202 results about "Mangrove" patented technology

The invention relates to the technical field of image processing, and discloses a mangrove forest ecological information extraction method and system based on an unmanned aerial vehicle. The method comprises the following steps: acquiring overlooking and side-looking photos through an unmanned aerial vehicle; correcting the overlook picture by using the coordinate information and superposing the overlook picture to the remote sensing image; generating a photographing direction pattern layer based on the side view photo; establishing a mangrove forest species interpretation mark library by using a random forest algorithm; classifying selected samples to generate a species distribution diagram; and finally evaluating the mangrove forest ecological function to form ecological monitoring data. According to the method, multi-angle and multi-level high-precision ecological information extraction of the mangrove forest is realized, so that a more accurate and comprehensive scientific basis is provided for species classification, ecological system function evaluation and protection management of the mangrove forest.

The invention relates to a mangrove forest space expansion-atrophy monitoring method based on a continuous time sequence spectral index. The mangrove forest space expansion-atrophy monitoring method comprises the following steps: step (1), preprocessing a remote sensing image; (2) generating an original time sequence; (3) removing abnormal observation data in the time sequence; step (4), constructing a vegetation index time sequence sensitive to the mangrove forest within the year; step (5), extracting a mangrove forest potential area every year based on a threshold method; step (6), identifying and correcting potential areas of the mangrove forest in data abnormal years; (7) capturing the years of space expansion and atrophy of the mangrove forest; (8) drawing a space-time distribution diagram of expansion and atrophy of the mangrove forest; and (9) quantitatively evaluating the annual mangrove forest area increase and loss. The method draws the spatial distribution diagram of mangrove forest expansion and atrophy based on long-time series multispectral remote sensing data, can accurately monitor the time point when the mangrove forest is subjected to atrophy and expansion events, and ascertains the spatial and temporal characteristics of the mangrove forest distribution range.

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 discloses an unmanned aerial vehicle aerial photograph mangrove forest target detection method based on an improved YOLOv11 algorithm, and belongs to the technical field of unmanned aerial vehicle aerial photograph target detection. In the invention, aiming at the characteristics of small, dense and overlapped to-be-detected targets in mangrove forest ecological monitoring, a GhostConv module is utilized to replace the original common convolution, the calculation amount and model parameters are reduced, an EMA attention mechanism is introduced to replace a common attention mechanism in a C2PSA module, the attention distribution is smoother and more stable, an MSAA module is embedded in a Neck layer, and the detection accuracy is improved. The feature information of different scales is effectively integrated, the multi-scale feature fusion is enhanced, and the detection performance of the small target under the complex background is effectively improved. Compared with the prior art, the detection accuracy of dense small targets in the mangrove forest is improved while efficient real-time detection is guaranteed, and the method is suitable for unmanned aerial vehicle aerial photography mangrove forest ecological monitoring scenes.

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 slope protection bricks, and discloses an engineering muck bionic slope protection brick and a mangrove forest root system synergistic reinforcement method thereof.The engineering muck bionic slope protection brick comprises a bionic slope protection brick body; the planting cavity is a cavity designed by simulating the root structure of a mangrove forest, and the planting cavity is formed in the bionic slope protection brick body and used for planting plant seeds or seedlings. The bionic slope protection brick body is obtained by taking engineering residue soil as a raw material, adding a certain proportion of adhesive, uniformly mixing and then pressing, and the planting cavity is formed in the bionic slope protection brick body by simulating the root structure of a mangrove forest, so that plant seeds or seedlings can be planted in the cavities of the main branches; and the roots of plant seedlings can grow downwards along the branch cavities and penetrate through the bionic slope protection brick body to enter the underground, so that the stability of the bionic slope protection brick body during slope protection is effectively improved.

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 seedling survival rate evaluation method and system based on deep learning. The method comprises the following steps: acquiring a digital orthoimage data set of a mangrove forest restoration target area; a C2fSE module, a DSACmix attention module and a C2fGP module are introduced, and an improved YOLOv8 network model is constructed; based on an improved YOLOv8 network model, performing identification detection on the digital orthoimage data set of the mangrove forest restoration target area to obtain a bounding box and confidence detection data of survived mangrove forest seedlings; and performing redundancy elimination and counting on the spliced detection data of the survived mangrove forest seedlings through a non-maximum suppression algorithm to obtain a mangrove forest seedling survival rate evaluation result. According to the method, the characteristics of the survival mangrove forest seedlings under the complex coastal zone environment background can be extracted from the unmanned aerial vehicle data, and the accuracy and efficiency of survival mangrove forest seedling identification are improved. The mangrove forest seedling survival rate evaluation method and system based on deep learning can be widely applied to the technical field of forest surveying and mapping remote sensing.

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 relates to the technical field of ecological environment science, in particular to a mangrove forest habitat core fish community prediction method, which comprises the following steps: acquiring fish eDNA data of a mangrove forest water area in different periods and performing data analysis to obtain composition structure data of a fish school; determining a food web; constructing an ecological network model; calculating a characteristic index of each species node in the ecological network model; carrying out standardization processing on the characteristic indexes; calculating a comprehensive score of the standardized feature indexes; important species and non-important species are distinguished according to the comprehensive score; important species data in different periods are preprocessed; calculating the highest similarity score between any two important species; evaluating the statistical significance; constructing an association network by using a species association result with statistical significance; and analyzing the association network to determine a core fish school. According to the method, the ecological network and correlation analysis are integrated, comprehensive analysis of the core fish community dynamics is realized, and the core fish school of the ecological system is accurately identified.

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 relates to the technical field of data analysis, in particular to a mangrove forest health assessment system based on tropical cyclones. The structure risk coefficient, the geological risk coefficient and the ecological risk coefficient are combined to obtain the health risk index of the mangrove forest, so that accurate health assessment is realized, meteorological data and cyclonic state data are monitored to predict a cyclonic prediction path, and the prediction accuracy is improved. And then the cyclonic prediction path and the health risk index of the mangrove forest are combined to form risk response management and control on damaged and undamaged mangrove forests, so that accurate judgment and supervision on key areas are formed, and the situation of excessive damage caused by untimely management and control on the mangrove forest is reduced.

The invention provides a mangrove forest growth monitoring method and system, and the method comprises the steps: obtaining a mangrove forest multi-temporal remote sensing image of a target monitoring region, extracting a spectral feature index sum of the mangrove forest multi-temporal remote sensing image, and obtaining a leaf area index based on a leaf area index model; obtaining a water body suspended sediment concentration inversion result based on a sediment content inversion model; performing spatial statistical analysis on the leaf area index and the inversion result of the suspended sediment concentration in the water body to obtain a spatial statistical index; according to the leaf area index and the water body suspended sediment concentration inversion result, the stress degree of water body suspended sediment on mangrove forest growth is analyzed, and mangrove forest growth condition change parameters are obtained; and according to the spatial statistical index and the mangrove forest growth condition change parameter, performing stress degree grading on the target monitoring area, and generating a corresponding early warning instruction. The problem that the influence of suspended sediment in the water body on mangrove forest growth cannot be recognized in time can be solved, and the mangrove forest growth monitoring accuracy is improved.

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 provides mangrove chitin bacillus FCG-7 and application of the mangrove chitin bacillus FCG-7 in preparation of N-acetylglucosamine, and belongs to the technical field of microorganisms, the mangrove chitin bacillus FCG-7 provided by the invention is preserved in Guangdong Microbial Culture Collection Center, and the preservation number is GDMCC No: 63488. The strain provided by the invention has good low-temperature resistance and extracellular chitinase production capability, and can efficiently degrade chitin under a low-temperature condition to produce N-acetylglucosamine with an excellent biological effect; and the strain is still stable after passage, and is expected to be applied to the production of high-added-value N-acetylglucosamine by using chitin biomass as a raw material under a low-temperature condition.

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 relates to the technical field of planting cylinders, in particular to an offshore mangrove forest growth environment simulation device which comprises a bottom frame and a planting cylinder arranged at the top of the bottom frame, a first partition plate and a second partition plate are arranged in the planting cylinder from top to bottom, a planting cavity is formed above the first partition plate, and a filtering cavity is formed between the first partition plate and the second partition plate. A water storage cavity is formed below the second partition plate; a water supply pump is arranged in the water storage cavity, the water outlet end of the water supply pump is connected with a water supply pipe, the free end of the water supply pipe extends into the planting cavity, a water return pipe is arranged in the planting cavity, and the tail end of the water return pipe extends into the filtering cavity; the filtering mechanism is arranged in the planting tank in an inserted mode, whenever the filtering mechanism needs to be cleaned, only the filtering mechanism needs to be pulled out, operation is easy, and use is convenient.

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 relates to the technical field of marine ecological environment regulation and control, in particular to an offshore wind power driven ecological floating island blue carbon capture and regulation self-adaption method, which comprises the following steps: S1, acquiring the flow velocity gradient, chlorophyll a concentration and dissolved inorganic carbon content of a target sea area, and acquiring torque fluctuation data of a main shaft of a wind power device; s2, calculating a unit area carbon flux threshold value of the floating island; s3, generating a floating island matrix permeability adjusting instruction according to the carbon flux threshold value; s4, triggering a salinity gradient power generation module to generate compensation electric energy; s5, activating a micro-fluidic chip by using the compensation electric energy, and controlling the opening and closing frequency of pores of the mangrove forest; and S6, dynamically correcting the distribution of balancing weights of the floating island, so that the deposition rate of blue carbon is stabilized in a target interval. According to the method, wind power energy prediction, carbon flux calculation and floating island self-adaptive structure regulation and control are integrated in real time, efficient and stable operation of the blue carbon capture process of the floating island in the complex marine environment is achieved, and therefore the carbon fixing efficiency is remarkably 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.

The present invention provides a method and system for monitoring mangroves in high-resolution remote sensing images. Among them, the method includes: performing image acquisition and processing covering the study area to obtain a fused image; calculating the normalized difference water index and the normalized difference vegetation index based on the fused image to obtain a first calculation result; obtaining a key feature layer according to the first calculation result, resampling the fused image to obtain a first resampled image; dividing and screening sub-regions according to the first resampled image, calculating an automatic threshold according to the division result, and extracting the whole-region water body; obtaining a threshold for vegetation extraction based on the first resampled image, the normalized difference vegetation index, and the non-water body regions in the whole-region water body; extracting the intertidal zone according to the water body regions in the whole-region water body; performing masking processing on the fused image to obtain a first masking processing result; and obtaining a mangrove distribution map according to the first masking processing result and the threshold for vegetation extraction.

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.