36 results about "Wetland vegetation" patented technology

The invention discloses an emergent vegetation management method for increasing a biological carbon sequestration function, and belongs to the technical field of ecological system management and restoration, and the method comprises the following steps: in the stable growth period of emergent vegetation, discharging part of wetland moisture, and carrying out first mowing on overground parts of plants above the water surface; after the emergent vegetation further grows, the water level is restored to the end of the growth season, finally water is drained, secondary mowing is carried out, and all the emergent vegetation on the ground is harvested. According to the method, the overground biomass is increased through the plant photosynthetic carbon sequestration function by adjusting the appropriate harvesting time and mode of the wetland emergent vegetation, and therefore the biological carbon sequestration amount is increased. Compared with a traditional method for harvesting wetland vegetation at a time, the method has the advantages that the plant growth rate is remarkably increased, the overground biomass of the plants and the accumulation of organic carbon in surface soil are promoted, and the improvement of the wetland carbon sequestration function is facilitated.

The invention relates to a wetland vegetation extraction method and system based on spatial-temporal feature fusion, and belongs to the field of wetland remote sensing intelligent interpretation. The problems that an existing wetland vegetation classification method does not make full use of remote sensing data time sequence features, and the wetland vegetation extraction precision is insufficient are solved. According to the technical scheme, a single optical multi-temporal remote sensing image is used as input, frequency domain decomposition and convolution long and short time memory network modeling long-term phenology and short-term fluctuation are carried out through a time-frequency enhancement time sequence branch, and spectral grouping of a spectral space branch and cavity convolution multi-scale feature extraction are combined; and a cross-domain cross attention fusion module is utilized to realize time sequence spectral space three-dimensional feature deep fusion, and finally a wetland vegetation community classification map is output. On the premise of not depending on multi-source data, the time-frequency enhanced double-branch space-time model is innovatively provided, the multi-temporal remote sensing data time sequence feature self-adaptive extraction capacity can be effectively improved, the classification stability and robustness are improved, and finally the remote sensing monitoring precision of a wetland ecosystem is improved.

The present application discloses a wetland vegetation carbon sequestration potential prediction method and system under the influence of future climate change, belongs to the technical field of wetland vegetation carbon sequestration potential prediction, and comprises the following steps: obtaining the net primary productivity of vegetation, the vegetation coverage, the monthly temperature, the precipitation and the atmospheric carbon dioxide concentration in the historical period and the future period; calculating the annual average net primary productivity of vegetation, the coverage and the multi-year average vegetation coverage; calculating the annual average temperature, the precipitation and the carbon dioxide concentration; extracting the wetland vegetation pixel distribution as the research area range; calculating the ratio of the annual average net primary productivity to the vegetation coverage to obtain the wetland vegetation carbon sequestration potential value in the historical period; adopting the multiple stepwise regression analysis method to build a per-pixel wetland vegetation carbon sequestration potential estimation model; and utilizing the annual temperature, the precipitation and the carbon dioxide concentration in the future period to realize the accurate prediction of the wetland vegetation carbon sequestration potential in the research area range in the future period.

A method for improving the germination of *Carex lumbricoides* seeds based on nano-zinc oxide initiation belongs to the field of wetland ecological engineering technology. The invention includes the following steps: First, nano-zinc oxide particles with a particle size of 30±10 nm are dispersed in deionized water by ultrasonic oscillation to prepare a suspension of 12.5-200 mg / L; second, collected *Carex lumbricoides* seeds are float-screened, and plump seeds that sink to the bottom are selected and subjected to surface sterilization; subsequently, the sterilized seeds are immersed in the nano-zinc oxide suspension and initiated by agitation under light-proof conditions for 6 or 24 hours, followed by air drying to restore the initial moisture content; finally, the initiated seeds are placed in an artificial climate incubator for germination culture under specific variable temperature and light conditions. This invention utilizes the biocompatibility of nano-zinc oxide and effectively solves the problems of long germination cycles and low germination rates of *Carex lumbricoides* seeds by precisely controlling the initiation concentration and time. This invention has the advantages of simple operation, low cost, and environmental friendliness, and can significantly improve the germination rate and germination percentage, providing high-quality seedlings for the restoration of degraded wetland vegetation.

The invention provides a method, a device and equipment for predicting influence of marsh vegetation change on surface temperature, and belongs to the technical field of ecological remote sensing and climate effect evaluation. The method comprises the following steps: firstly, determining vegetation pixel distribution as a research area range in an unchanged marsh wetland distribution range; a year-by-year and multi-year average growth season data set is constructed through a maximum value synthesis and arithmetic mean method; in the grid units, the pixels are divided into a high value group and a low value group according to the multi-year average growth season leaf area indexes, the difference value between the multi-year average growth season leaf area indexes of all the pixels in the high value group and the low value group and the average surface temperature value is calculated, and then the surface temperature change degree caused by the marsh wetland vegetation coverage change is quantified; and finally, realizing accurate prediction of future surface temperature influence in combination with a vegetation change trend on a pixel scale.

The application relates to a drought area salt water lake shore wetland stress-resistant vegetation configuration repair method, and belongs to the technical field of wetland ecological repair, and specifically relates to a drought area salt water lake shore wetland stress-resistant vegetation configuration repair method. The method solves the problem that the existing lake shore wetland vegetation repair technology cannot adapt to the extreme compound stress habitat of the inland closed salt water lake in the drought area, that is, no external fresh water supply, high mineralized water body, heavy saline soil and strong wind erosion, and overcomes the technical bottlenecks in the application of traditional technology in the region, such as low vegetation survival rate, poor community structure stability, insufficient directional conservation of local halophytes, secondary degradation after repair and the like. The method comprises the following steps: dividing the drought area salt water lake shore wetland into four functional repair regions according to four stress factors as quantitative partition basis; configuring stress-resistant vegetation for the four functional repair regions; and performing auxiliary repair measures for the four functional repair regions.

The application discloses a Landsat-based tidal wetland spatio-temporal evolution monitoring method, which comprises the following steps: S1, acquiring multi-temporal Landsat remote sensing image data of a target region and performing consistency preprocessing; S2, constructing a spectral index time sequence comprising NDWI, NDVI, PSRI and NIRv based on the preprocessed multi-temporal Landsat remote sensing image data; S3, generating key period images based on the spectral index time sequence; S4, performing initial classification of tidal flats and wetland vegetation based on the key period images to obtain initial classification maps year by year; S5, performing segmentation by using a LandTrendr algorithm based on the spectral index time sequence, and performing time sequence consistency optimization on the initial classification maps according to the obtained segmentation results to obtain optimized classification results; and S6, performing post-processing on the optimized classification results to generate a tidal wetland year-by-year dynamic classification data set. The application can generate a high-quality data set accurately reflecting the year-by-year dynamic evolution of a tidal wetland.

The invention provides modified biochar, a seed coating agent and a seed coating method for wetland vegetation restoration, and belongs to the technical field of wetland vegetation restoration, the modified biochar for seed coating comprises biochar, a microbial agent, a plant growth regulator and chitosan oligosaccharide; the mass ratio of the biochar to the microbial agent to the plant growth regulator to the chitosan oligosaccharide is (10-25): (0.1-0.5): (1-3): (5-10). The invention also provides a seed coating agent for wetland vegetation restoration. The seed coating agent comprises the modified charcoal, a binder and a filler, the mass ratio of the modified charcoal to the binder to the filler is (20-40): (5-15): (50-80). Pelleted seeds prepared by the seed coating agent provided by the invention can overcome high-strength water and sand dynamic changes of wetlands, especially coastal wetlands, and the seed germination rate and the plant stress resistance are improved.

A sea water desalination device and method based on coastal wetland vegetation transpiration belong to the technical field of ecology and water resource treatment crossing, and comprise a fresh water collection system, a fresh water storage system, and an auxiliary regulation and display system. According to the method, sea water desalination is achieved through the natural transpiration effect of coastal wetland vegetation (such as mangrove forests), condensed fresh water is collected through a transparent air chamber / ceiling, and operation is guaranteed by combining solar power supply and environment regulation and control. Compared with a traditional seawater desalination technology, the method is wide in application range, easy to manufacture and extremely low in energy consumption, the efficiency and sustainability of obtaining fresh water in the coastal area are improved, meanwhile, ecological protection is considered, a stable water supply solution is provided for coastal and island areas lacking fresh water resources, and technical support is provided for related resource utilization and ecological restoration research.

The utility model relates to the technical field of forest land wetland plant cleaning, in particular to a forest land wetland plant cleaning device which comprises an obliquely arranged conveying belt and further comprises a collecting box arranged on the lower side of the conveying belt and used for collecting cleaned plants; a plurality of evenly-distributed scraping rods are fixedly arranged on the conveying belt, side baffles are fixedly arranged on the two sides of the conveying belt, and the lower ends of the side baffles are fixedly connected with the two sides of the collecting box. The stripping mechanism comprises a stripping plate, is arranged between the upper side of the collecting box and the conveying belt and is used for scraping the plants between the scraping rods; the squeezing mechanism comprises a squeezing plate and a reciprocating unit, the squeezing plate is arranged in the collecting box, and the reciprocating unit is arranged between the squeezing plate and the collecting box and used for squeezing the collected plants. According to the plant collecting box, the collected plants can be automatically extruded while the plants are cleaned, and the space utilization rate of the collecting box is increased.

The invention relates to a method for rapidly increasing the vegetation carbon sink of a small-leaf badge wetland based on moisture and rhizome increase, and belongs to the technical field of methods for increasing the vegetation carbon sink of the small-leaf badge wetland. Diagnosing the degeneration degree of the small-leaf badge wetland, determining the degeneration degree according to the important value of the dominant species and the soil water content, and taking different recovery measures according to different degeneration degrees; in the step of recovering the lightly-degraded small-leaf badge wetland, water regulation is adopted, the surface water level is controlled to be 1-5 cm, and the regulation time is the first ten days of May; recovering the moderately-degraded small-leaf badge wetland, and combining rhizome separate increase and moisture regulation and control to recover the moderately-degraded small-leaf badge wetland; and restoring the severely-degraded lobular octopus wetland, wherein rhizome separate increase, species transplantation and moisture regulation are combined. The method has the outstanding characteristics that according to a recovery ecology limiting factor theory and a cloned plant propagation ecology principle, scientific recovery of dominant environment limiting factors and biological factors is taken as a main line, and through technical measures such as scientific regulation and control of moisture, separate increase of rhizomes and species transplantation, rapid recovery of degraded small-leaf badge wetland vegetation is realized, and the carbon sequestration amount of the vegetation is increased.

The invention discloses a wetland vegetation classification method and system, and the method comprises the steps: obtaining a synthetic aperture radar SAR image and a multispectral MSI image of a to-be-classified region, and carrying out the preprocessing; inputting the MSI image into a convolutional neural network (CNN) branch, and extracting spectral texture features by using an adaptive scale sensing module (ASPM); performing super-pixel segmentation on the SAR image to construct a super-pixel image; inputting the superpixel image into a GCN (Graph Convolution Network) branch, and extracting structural features by using an ADMS (Adaptive Graph Structure Module); performing pixel-level fusion on the spectral texture features and the structural features by using a gating fusion module GFM to obtain fusion features; and inputting the fusion features into a classifier, and outputting a classification result of the wetland vegetation.

The invention discloses an arid region degraded wetland community restoration method built based on a fertilizer island, belongs to the technical field of vegetation restoration, and aims to promote restoration of arid region degraded wetland vegetation and improve community productivity and stability, the method comprises the following steps: introducing indigenous drought-tolerant and saline-alkaline-tolerant shrubs or small arbors, supplementing water to promote growth of the shrubs or small arbors, and building a fertilizer island habitat; when the crown breadth grows to a proper specification, planting stress-tolerant wetland herbaceous plants, and constructing herbaceous plaques by utilizing species collocation and density regulation and control; in the herbaceous vegetation recovery process, field planting is promoted through water and fertilizer management, and the stability of degraded wetland communities is improved through shrub and grass matching. The productivity and stability of the plant community are recovered by building the fertilizer island habitat, and the method has the advantages of being reliable, stable in effect and capable of saving water and fertilizer.

The invention relates to the technical field of wetland ecological restoration equipment application, in particular to a pollution-reducing and carbon-sequestration restoration machine for directional cultivation of wetland vegetation, which comprises an advancing assembly, a drilling assembly, a transplanting assembly, a carbon sequestration assembly and a pushing assembly, all the modules are integrated on a crawler-type advancing platform, and can stably work under the complex terrain of the wetland; the moving assembly moves to a to-be-repaired area, and the drilling assembly drills planting groove holes in soil; the transplanting assembly is used for accurately planting the vegetation in the storage box into the groove holes through a multi-dimensional adjustable pneumatic clamping device; the carbon sequestration assembly directionally sprays the fungicide to the roots of the vegetation, so that the carbon sequestration capacity is improved; the pushing assembly automatically backfills the soil, planting and fixing are completed, integrated and automatic operation of wetland vegetation cultivation is achieved, the remediation efficiency is greatly improved, meanwhile, through precise pesticide application and standardized planting, the ecological benefits of wetland pollution reduction and carbon sequestration are enhanced, and the device is suitable for large-scale ecological remediation engineering of large-area wetlands.

The application discloses a kind of dry area wetland vegetation recovery methods based on soil seed bank activation, belong to ecological environment restoration technical field.The present application is to solve the problem that wetland recovery technology in prior art has not fully considered the special environmental conditions of dry area wetland, and the effect is poor in solving the problems of water shortage, salinization and soil hardening, etc., provides a kind of dry area wetland vegetation recovery method based on soil seed bank activation, by targeted regulation of soil moisture conditions, improve soil physical and chemical properties, create suitable microenvironment for the germination of seed in soil seed bank, and effectively activate the natural germination potential of soil seed bank;At the same time, combined with artificial intervention to supplement the missing dominant species, ultimately promote the natural recovery of wetland vegetation and the construction of stable community, realize the improvement of dry area wetland ecological environment, fill the technical blank of prior art that lacks special limiting conditions for dry area wetland and is difficult to activate soil seed bank efficiently, improve the pertinence and sustainability of vegetation recovery.

The utility model discloses modularized water and fertilizer drip irrigation equipment for plateau wetland vegetation recovery, which comprises a liquid storage tank and a drip irrigation main pipe, one side of the liquid storage tank is communicated with a water pump, the water outlet end of the water pump is provided with a water supply pipe, one end of the water supply pipe is provided with a quick-connection female head, the drip irrigation main pipe is communicated with a connecting pipe, and the connecting pipe is provided with a quick-connection female head. A connecting pipe is arranged on the drip irrigation main pipe, a quick connecting male head is installed at one end of the connecting pipe, the quick connecting male head is matched with a quick connecting female head for use, the drip irrigation main pipe communicates with a drip irrigation branch pipe, the surface of the drip irrigation branch pipe communicates with a drip irrigation pipe, and a drip irrigation opening is formed in the surface of the drip irrigation pipe. According to the utility model, the plurality of modularized drip irrigation pipes are arranged, and the water outlet ends of the water pumps are communicated with the corresponding modularized drip irrigation pipes, so that drip irrigation can be carried out on vegetation in a corresponding area, and compared with a mode of carrying out drip irrigation work by adopting a single row of drip irrigation pipes, the drip irrigation efficiency is higher, and the use is more convenient; and modular drip irrigation operation can be carried out, practicability is high, and the device is worthy of popularization.

The invention provides a wetland vegetation LAI inversion method based on unmanned aerial vehicle multi-source remote sensing. The method comprises the following steps: collecting multi-source remote sensing data of a target area through an unmanned aerial vehicle; the multi-source remote sensing data comprises hyperspectral reflectivity data and point cloud data; performing feature extraction and screening on the multi-source remote sensing data to obtain an optimal feature subset; and performing inversion modeling and result comparison of the leaf area index through the optimal feature subset to obtain an optimal modeling scheme. According to the method, multi-source remote sensing characteristic variables are optimized and screened, and the LAI inversion model of the wetland vegetation is constructed by using a machine learning algorithm, so that the LAI estimation precision of the wetland vegetation is improved.

The application discloses a kind of based on water-permeable siltation promoting seedling and reed transplanting seedling coastal wetland vegetation restoration and carbon sink promotion method, belong to ecological restoration technical field.The coastal wetland vegetation restoration and carbon sink promotion method includes the following steps: S1.in target recovery area, several single-section water-permeable siltation promoting seedling components are arranged along the direction of tidal ditch or the direction substantially parallel to tidal ditch direction;Water-permeable siltation promoting seedling component includes at least 2 pile bodies and water-permeable rough material;Water-permeable rough material is plane structure, and is connected in parallel with pile body;Adjacent 2 pile bodies have pile body spacing;Water-permeable rough material is arranged with several water-permeable through holes;Each adjacent single-section water-permeable siltation promoting seedling component has section drainage gap;Component parameters are calculated by formula I-VII: H=clip (H min ,H max ,H0+Σb H, j Z j ).The application estimates deposition enhancement and carbon sink promotion effect by simplifying flow velocity reduction-deposition-carbon burial model, combined with reed transplanting seedling point planting, to realize more efficient and better coastal wetland vegetation restoration and carbon sink promotion effect.

The invention relates to the technical field of remote sensing image processing, in particular to a high dynamic water level flood area wetland classification method based on time sequence remote sensing and an intelligent algorithm, which comprises the steps of data preprocessing, hydrological period division, feature set construction, feature optimization, wetland classification and dynamic wetland classification. According to the invention, complementary advantages of a Sentinel-1 radar image and a Sentinel-2 optical image are fully fused, all-weather and multi-temporal wetland monitoring is realized, high time resolution and image availability can still be maintained in cloudy, frequent rainfall or severely fragmented areas, the continuity and stability of dynamic monitoring are remarkably improved, and the real-time performance of the system is improved. By extracting NDVI statistical characteristics (mean value, extreme value, standard deviation, skewness, kurtosis, quartile and the like) in each hydrological period, vegetation growth intensity, stability and volatility are comprehensively represented, so that the model can effectively capture the time change rule of wetland vegetation.

This invention discloses an AI-based early warning method for estuarine wetland degradation risk, belonging to the field of ecological remote sensing monitoring technology. Addressing the shortcomings of existing wetland degradation prediction methods, such as reliance on multi-source data, lack of bidirectional temporal modeling and key-step focusing capabilities, and absence of critical warning mechanisms, this invention uses only MODIS NDVI time series data as the sole data source. It simultaneously captures the forward and backward temporal dependencies of the NDVI sequence through a bidirectional long short-term memory network (BiLSTM) and introduces an attention mechanism to adaptively focus on the key time steps that contribute most to the degradation trend. After model training, multi-step predictions are made for wetland vegetation cover dynamics over the next 6–24 months. Based on this, the degradation rate and acceleration of the predicted sequence are calculated and compared with historical thresholds. When the predicted NDVI consistently falls below the critical degradation threshold and the degradation rate accelerates significantly, a graded early warning signal is automatically issued, particularly identifying critical degradation states about to cross the irreversible inflection point. Using Chongming East Beach in the Yangtze River Estuary as an example, this method achieves a prediction error (MAE) as low as 0.028 when using only the NDVI single indicator and can issue effective critical warnings 6–12 months in advance. This invention has the advantages of simple data acquisition, high prediction accuracy, interpretable attention weights, and strong transferability, providing a new technical means for the protection and management of estuarine wetlands.

The invention relates to a wetland vegetation dynamic monitoring method based on phenological characteristics and a random forest model. The method comprises the following steps: S1, constructing a wetland multi-source data set; s11, constructing a wetland sample set; s12, acquiring and screening remote sensing data, and calculating a vegetation index after cloud removal processing; s13, screening a wetland sample set; s2, phenological parameters are extracted; s21, sorting and screening the vegetation time sequence data; s22, fitting a vegetation index time sequence curve by adopting a double-logic model; s23, vegetation phenological parameters are calculated; s3, constructing a random forest classification model and performing training verification; s31, redundant features are removed through a feature screening method, and an optimal feature combination is obtained; s32, parameters of the random forest model are adjusted and optimized; s33, training a random forest classification model by using the training set, calculating a precision evaluation index by using the test set, and verifying the model; and S4, analyzing the dynamic change of the wetland ground objects. The method is high in precision and strong in robustness.

The invention discloses a coastal wetland vegetation hyperspectral image detection method based on cross-domain small sample learning, and belongs to the technical field of image processing. According to the method, prototype calibration is adopted, and a Gaussian mixture model is utilized to select features from a query set for prototype reconstruction, so that the consistency with prototype distribution is improved. The distance measurement method based on ridge regression is used for solving the problem of covariable offset between a prototype set and a query set, so that the accuracy of distance measurement is improved. In order to overcome the complexity of domain adaptation, domain adaptation based on graph matching is introduced, features of a source domain and a target domain are reconstructed into heterogeneous graphs, and the domain adaptation is regarded as a graph matching problem. In addition, the adoption of the shared transmission matrix facilitates the matching of nodes and edges, thereby reducing domain offset. According to the method, through the cooperation of the steps, the excellent classification capability is finally obtained, and the method has good performance on the coastal wetland vegetation hyperspectral data set shot on the spot.

The application discloses a kind of salinization wetland vegetation recovery method based on gradient moisture control, first, according to moisture gradient, degraded wetland is divided into three levels of moisture zone, namely low moisture zone, medium moisture zone and high moisture zone, and according to moisture zoning, vegetation recovery scheme is formulated, based on the quantitative relationship between soil moisture content and vegetation aboveground biomass, water supplement measures are formulated, and soil is improved through salt-nutrient synergistic control.The application innovatively integrates hydrological engineering and ecological process control, breaks through the differentiated water supply mode, realizes the synergistic effect of increasing soil and salt inhibition, provides a replicable recovery paradigm for semi-arid region wetlands, and provides a quantifiable natural solution for coping with land desertification and climate change.

The invention discloses an arid region wetland vegetation restoration method based on soil seed bank activation, and belongs to the technical field of ecological environment restoration. The invention provides an arid region wetland vegetation restoration method based on soil seed bank activation in order to solve the problems that in the prior art, special environmental conditions of arid region wetlands are not fully considered mostly in wetland restoration technologies, and the effect on solving the problems of water shortage, salinization, soil hardening and the like is poor. Soil moisture conditions are regulated and controlled in a targeted manner, soil physical and chemical properties are improved, a suitable microenvironment is created for germination of seeds in a soil seed bank, and then the natural germination potential of the soil seed bank is effectively activated; meanwhile, deficient dominant species are supplemented in combination with artificial collaborative intervention, natural recovery of wetland vegetation and stable community construction are finally promoted, the ecological environment of the arid region wetland is improved, and the technical blank that in the prior art, special limiting conditions for the arid region wetland are lacked, and a soil seed bank is difficult to activate efficiently is filled up; and the pertinence and sustainability of vegetation recovery are improved.

The invention belongs to the technical field of multi-source remote sensing data processing and fusion, discloses a multi-source remote sensing fusion model and method for coastal wetland vegetation species level classification, and aims to solve the problems of insufficient resolution, low species identification precision and poor multi-source data complementarity in the prior art. According to the scheme, the method comprises the following steps: preprocessing sentinel No.1 SAR data and sentinel No.2 optical data, and unifying the data to 10-meter resolution; constructing an SAR feature set and an optical spectrum phenological feature set; a CMVN double-branch model is adopted, after preprocessing is conducted through a head module, features are fused through six SOCM cross-modal fusion modules, and after field label training is combined, six types of vegetation species-level results such as spartina alterniflora are output. The overall precision is 0.916, the anti-interference performance is high, and support is provided for dynamic monitoring and ecological evaluation of the wetland.

This application discloses a method for retrieving wetland biomass from satellite imagery based on UAV data, relating to the field of remote sensing information processing technology. The method includes: acquiring satellite imagery of the target study area; preprocessing the satellite imagery; calculating the broadband vegetation index distribution of the satellite imagery based on the preprocessed satellite imagery; inputting the broadband vegetation index distribution into a trained wetland biomass retrieval model; retrieving the wetland biomass of each pixel based on the broadband vegetation index in the satellite imagery; and outputting a wetland biomass distribution map of the target study area. This application, by combining high-resolution satellite data, establishes a multivariate wetland biomass retrieval model, improving the accuracy of directly retrieving vegetation biomass from satellite imagery. This can enhance the accuracy of data obtained from wetland vegetation biomass monitoring and provide data support for wetland vegetation parameter retrieval, environmental monitoring, and carbon sequestration research.

The invention relates to a multi-source remote sensing and artificial intelligence fused wetland system plant carbon reserve fine estimation method, which belongs to the technical field of wetland ecological monitoring and remote sensing inversion, and comprises the following steps: obtaining and preprocessing multi-source remote sensing image data; a two-stage progressive classification system is constructed in combination with land utilization types and vegetation community spatial distribution data acquired in the field, and vegetation fine class identification is realized based on a random forest and a graph convolutional neural network; the method comprises the following steps: fusing spectrum and texture features of multi-source remote sensing, combining actual measurement data of a biomass quadrat, and constructing a biomass regression estimation model through feature variable optimization; and based on the biomass estimation result and the carbon conversion coefficient corresponding to the vegetation subclass, calculating the carbon reserve and drawing a carbon density spatial distribution diagram. According to the method, the problems of low wetland vegetation classification precision, insufficient multi-source data collaborative utilization and poor biomass estimation model stability are effectively solved, and reliable technical support is provided for wetland ecosystem protection and dual-carbon strategy implementation.

The application discloses a fusion ecological prior coastal wetland vegetation class and key parameter identification method, relates to the technical field of hyperspectral remote sensing, and is used for identifying the type of coastal wetland vegetation. The method comprises the following steps: inputting hyperspectral image data of the coastal wetland, extracting shared spectral features, shared spatial features and input image features, providing a knowledge-guided spectral discrimination mechanism for the spectral intra-class dispersion problem caused by the same type of different forms of single vegetation area, providing a local Randers geometry refinement mechanism based on the class pair relationship knowledge for the boundary transition and mixed pixel expression difficulty problem of the mixed area, providing a dynamic decision mechanism supported by template knowledge for the characteristics of the bare beach, such as high dynamicity and fuzziness, and finally performing unified label alignment, outputting the class, state and mixed degree, and improving the accuracy, interpretability and applicability of the fine identification of the coastal wetland.

This application provides a method, apparatus, and equipment for predicting the impact of marsh vegetation change on land surface temperature, belonging to the field of ecological remote sensing and climate effect assessment technology. Based on long-term series leaf area index, land surface temperature, digital elevation model, and marsh wetland distribution data, it first determines the vegetation pixel distribution as the study area within the unchanged marsh wetland distribution range; it then constructs annual and multi-year average growing season datasets using the maximum value synthesis and arithmetic mean methods; within the grid cells, pixels are divided into high and low value groups based on the multi-year average growing season leaf area index, and the difference between the multi-year average growing season leaf area index and the average land surface temperature of all pixels in the high and low value groups is calculated to quantify the degree of land surface temperature change caused by marsh wetland vegetation cover change; finally, by combining the vegetation change trend at the pixel scale, it achieves accurate prediction of the future impact on land surface temperature.

The invention provides a beach wetland hydrological simulation and vegetation updating capability evaluation method and device. The method comprises the steps that hydrological simulation device parameters are determined according to soil hydraulic characteristics of a target area, a vegetation updating strategy and target simulation hydrological scene characteristics; outputting a water level change sequence according to the climate and hydrological historical data and change parameters thereof; driving and adjusting a hydrological simulation device according to the water level change sequence to simulate a beach vegetation hydrological process; and a wetland vegetation updating process experiment is carried out, and the updating capability is evaluated through real-time observation. By accurately and uniformly regulating and controlling the water level in the plurality of plant growth container units and simulating the hydrological dynamic state of the beach wetland under the climate change background, observation and evaluation of the beach wetland vegetation renewal capacity are realized, the method is used for deeply researching the influence of extreme hydrological events on the natural recovery potential of a beach wetland ecosystem, and the research and development of the beach wetland ecological system are facilitated. And a key technical support and a scientific basis are provided for wetland protection, adaptive management and ecological restoration engineering.