1226 results about "Vegetation" patented technology

Disclosed are a three-dimensional laser point cloud-based method and system for in-depth vegetation management in transmission corridors. The method comprises: collecting multi-source data by means of an unmanned aerial vehicle- and helicopter-mounted multi-sensor system, and preprocessing the multi-source data; using the processed multi-source data to create a digitalized power grid corridor and construct a vegetation management analysis model; on the basis of data analyzed by the vegetation management analysis model and geofencing technology, designing a hazard vegetation clearing-cost-compensation prediction model; and developing a mobile application for analyzing hazard vegetation data flows and the closed-loop management of vegetation management service flows. The described method achieves high-precision and high-coverage data collection, precisely identifies transmission line digital twins and hazard vegetation, standardizes and automates the management of hazard vegetation clearing and related compensation, and develops a mobile application for analyzing hazard vegetation data flows and the closed-loop management of vegetation management service flows, thereby achieving the end-to-end digital and mobile management of vegetation management. The organic combination of steps comprehensively improves the intelligence and efficiency of transmission line vegetation management.

The invention provides a desertification monitoring grading and vegetation extraction method and system, and relates to the field of desertification remote sensing monitoring and ecological assessment, and the method comprises the steps: selecting a multi-temporal satellite image and an unmanned aerial vehicle image which cover a full research region according to a preset condition, and carrying out the data preprocessing of the multi-temporal satellite image and the unmanned aerial vehicle image; the method comprises the following steps: constructing a desertification difference index by fitting a feature space of a vegetation index and a surface albedo by using a multi-temporal satellite image, grading the desertification degree of a research area to obtain a grading result, and locking a key monitoring area in the research area according to the grading result; a vegetation sample image of a key monitoring area is obtained from an unmanned aerial vehicle image, HSL color space conversion and hue optimization processing are carried out on the vegetation sample image, and a normalized vegetation index based on HSL is constructed, so that vegetation information of the key monitoring area is finely extracted, a key desertification disaster area is effectively positioned, and the accuracy of the desertification disaster area is improved. And vegetation information in the region is finely extracted.

The invention discloses a soil water content inversion method and system based on L-band dual-polarization data and a medium, and belongs to the field of remote sensing technology and environment monitoring. The system comprises an SAR data acquisition module, an optical data processing module, a vegetation scattering separation module, an inversion model construction module, a spatial distribution calculation module and a risk assessment module, wherein the SAR data acquisition module is used for regularly acquiring L-band dual-polarized SAR data of a target area; the optical data processing module assists in identifying and separating vegetation scattering components by calculating vegetation indexes; the vegetation scattering separation module is used for extracting pure soil back scattering signals; the inversion model building module is used for training and optimizing a soil water content inversion model based on the pure soil back scattering signals and field sampling data; the spatial distribution calculation module is used for calculating a spatial distribution diagram of the surface soil water content of the target area; and the risk assessment module is used for carrying out landslide risk grade division and early warning management.

The invention relates to the technical field of water ecological restoration, solves the technical problems that in the prior art, fine design, adaptability and robustness are insufficient, and particularly relates to a lakeside zone ecological restoration method based on microtopography and vegetation community integration. Constructing a lakeside zone water depth and flooding duration pattern, parameterizing a bank slope microtopography by step elevation, width and gradient, and calculating a corrected water depth and a hydrodynamic exposure index by adjusting parameters; introducing a suitability scoring function to establish a functional response model for realizing quantitative matching of the microtopography steps and the vegetation communities; and constructing a comprehensive evaluation function and a robustness index by integrating ecological function indexes under various hydrological scenes, and carrying out joint optimization on microtopography and vegetation configuration. According to the method, the lakeside zone recovery design is converted from experience zoning into a computable and comparable integrated scheme, and the method is suitable for various lakeside zones such as natural gentle slope lakesides and urban hard revetments.

The invention discloses a vegetation-submerged embankment coupled tough coast protection method and system and a storage medium, and the method comprises the steps: firstly, determining a research region, and systematically collecting the near-shore terrain, water and sand power conditions, vegetation parameters and other data; secondly, constructing a water-sand-vegetation coupling numerical simulation model based on the collected data, and performing precision verification on the model; then selecting a suitable vegetation type in combination with environmental characteristics of the research area, carrying out artificial seeding and planting work in a vegetation potential habitat of the tidal flat, and determining a design elevation and an offshore distance of the submerged dike; and finally, evaluating the coast protection effect under the synergistic effect of the submerged embankment and the vegetation, and quantitatively analyzing the improvement degree of the coast protection effect on wave attenuation and beach stability. A sustainable tough coast protection system can be formed under the climate change background through a collaborative protection mechanism integrating natural vegetation and an artificial submerged embankment. The method has the advantages of low cost, remarkable wave reduction effect and the like, and the capability of coping with climate change challenges and comprehensive protection of coastal zones can be effectively improved.

The invention relates to the technical field of forestry environment monitoring, and discloses a forestry environment monitoring method and system based on big data analysis. The method comprises the following steps: acquiring multi-dimensional environmental parameters such as soil moisture content, vegetation coverage and meteorological factors through a distributed sensor network, and generating a real-time weight coefficient through a dynamic weight distribution engine; and completing anomaly detection by using the space-time correlation analysis model, and triggering a self-adaptive sampling strategy to perform high-density acquisition on an abnormal region. Real-time data and satellite remote sensing data are integrated through a multi-source data fusion algorithm, a forestry environment state matrix is generated, and a reference threshold is dynamically updated in combination with an incremental learning mechanism. And generating a regulation and control instruction set for soil improvement, vegetation maintenance and disaster early warning according to the updated threshold value, and executing and collecting feedback data by the edge computing node. And comparing feedback data with an expected index through a bidirectional verification mechanism, generating system optimization parameters, and returning the system optimization parameters to a dynamic weight distribution engine, thereby realizing accurate monitoring and efficient regulation and control of a forestry environment.

The invention relates to an earthwork balance calculation method, system and device and a storage medium, and relates to the technical field of earthwork balance design. The method comprises the steps of performing fusion processing on oblique photography data and laser radar point cloud data to generate a live-action three-dimensional model; performing grid division on the live-action three-dimensional model to obtain a grid model; based on the gridding model, defining a decision variable set including the design elevation of each grid point, the overall slope of the site, the slope height of each slope unit and the earthwork allocation amount, and constructing a comprehensive objective function including an earthwork balance objective, an economical objective and a safety objective; and under the engineering constraint condition, performing multi-objective optimization solution on the comprehensive objective function to obtain a Pareto optimal solution set, and selecting a final implementation scheme from the Pareto optimal solution set. According to the method, the problem of topographic data missing of the dense vegetation region is effectively solved, the limitation of traditional single-target optimization is broken through, and the maximization of comprehensive benefits is realized.

The invention discloses a vegetation carbon sink space optimization method and system based on interpretable machine learning. The method comprises the steps that firstly, multi-source environment data such as administrative division, environment factors and target variables of a target area are acquired, and a space table data set is formed; training an XGBoost regression model based on the data set, predicting the vegetation carbon sink amount and evaluating the performance of the model; quantifying the contribution of each environmental factor to the carbon sink by combining an SHAP interpreter, generating a feature importance and interpretability map, fitting through a LOWESS algorithm to obtain an environmental factor and carbon sink response relation curve, and extracting an optimal threshold interval; further predicting a carbon sink gain according to an optimization threshold value, delimiting optimization partitions, and forming a space optimization scheme; and finally outputting a vegetation carbon sink optimization suggestion report. According to the method, scientific quantification of carbon sink space optimization can be realized, and the interpretability and decision support value of a carbon sink improvement strategy are improved.

The invention relates to the technical field of remote sensing geological prediction, in particular to a slope support stability prediction method and system based on remote sensing data, and the method comprises the steps: obtaining a multispectral remote sensing image of a target slope region, and generating a vegetation mask through employing a normalized vegetation index; removing interference pixels of a vegetation coverage area in combination with morphological filtering and connected area analysis, then identifying support structure features through an improved edge detection algorithm, delimiting an influence area by adopting a region growing algorithm based on machine learning, inputting an area image into a convolutional neural network model, and obtaining an image of the support structure; a deformation probability graph is output by using a data enhancement technology and a weight optimization layer, finally, a threshold value is determined according to historical data, slope support stability categories are divided in combination with spatial neighborhood information and a voting mechanism, vegetation interference is effectively eliminated, a support structure is accurately recognized, prediction precision is improved, slope support stability can be accurately judged in time, and the method is suitable for popularization and application. And a reliable basis is provided for early warning and protection of slope disasters.

The invention relates to the technical field of forest wetland carbon sink monitoring and evaluation, and particularly discloses a forest wetland carbon sink intelligent monitoring and evaluation system and method. According to the method, a vegetation carbon sequestration potential evaluation result is obtained by analyzing vegetation structure feature data of forest, water and soil composite ecology of the forest wetland, a water-soil-carbon cooperative evaluation result is obtained by analyzing specific hydrological-soil carbon cycle key elements of the forest wetland, and environmental disturbance response factors are obtained by combining various environmental disturbance factors. Through multi-source data fusion and intelligent modeling, a carbon sink quantitative evaluation value is output, and abnormal feedback is triggered; the problems that in the prior art, adaptability to a forest wetland compound ecological system is insufficient, evaluation refinement is insufficient, and dynamic response is lacked are solved. According to the method, accurate and dynamic monitoring and evaluation of forest wetland carbon sink are realized, carbon sink short plates can be accurately positioned, optimization support is provided, and technical support is provided for protection, restoration and optimization management of forest wetland carbon sink resources.

The invention discloses a rapid restoration method for degraded grassland based on a microbial agent. The microbial agent is formed by compounding 15-30 parts of a nitrogen-fixing microbial agent, 10-20 parts of a phosphate-solubilizing microbial agent, 8-15 parts of a plant growth-promoting microbial agent, 25-40 parts of a humic acid carrier, 3-8 parts of an embedding agent sodium alginate and 5-12 parts of a nutritional agent molasses. Wherein the nitrogen-fixing bacteria are azotobacter chroococcum and alfalfa rhizobium (the viable count is greater than or equal to 5 * 10 < 9 > CFU / g), the phosphate solubilizing bacteria are bacillus megatherium (the viable count is greater than or equal to 1 * 10 < 10 > CFU / g), and the growth-promoting bacteria are bacillus subtilis and pseudomonas fluorescens (the viable count is greater than or equal to 2 * 10 < 10 > CFU / g). The microbial inoculum is subjected to activation, carrier adsorption, sodium alginate embedding granulation and CaCl2 cross-linking processes to be prepared into slow-release particles of 1-3 mm. 200-250 kg of organic fertilizer is applied to each hectare in a mild area, 300-400 kg of organic fertilizer is applied to each hectare in a moderate and severe area, and leymus chinensis and wheatgrass are sown in a mixed mode. After 90 days of application, the vegetation coverage is improved by 40% or above, the soil organic matter is increased by 25-40%, the microbial diversity index is larger than or equal to 3.5, and the comprehensive remediation cost is reduced by 53%.

The invention provides a wetland ecosystem health evaluation method based on multi-source remote sensing data, and belongs to the technical field of wetland ecosystems, and the method comprises the steps: carrying out the dense dark pixel and Kalman filtering cooperative atmospheric correction of a multi-spectral remote sensing image to obtain a surface reflectance image, and achieving the precise segmentation of a wetland landscape through a graph cut theory, a spectral unmixing model based on an improved Gaussian kernel is utilized to embed physical constraints to invert water quality parameters, a laser radar canopy height priori constraint three-dimensional radiation transmission model is combined to invert vegetation parameters, and a mixed pixel decomposition result is optimized through a Markov random field. A water quality, vegetation and landscape three-dimensional comprehensive health evaluation system is constructed, degradation dominant factors are identified, and the technical problem that it is difficult for multi-source remote sensing data to cooperatively invert wetland ecosystem multi-dimensional health state parameters is solved.

The invention relates to the technical field of forestry intelligent management and control, and discloses a forestry optimization management and control method and system based on artificial intelligence. The method comprises the following steps: collecting multi-source environment monitoring data such as soil humidity, illumination intensity and vegetation growth indexes in a forestry area, aligning a time sequence through a dynamic time warping algorithm, and separating steady-state and transient components; detecting anomalies through an isolated forest algorithm and generating a thermodynamic diagram, and obtaining a potential disease and pest outbreak area through causal reasoning in combination with historical records; constructing a dynamic growth reference curve, and identifying a disturbance sensitive area by using a generative adversarial network and a convolutional neural network; and integrating and generating a management and control priority partition map, optimizing resource allocation through deep reinforcement learning, outputting a task sequence, and driving equipment to execute irrigation, fertilization or pest control operation. According to the method, multiple artificial intelligence algorithms are fused, accurate control over the whole forestry process is achieved, and the method adapts to the dynamically-changing forestry environment.

The invention belongs to the technical field of drought risk prediction and vegetation influence evaluation, and particularly discloses a vegetation-to-drought resource utilization coupling response strategy evaluation method, which comprises the following steps: acquiring target data; constructing a drought index according to the target data, and extracting a drought event; extracting vegetation total productivity, vegetation net level productivity and evapotranspiration abnormal data before and after the drought event, and calculating response intensity of vegetation variables; calculating the average variation of the light energy utilization efficiency, the carbon utilization efficiency and the moisture utilization efficiency; comprehensively considering the multivariable resistance-recovery state of the vegetation physiological process and the regulating variable of the resource utilization efficiency of the vegetation in response to drought stress, and analyzing the drought event resistance-recovery response capability of the vegetation. The method can more accurately and objectively understand the physiological mechanism difference of the vegetation to the drought stress response.

The invention provides a forest resource remote sensing data spatio-temporal change analysis method and system, and relates to the technical field of data processing, and the method comprises the steps: carrying out the ground object type classification processing of each pixel according to a spectral unmixing processing result, and generating a forest coverage distribution diagram; extracting all pixel center points on the boundary of the change region as an input point set by using the forest coverage distribution map of the plurality of time points; finding out the outermost points in the input point set through iterative calculation, and connecting the points to form a convex polygon; performing standardized integration on the shape and the range of the change area based on a convex polygon to obtain a standardized description result; calculating the annual change rate of the forest area based on the normalized description result; and calculating vegetation coverage and forest biomass ecological indexes based on the annual change rate of the forest area. According to the method, more accurate and coherent monitoring and evaluation of the space-time change of the forest resources can be realized.

The invention relates to the technical field of remote sensing measurement, in particular to a power transmission line tree high-risk assessment method and device based on satellite remote sensing and a medium. And based on the result and line data, calibrating a safety distance, combining terrain elevation correction, identifying the tree, calculating the height of the tree and the clearance distance of the line, comparing the clearance distance with a safety standard, dividing high, medium and low risk levels, and generating a high-risk assessment result of the power transmission line tree. According to the method, the vegetation distribution is described by extracting the spectral features of the remote sensing image, the dynamic safety interval is constructed in combination with the position of the power transmission line and the vegetation distance, the model is established by fusing terrain and crown elevation data, clearance parameters are quantitatively calculated to generate the risk level, the complex terrain adaptability and the risk point automatic identification precision are improved, and the early warning reliability is improved. And intelligence and decision-making accuracy of power transmission line inspection are promoted.

The invention relates to the technical field of intelligent monitoring of protective forests, and discloses a protective forest monitoring and evaluating system based on intelligent visual identification. According to the system, synchronous perception information of vision, terrain and weather is captured through a multi-source information synchronous acquisition module, and a vegetation growth space field containing canopy form topology and canopy density change process is generated through multi-dimensional feature extraction operation. A degeneration characteristic evolution graph constructed according to the method can present a degeneration plaque contour, a spreading track and an intensity distribution thermodynamic diagram. The map is subjected to deep feature analysis through a trained multi-level degradation identification network to obtain a degradation level and a trend vector, and finally a comprehensive evaluation report with spatio-temporal information is formed. According to the invention, high-precision and automatic monitoring and evaluation of the dynamic degradation process of the protection forest are realized.

The invention discloses an energy corridor forest fire risk prediction method and system based on multi-source data fusion, and the method comprises the steps: constructing a fusion data set, and determining risk evolution parameters; if the risk evolution parameter exceeds a preset threshold value, triggering a vegetation difference analysis function to obtain a section heterogeneity label; after section heterogeneity labels are obtained, time-space non-uniformity compensation correction is conducted on historical data through a time sequence change tracking method, the risk transition probability of each section is calculated, and potential critical points are judged; if the potential critical point is judged to be high in probability, activating a difference early warning mechanism, generating a targeted risk level map according to a section heterogeneity label and a risk transition probability, and obtaining a dynamic early warning signal; and carrying out iterative verification on the dynamic early warning signal by adopting a real-time update flow in the fusion data set, adjusting a risk level in combination with prediction output of the long and short-term memory network, and determining a final risk prediction result. According to the invention, the timeliness and accuracy of fire risk prediction are improved.

The invention provides a coastal wetland ecological corridor identification and optimization method and system, and relates to the technical field of ecological environment monitoring. The method comprises the following steps: integrating optical, radar, terrain, night light and road data on an earth engine cloud platform, and unifying time and space references to form an initial data set; extracting a normalized vegetation index and a vegetation coverage degree, and combining with a water body frequency to identify an ecological source land; generating a comprehensive resistance surface according to the land utilization and coverage type and the road distance; and performing minimum cost path and circuit connectivity analysis in a geographic information system to obtain potential galleries, key nodes and barrier regions, and outputting gallery grades and optimization suggestions in a grading manner according to a cost threshold, thereby realizing high-precision identification and optimization of the coastal wetland ecological network.

The invention relates to the field of intelligent transportation and urban landscaping management, and discloses an LIM transportation junction landscaping landscape intelligent operation and maintenance method and system and electronic equipment, and the method comprises the following steps: constructing a landscaping landscape information model integrating a three-dimensional space model, physiological attributes and infrastructure topology; deploying a multi-source sensing network based on the model, and collecting environment, vegetation and operation and maintenance data; performing time-space association on the data and the model entity, and establishing a dynamic data management platform; constructing a vegetation digital twinborn prediction engine in combination with the physical information neural network and outputting a result; based on the prediction result, adopting multi-target reinforcement learning to generate a maintenance strategy; and executing an intelligent maintenance task bound with the model space coordinates according to a strategy. According to the method, unified integration and dynamic management of greening landscape data are realized, vegetation state sensing precision and prediction capability are improved, intelligent decision-making of multi-objective optimization is supported, and scientificity, efficiency and resource utilization rate of greening maintenance are effectively improved.

The invention provides an all-dielectric film system structure with visible near-infrared low transmission vegetation simulation, intermediate infrared high reflection and microwave high transmission. The device sequentially comprises, from top to bottom, a visible near-infrared simulated vegetation layer which is composed of first to fourth dielectric layers and is configured to simulate vegetation reflection spectrum characteristics in a wave band of 380-2500 nm and has a low transmission characteristic; the intermediate infrared high-reflection layer is formed by alternately stacking a first dielectric layer and a third dielectric layer, and is configured to form high reflection in a wave band of 3-14 microns; and a substrate layer; the thickness of the whole film layer is far smaller than the microwave wavelength, so that the film system keeps high transmission in the microwave band. According to the invention, the amorphous InSb material with high loss characteristic in visible near-infrared light is introduced, so that the average transmissivity of 1500-2500 nm is reduced to 0.095 while high-precision simulation of the vegetation reflectivity spectrum is realized, thereby realizing simulation of low transmissivity of the wave band vegetation.

The invention relates to the technical field of agricultural quantitative remote sensing and intelligent agriculture, and discloses a multi-source remote sensing data radiation correction and chlorophyll content cross-platform inversion method, which comprises the following steps: synchronously obtaining ground actual measurement, unmanned aerial vehicle and satellite remote sensing data, calculating vegetation indexes, and screening sensitive characteristic variables by using Pearson correlation analysis; establishing a radiation correction model by adopting a ratio averaging method, performing radiation normalization processing on satellite data by taking unmanned aerial vehicle data as a reference, and eliminating sensor response differences; an SPAD inversion model is constructed and optimized based on Z-score standardization, the SPAD inversion model is migrated and applied to corrected satellite data, a regional scale chlorophyll distribution diagram is generated through pixel-by-pixel calculation, and then a differentiated farmland management strategy is formulated. According to the method, the difficulty of multi-source data collaboration is solved, the regional scale crop chlorophyll monitoring precision is improved, and scientific decision support is provided for large-area crop fertilization.

The invention provides a laser-point-cloud-based safety inspection identification evaluation method and system for an overhead transmission line, and relates to the technical field of vegetation growth evaluation.The method comprises the steps that multi-period multi-view-angle laser point cloud and meteorological data of a transmission line corridor are collected, and vegetation point cloud distribution data are obtained through point cloud splicing and segmentation; the method comprises the following steps: performing three-dimensional reconstruction in combination with multi-dimensional indexes to generate a vegetation model, extracting point cloud features from the vegetation model, performing space-time coding fusion on the point cloud features and meteorological features, calculating vegetation growth change data through a growth model based on fused features, and comparing the vegetation growth change data with a safety threshold to generate a growth evaluation result, thereby realizing accurate monitoring of the vegetation growth state. Automatic and accurate monitoring and safety early warning of the vegetation growth state of the power transmission line corridor can be realized.

The invention discloses a forest land ecosystem carbon reserve accounting method and system, and the method comprises the steps: obtaining and screening feature variables, such as optical remote sensing data, vegetation indexes, texture factors, satellite-borne laser radar feature parameters, so as to determine features having the most influence on a target variable, and removing redundant and irrelevant variables; the method comprises the following steps of: screening characteristic variables, reserving variables which contribute to the model prediction effect, improving the precision and stability of the model, constructing a canopy height inversion model by adopting a random forest method based on the screened characteristic variables, realizing accurate estimation of the canopy height, and finally calculating the biomass of vegetation of the forest ecological system by using a different-speed growth equation. And finally, summarizing the biomass of the vegetation of the forest ecological system to obtain the total biomass of the vegetation, and multiplying the total biomass of the vegetation by the carbon-containing coefficient to obtain the vegetation carbon reserve of the forest ecological system, so that wide-area and efficient forest parameter monitoring is realized, the monitoring cost is reduced, the environmental adaptability is improved, and the problems of data continuity and standardization are solved.

The invention discloses a sea-land collaborative coastal wetland ecological quality remote sensing evaluation method under a spartina alterniflora expansion background, and the method comprises the steps: obtaining multi-source remote sensing data of a research region, and carrying out the preprocessing of the data, and obtaining a preprocessed remote sensing image; according to the preprocessed remote sensing image, obtaining a salt marsh vegetation space-time distribution diagram of the research area; remote sensing evaluation is conducted on coastal wetland land and water body ecological quality, land ecological quality remote sensing evaluation indexes integrate NDVI, WET, NDSI, LST and DHI, and water body ecological quality remote sensing evaluation indexes integrate TSM, WST, CDOM and Chl-a; and finally, quickly obtaining the ecological quality change of the affected area of the spartina alterniflora through a spartina alterniflora change condition mask evaluation result. The ecological quality remote sensing evaluation method provided by the invention is more suitable for the coastal wetland under the background of spartina alterniflora expansion, and a scientific basis is provided for making coastal wetland ecological management and protection strategies.

The invention relates to the technical field of remote sensing image recognition, in particular to a method for determining a moso bamboo forest snow disaster affected area based on a GEE platform. The method mainly solves the problems of low efficiency, high cost and difficulty in realizing large-range rapid evaluation caused by dependence on manual field investigation in the prior art. According to the technical scheme, the method comprises the following steps: firstly, obtaining a multi-temporal Sentinel-2 image before and after a snow disaster, and carrying out the preprocessing of the multi-temporal Sentinel-2 image; extracting spectral bands, vegetation indexes and texture features to form an initial feature set; then, a key feature variable combination is determined through statistical significance filtering and machine learning optimization; on this basis, establishing a logistic regression discrimination model and determining an optimal classification threshold; and finally, carrying out snow disaster state classification on the moso bamboo forest region by utilizing the trained model, and generating a disaster region spatial distribution diagram.

The invention relates to a coastal zone mangrove forest biomass prediction method and device, a storage medium and equipment, and the method comprises the steps: obtaining the image data of a target region and the text description information of the image data, extracting a visual feature vector of the image data based on a pre-trained image feature extraction model, and carrying out the prediction of the biomass of a mangrove forest based on a normalized vegetation coefficient and the image data; the method comprises the following steps: acquiring a visual feature vector, acquiring a spectral feature vector, fusing based on text description information and knowledge graph information to obtain a text feature vector, splicing the visual feature vector, the spectral feature vector and the text feature vector to obtain a fusion feature vector fusing multiple modes of vision, spectrum, text and knowledge graph, and then based on the fusion feature vector, obtaining a fusion feature vector based on the fusion feature vector. And the predicted biomass is obtained by using the trained biomass prediction model, so that the accuracy of predicting the biomass of the mangrove forest in the coastal zone is improved.

The invention relates to the technical field of environment monitoring, in particular to a wetland ecological monitoring method based on multi-source heterogeneous data fusion, which comprises the following steps of: acquiring a water level vegetation water quality multi-source sequence, generating a multi-source wetland monitoring data set through wavelet transform alignment, calculating water level and vegetation water quality response time lag to obtain a water power driving coefficient, and calculating the water power driving coefficient; the method comprises the following steps: performing frequency domain alignment on multi-source monitoring data, distributing weights to obtain ecological response intensity, calculating energy flow offset, performing tensor decomposition to obtain causal weight, screening high values to construct a wetland ecological causal chain network, and performing sorting to select first three leading chains to extract parameter intervals to generate wetland ecological monitoring and early warning information. The method solves the problems of data discretization and time sequence mismatch, calculates a response time lag and weighted optimization coupling relation, improves the dynamic response precision, identifies a causal path based on energy flow offset and tensor decomposition, quantifies the intensity, constructs a dominant causal chain, and achieves the directional early warning and dynamic evaluation of wetland ecology.

The invention discloses an ecological bank protection intelligent irrigation control system and method based on multi-source data fusion, and relates to the field of irrigation control. Soil moisture content data is collected in real time, meteorological and vegetation growth information is combined, an irrigation strategy is dynamically adjusted through a prediction model, and accurate matching of vegetation water demand and water supply is ensured; meanwhile, sonar echoes are used for constructing underwater digital twin bodies, the state of a fish nest channel is monitored in real time through image recognition, when blockage is detected, the system immediately triggers a cleaning mechanism and synchronously optimizes an irrigation threshold value, and it is avoided that vegetation irrigation is affected due to the fact that water quality is affected by blockage; in the cleaning process, the main operation machine efficiently dredges the channel through rotary cleaning and water flow scouring, and the cleaning effect is verified in real time by means of digital twin bodies; in addition, the system further guarantees the irrigation continuity by presetting the cleaning time limit and a standby water source switching mechanism, effectively prevents the risk of bank slope instability, and remarkably improves the durability and ecological stability of the bank protection structure.

A method and system for deep learning-based automated detection of vegetation encroachment in overhead power distribution networks. A deep learning model can be used to detect vegetation encroachment in preprocessed images and frames and deep learning explainability tools can be used to identify irrelevant objects in the images that contribute to misclassification.