248 results about "Sample plot" patented technology

The invention relates to the technical field of remote sensing space information, in particular to a large-scale forest green carbon inversion method and system based on multi-source remote sensing data, and the method comprises the steps: resolving remote sensing parameters, and obtaining the crown breadth and height of a single-plant crown of a sample plot; taking the crown breadth and the height of the single tree crown of the sample plot as independent variables, and establishing different-speed growth models of different tree species; performing inversion based on original echo signal waveform data of the satellite-borne laser radar to obtain forest structure parameters; according to the forest structure parameters, calculating laser spot scale biomass by using a different-speed growth model; and based on the laser spot scale biomass and the multi-source remote sensing image features, using a U-Net deep learning model to obtain large-scale biomass data with continuous space, and obtaining a large-scale forest above-ground biomass distribution map according to the large-scale biomass data. According to the method, high-precision inversion from single-plant crown parameters to large-scale forest above-ground biomass is realized, and the efficiency of forest biomass estimation is improved.

The invention discloses a forest grass wet carbon sink metering method and system, and relates to the field of carbon sink metering, and the method comprises the following steps: 1, collecting and processing multi-source data, 2, fusing the data, aligning the multi-source data, adopting a geographic registration and time synchronization technology, and unifying a spatial resolution and a timestamp, and 3, extracting features. 4, constructing a carbon sink dynamic model; and 5, carrying out carbon sink visualization and decision support. According to the forest and grass wet carbon sink metering method and system, multi-source data collaboration is achieved, optical, radar and laser radar data are fused, the defect that traditional carbon sink metering depends on ground sample plot survey or single remote sensing data is overcome, carbon sink contributions of forests, grass and wet lands are accurately distinguished, the overall spatial resolution is higher, cloud and mist interference is avoided, the single data source error is solved, and the measurement accuracy is improved. Through multi-source data fusion and dynamic model optimization, high-precision measurement and real-time monitoring of forest grass wet carbon sink are realized.

The invention discloses a fine individual tree segmentation method fusing air-based laser radar point clouds and ground-based laser radar point clouds, and aims to solve the challenges that the data volume of combined point clouds is large, crown staggering is complex in a dense forest environment, and part of tree trunk point clouds are lost due to shielding, and the method combines an individual tree segmentation strategy from top to bottom and an individual tree segmentation strategy from bottom to top. A weighted height-density map is adopted to carry out individual tree positioning, a mark control watershed algorithm is combined to realize preliminary tree crown region extraction, then multi-label local graph segmentation optimization is introduced, segmentation boundaries are corrected, an under-segmentation unit is optimized by adopting a shortest path, and a complete segmentation framework is formed. On the basis of airborne and foundation point clouds of six actually measured sample plots, the method is verified after registration and combination. The advantages of airborne point cloud and foundation point cloud are combined, and the proposed individual tree segmentation method provides technical support for refined forest modeling and ecological monitoring.

The invention discloses a forest global single-tree-level crown index system establishment method based on an airborne laser radar, and belongs to the technical field of intelligent forestry remote sensing investigation and monitoring. According to the method, on the basis of the unmanned aerial vehicle laser radar point cloud of the target forest stand, a derivative model is constructed, and then full-forest stand individual tree segmentation is carried out. According to the whole forest single-tree point cloud; a three-dimensional space extreme value detection method, an elevation detection slice segmentation method of an RANSAC (Random Sample Consensus) algorithm, a boundary adaptive detection method of horizontal projection and direction range decomposition, a canopy projection mutual embedding and contact height screening judgment method, a projection convex hull method based on a QuickHull algorithm and the like are respectively adopted to automatically extract the height, the crown length, the height under branches, the crown diameter and the effective crown length of a single tree; according to 17 individual tree crown indexes such as crown projection area and crown volume, a whole forest individual tree crown index system database is constructed through spatial topological correlation mapping. According to the method, the limitation of traditional sample plot investigation is broken through, and visual monitoring extraction and accurate establishment of the forest global single-tree-level crown growth index system are realized.

The invention discloses a pheromone binding protein derived peptide capable of effectively monitoring sex pheromones of fall webworms and a biosensor, and belongs to the field of biosensors. At present, methods such as sample plot survey and sex traps commonly used in fall webworm monitoring have the problems of poor timeliness, high labor cost, insufficient data continuity and the like. In order to solve the problem, the pheromone binding protein-derived peptide provided by the invention has an amino acid sequence as shown in SEQ ID NO: 2, the biosensor comprises a substrate and an interdigital electrode, a single-walled carbon nanotube is attached to the electrode, and the electrode is also connected with the pheromone binding protein-derived peptide. The sensor can specifically detect sex pheromones of fall webworms, has no response to 17 plant volatile matters, and can detect sex pheromones released by as low as five live female fall webworms under laboratory conditions. The method has application potential in the fields of early pest monitoring, prevention and control efficiency improvement, ecological safety guarantee and the like.

The invention discloses a secondary forest development stage division method based on forest stand state characteristics, and belongs to the technical field of forest development stage division. Comprising the following steps: selecting an initial index set, performing index forward and standardization processing on sample plot survey data, and forming a data set; classifying the data by adopting a system clustering analysis method, checking and analyzing a state index influencing a classification result, and selecting plt; the index of 0.05 constitutes a secondary forest development stage division index system; carrying out weight assignment, then correcting the weight by adopting an entropy evaluation method, and determining the comprehensive weight of the index by utilizing a Lagrange multiplier method; the sum of the dimension-removed value of the division index and the comprehensive weight value product is a development stage value; according to an equidistant method, the development stage of the secondary forest is divided into a forest gap stage, a renewal stage, a differentiation stage, a built-up stage and a stable stage. Depending on forest age is avoided, and the method is suitable for multi-tree mixed secondary forests; the indexes are easy to obtain, and the division result can accurately guide forest management.

The invention relates to the technical field of forestry measurement, in particular to a forest sample plot investigation and measurement method and system based on fusion of multiple point cloud technologies. The forest sample plot investigation and measurement system comprises an ALS point cloud collector, an MLS point cloud collector, a TLS point cloud collector, a path planning module, a preprocessing module, a registration module, a classification extraction module, a database and a visualization module. According to the forest sample plot investigation and measurement method and system based on fusion of multiple point cloud technologies, all-around point cloud data acquisition is carried out in the air, the ground and marked fixed points of a forest through fusion of ALS point cloud acquisition, MLS point cloud acquisition and TLS point cloud acquisition technologies, different technical advantages are fully played, and the forest sample plot investigation and measurement method and system based on fusion of multiple point cloud technologies have the advantages of being high in accuracy and high in accuracy. The forest sample plot survey and measurement precision and efficiency are improved, multiple point cloud acquisition modes are fused and dynamically adjusted, the fusion strategy is automatically adjusted according to the data acquisition characteristics of the acquirer in different scenes, and the acquisition efficiency is improved.

The invention relates to the technical field of forestry management, in particular to a forest and grass resource dynamic monitoring system which comprises the steps that a graph attention network is adopted to conduct high-low weight recognition processing on the structure difference value between node pairs, feature vectors are established through the canopy height difference and canopy density difference between adjacent nodes, and the canopy height difference and the canopy density difference between adjacent nodes are obtained; an edge weight scoring system is constructed in the form of segmented statistics and proportion weighting, node edge pairs with high influence relation strength are dynamically screened, ordered aggregation of spatial communication strength is realized, a graph neural network is introduced in a parameter fusion stage to construct a node parameter representation mechanism, and the spatial communication strength is improved. The tree species proportion, the grade of diameter at breast height and the community vertical structure in a regional sample plot are used as input features, unified mapping of node features of each region is completed in multiple rounds of iteration, cross-regional difference analysis is executed on model output in combination with biomass change frequency, parameter items with the difference proportion lower than a set threshold value are replaced with unified expression, and the model output is obtained. And parameter synchronization is realized to realize space nesting and feature integration.

The invention provides a method for evaluating grassland degradation based on microbial community structures and composition, and belongs to the technical field of grassland degradation degree evaluation. The method comprises the following steps: selecting a representative sample plot in a to-be-evaluated grassland degradation area, selecting a sample square in the sample plot, and collecting rhizosphere soil in the sample square; mixing rhizosphere soil of all quadrat belonging to the sample plot to obtain a soil sample; analyzing the OTU number of bacteria and the OTU number of fungi in the soil sample by adopting high-throughput sequencing; calculating a grassland degradation grade evaluation value according to the OTU number of the bacteria and the OTU number of the fungi; according to the grassland degradation grade evaluation value and the grassland degradation grade evaluation standard, grassland degradation evaluation is carried out, and an evaluation result is obtained. The grassland degradation grade evaluation value of the to-be-evaluated area is calculated by taking the microflora structure and composition of the to-be-evaluated area as evaluation indexes, and the grassland degradation grade of the to-be-evaluated area is determined in combination with the grassland degradation grade evaluation standard.

The invention relates to the technical field of forest resource investigation and monitoring, and discloses a natural forest multi-source point cloud registration method and system based on point cloud segmentation optimization, and the method comprises the steps: laying a sample plot, collecting the diameter at breast height, the height and the crown breadth of a single tree, and obtaining the space coordinates of the single tree in combination with RTK; according to the method, unmanned aerial vehicle laser scanning and backpack laser scanning are adopted to collect and preprocess point cloud data of a research area, an ICP algorithm is adopted to align a source point cloud with a target point cloud through rotation and translation, point cloud registration is achieved through a minimization error function, point cloud registration precision and errors are quantified through evaluation indexes, and point cloud registration evaluation is carried out. Therefore, the point cloud registration precision is effectively improved through a segmented registration strategy, limitation caused by single sensor data can be overcome through fusion of BLS and ULS multi-source point cloud data, the extraction precision of tree structure parameters is remarkably improved, and the method is not only suitable for point cloud registration of natural forests, but also suitable for point cloud registration of natural forests. And reliable data support and technical support are provided for forest resource monitoring and ecological protection.

The invention relates to the technical field of plant ecological carbon sink measurement and calculation, in particular to a tea garden carbon reserve dynamic monitoring and prediction method based on meteorological data, a typical sample plot in a tea garden is selected, meteorological data and basic carbon reserve of the typical sample plot are acquired, and the meteorological data comprise real-time data and forecast data; establishing dynamic monitoring, and performing heat damage identification and freeze damage identification on real-time data; establishing a carbon reserve dynamic correction model; a carbon reserve prediction model is established, and when heat damage and / or freezing damage occur in prediction data, the carbon reserve dynamic correction model is used for prediction to obtain a prediction result; according to the method, the damage of high-temperature stress to the tea trees has a gradual superposition characteristic, high-temperature inhibits photosynthesis, so that calculation is performed in a summation form, cold waves increase respiratory consumption, and the damage mainly depends on the intensity peak value and the cooling rate of a single event; by providing more accurate dynamic measurement and calculation as the supplement of static measurement and calculation, the change condition of the carbon reserves of the tea garden can be reflected in time.

The invention discloses a forest vegetation health index construction and diagnosis method based on multi-source remote sensing data, and the method achieves the fusion of a feature level and a decision level of data through selecting sample plots containing different health levels, collecting the multi-source remote sensing data of a satellite, an unmanned plane and a ground sensor, and constructing a multi-source data fusion network. Indexes reflecting forest health are extracted from the fused data, a hierarchical clustering method is adopted for screening and standardization processing, a health index model is constructed, and a machine learning algorithm is utilized for optimizing the weight. And finally, calculating a forest health index based on the optimized model, dividing health levels, and generating a forest health level distribution map. The method is suitable for large-area forest vegetation health diagnosis, has the characteristics of high efficiency, accuracy and comprehensiveness, can remarkably improve the scientificity and rationality of forest health monitoring, and can be expanded and applied to the fields of fire / insect pest early warning, carbon sink evaluation and the like.

The invention discloses a forest carbon sink dynamic closed-loop regulation and control method and system based on multi-source monitoring data, and relates to the technical field of forest ecological monitoring and carbon sink management, and the system comprises a data collection layer which is used for obtaining multi-source monitoring data including carbon flux data, plant physiological data, microclimate data, soil flux data and sample plot survey data; the intelligent fusion layer is used for processing and feature extraction of multi-source monitoring data and outputting a standardized carbon sink driving factor; the dynamic prediction layer predicts the carbon sink trend through an integrated model by using the standardized carbon sink driving factor; and the regulation and control decision-making layer is respectively connected with the dynamic prediction layer and the forest ecosystem, comprises a regulation and control rule base and a feedback optimization unit, and is used for generating a quantitative regulation and control strategy and optimizing a model and a rule. Therefore, by adopting the forest carbon sink dynamic closed-loop regulation and control method and system based on the multi-source monitoring data, the precision and efficiency of forest carbon sink management can be improved, and real-time and accurate closed-loop regulation and control are realized.

The invention discloses a ground monitoring method for water and soil loss and carbon loss of a road slope, and belongs to the field of ecological monitoring. The method comprises the following steps: setting an engineering disturbance area and a natural contrast area according to regional water and soil loss, vegetation coverage and altitude gradient, and arranging an erosion pile sample plot to form a contrast observation network; removing impurities on the surface layer of the sample plot, collecting a soil sample, obtaining volume-weight data, and recording environmental parameters such as geographic position, altitude and gradient; the method comprises the following steps: pretreating a soil sample, measuring organic carbon content and volume weight, and obtaining carbon components and physical parameters; the exposed height of the erosion pile is measured regularly, erosion / accumulation modulus is calculated, and a space-time database is established in combination with weather, disturbance events and the like; and based on the erosion / accumulation data and the physical and chemical parameters, constructing a model to quantify carbon loss and carbon sequestration caused by water and soil loss. According to the method, through contrast point distribution, high-frequency observation and multi-parameter analysis, whole-process dynamic monitoring of complex terrain carbon migration is achieved, and the method is suitable for accurate evaluation of water and soil loss induced carbon loss in the field of ecological monitoring.

The invention discloses a handheld forest region sample plot calibration surveying and mapping device and method, and relates to the technical field of forest resource investigation, and the device comprises a laser radar, a visual camera and a global navigation satellite system / inertial orientation positioning navigation system integrated navigation unit; when a user carries the device to move, the processing unit constructs a three-dimensional point cloud map and calculates a six-degree-of-freedom pose; when the signal of the global navigation satellite system is unavailable, calculating a current position by combining the three-dimensional point cloud map, the six-degree-of-freedom pose and inertial data; and the processing unit calculates parameters of the stumpage and the candidate area and generates a sample plot calibration surveying and mapping report. According to the invention, the device achieves the synchronous capturing of the characteristics of the trunk base and the canopy at a handheld height, an operator only needs to click and select an initial position on a touch screen, and the system combines laser instant positioning and map construction with a global navigation satellite system / inertial orientation positioning navigation system for tight coupling positioning. And automatically generating sample plot boundaries and ecological parameters conforming to regulations.

The invention discloses a forest fixed sample plot measuring device and method based on a high-precision foundation laser radar in the technical field of forest fixed sample plot measurement, and the device comprises a mobile vehicle body which is provided with a mounting seat, the mounting seat is provided with a processing device and a laser radar, and the two sides of the mobile vehicle body are each provided with a buffer assembly; the buffering assembly comprises a sleeve piece, a moving piece arranged in the sleeve piece in a sliding mode, an elastic piece arranged between the sleeve piece and the moving piece, a supporting plate arranged on the moving piece, a triggering assembly arranged in the sleeve piece and an air supply assembly communicating with the sleeve piece. The three-dimensional structure parameters of the vegetation are obtained through the laser radar, a traditional large fixed sample plot measurement method is improved, the monitoring precision of the individual height of the tree is greatly improved, and the problem that the height of the tree is difficult to measure in construction and monitoring in a traditional fixed sample plot or a large number of visual estimation modes are adopted is solved. And the precision and efficiency of forest fixed sample plot monitoring are improved.

The invention is suitable for the technical field of forest assessment methods, and provides an urban forest community carbon reserve assessment method and system based on multi-source data, and the method comprises the steps: obtaining research region information; the method comprises the following steps of: obtaining a geometrical relationship between an orientation in a panoramic image and a point cloud center direction after equipment installation, generating individual tree data units according to a relationship model, and binding the individual tree data units with unique numbers; extracting contour features of image areas corresponding to the individual tree data units, identifying whether the contour features have contradictions, performing secondary stripping on abnormal contours to obtain final individual tree data units, and performing species classification according to the final individual tree data units; a structure weighting factor is constructed by combining the area of the sample plot, a corresponding grade is judged according to the total carbon storage value of the whole sample plot, a theoretical carbon storage upper limit is calculated, the relative deviation rate of the current carbon storage is calculated according to the theoretical carbon storage upper limit and the total carbon storage value of the sample plot, and the method has the advantages that structure expression is refined, model matching is flexible, and the distinguishable grade of an evaluation result can be improved.

The present invention is applicable to the technical field of plot monitoring, and provides an ecological plot monitoring method and system based on computer vision, including the following steps: collecting aerial images of an ecological plot, where each aerial image corresponds to an aerial parameter information; performing edge cropping on the aerial images according to the aerial parameter information, and stitching all the edge-cropped aerial images to obtain an ecological plot image; performing image processing and image segmentation on the ecological plot image, segmenting the ecological plot image into several regions, and extracting key feature information of each region; identifying the ecological information of each region according to the key feature information, and at the same time obtaining the recognition credibility; determining the area range and collection coordinates of the regions that need to be recollected based on the recognition credibility; and re-determining the ecological information of each region according to the recollected aerial images. The present invention avoids blind re-sampling according to the recognition credibility, and further improves the accuracy and integrity of the ecological information.

The invention discloses a forest stock calculation method based on three-dimensional point cloud data and a neural network, and the method comprises the steps: carrying out the preprocessing of point cloud data, including point cloud denoising, filtering and normalization, and removing the redundant data, such as the surrounding ground feature environment and non-ground points, except for single tree information; a mark control watershed algorithm based on morphological filtering is adopted to mark a crown center point, individual tree segmentation is achieved, and individual tree parameters such as individual tree diameter at breast height, tree height and crown breadth are extracted; a neural network volume inversion model is established, the model takes the individual tree parameters as an input layer and the forest volume as an output layer, and an optimization algorithm is adopted to optimize the training process; and estimating the forest stock by using the stock inversion model, inputting the diameter at breast height, the tree height and the crown breadth in the sample plot checking data into the model, and outputting a forest stock estimation result.

The invention discloses a carbon source and sink accounting method based on flux observation, an ecological system carbon reserve accounting system and a flux observation carbon source and sink accounting system are established, and the ecological system carbon reserve accounting system obtains carbon reserves of plants, soil, water and ground mud based on a sample point investigation method to estimate the carbon reserves of the ecological system. Estimating regional carbon reserves in combination with remote sensing survey; the flux observation carbon source sink accounting system is based on the constructed wetland carbon sink monitoring system and monitors data in real time; regional ecological system carbon reserve dynamic accounting is carried out by combining sample plot investigation with a carbon sink model and a remote sensing image technology, compared with the prior art, long-term fixed observation sample plots are set from multiple angles of lake wetland water, soil and gas generation, and Hongze lake wetland carbon sink observation data acquisition is carried out; and carrying out parameter adjustment and correction by utilizing a modern technical means and a large model technology, carrying out Hongze lake carbon sink model construction, and realizing Hongze lake carbon sink value accounting.

The invention provides a method and a device for determining urban plot evacuation identification information, which can be applied to the technical field of artificial intelligence and emergency evacuation. The method comprises the steps that an identification agent obtains an action of layout evacuation identification based on land parcel information and initial evacuation parameters; executing an action in the environment, and determining a layout result of the evacuation identifier and an evacuation state result; the initial strategy network obtains a sample action, a layout result of a sample evacuation identifier, a sample evacuation state result and a sample distribution parameter based on the sample plot information and the sample initial evacuation parameter; the initial value network evaluates a sample action based on an evaluation strategy, a layout result of a sample evacuation identifier and a sample evacuation state result to obtain an evaluation result; and based on the evaluation result, the sample distribution parameter and the loss function, updating respective initial parameters of the initial policy network and the initial value network to obtain an identified agent. Through the method, dependence on a mathematical model and an algorithm can be reduced, and consumption of computing resources is reduced.

The invention discloses a multi-scale forest above-ground biomass estimation method based on an individual tree detection technology, relates to the technical field of individual tree detection, and solves the problems that in an existing AGB estimation method, the overall accuracy of area AGB estimation is influenced by uncertainty of an individual tree method, and the overall accuracy of area AGB estimation is influenced due to the fact that a model adopts a complex mechanism. And therefore, the accuracy of the prediction is influenced by the random residual error in the actual predicted value, and the precision of the estimation result is reduced, and the like. According to the method, AGB estimation from the individual tree scale to the sample plot scale is carried out by adopting an individual tree method; based on the EIV model, constructing an AGB upscaling estimation model from the sample plot scale to the forest stand scale; amplifying a forest stand scale reference AGB sample by adopting an EIV model and canopy height extracted by ICESat-2 / ATL08; estimating a high-resolution forest AGB by using a random forest model, and the like. According to the method, multi-scale AGB high-precision estimation from a single tree to a region can be realized, and the estimation efficiency and precision of the forest biomass are greatly improved.

The invention discloses a vegetation biomass monitoring method fusing unmanned aerial vehicle hyperspectrum and Sentinel-2, and belongs to the field of ecological remote sensing information processing. In order to break through the data fusion bottleneck, a sample plot sample is collected to measure biomass, and a shrub different-speed growth equation is constructed; preprocessing the hyperspectral image of the unmanned aerial vehicle; segmenting and identifying grassland and shrubs; extracting feature inversion single-year biomass; constructing a buffer area to obtain sample plot AGB reference data; training the model by using a Sentinel-2 image; and long-time-sequence monitoring is realized through batch inversion. The method breaks through the bottleneck of data fusion, improves the inversion precision and generalization ability, reduces the cost, and supports long-time-sequence dynamic monitoring and multi-scene application.

The invention discloses a single plant afforestation carbon sink metering and monitoring method based on machine vision recognition, and belongs to the technical field of geographic spatial data processing, and the method comprises the steps: S10, obtaining spatial distribution data of a preset monitoring sample plot, the spatial distribution data comprising forest types and forest distribution; monitoring deployment is carried out in a preset monitoring sample plot, and the monitoring deployment mode comprises the step of arranging a plurality of observation cameras in the preset monitoring sample plot; and step S20, video picture data of the forest is acquired based on the arranged observation camera so as to acquire growth data information of the forest, and the growth data information comprises the height, the diameter and the coverage of the forest. According to the invention, through a machine vision identification technology, effective monitoring of forest tree growth data is realized, accuracy and efficiency of carbon sink metering are improved, and through periodic recording and analysis of growth data information, a change rule of forest tree growth can be found in time, and a scientific basis is provided for dynamic monitoring of carbon sink.

The invention discloses an automatic forest checking method integrating point cloud precise segmentation and parameter inversion, and relates to the field of forest resource checking, and the method comprises the steps: constructing an automatic 3D forest checking frame with a double-branch point cloud segmentation network as a core, carrying out the preprocessing of forest point cloud data, extracting multi-scale features, and carrying out the segmentation of a point cloud segmentation network; performing mask scoring processing on the multi-scale features, and finally outputting a mask score, a semantic segmentation result and an instance segmentation result; dividing the point cloud into three types of semantic tags of ground, branches and leaves according to the semantic segmentation result, dividing the instance segmentation result into independent individual tree instances, performing convex hull or voxelization processing on the accurately segmented point cloud, inverting forest structure parameters, and completing automatic checking of forest resources. The automatic 3D forest checking framework is excellent in performance, efficiently and accurately draws the boundary of a single tree in a point cloud, realizes high-precision identification and separation of branches and leaves on a sample plot and a tree level, and promotes digitization, automation and intelligentization of forestry resource management.

The invention discloses a forest resource estimation method and device based on deep learning. Relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining initial point cloud data of a target forest region, and carrying out the preprocessing of the initial point cloud data, and obtaining target point cloud data; performing feature extraction on the target point cloud data through a target backbone network of a target deep learning model to obtain target feature information; processing the target feature information through a plurality of full connection layers of the target deep learning model to obtain an output result corresponding to each full connection layer; and obtaining the forest resource quantity of the target forest region according to the output result corresponding to each full connection layer. According to the method and the device, the problem that the efficiency of estimating the forest resources is relatively low due to the fact that the forest resources in the forest area are estimated through a sample plot checking method in the related technology is solved.

The invention relates to the technical field of grassland ecological evaluation, in particular to a dynamic reference index calculation method and system for grassland ecological quality evaluation. The method comprises the following steps: acquiring historical reference data of a non-degraded sample plot covered with different interference degrees in a target area, and constructing a reference system time sequence prediction model based on the historical reference data; based on pre-acquired historical monitoring data of a to-be-evaluated grassland, utilizing the reference system time sequence prediction model to obtain a dynamic reference index potential value; extracting an interference factor from the historical reference data, and setting an interference weight for the interference factor; and correcting the potential value of the dynamic reference index by using the interference weight based on the historical monitoring data and the historical reference data to obtain the dynamic reference index. The problem that in the prior art, a fixed reference index is difficult to dynamically respond to the change of the environment, so that the evaluation of the ecological condition of the grassland is inaccurate is solved, and the scientificity of reference index calculation is greatly improved.

The invention provides an urban green land carbon storage and sink quantification method based on remote sensing and field investigation, and relates to the technical field of environmental science and engineering, remote sensing technology and ecological assessment, and the method comprises the steps: obtaining a high-definition remote sensing image, combining a green land planning file, determining the distribution of various types of green lands, calculating an NDVI, and extracting an effective green land space; and generating a sampling point scheme according to the area proportion. Growing parameters of arbors and shrubs in the quadrat are investigated on site, biomass is calculated and converted into carbon reserves, an NDVI and carbon density model is established, and a carbon density spatial distribution diagram is generated. And constructing a carbon sink model based on the relationship between the age of the arbor and the carbon reserves, calculating the annual carbon sink, establishing a relationship model between the NDVI and the carbon sink, and generating a carbon sink spatial distribution diagram. And integrating the two diagrams to output carbon storage and sink capability evaluation results of different green land types, thereby providing a scientific basis for urban ecological planning.

The invention provides a multi-mode remote sensing highway vegetation carbon reserve accurate inversion method based on tail gas stress correction, and belongs to the field of remote sensing images. Comprising the following steps: acquiring satellite remote sensing data, unmanned aerial vehicle data, ground actual measurement data and traffic flow data; vegetation biomass inversion based on multivariate data fusion, including biomass inversion of individual tree scale, sample area scale and road domain scale; converting the road domain biomass into road domain carbon reserves; calculating the traffic pollutant discharge amount; the day-by-day and hour-by-hour pollutant concentration of the vegetation sample plot is simulated according to the traffic pollutant discharge amount; constructing a carbon reserve dynamic correction model, and correcting road domain carbon reserves based on a photosynthetic inhibition effect and a respiration promotion effect; according to the method, satellite remote sensing, unmanned aerial vehicle remote sensing and ground measured data are fused, a multi-modal data closed loop is constructed, the limitation of a single data source in temporal-spatial resolution and coverage range is solved, multi-scale biomass inversion from a single tree to a road domain is realized, and the carbon reserve estimation precision is improved.

A tree-dwelling primate identification method based on thermal infrared and visible light image fusion relates to the technical field of target detection, and comprises the following steps: synchronously acquiring a visible light image and a thermal infrared image in a target sample plot; performing image processing on the visible light image and the thermal infrared image; extracting thermal infrared features in the thermal infrared image and biological features in the visible light image; based on the extracted thermal infrared features and biological features, performing a three-layer matching mechanism of rough matching, static matching and biological constraint; carrying out image fusion on the visible light image and the thermal infrared image; carrying out model training and optimization; and outputting a species identification result. According to the method, adaptive infrared radiation correction enhancement is carried out on the complex inhabiting environment of the tree-dwelling primates in the high-altitude area, then thermal infrared features and visible light features are fused to carry out cross-modal feature three-layer matching and modeling, interference of environmental conditions can be effectively eliminated, the recognition precision is improved, and the recognition efficiency is improved. And a key technical support is provided for physiological monitoring of the tree-dwelling primates.