289 results about "Mangrove" patented technology

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

The invention provides a northbound mangrove forest population dynamic state, primary productivity and carbon flux evaluation system, and the system comprises a data collection unit which is used for carrying out the data collection of an ecological system of a mangrove forest planting region to be evaluated, and obtaining an original data set; the data analysis unit is used for analyzing and identifying the original data set; the evaluation unit is used for evaluating community development and carbon sink dynamic states and future change trends after mangrove forest north migration by using a mangrove forest community and carbon sink dynamic evaluation model; and the data storage unit is used for storing the original data and the evaluation result. The model is helpful for understanding and predicting the health condition and the carbon sequestration function of a coastal wetland mangrove forest system, and can realize comprehensive evaluation of community development and carbon sequestration capability after mangrove forest north migration and prediction of a future trend. Scientific basis and data support are provided for accurate accounting of coastal wetland protection and restoration after mangrove forest north migration and effective management of blue carbon resources.

The invention discloses a mangrove forest disease and insect pest remote monitoring and intelligent prevention and control system based on the Internet of Things. A plurality of modules such as an Internet of Things sensor network, an unmanned aerial vehicle inspection system, a high-definition monitoring camera network, intelligent prevention and control equipment and a software platform are integrated; the Internet of Things sensor network monitors environmental parameters and pest and disease damage signs in real time, the unmanned aerial vehicle inspection system provides aerial inspection support, and the high-definition monitoring camera network realizes 24-hour uninterrupted monitoring on the ground; the software platform is responsible for data acquisition, transmission, analysis and early warning release, and provides a remote control interface for management personnel to perform accurate prevention and control; the intelligent prevention and control equipment automatically or remotely executes prevention and control tasks according to the monitoring data and the early warning information; the system also has additional functions of energy management, network communication, user authority management and the like, and continuous and stable operation and data security of the system are ensured. The implementation of the system can effectively improve the intelligent level of mangrove forest pest control, and provides powerful support for protecting a mangrove forest ecological system.

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 metarhizium aureoviride strain YEC and application of the metarhizium aureoviride strain YEC in prevention and treatment of teak camel moths. The Metarhizium anisoviride YEC provided by the invention is separated from soil in Guangdong province, can infect mangrove forest pests, namely teak camel moths, and can have relatively high fatality rate and stiff insect rate on the teak camel moths within a short time. The metarhizium aureoviride strain YEC is a biological control strain with potential development and application value in control of mangrove forest pests.

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 the field of ecological monitoring, in particular to an unmanned aerial vehicle monitoring network layout method for mangrove forest ecological monitoring, and the method comprises the steps: constructing a mangrove forest ecological safety source region and an ecological resistance surface of the mangrove forest ecological safety source region, and recognizing an ecological corridor region and an ecological strategy point; according to the mangrove forest ecological safety source area, the ecological corridor area and the ecological strategy point, an unmanned aerial vehicle monitoring network is constructed to form a unified and independent unmanned aerial vehicle mangrove forest ecological system monitoring system, reasonable layout of unmanned aerial vehicle networking is achieved, the high-frequency, real-time and dynamic monitoring requirements for mangrove forests are met, and the monitoring efficiency is improved. Accurate matching of monitoring area coverage and monitoring requirements of the unmanned aerial vehicle is realized.

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 provides a high-salinity high-tidal-flat mangrove forest ecological restoration method. The high-salinity high-tidal-flat mangrove forest ecological restoration method comprises the following steps of S1, soil improvement, S2, seedling cultivation, S3, seedling planting, S4, seedling reseeding, S5, bank protection device arrangement and S6, pest control. The soil conditioner is used for improving the soil structure and reducing the soil salinity. Salt-resistant domestication is performed on the seedlings by gradually improving the salinity of irrigation water, so that the salt resistance of the seedlings is improved. Organic fertilizer, ferriporphyrin, abscisic acid and brassinolide are used for providing nutrients for seedlings, osmotic equilibrium in plants is adjusted, opening and closing of air holes are adjusted, plasticity of cell walls is adjusted, and therefore the salt resistance of the plants is enhanced. According to the method, the roots of seedlings are inoculated with the halorhodobryum terbach and the proteobacteria, the halorhodobryum terbach and the proteobacteria terbach form a symbiotic relationship with the roots of plants, a microbial community is formed, and the absorption efficiency of the plants to nutrients in soil is According to the method, the planting survival rate and the plant growth rate are improved through the multi-way overall synergistic effect, and then the biological diversity is improved.

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 relates to a mangrove forest carbon reserve prediction method, electronic equipment and a storage medium, and belongs to the technical field of computer vision and remote sensing, and the mangrove forest carbon reserve prediction method comprises the steps: obtaining remote sensing data and measured data of a mangrove forest, and marking the remote sensing data based on the measured data to construct a mangrove forest species data set; the constructed MFPA-YOLO semi-supervised model is trained on the basis of the mangrove forest species data set, the MFPA-YOLO semi-supervised model comprises an input layer, a target detection layer and an output layer, the target detection layer comprises a teacher network, a pseudo label generation network and a student network, a mangrove forest remote sensing image to be predicted is predicted on the basis of the fully trained MFPA-YOLO semi-supervised model, and a mangrove forest remote sensing image to be predicted is obtained. The mangrove forest carbon reserve initial prediction value is obtained, the ecological environment index is determined, the mangrove forest carbon reserve prediction value is determined based on the ecological environment index and the mangrove forest carbon reserve initial prediction value, and the efficiency and accuracy of the model are improved.

The invention relates to the technical field of microorganisms, in particular to an antibacterial bacillus subtilis subsp. Deserticola strain and application thereof.Bacillus subtilis subsp. Deserticola D7-HSJ9 is obtained from mangrove forest rhizosphere soil by the applicant, and it is detected that the strain has good salt resistance, alkali resistance and antibacterial characteristics, and the bacillus subtilis subsp. Deserticola D7-HSJ9 can be used as a bacillus subtilis subsp. Deserticola strain. According to the present invention, the bacterial strain has characteristics of good antibacterial effect, particularly good antibacterial effect on streptococcus iniae, and good antibacterial effect on a plurality of pathogenic bacteria such as photobacterium dama subspecies, aeromonas salmonicida, staphylococcus aureus and the like, such that the bacterial strain can effectively replace the antibiotic so as to improve the prevention and control of fish diseases, and a good germplasm resource is provided for later preparation of microecological preparation products.

The invention discloses a system for providing mangrove forest seedlings with natural seawater for salinity exercise and a seedling raising method. The system for salinity exercise comprises a settling pond, a settling and filtering device, a salinity adjusting device and a circulating pump. Wherein the bottom of the sedimentation tank is obliquely arranged, a drain pipe is arranged on the side wall of the sedimentation tank and is higher than the bottom of the sedimentation tank, the sedimentation tank is used for introducing seawater, and a desilting opening is further formed in the sedimentation tank and is formed in the lowest position of the bottom of the sedimentation tank. The settling and filtering device comprises a box body, a plurality of settling plates and a plurality of retention filter plates, and is used for further settling and filtering seawater conveyed from the settling pond; the salinity adjusting device is used for adjusting the salinity of the seawater by utilizing an evaporation effect and a condensation effect; and the circulating pump circularly pumps seawater, so that the concentration degree of the salinity of the salinity adjusting device at the first line is increased, the salinity of the finally output seawater is ensured to meet the expectation, the salinity exercising process of the mangrove seedlings is ensured to be smoothly carried out, and the trouble of manually adding salt and water is also reduced.

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 relates to the technical field of carbon reserve estimation, and discloses a mangrove forest biomass carbon reserve estimation method based on a different-speed growth inversion model, and the method comprises the following steps: 1, setting a quadrat region; 2, collecting aerial images, kandelia candel morphological data, kandelia candel destructive data and soil profile organic carbon density data; step 3, establishing an allotropical growth inversion model; processing the kandelia candel destructive sampling parameters to obtain a kandelia candel carbon conversion coefficient; performing image processing and visual interpretation on the aerial image of the research area to obtain the crown breadth coverage total area and the mud flat coverage area of the kandelia candel in the research area; 4, the overall kandelia candel carbon reserve is obtained by combining the kandelia candel morphological data, the total crown breadth coverage area of the kandelia candel, the different-speed growth inversion model and the kandelia candel carbon conversion coefficient; and combining the organic carbon density data of the soil profile and the mud flat coverage area to obtain the overall soil carbon reserve. Meanwhile, the invention further provides a mangrove forest biomass carbon reserve estimation system based on the different-speed growth inversion model.

The invention relates to the technical field of image processing, and discloses a dense mangrove forest counting method based on an improved U2Net model, and the method comprises the steps: collecting unmanned plane laser radar data of a mangrove forest growth region, and constructing an unmanned plane visible light remote sensing image data set; an improved U2Net model is constructed; the improved U2Net model comprises an encoder, a dense jump connection module, a decoder and a significant feature map fusion module; and generating a saliency probability graph by using the improved U2Net model to obtain a mangrove forest counting result. According to the method, a data set is made in combination with Lidar remote sensing images, so that crown breadth characteristics of mangrove forest can be better reflected; according to the mangrove forest counting method, the multi-scale features and the attention mechanism are fused on the network model, the mangrove forest counting result in the dense scene is improved, meanwhile, the good generalization ability can be reflected on mangrove forest pictures with different spatial scales and different canopy densities, and the precision of the mangrove forest counting result 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.