157 results about "Mangrove" patented technology

The invention discloses a mangrove forest super-resolution mapping method for depth extraction of space-time spectrum features, and belongs to the technical field of intelligent processing of remote sensing images, and the method comprises the following steps: S1, obtaining multi-source remote sensing image data in a research area, and carrying out the preprocessing of the multi-source remote sensing image data; s2, mangrove forest classification extraction is carried out on the preprocessed data based on an object-oriented method; s3, extracting the mangrove forest category graph based on an SVR algorithm; s4, based on the target time-phase mangrove forest abundance map, constructing a super-resolution cartographic model training data set of time-space matching; s5, constructing a space-time super-resolution cartographic network framework combining coding-decoding and a VSS module based on the super-resolution cartographic model training data set of space-time matching; s6, training and verifying the space-time super-resolution cartographic network framework model combining the coding-decoding module and the VSS module; and S7, batch space-time super-resolution mapping is carried out based on the optimization model.

The invention discloses a mangrove forest distribution information extraction method based on deep learning and remote sensing images, and the method comprises the steps: firstly, constructing a spatial diagram fusing ecological priori knowledge such as hydrological connectivity, and extracting high-dimensional features containing a complex space-time dependence relation from an irregular satellite image time sequence through employing a PG-STGT network; a physical information guided antagonism decoupling mechanism is subsequently adopted to explicitly decompose the high-dimensional feature into three subspaces. And finally, according to the method, a high-robustness distribution diagram is generated by applying the decoupled identity feature vectors, and a multi-dimensional mangrove forest health index is generated by using residual error abnormal features. According to the method, the physical mechanism and deep learning are deeply fused, the time consistency of the classification result is improved, and the ability of diagnosing the health condition of the ecological system is provided.

The invention relates to a method for separating micro-plastics from sediment rich in organic matters in mangrove forest, which comprises the following steps: sediment pretreatment: freeze-drying a sample, sieving, and oscillating and dispersing by using an alkaline buffer solution with the pH value of 9.0-10.0; performing density gradient centrifugation: sequentially performing step-by-step centrifugation by using a NaI solution with the concentration of 1.1-1.3 g / cm < 3 > and a ZnCl2 solution with the concentration of 1.5-1.7 g / cm < 3 >; enzymatic hydrolysis purification: digesting a centrifugal product with a compound enzyme solution at 35-40 DEG C for 4-8 hours; and vacuum filtration: collecting the micro-plastics by using a filter membrane with the aperture of 0.2-0.8 mu m. According to the method for separating the micro-plastics from the sediments rich in the organic matters in the mangrove forest, the density range of all common plastics is accurately covered by dual-density gradient centrifugation, the average recovery rate of 0.1-5mm micro-plastics in the sediments in the mangrove forest reaches 90.2 + / -3.5%, the degradation efficiency of typical organic matters in the mangrove forest by adopting organic matter interference elimination and cellulase and protease combination reaches 92.4%, and the degradation rate of the typical organic matters in the mangrove forest reaches 92.4%. Therefore, the separation efficiency is effectively improved, the contact ratio of particle size distribution curves of 1 mu m-5 mm before and after treatment reaches 98.2%, and the integrity of the micro-plastic can be effectively guaranteed.

The invention relates to the technical field of mangrove forest protection, in particular to a forest protection fence based on biophysical coordination and a prevention and control method thereof.The forest protection fence comprises a plurality of supporting components and an annular protection screen body, the multiple supporting components are arranged around target plants and form an annular supporting system, and each supporting component comprises a supporting rod and an expansion supporting assembly; the supporting rods are vertically arranged, the lower ends of the supporting rods can be inserted into a soil layer, the extension supporting assemblies are arranged on the supporting rods and can partially enter the position below the soil layer along with the supporting rods, and the extension supporting assemblies between every two adjacent supporting components can be connected in a matched mode. The annular protective screen body is connected with the annular supporting system and at least partially extends below the soil layer; through cooperation of the supporting component and the annular protective screen body, benthonic organisms around the target plant can be limited to move in the preset area, and growth of the target plant is assisted through movement of the benthonic organisms. According to the technical scheme, the growth of the target plant is protected by virtue of the cooperation of benthic organism activity, the supporting component and the annular protective screen body.

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 discloses a mangrove forest carbon sink potential dynamic evaluation method and system based on AI model quantification. The method comprises the steps of obtaining air data of a mangrove forest based on an unmanned aerial vehicle; acquiring ground data of the mangrove forest based on a sensor arranged on the ground; performing integration processing on the air data and the ground data to obtain detection data; and inputting the detection data into a pre-trained AI model for quantitative processing to obtain a mangrove forest carbon sink potential dynamic evaluation result. According to the mangrove forest carbon sink potential dynamic evaluation method, the air data of the mangrove forest and the ground data of the mangrove forest are collected based on the unmanned aerial vehicle and are integrated, so that the detection data are more accurate and comprehensive, and the accuracy of the mangrove forest carbon sink potential dynamic evaluation is improved.

The invention relates to the technical field of seedling transplanting, and discloses mangrove forest seedling transplanting equipment which mainly comprises a high-pressure water gun integrated with a high-pressure water pump, a bearing mechanism mounted on a gun barrel and a water inlet head. The bearing mechanism is provided with a clamping assembly for clamping stems of seedlings and a bearing table for bearing roots of the seedlings, and is provided with an ejection assembly in a matched mode. Through mechanical linkage of a sliding sleeve, a driving plate, a movable connecting rod and a bearing disc, when a lifting rod is operated, the ejection assembly can automatically complete the actions of firstly opening a clamping assembly to release a stem and then ejecting the bearing disc to support the root of a seedling in sequence. During working, a high-pressure water gun is used for spraying water flow to impact on the mud flat to form a planting pit, and seedlings are placed at the bottom of the pit without damage in a water-rich environment. The problem that traditional manual transplanting easily causes mechanical damage to seedling rhizomes is solved.

A mangrove forest extraction method based on a multi-source data time sequence comprises the steps that 1, a remote sensing image of a research area is acquired and preprocessed; 2, after the image is preprocessed, index features of the image are obtained through wave band calculation; 3, mangrove forest and non-mangrove forest pixel points are manually selected to serve as time sequence sample point positions, and meanwhile an index time sequence curve of the sample points is extracted; 4, inputting the sample point time sequence curve into a convolutional long short-term memory network, and training a mangrove forest extraction model; and 5, extracting an index time sequence of all pixels in the mangrove forest extraction region, inputting the index time sequence into the mangrove forest extraction model, and extracting a mangrove forest distribution range. According to the method, the advantages of various data sources are combined, the mangrove forest growth background and the time sequence dynamic characteristics are considered, the mangrove forest planting range is extracted through the multi-remote-sensing-index time sequence, the robustness of the model in a complex environment is improved, and the problems that the mangrove forest range boundary is unstable and is easily interfered by the environment are solved.

The invention provides a mangrove forest degradation type and reason identification method and system based on remote sensing and a multi-factor index system. The method comprises the following steps: firstly, integrating a multi-temporal remote sensing image, ecological monitoring data and ground survey data, and constructing a systematic degradation factor index system covering natural, climate, biological and human factors; secondly, extracting typical degradation characteristics such as vegetation coverage decline and species composition change by using a remote sensing change detection technology and a principal component analysis method; then, through K-means clustering, an ecological classification algorithm based on rules and an ecological threshold standard, mangrove forest degradation types are identified and classified, and the types comprise a man-made interference type, a biological erosion type, a natural succession type and a multi-factor coupling type; then, adopting a supervised machine learning model and combining a feature importance evaluation method such as a Shapley value to identify a degradation cause and a weight thereof; and finally, verifying the precision of the model through an on-site monitoring quadrat and an expert interpretation result, and outputting visual results such as a degradation type distribution diagram, a factor contribution distribution diagram and the like.

The invention discloses a method for evaluating the growth performance of mangrove forest seedlings in an estuary high-salinity environment, and the method comprises the steps: constructing an analysis framework in which a dynamic salinity stress system and a multi-dimensional monitoring technology are coupled, and achieving the precise evaluation of the environmental adaptability of the mangrove forest seedlings; the method comprises the following steps: firstly, carrying out sterile germination and matrix domestication on a target estuary provenance, then establishing a three-dimensional stress model containing basic salinity maintenance, sudden salinity impact and tidal cycle fluctuation, and strictly controlling the sudden salinity impact amplitude within 20% of the historical extreme value of the estuary; synchronously acquiring canopy volume and root architecture parameters of the seedlings by adopting a three-dimensional laser scanning technology, and constructing a morphological database in combination with leaf apparent characteristic records; subcellular structure indexes such as cell wall thickening, chloroplast envelope integrity and mitochondrial ridge density are observed and analyzed through a transmission electron microscope, the molecular mechanism of osmotic regulation and energy metabolism is disclosed, and the concentration of suspended solids in the water body, the oxidation-reduction potential of sediments and the functional diversity of microorganisms are synchronously monitored.

The invention provides a mangrove forest dense time sequence detection method and device, and relates to the technical field of target recognition, and the method comprises the steps: obtaining a radar image and an optical remote sensing image of a to-be-detected region; calling a pre-constructed double-branch feature extraction network to independently perform feature extraction on the radar image and the optical remote sensing image to obtain radar features and optical remote sensing features; performing feature fusion on the radar feature and the optical remote sensing feature to obtain a first fusion feature; sSMI features are calculated based on the radar and optical remote sensing image reflectivity data; performing feature fusion on the first fusion feature and the SSMI feature to obtain a second fusion feature; a pre-constructed target network is called to process the second fusion feature, a candidate detection result is obtained, and the target network is used for recognizing the change position and the change type of the target object at the same time; and performing space-time consistency correction on the candidate detection result to obtain a detection result for output.

The invention discloses Moraxella roselle MT01 and application thereof in CO2 fixation and / or sediment carbon emission reduction, and belongs to the technical field of microorganisms. According to the invention, a strain of Moraxella roselle MT01 is obtained through screening and identification, and the preservation number is CCTCC (China Center For Type Culture Collection) NO: M 20252028. Through verification, the strain MT01 contains two key carbon sequestration genes, namely fhs and cbbM; whether the strain MT01 has the carbon sequestration capability or not is detected by using isotope, and the result shows that delta 13C exceeds 60 per thousand; the carbon sequestration capability of the strain MT01 is detected by adopting sediment carbon emission reduction, the result shows that the strain MT01 can obviously reduce the emission of CO2 in mangrove forest sediment, and the emission reduction efficiency is about 16.9%. The invention provides new strain selection and technical basis for microbial carbon sequestration.

The invention discloses a mangrove forest protection effect intelligent evaluation and scene prediction system, and relates to the field of ecological environment information intelligence, and the system comprises a key driving factor recognition module which is used for determining a core variable influencing the protection effect and a dynamic response interval of the core variable based on a statistical analysis and nonlinear fitting method; the protection effect quantitative evaluation module is used for calculating a net improvement effect of protection intervention by comparing ecological index differences inside and outside the protection area; the causal effect analysis module is used for separating independent contributions of natural factors and artificial protection by adopting a dual machine learning framework; the space-time dynamic modeling module is used for performing uncertainty modeling on the space-time evolution of the ecological indexes by using a Bayesian hierarchical structure; and the multi-scene prediction module is used for realizing ecological response prediction under multi-factor driving and supporting input and simulation of user-defined scenes. According to the scheme, multi-dimensional and high-precision evaluation and future trend reliable prediction of mangrove forest protection effects can be realized.

The invention discloses an artificial mangrove forest pest monitoring and early warning method and system, and belongs to the technical field of ecological environment monitoring, and the method comprises the steps: collecting multi-frequency pest acoustic signals and low-altitude thermal infrared image data, constructing a pest energy density field, and forming a multi-dimensional propagation feature matrix; establishing a pest risk node map based on the matrix, simulating a propagation path and calculating a pest energy coupling strength coefficient; carrying out energy refraction remapping and abnormal superposition region identification, extracting an energy convergence core region, and generating a propagation trend trajectory diagram; constructing a combined prediction model of the microscopic infection characteristics and the macroscopic diffusion situation of the insect pests, outputting an evolution stage risk curve, and identifying secondary outbreak latent points; the method has high adaptability, high accuracy and response preposition, and the intelligent level of mangrove forest pest monitoring and early warning is remarkably improved.

The invention discloses a mangrove forest recognition method and system based on ensemble learning, and relates to the technical field of remote sensing data processing.A mangrove forest integrated recognition model driven by feature indexes is constructed, and the model takes various heterogeneous random forest models, gradient elevator models, support vector machine models and K neighbor models as base classifiers; the mangrove forest identification method effectively utilizes respective learning capabilities for different data models, reduces single model deviation, and then couples with the meta-classifier comprising the logistic regression model, so that the classification performance of the model can be further improved, the mangrove forest identification efficiency and accuracy are remarkably improved, and the mangrove forest identification efficiency and accuracy are improved. And meanwhile, the adaptability and the recognition capability of the model under a complex background condition are improved.

The invention discloses a mangrove forest state diagnosis method and system based on AI assistance. The mangrove forest state diagnosis method comprises the steps that first feature extraction is executed on mangrove forest remote sensing data to be diagnosed to obtain remote sensing visual features; aiming at each mangrove forest state of the last-stage classification unit, integrating self soft prompts, soft prompts of the upper-stage classification unit and state information to construct mangrove forest state information; performing second feature extraction on the mangrove forest state information to obtain state features; calculating a matching coefficient between the remote sensing visual feature and each state feature; and determining a diagnosis conclusion according to the mangrove forest state corresponding to the state characteristic with the maximum matching coefficient. According to the method, the multi-level soft prompt is fused with ecological priori knowledge, and a double-feature extraction and matching mechanism is combined, so that precise and hierarchical diagnosis of the mangrove forest state is realized, and technical support is provided for coastal ecological protection and restoration decision making.

The invention provides a natural shoreline and mangrove forest supervision method and system based on video recognition, and the method comprises the steps: S1, installing and deploying information collection equipment, and setting a preset position and a cruise plan according to different monitoring targets; s2, accessing original sample data acquired by information acquisition equipment, reinforcing an original sample data set through a three-level data enhancement framework, and constructing a shoreline target sample data set; s3, optimizing by taking the YOLOv8n model as a basic model, and constructing a shoreline target recognition model; s4, constructing a target entity-spatial constraint-ownership state triple verification rule fusion judgment model as a behavior recognition model; and S5, inputting a recognition result of the shoreline target recognition model into a behavior recognition model, judging an illegal behavior through a triple verification rule, and generating early warning information. According to the invention, the working efficiency of natural shoreline supervision can be improved, and a replicable solution is provided for fine management of the coastal zone and the mangrove forest.

This invention relates to a method for constructing a biodegradable mangrove ecological island based on dynamic tidal energy regulation, and an artificial island siltation device. The artificial island siltation device includes a bamboo gate system, a hemp rope net system, a fixed pile system, and a limiting system. A dynamic filtration structure is formed by the liftable bamboo gate and the hemp rope net. Utilizing the buoyancy difference and water flow velocity changes generated by tidal rise and fall, a synergistic effect is achieved: accelerating the passage of sediment-laden water during high tide and promoting siltation during low tide. A fully biodegradable material is used to construct a growing silt body. The buoyancy balance is regulated by the bamboo joint holes, ensuring the device remains suspended on the surface layer of the silt body. This allows for continuous siltation and elevation of the tidal flat, forming an artificial island that provides a site for mangrove seedling planting and meets the required daily sunrise duration for mangrove growth. The method and device of this invention have technical advantages such as low construction cost, dynamic adaptation to tidal changes, and avoidance of secondary pollution.

The present application relates to the technical field of climate change warning, and discloses a mangrove blue carbon ecological risk identification and warning method and system for climate change, comprising the following steps: acquiring time series of global land surface characteristic satellite leaf area index data and environmental factors, performing detrending, deseasonalization and standardization processing to extract abnormal sequences; determining climate oscillation windows and neutral periods according to multivariate El Nino-Southern Oscillation index and dipole mode index, and calculating the mean value of the environmental factors in the neutral period as a reference; inputting the environmental factors into a long short-term memory network to extract attention weights, calculating the factor difference between the mean value of the climate oscillation window and the reference, mapping the factor difference by using a nonlinear saturation kernel function, combining the weights to obtain contribution amplitude, and selecting a dominant risk factor by comparing the direction; processing the absolute value of the leaf area index anomaly by using an S function to output risk intensity, and predicting future leaf area index anomaly based on the dominant risk factor to generate a warning evaluation.

The present invention relates to the field of image processing technology and discloses a dense mangrove counting method based on an improved U2Net model. The method comprises collecting UAV lidar data from mangrove growth areas to construct a UAV visible light remote sensing image dataset; constructing an improved U2Net model; the improved U2Net model comprises an encoder, a dense skip connection module, a decoder, and a salient feature map fusion module; and utilizing the improved U2Net model to generate a saliency probability map to obtain mangrove counting results. The present invention combines lidar remote sensing imagery to produce a dataset that can better reflect the crown width characteristics of mangroves. The network model incorporates multi-scale features and an attention mechanism to improve mangrove counting results in dense scenes. Furthermore, the method demonstrates good generalization capabilities for mangrove images at different spatial scales and different canopy densities, thereby improving the accuracy of mangrove counting results.

This invention provides a method and apparatus for detecting dense mangrove forests over time, relating to the field of target recognition technology. The method includes: obtaining radar images and optical remote sensing images of the area to be tested; using a pre-constructed dual-branch feature extraction network to independently extract features from the radar images and optical remote sensing images, obtaining radar features and optical remote sensing features; fusing the radar features and optical remote sensing features to obtain a first fused feature; calculating SSMI features based on the reflectivity data of the radar and optical remote sensing images; fusing the first fused feature and the SSMI feature to obtain a second fused feature; using a pre-constructed target network to process the second fused feature to obtain candidate detection results, wherein the target network is used to simultaneously identify the changing location and type of change of the target object; and performing spatiotemporal consistency correction on the candidate detection results to obtain the detection results for output.

The invention relates to an in-situ detection device for heavy metals in mangrove forest sediment pore water, which comprises a support frame body, a sampling mechanism comprises a sampling cylinder, a piston is coaxially arranged in the sampling cylinder, and the bottom of the sampling cylinder is provided with a sample outlet provided with an electromagnetic valve and a sampling port provided with a one-way valve. The sampling port is connected with a sampling pipe with percolation sieve holes; the detection mechanism comprises a detection chamber, a plurality of groups of detection stations are arranged in the detection chamber, detection test paper release assemblies and detection test paper recovery assemblies are arranged on the detection stations, and liquid dropping branch pipes are arranged above the heavy metal detection test paper between the detection test paper release assemblies and the detection test paper recovery assemblies; the liquid dropping branch pipes are connected to the sample outlet; and the detection chamber is provided with a camera assembly at the top of the detection station. The sampling pipe is inserted into a specific position of the sediment, so that in-situ long-term monitoring of the heavy metal in the mangrove sediment pore water in the sediment is realized, disturbance to the sediment is avoided, and the accuracy of a measurement result is ensured.

The invention discloses an atrazine degrading strain and application thereof, the strain is a strain HB172176, the taxonomic name of the strain HB172176 is Paenibacillus Danchanensis, the preservation number of the strain HB172176 is CGMCC (China General Microbiological Culture Collection Center) NO.1. 16820, and the preservation date of the strain HB172176 is September 9, 2021. The invention further discloses application of the atrazine degrading strain in preparation of atrazine degrading products. The strain is separated from mangrove forest habitat with changeable environmental conditions, has atrazine degradation activity, has wide adaptability to pH and salinity, and can be applied in more environments.

The invention relates to the technical field of medicines, and particularly discloses a benzaldehyde compound produced by mangrove forest deposited mud-derived fungi as well as a preparation method and application of the benzaldehyde compound. The structure of the compound is shown as a formula (I). The compound (I) provided by the invention can significantly inhibit LPS-induced NO release, and has low cytotoxicity and good cytocompatibility. In addition, the compound provided by the invention can be prepared by adopting a fungus Pseudalleschelia angusta SYSU-M4 from mangrove forest sedimentary mud, and the extraction and separation method of the compound is simple and low in cost.

The application provides a mangrove damage early warning method and system based on remote sensing, comprising the following steps: determining a research area and constructing a spatial index database; determining a baseline period and a mangrove distribution area; extracting a vector boundary layer from the mangrove distribution area according to the baseline period, thereby constructing a baseline database; downloading the latest remote sensing image of the research area every month from the baseline period, and calculating NDVI of each latest remote sensing image, thereby forming a time series NDVI dataset; comparing the difference between the monthly NDVI and the NDVI of the same period of the previous year based on each grid unit in the spatial index database; determining the mangrove damage risk according to the difference result; the early warning response is more timely and accurate; and the method is simple and easy to implement automatically.

An improved marine life sanctuary assembly that is completely underwater and is designed to duplicate a natural mangrove that provides protection and promotes an underwater ecosystem, which can help reverse the decline in the fish population, aviary habitats, erosion, and the like and help to promote land and marine colonization.

The invention relates to the technical field of tree height statistics, in particular to a mangrove forest tree height estimation method and device, equipment and a medium. According to the method, the spectral band, the topographic feature and the radar backscattering feature of the boundary area where the mangrove forest is located are collected, and the tree height mean value of the mangrove forest in the boundary area is estimated through the spectral band, the topographic feature and the radar backscattering feature. As the topographic features and radar back scattering are satellite remote sensing data acquired by the radar, the spectral band is data acquired by the spectral imager sensor, and the radar can acquire the satellite remote sensing data of any boundary area where the mangrove forest is located, even if the boundary area is located in a shallow sea area, the data can be accurately acquired. Therefore, the method for estimating the height of the mangrove forest is not limited by the growing environment of the mangrove forest. And the tree height is estimated by adopting the multi-source satellite remote sensing data, so that the accuracy of the tree height estimation value can be improved.

The utility model relates to the technical field of ocean engineering discloses a kind of ecological breakwater of submerged dike, artificial reef and mangrove composite, including artificial reef, the inside of artificial reef is provided with submerged dike, the outside of submerged dike is fixedly connected with node column, the node column is fixedly connected in the inside of artificial reef, the side of artificial reef is fixedly connected with mangrove bottom protection, the top of mangrove bottom protection is provided with multiple mangrove, artificial reef is hollow triangular structure, the top angle of artificial reef is 60 degrees, submerged dike is triangular structure, the top angle of submerged dike is 60 degrees.In the utility model, under the combination of submerged dike, mangrove and artificial reef, through double wave dissipation effect, effectively reduce the erosion of wave to structure, enhance the dissipation ability of hydrodynamic force, promote ecological restoration, reduce scour, enhance overall stability, prolong service life.

The present application relates to a kind of improved radiation transmission model mangrove chlorophyll estimation method, comprising: the measured data of the tree height, crown width and leaf area index of mangrove are collected;Mathematical relationship between leaf area index and tree height, crown width is constructed;Simulated spectral data is generated as training set using improved radiation transmission model;The sensitive feature screening of mangrove functional traits is carried out;Random forest inversion model based on physical constraint is constructed;The present application estimates the chlorophyll content of mangrove using the random forest inversion model based on physical constraint.The beneficial effects of the present application are: the present application can effectively improve the precision of chlorophyll inversion, provide scientific basis for the fine inversion of mangrove chlorophyll, provide data support and decision-making reference for the protection and scientific management of mangrove.