematically analyzed, data are accurate, and presentation is visual.

The invention provides a mining area vegetation reconstruction method based on vegetation classification and division. The method relates to the technical field of ecological environment restoration, and comprises the following steps: S1, acquiring mining area environment data by adopting a mode of combining remote sensing images, unmanned aerial vehicle aerial photography and ground sampling, performing spatial interpolation, normalization and filtering preprocessing on the environment data, and outputting a mining area ecological factor database; s2, on the basis of the ecological factor data, constructing a multi-index evaluation model by combining an analytic hierarchy process with clustering analysis to perform vegetation type division on the mining area, and generating partition templates with different ecological functions; and S3, constructing a plant phenological growth prediction model based on years of phenological observation data and weather forecast information. According to the mining area vegetation reconstruction method based on vegetation classification and division, precise planting and spatial layout are completed, the vegetation survival rate, biomass production and community diversity are remarkably improved, and the strict requirements of mining area ecological reconstruction for diversity and adaptability are met.

The invention provides a wetland forest and grass ecosystem restoration strength evaluation method under drought stress. The method comprises the steps of data collection, daily-scale ecosystem service function index ESS construction, ecological drought identification and restoration elasticity curve construction. Wherein the time resolution of the index is accurate to the day, the capture of short-time drought below the monthly scale is enhanced, and the accurate description of the dynamic process of ecological system restoration under the drought stress is realized; combined identification is carried out on the drought events in combination with the standardized ecological water deficit, and the accuracy of identification of the drought events causing significant differences of ecological system service functions is improved; a quantile regression model is utilized to quantify the ecological system recovery capability of different vegetation types, and data characteristics of different recovery time distributions of the ecological system under the same drought intensity are considered, so that the defect that a traditional mean value regression model cannot fully describe drought complex characteristics is overcome; and a theoretical support is provided for sustainable development of an ecological system under an extreme climate condition.

The invention discloses a soil humidity inversion method and device, a medium and equipment, and relates to the technical field of microwave remote sensing inversion. According to the method, the total backscattering coefficient, the normalized vegetation index, the leaf area index and the average leaf inclination angle of the electromagnetic waves of the target area pixel by pixel are obtained in combination with microwave remote sensing data and atmospheric apparent reflectance data of the target area; according to the normalized vegetation index, the leaf area index and the average leaf inclination angle, spatial clustering is carried out on vegetation, spatial heterogeneity of crop types is captured, and then based on the constraint that the surface roughness parameters and the vegetation parameters of the same vegetation type are the same and the soil humidity in the same surface space window is the same, the spatial heterogeneity of crop types is obtained. Through two-stage iteration inversion of the intra-class scale and the ground surface space window scale, the time dynamic state is adapted, the parameter calibration function of the in-situ soil humidity data is replaced, and the model parameters and the soil humidity can be determined without the in-situ soil humidity data. According to the invention, the accuracy and reliability of the soil humidity inversion method are improved.

The invention provides a global vegetation canopy moisture content inversion method, which belongs to the field of remote sensing geosciences, and comprises the following steps: acquiring vegetation character data in a global vegetation character database, and counting the distribution condition and correlation of each character in different vegetation types; training samples of different vegetation types are generated through forward simulation; performing convolution conversion on the simulated spectrum data by adopting a spectrum response function to obtain an equivalent spectrum; evaluating feature combinations of different spectrums and vegetation indexes, and determining an optimal feature combination and a machine learning algorithm suitable for different vegetation types; a corresponding inversion model is adaptively called according to the vegetation type and the canopy structure, and remote sensing estimation of the global vegetation canopy moisture content is achieved. According to the method, the canopy moisture content of different vegetation types can be efficiently inverted to generate a global vegetation canopy moisture content remote sensing product with space adaptability and relatively high precision, and data support is provided for applications such as ecological environment monitoring, drought evaluation and carbon-water flux simulation.

The invention discloses a vegetation carbon density and carbon reserve estimation method and device based on machine learning, and relates to the field of vegetation carbon density and carbon reserve accounting, the vegetation carbon density and carbon reserve estimation method based on machine learning mainly comprises the following steps: obtaining land cover and vegetation type data of a research area; setting a standard sample plot in the research area and investigating to obtain overground, earth surface and underground vegetation carbon density and carbon reserve data of the sample plot; performing preprocessing and information extraction on the land cover data of the research area to obtain remote sensing ecological factors of the sample plot; according to the sample plot remote sensing ecological factors and sample plot carbon density measured data, vegetation carbon density model construction and optimization are carried out, and an optimal estimation model is obtained; and according to the vegetation type and the vegetation area, utilizing the optimal estimation model to obtain the vegetation total carbon reserve in the research area. According to the vegetation carbon density and carbon reserve estimation method and device based on machine learning provided by the invention, rapid, dynamic and accurate estimation of multi-element vegetation carbon density and carbon reserve of regional scale forest grass wet and rough vegetation can be realized.

The invention discloses a road construction carbon emission monitoring method and system, and relates to the technical field of carbon emission monitoring. The method comprises the steps of obtaining earth surface feature information, vegetation types and distribution areas of a construction area, obtaining disturbance areas of different vegetation types, calculating disturbance coefficients, loss coefficients and carbon sink loss amounts of the disturbance areas, calculating the total carbon emission amount of a construction period, constructing an emission evaluation model, and outputting a predicted emission amount of a subsequent construction period in combination with a project scheme. Judging whether the predicted emission exceeds a preset emission threshold value or not, if not, keeping the project scheme unchanged, and if yes, suggesting to modify the project scheme; the carbon sink loss amount caused by vegetation damage can be accurately calculated, so that the total carbon emission amount is more comprehensively and accurately evaluated, and a scientific basis is provided for construction scheme optimization.

The invention relates to the field of urban green land evapotranspiration zoning, in particular to an urban green land surface evapotranspiration zoning method and system based on impedance parameters, and aims to distinguish arbor, shrub and lawn transpiration in an urban green land. The method comprises the following steps: firstly, collecting and preprocessing vegetation morphological parameters, meteorological data, canopy surface temperature, soil heat flux, vegetation transpiration and vortex related flux data; calculating leaf boundary layer impedance and canopy stomatal impedance of each vegetation type by using a backstepping Penman-Monteith formula and an earth surface energy balance equation; determining the proportion of each vegetation type in the flux contribution source area through a flux footprint model, and reversely deducing the total aerodynamic impedance and the total stomatal impedance of the earth surface; solving aerodynamic impedance of each vegetation according to an earth surface energy balance equation and performing fitting correction; and finally, the corrected impedance parameters are substituted into a Penman-Monteith model to calculate the transpiration amount of each vegetation. According to the method, the problem of mismatching between single plant scale data and community scale data is effectively solved, and the evapotranspiration partition calculation precision is improved.

The invention discloses a vegetation type-considered wetland carbon reserve inversion and contribution analysis method, which comprises the following steps of: (1) obtaining a spatial mode of a wetland dominant vegetation type according to multi-source remote sensing data and ground observation plant community sample data; (2) constructing a multi-model collaborative learning framework on the spatial mode of the wetland advantageous vegetation type obtained in the step (1); (3) analyzing the carbon reserve difference of different vegetation types on the basis of the spatial pattern of the carbon reserve of each advantageous vegetation type of the wetland predicted in the step (2) so as to evaluate the influence value of the vegetation types on the soil carbon reserve; and (4) preferentially selecting characteristic factors from the key environment factors identified in the step (3) to obtain direct and indirect influence of each characteristic on carbon reserve change. The method has the following beneficial effects that a multi-model collaborative learning framework integrated with the advantages of various machine learning is combined with a data-driven regression model, and scientific support is provided for fine management and ecological protection strategies of the wetland.

The invention relates to the technical field of water and soil conservation and ecological restoration, in particular to a rapid restoration method for native landforms and vegetation in a northwest construction disturbance area. The method comprises the following steps: before construction, performing investigation and sampling analysis on vegetation types and soil layers of a to-be-disturbed area, and acquiring topographic and geomorphic information of the construction disturbed area by adopting an unmanned aerial vehicle; in the construction process, shrubs in a construction disturbance area are numbered and transplanted, and turfs and soil of all layers are stripped in a layered mode; after construction is finished, soil backfilling maintenance is conducted according to the soil profile hierarchical configuration; shrub plants are transplanted, herbaceous plants are sown, and meanwhile vegetation in the construction disturbance area is managed and maintained. According to the method, the steps are clear, a complete system is formed through a series of operations including vegetation and soil investigation, transplanting, stripping, backfilling, sowing, maintenance and the like, all the steps are closely connected, operation confusion and repeated labor are avoided, and the efficiency and effect of landform disturbance and vegetation recovery work are improved.

The invention provides a desert steppe area mining area gangue discharge field ecological vegetation screening method and system, and the method comprises the steps: determining the classification factors of the site types of topographic regions based on site attribute information, and dividing a plurality of topographic regions into a plurality of different site types; identifying vegetation state information on the terrain area of the target site type to obtain a vegetation mode; calculating an evaluation score of the vegetation mode based on the vegetation type related to the vegetation mode; optimizing the vegetation mode to obtain a target vegetation type on the basis of correlation features among various types of vegetation in the vegetation mode; the method comprises the following steps: determining classification factors of site types according to real area information, dividing a waste rock discharge field into multiple types, determining vegetation modes based on vegetation states of the site types, calculating evaluation scores of the vegetation modes, and further optimizing the vegetation modes to obtain optimal vegetation types of the site types. Thus, vegetation types adapted to various site types are generated to obtain a stable and effective ecosystem.

The invention provides a mountain freeze-thaw desertification early warning method and related equipment. The method comprises the following steps: acquiring thermal neutron intensity and epithermal neutron intensity of a target area; based on a target intensity ratio and a pre-obtained empirical fitting parameter set, target parameters are determined, and the target intensity ratio is the ratio of the thermal neutron intensity to the epithermal neutron intensity of the target area; determining a target moisture content according to the target parameter and the thermal neutron intensity or epithermal neutron intensity of the target area; and when the target moisture content is lower than a preset critical soil moisture threshold value, carrying out mountain freeze-thaw desertification early warning. Soil moisture is inverted through a cosmic ray neutron detection technology, environmental interference factors such as a non-soil water hydrogen source are corrected, local soil moisture of different vegetation types and soil types is accurately captured, freeze-thaw desertification is judged in combination with a critical soil moisture threshold, and early warning is given.

The invention relates to the field of coastal salt marsh wetland vegetation monitoring, and discloses a suaeda salsa adaptive hyperspectral index and identification method based on a hyperspectral remote sensing image. The method mainly aims at solving the problem of difficulty in identifying the suaeda salsa in the intertidal zone caused by serious phenomena of same object and different spectrum and same spectrum and foreign matter in the coastal wetland; the method comprises the following steps of: firstly, selecting representative samples and acquiring an average spectral reflectivity curve of the representative samples by considering different vegetation types, coverage difference and soil water-salt difference of the coastal wetland; then, analyzing the spectrum curve and selecting a hyperspectral image optimal wave band combination which is most suitable for distinguishing the suaeda salsa from other surface features; then, setting an adaptive rule and constructing a suaeda salsa adaptive hyperspectral index (SSAHI); and finally, quickly and accurately identifying and extracting the range of the suaeda salsa in the intertidal zone based on SSAHI. According to the method, the range of the suaeda salsa in the intertidal zone can be rapidly and accurately monitored based on the hyperspectral remote sensing images of different sensors and different time, and the problems that existing suaeda salsa identification based on the hyperspectral remote sensing images is difficult and low in precision are solved.

The invention discloses an under-forest plant growth monitoring method and system based on image recognition, and belongs to the field of plant growth monitoring, and the monitoring method specifically comprises the following steps: I, selecting an under-forest plant growth region, and carrying out the region division according to the factors of terrain, vegetation type and ecological sensitivity; iI, acquiring various environmental data such as soil parameters and meteorological data of a plant growth area in real time, capturing plant image data at regular time, and constructing data pairs accurately matched with timestamps; iII, uploading the collected data to a cloud platform, carrying out unified storage and classified arrangement on the environment and image data, and preprocessing the image data at the same time; according to the invention, simultaneous acquisition, transmission and storage of various data of environmental soil and plant images can be realized, the accuracy of state identification is improved, the ability to understand and predict a complex growth process is enhanced, automatic identification and instant early warning of an abnormal state are realized, and the management response efficiency of the system is improved.

The invention relates to a circulation quality data evaluation method, system and equipment for carbon circulation, and relates to the field of carbon circulation quality evaluation, and the method comprises the steps: carrying out the carbon emission prediction of a coal mining subsidence land, and obtaining the predicted carbon emission; scale change prediction of soil, vegetation and water is carried out according to the collapse characteristic information; soil physicochemical property parameters, vegetation type parameters and water body component parameters are collected, and the predicted carbon sequestration amount is obtained through calculation in combination with the change scale parameters of soil, vegetation and a water body; and calculating according to the predicted carbon sequestration amount and the predicted carbon emission amount to obtain a carbon cycle quality evaluation result. According to the method, the technical problem that the carbon cycle quality cannot be effectively and quantitatively evaluated due to the fact that the carbon emission and the carbon sequestration capacity cannot be accurately predicted because the special geographical environment characteristics and the change rule of the coal mining subsidence land are not fully considered in a traditional evaluation method can be solved; the scientificity, accuracy and reliability of carbon emission and carbon sequestration capability prediction can be remarkably improved, and accurate quantitative evaluation of carbon cycle quality is realized.

The invention discloses a mountain vegetation classification method based on a remote sensing image and a vegetation index, and relates to the technical field of mountain vegetation classification, and the method comprises the steps: obtaining remote sensing image data of a research region in a set time span, and dividing the remote sensing image data into a plurality of sub-regions; and analyzing and extracting vegetation index time sequence characteristics and seasonal growth characteristics of each sub-region in a set time span, preliminarily determining the vegetation type of each sub-region as an initial label, and using the obtained initial label, vegetation index time sequence characteristics and seasonal growth characteristics of each sub-region to train a classification model. According to the method, the change of the vegetation index of each sub-region in the time sequence is calculated, and the change of the seasonal vegetation index is combined, so that the static vegetation type is considered, the growth cycle of the vegetation and the change characteristics of the vegetation in different seasons are dynamically captured, and the real change condition of the mountain vegetation can be reflected more accurately.

The invention discloses a method, a device, equipment and a medium for formulating a vegetation recovery scheme of a power transmission and transformation project in a Hebei earth-rock mountainous area based on consideration of weather resistance of plants. According to the technical scheme provided by the invention, field survey data of a power transmission and transformation project range is acquired, and corresponding first vegetation type information and soil condition information in the project range are extracted; determining a candidate list based on the vegetation type information corresponding to the engineering range, evaluating the engineering range based on the soil condition information, and dividing the engineering range into at least one block based on an evaluation result; based on the evaluation result corresponding to the block, selecting a target vegetation type from the candidate list as a vegetation restoration object of the block; and determining a planting scheme based on the vegetation restoration object selected by each block and the evaluation result. According to the invention, the vegetation type and the soil condition of the engineering area are analyzed, and the climate type of the power transmission and transformation engineering area is combined, so that the vegetation recovery scheme according with climate and geological conditions is scientifically and specifically determined.

The invention discloses a forest fire danger early warning system and method based on meteorological and vegetation data analysis, and relates to the technical field of fire risk prediction and control. According to the forest fire danger early warning system and method based on meteorological and vegetation data analysis, fine grid division is carried out on a target forest area through a high-resolution DEM and a multispectral or hyperspectral image, and vegetation type identification is carried out on each monitoring subunit; calculating a wet-dry cycle index WDI, a monsoon effect index MFI and a dry thunderstorm risk factor DRF by combining hourly wind speed, wind direction, relative humidity, precipitation and vapor pressure difference meteorological data; through extracting canopy height, canopy density, under-forest shrub and grass layer thickness and high VOC tree species coverage rate, fuel continuity coefficient FCI and volatile matter flammability index VEI are calculated, and accurate quantification of continuity and flammability of vertical ladder fuel and horizontal canopy is realized. And a comprehensive fire danger index FFI is constructed and subjected to hierarchical management after judgment, so that the dynamism, the hierarchical property and the pertinence of fire danger early warning are realized, and the forest fire danger prevention and control precision and the patrol resource utilization efficiency are improved.

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 provides an intelligent irrigation control method and system for vegetation in front of a seawall, and relates to the technical field of vegetation watering. Obtaining a vegetation image; according to the vegetation image, through a convolutional neural network, vegetation growth stage identification is carried out; dividing vegetation areas according to vegetation types and the identified vegetation growth stages; according to the soil granularity, the average temperature, the precipitation amount, the air humidity, the normalized water body index, the vegetation type and the vegetation growth stage, predicting the water supply amount per unit area required by each vegetation region through an artificial neural network; and according to the water supply amount per unit area required by each vegetation area, irrigating vegetation in each vegetation area through a seawall front irrigation system. The method can provide the best growth condition for the vegetation in front of the seawall, promote the health and growth of the vegetation, help to maintain and improve the stability of the ecological system of the area in front of the seawall, and support biodiversity.