3740 results about "Vegetation" patented technology

The invention provides an ecological meteorology and satellite remote sensing combined dynamic environment monitoring method and system. Wherein spatio-temporal dynamic concentration data and spectral reflection characteristic data are obtained at a pollutant emission node; generating an associated data block by the spatio-temporal dynamic concentration data and the spectral reflection characteristic data according to a pollution concentration abrupt change event trigger time sequence, and performing chain storage on Hash fingerprints and pollution source geographic coordinate information through a distributed node consensus mechanism; integrating the ecological meteorological observation data to generate a pollutant migration path map; and establishing a pollution influence boundary judgment model according to the pollutant migration path map and the vegetation stress response characteristic data, and monitoring the diffusion range and influence boundary of pollutants in real time through the model to generate an environment monitoring report. According to the technical scheme provided by the invention, the industrial environment pollution dynamic monitoring precision and the decision response efficiency are remarkably improved.

The invention relates to a power transmission line forest fire risk assessment early warning system and method, and belongs to the technical field of power transmission line risk assessment. The system comprises an environment DNA power transmission line forest fire risk assessment and early warning system, a quantum sensing power transmission line fire remote monitoring system, a power transmission line forest fire early warning emergency training system, a digital twin power transmission line fire risk dynamic management system, and a multi-node power transmission line fire early warning system of an edge AI and a low orbit satellite. The system comprises a thermal imaging and deep learning integrated power transmission line fire risk distribution prediction system, an AI-driven power load dynamic adjustment and fire prevention system, an intelligent fire-fighting equipment linked power transmission line fire spreading path prediction system and a distributed quantum computing power transmission line fire emergency simulation and optimization response system. According to the method, the inflammable vegetation density and biocenosis change can be dynamically analyzed through a plant and microorganism gene sequencing technology.

The invention provides an intelligent ecological restoration system, method and device for a high and steep slope of a strip mine in an arid region. Comprising a data acquisition layer which realizes real-time acquisition of multi-dimensional environmental data through InSAR satellite remote sensing, a ground sensor network and unmanned aerial vehicle multispectral imaging; the transmission layer adopts LoRa and 5G hybrid networking; the platform layer is used for constructing a slope stability prediction and restoration scheme optimization platform based on a digital twinborn model and a deep learning algorithm; and the application layer is used for remotely controlling the repairing device through a mobile terminal and a Web terminal and monitoring the repairing progress in real time. The problems that a traditional restoration technology is poor in adaptability, low in vegetation survival rate, high in ecological restoration cost, insufficient in monitoring technology application, insufficient in monitoring feedback mechanism, insufficient in intelligence, long in ecological restoration period and the like are solved.

The invention relates to the technical field of remote sensing monitoring, in particular to an intelligent forest disease and pest monitoring method and system based on unmanned aerial vehicle remote sensing, and the method comprises the following steps: collecting multispectral data by an unmanned aerial vehicle, extracting reflectivity and smoothing the reflectivity, carrying out differential recognition on abnormal pixels, extracting curves and screening significant changes, segmenting scab boundaries, and classifying health states. And generating a pest and disease map layer prediction trend. According to the method, the reflectivity time sequence is constructed, differential processing is carried out, the vegetation change trend is dynamically captured, abnormal areas are identified by combining slope offset and persistence analysis, significant pixels are screened according to main peak wavelength offset, boundary information is extracted, the scab positioning precision is improved, and the recognition resolution of the lesion state is enhanced through reflectivity combined analysis; accurate description of disease spot dynamic changes is realized, static image dependence limitation is broken through, monitoring time continuity and space response capability are enhanced, and disease and insect pest change capture efficiency and state classification accuracy are effectively improved.

The invention provides a forestry pest and disease prediction method and system based on artificial intelligence, and relates to the technical field of artificial intelligence, and the method comprises the steps: firstly obtaining a dynamic monitoring data set of a target forest region, covering an environment and vegetation physiological parameter sequence collected by a plurality of monitoring nodes, and a space coordinate region corresponding to each node; extracting features of the dynamic monitoring data set to obtain a forestry environment feature set containing vegetation growth, environment fluctuation and potential identification features of plant diseases and insect pests, and analyzing the forestry environment feature set by using a pre-trained plant disease and insect pest prediction model to generate a prediction parameter set representing the occurrence probability and influence range grade of the plant diseases and insect pests; executing dynamic prediction matching, determining a disease and insect pest distribution thermodynamic map and generating a control priority strategy, and finally fusing the disease and insect pest distribution thermodynamic map and the disease and insect pest control priority strategy to generate a forest region control optimization instruction set, and feeding back the instruction set to a forestry monitoring platform to trigger resource scheduling so as to realize efficient disease and insect pest control.

Methods of creating and using digital twins of vegetation / crops in relation to their growing environments. Some embodiments include creating a digital twin of the growing environment using sensor data from a subsurface sensor suite, creating a digital twin of the vegetation crop using data from an overhead sensor suite, and using at least one mathematical model to relate the digital twin of the crop to the digital twin of the growing environment. In some embodiments, the mathematical model(s) implement a root cause analysis that uses a limiting factor approach. In some embodiments, the subsurface sensor suite is on a multi-sensor soil probe. In some embodiments, the multi-sensor soil probe comprises a video microscope, a tip stress sensor, a sleeve stress sensor, and a volumetric- water-content sensor. Other related systems, devices, methods, and software are also disclosed.

The invention provides a forest area monitoring method and system based on unmanned aerial vehicle routing inspection, and the method comprises the steps: firstly obtaining a historical routing inspection data set containing a geographic position identifier and a topographic feature parameter in a target forest area, and generating an initial routing inspection route indicating a flight path and an image collection node according to the historical routing inspection data set; then, an unmanned aerial vehicle carrying a multispectral sensor is called to carry out dynamic routing inspection according to an air route, a real-time monitoring image set composed of vegetation coverage images of a plurality of monitoring areas under different timestamps is obtained, and then feature extraction and anomaly detection are carried out on the real-time monitoring image set; according to the method, an image anomaly feature set containing vegetation and surface structure anomaly indexes is determined, and finally, a forest region monitoring optimization strategy is generated based on the image anomaly feature set, so that the unmanned aerial vehicle inspection frequency and image acquisition node space distribution are adjusted, and more efficient and accurate monitoring of a forest region is realized.

The invention relates to the technical field of forest carbon sequestration, in particular to a forest carbon sequestration remote sensing monitoring method and system, and aims to solve the problems that in the prior art, a carbon sequestration prediction result can be obtained by inputting a target area image into a preset carbon sequestration model, a more comprehensive carbon reserve prediction model cannot be constructed in combination with a vegetation index, and the carbon sequestration prediction result cannot be obtained by using the preset model. The problem that the prediction result is inaccurate due to the fact that the features of specific tasks cannot be effectively handled is solved. According to the invention, ground survey data and vegetation indexes are combined through the prediction model construction module to construct a comprehensive carbon reserve prediction model, and the carbon reserve prediction model is obtained through machine training learning and can effectively cope with the characteristics of a specific task, so that the prediction accuracy and flexibility are improved; and a high-resolution carbon reserve distribution map is generated through an efficient machine learning algorithm, scientific decision and management are supported, and an important tool is provided for carbon reserve research.

The invention relates to the technical field of growth state evaluation, and discloses a crop growth state evaluation method and system based on multi-dimensional monitoring. The method comprises the steps of collecting multi-source remote sensing data of a farmland area according to a crop growth period, and performing topographic correction on the multi-source remote sensing data to obtain target vegetation data; based on the multi-source remote sensing data, farmland plot boundaries are extracted, and a farmland space association graph is constructed; inputting the target vegetation data and the farmland space association graph into an elevation perception graph convolutional network for elevation feature analysis, and calculating to obtain a crop abnormal growth index; and generating a growth state evaluation result based on the target vegetation data and the crop abnormal growth index. According to the method, crop growth abnormity caused by regional factors can be accurately identified, so that the accuracy of evaluation results under different terrain and environmental conditions is ensured.

The invention discloses a seawall overtopping amount and overtopping risk calculation method considering a mangrove forest wave dissipation effect, and relates to the technical field of coast engineering. The method comprises the following steps: acquiring typhoon path data, inputting the typhoon path data into a Holland typhoon empirical model, and accurately generating a typhoon wind field; calculating coastal site tide level data in combination with a coastal storm surge numerical model; obtaining wave data during a typhoon by using a storm surge typhoon wave forecasting model based on the SWAN; historical typhoon data and relevant information of the seawall and the mangrove forest are collected, a water level-wave-vegetation-seawall coupling model is constructed, the wave dissipation effect of the mangrove forest is accurately simulated, and wave characteristic calculation is optimized; constructing a database, training the database through a CNN neural network model, and improving the prediction precision of seawall front wave height and wave force parameters; the wave overtopping amount is calculated based on the prediction parameters, a scientific basis is provided for wave overtopping risk assessment, it is ensured that real-time assessment of the wave overtopping risk is more accurate and efficient, and the disaster prevention and reduction capacity is effectively improved.

The invention discloses a soil organic matter prediction mapping method, device and equipment based on deep learning and a medium, and relates to the field of digital soil mapping, and the method comprises the steps: obtaining a remote sensing image of target region soil, and setting a preset number of soil sampling points; comprehensively acquiring environmental data indexes such as terrain factors, vegetation factors, climate factors and land utilization factors; constructing three-dimensional environment tensor data in a preset range of each sampling point, and inputting the three-dimensional environment tensor data into the soil organic matter content prediction model to obtain an organic matter content prediction value set of the soil sampling points in the target area; and generating a spatial distribution prediction map of the soil organic matter content of the target area by combining the prediction value set and the spatial coordinates of the soil sampling points, thereby improving the accuracy of soil organic matter prediction and the mapping efficiency, and providing technical support for soil resource management and agricultural sustainable development.

The invention provides an AI assistance-based landscape garden scheme auxiliary generation method and system, and the method comprises the steps: firstly receiving initial topographic data and a landscape adjustment instruction containing a visual presentation requirement and an ecological function requirement, which are inputted by a user in an interactive interface, and then processing the initial topographic data through a topographic segmentation model; the method comprises the following steps: extracting gradient distribution, altitude interval and land cover features, dynamically correcting the gradient distribution features into stepped topographic features according to visual requirements, matching the land cover features according to ecological requirements to obtain vegetation distribution features, performing spatial superposition on the two features, and performing path connectivity analysis in combination with tourist flow data to determine touring path features. Finally, a landscape garden scheme containing a three-dimensional visualization model and ecological evaluation parameters is generated through multi-dimensional rendering according to the sightseeing path features and sent to a user terminal to achieve interactive feedback, so that various requirements are efficiently integrated, and scientificity and intuition of landscape garden design are improved.

The invention provides a surface soil humidity multi-source data fusion and downscaling method and system. The surface soil humidity multi-source data fusion and downscaling method comprises the following steps: performing quality evaluation and standardization processing on multi-source soil humidity data; carrying out anomaly detection and restoration by adopting a hierarchical spatial-temporal feature extractor; carrying out regional partitioning based on an adaptive boundary generation algorithm and a soft boundary transition model; data fusion and error control are realized through a regional cascade fusion network and an error chain tracking algorithm; and carrying out downscaling processing by utilizing a terrain adaptive downscaling framework and a multi-scale residual learning model to obtain a soil humidity data product with the resolution of 1km. The soil humidity data of complex terrains and high vegetation coverage areas can be effectively processed, the data fusion precision and downscaling effect are remarkably improved, and the technical problems of a traditional method in the aspects of data quality evaluation, anomaly detection, partition boundary processing, error control and the like are solved.

The invention discloses a forest carbon sink function monitoring method based on multi-source data. The method comprises the following steps: collecting data such as vegetation, soil, weather, landform and interference factors; the collected data are preprocessed; the preprocessed data are input into a Biome-BGC model for processing, and ecological system carbon flux parameters are obtained; determining a vegetation carbon sink amount and a soil carbon sink amount based on an ecological system carbon flux parameter, and obtaining a comprehensive carbon sink amount by combining artificial and natural interference carbon emission amounts; determining the forest carbon sink potential through a constructed forest carbon sink potential calculation formula based on the calculated comprehensive carbon sink amount; based on the calculated comprehensive carbon sink amount, time stability, space stability and anti-interference stability are determined, weighted summation is carried out on the time stability, the space stability and the anti-interference stability, and the carbon sink stability is determined; according to the method, the forest carbon sink function is monitored through multi-source data, the monitoring accuracy is improved, and the forest carbon sink potential, the carbon sink stability and the investment value are accurately quantified.

The invention provides a mangrove forest restoration area carbon sink quantity data monitoring system, and relates to the technical field of ecological carbon sink metering, and the system comprises an acquisition module which is used for collecting a carbon exchange original data set of an atmosphere-vegetation-soil interface based on a multi-type sensor network arranged in an intertidal wetland of a mangrove forest restoration area; the data generation module is used for carrying out space-time coordinate matching based on the original data set and generating a multi-dimensional carbon flux fusion data set of geographic position and time joint indexing; and the identification module is used for calculating a local space-time dispersion and a global correlation threshold value based on the fused data set, and identifying and marking abnormal data points. According to the invention, real-time and multi-dimensional monitoring of the carbon sink amount of the mangrove forest restoration area is realized, the monitoring efficiency and the data reliability are improved, and support is provided for assessment of restoration and management strategies.

The invention provides a plateau lake agricultural non-point source pollution treatment method which comprises the following steps: acquiring vegetation indexes and surface temperature field data by using a remote sensing satellite, and generating a pollution source space thermodynamic diagram in combination with water quality and soil parameters of ground sampling points; performing space-time alignment and data fusion on the thermodynamic diagram and real-time runoff and soil permeability data acquired by the hydrological sensor network, and constructing a structured pollution migration database; on the basis of the database, a hybrid neural network model embedded with physical constraints is utilized to predict pollutant concentration distribution within 72 hours in the future; inputting the predicted value into a multi-stage optimization controller, and generating a control parameter set comprising treatment intensity, engineering parameters and a fertilization ratio; generating a treatment strategy map covering the drainage basin through a GIS; and deploying an Internet of Things monitoring node to collect the treated water quality data to form a closed-loop control link. The treatment efficiency and effect can be improved, the treatment cost is reduced, and the negative influence on the ecological environment is reduced.

The invention relates to the technical field of hydrological process simulation, in particular to an ecological hydrological process simulation method and system based on dynamic vegetation and a medium. The method comprises the following steps: acquiring a vegetation physiological monitoring parameter set and a root system water absorption dynamic data set; performing time-space unified registration on the vegetation physiological monitoring parameter set and the root system water absorption dynamic data set to obtain a vegetation physiological dynamic parameter set; performing soil dry and wet process division on the target area based on the vegetation physiological dynamic parameter set to obtain a segmented soil moisture state sequence; dividing the vegetation physiological dynamic parameter set according to the segmented soil moisture state sequence to obtain a state classification vegetation physiological data set; and constructing a vegetation water stress adaptation strategy model based on the state classification vegetation physiological data set. According to the method, the simulation precision of vegetation coverage and soil moisture dynamic balance is improved, and the problem of simulation errors caused by vegetation parameter static processing in the prior art is solved.

The invention aims to provide a mine ecological restoration carbon sequestration rate prediction method for solving the problems of an existing ecological restoration carbon sequestration rate prediction method. The method comprises the following steps: step 1, collecting multiple parameter multi-source data including vegetation growth, earth surface change and soil change of an ecological restoration area; 2, integrating the data, and constructing a dynamic prediction model for the change of the mine ecological restoration carbon sequestration rate; 3, verifying and optimizing the prediction model by using historical data and field observation data; and 4, adopting a machine learning technical means for the optimized prediction model, and establishing a quantitative relationship between the carbon sequestration rate change and ecological restoration means of land utilization, vegetation coverage and soil attributes. The method overcomes the influence of extreme climate events on the carbon sequestration rate of an ecological system, can accurately predict the change trend of the carbon sequestration rate under different ecological restoration strategies in the future, and provides a powerful scientific basis for ecological restoration of a mining area.

The invention discloses a guidance planning method for ecological restoration of a side slope, and relates to the technical field of ecological restoration, and the main scheme is as follows: collecting multiple data of the side slope; calculating a slope stability index, and presetting a threshold value to judge whether the slope area is in a risk state; calculating a vegetation coverage area, and further calculating a plant suitability index; calculating a plant variety selection index, and further calculating a planting quantity; calculating the water and soil loss rate after restoration; calculating a plant adaptation comprehensive index; calculating an ecological restoration effect index and a vegetation density, and further obtaining a planting layout rationality index; calculating a soil stability index, further calculating a water and soil loss control index, and judging whether the soil loss control state is abnormal or not according to a preset threshold value; calculating a water and soil conservation effect index, further obtaining a vegetation growth comprehensive index, presetting a threshold value to judge the vegetation growth condition, and taking corresponding remediation measures; the method can effectively guarantee scientificity and effectiveness of ecological restoration of the side slope.

The invention relates to the technical field of resource management, in particular to a seawall ecological protection-oriented vegetation water demand prediction method and system, and the method comprises the steps: collecting the environment monitoring data of a seawall region; establishing a salinity stress factor model based on the soil data, calculating a comprehensive salinity stress coefficient of the monitoring point, and performing multi-factor dynamic correction on the vegetation water demand to obtain a vegetation basic water demand; establishing a multi-dimensional time sequence model of a tidal cycle and a soil moisture supplement relationship considering geologic structure differences, and obtaining resource scheduling weights under different tidal stages and environmental conditions; according to the vegetation data, the underground data and the environmental data of the different seawall regions, based on a multi-region collaborative water resource allocation network, obtaining a deployment optimization scheme based on regional characteristic parameters; and generating a vegetation water demand prediction result and a graded resource scheduling scheme by integrating the vegetation basic water demand, the resource scheduling weight and the allocation optimization scheme, and performing irrigation.

The invention provides a vegetation greening layout optimization method, and relates to the technical field of environmental engineering and garden design, and the method comprises the steps: carrying out the homologous retrieval through taking a target region plant growth influence factor as a constraint, and obtaining a plant type and a planting density; calculating a high-frequency planting density threshold value of the plant type; inputting the plant types into a type-attribute comparison table for matching to obtain time zone attributes, determining a plant combination and performing adaptive screening; carrying out vegetation layout design based on the adaptive plant combination and the high-frequency planting density threshold to generate an initial layout scheme; constructing a simulated growth environment to perform growth prediction and simulated viewing on the layout scheme; and performing evaluation and feasibility evaluation on the simulated landscape according to the landscape evaluation function, and determining an optimal layout scheme, thereby solving the technical problems that the vegetation configuration cannot be accurately performed and the vegetation growth effect and the landscape aesthetic property cannot be effectively predicted and evaluated due to lack of systematic analysis on the plant growth. The technical effect of improving the accuracy and predictability of the plant layout is achieved.

The invention discloses an ecological slope protection structure and a construction method thereof.The ecological slope protection structure comprises an ecological vegetation layer formed by splicing a plurality of units, and a vegetation planting groove is formed in each unit; the regulation and control layer comprises a honeycomb-shaped coarse aggregate layer, a corrugated plate laminated middle aggregate layer and a fractal tree-root-shaped fine aggregate layer of which the porosity decreases progressively from top to bottom; the drainage mechanism comprises a drainage channel in the ecological vegetation layer, a porous drainage framework and a water collecting well arranged at the slope bottom; the anchoring mechanism comprises a plurality of umbrella-shaped expansion anchor rods, and the lower end of each anchor rod is perpendicular to the slope surface and is inserted into the slope surface soil layer; the lower end of each anchor rod is provided with four sets of extensible 120-degree arc-shaped anchor flukes, and the surfaces of the anchor flukes are provided with bionic ground gripping grooves consistent with the anti-sliding ribs in trend. By innovatively designing the ecological vegetation layer, the regulation and control layer, the drainage mechanism and the anchoring mechanism, stability of the slope protection structure in mechanical property and sustainable development of the ecological environment are achieved, and coordination and unification of vegetation growth and slope stability are promoted.

The invention relates to the technical field of power detection, and discloses a vegetation risk hidden danger detection method and system based on sparse point cloud segmentation, and the method comprises the steps: collecting image data of a monitoring region at different angles, generating sparse point cloud data, and carrying out the preprocessing of the sparse point cloud data; and performing point cloud segmentation on the sparse point cloud data through a neural network, dividing the monitoring region according to region types, analyzing a spatial relationship between vegetation and power equipment in a target region, performing risk assessment, and generating an early warning signal in combination with multi-modal data. According to the method, the sparse point cloud data is precisely segmented, the three-dimensional space characteristics of the tree are extracted, and the potential risk of the tree and the power transmission facility can be dynamically monitored in real time by precisely calculating the space relation between the tree and the power transmission facility; according to the multi-modal fusion data, multi-level early warning information is generated, so that the efficiency and precision of power transmission line inspection are greatly improved, and the pre-judgment and timely treatment of hidden dangers are realized.

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

The invention discloses a method for evaluating the influence of black land ecological barrier pattern evolution on agricultural climate and yield, and relates to the technical field of agricultural ecological environment monitoring and remote sensing model coupling. Comprising the following steps: S1, acquiring a high-resolution satellite image, microwave remote sensing earth surface humidity data, optical remote sensing vegetation coverage data and thermal infrared remote sensing earth surface temperature data of a target area; according to the method for evaluating the influence of the black land ecological barrier pattern evolution on the agricultural climate and the yield, the problem of lack of ecological-climate-agricultural system collaborative modeling in the traditional technology is solved. A high-resolution satellite image is used for analyzing a network topology structure of the protection forest, microwave and thermal infrared remote sensing are fused to invert'water, soil and gas generation 'multi-element parameters, and dynamic quantitative evaluation of ecological barrier spatial heterogeneity evolution on local climate and crop growth is realized in combination with a dynamic weight distribution and two-way nesting technology.

The invention discloses a method for quantifying the influence of climate change and human activity on watershed vegetation, which comprises the following steps: acquiring data of a research area from a plurality of data sources, performing time-space consistency processing, and calculating saturation vapor pressure difference; performing feature extraction on the processed data, and constructing a high-dimensional feature tensor; constructing a space-time diagram based on the high-dimensional feature tensor, performing dimension reduction and dynamic modeling by applying a map embedding technology, and analyzing space-time dynamic features of watershed vegetation; separating and quantifying direct influence of climate and human activities on vegetation by adopting a causal network and a regression decomposition method; comprehensive evaluation is carried out, and uncertainty analysis is carried out. According to the method, the ability of understanding the dynamic state of a complex ecosystem is remarkably improved, powerful scientific support is provided for watershed ecosystem management, and more accurate, reliable and comprehensive decision support can be provided.

The invention discloses a monitoring system for forest ecological carbon sink calculation, and belongs to the field of ecological environment monitoring and carbon sink evaluation. Comprising a foundation optical remote sensing module, a crown biomass calculation module, a soil carbon component identification module, a vertical carbon flux detection module, a regional microclimate analysis module, a time sequence big data coupling module, a carbon source and carbon sink model fusion module and a result correction feedback module. The method comprises the following steps: collecting vegetation three-dimensional structure data through a multi-frequency laser radar, calculating unit-area canopy carbon storage acceleration in combination with seasonal climate interference parameters, and fusing turbulent carbon exchange and soil carbon change data to construct a regional carbon flux coupling model; the system collects cross-rosa chinensis carbon change data to construct a multi-dimensional feature tensor diagram, dynamically updates a weight and a fitting function, realizes carbon sink dynamic estimation and feedback optimization based on a biology-climate-time three-dimensional cooperation mechanism, and improves forest carbon sink monitoring precision and stability. The beneficial effect is that scientificity and accuracy of ecological environment monitoring and carbon sink evaluation are improved.

The invention discloses a method for predicting the risk of a valley-type tailing pond being threatened by extra-small watershed flood, and the method comprises the steps: S1, obtaining the soil type, vegetation coverage and gradient of the extra-small watershed of the valley-type tailing pond, and the spatial distribution data of a valley, and building an underlying surface classification system; s2, non-linear key inducements in the rainfall-runoff process in the valley terrain extra-small watershed are extracted, dominant factors influencing runoff formation are recognized, and a non-linear confluence calculation numerical model is constructed; s3, constructing a distributed sensing monitoring network through a rainfall monitoring station and a runoff monitoring station; s4, constructing a layered three-dimensional hydrological hydrodynamic model of coupling rainfall runoff generation-slope confluence-shallow groundwater flow; and S5, establishing a risk assessment index system and a risk prediction model through a layered three-dimensional hydrological hydrodynamic model in combination with flood risk influence factors. According to the method, accurate prediction of the valley-type tailings pond under the flood threat in the extra-small watershed is realized, and the problem of poor predictability of the flood threat in the extra-small watershed is solved.

The invention relates to the technical field of disease and insect pest detection, and further relates to a disease and insect pest image multispectral imaging and deep learning intelligent detection system, which comprises a multispectral radiance calibration unit, an image analysis and processing unit and a disease and insect pest probability detection unit, the multispectral radiance calibration unit is used for carrying out multiband imaging on vegetation leaf surfaces in the forest region, deducting dark field digital counts from original digital counts and then converting the original digital counts into radiance of corresponding bands; the image analyzing and processing unit is used for converting the radiance into surface feature apparent reflectivity; multiplying the scab spectrum gradient contrast index by the spectrum heterogeneity index to obtain coupled pest and disease damage characteristics; and the pest and disease damage probability detection unit is used for mapping the convolution output of each layer into a pest and disease damage probability value based on a generalized error function. The accuracy and practicability of disease and pest detection are remarkably improved.

The invention provides a forest fire prediction method and system using a remote sensing image, and relates to the technical field of data processing, and the method comprises the steps: defining a fitness function used for evaluating the advantages and disadvantages of each individual, and each individual represents a group of weight parameters; randomly generating an initial population meeting a coding mode, wherein each individual represents a group of weight parameters; the fitness of each individual is calculated according to a fitness function, and a new generation of population is generated through selection, crossover and mutation operation; judging whether a preset termination condition is met or not, if the termination condition is met, stopping iteration, and outputting a current final weight parameter combination; according to the final weight parameter combination, combined with land surface temperature, vegetation coverage rate and vegetation water content data, calculating a forest fire danger dynamic danger index; and classifying the fire danger grades according to the forest fire danger dynamic danger index. The accuracy of the prediction result can be improved.