57 results about "Forest type" patented technology

The invention discloses a forest classification method based on an object-oriented high-resolution remote sensing image. The method is based on the high-resolution remote sensing image, an object-oriented image classification method is used, an orienting remote sensing forest second-level classification system is established, a forest remote sensing classification auxiliary data set and an integrated image are created, key indexes which can distinguish forest types are selected, and a layered step-by-step classification extraction method is provided to be used for establishing information extraction knowledge rules of various forest types. The processes of the method are suitable for middle-small-scale forest resource remote sensing monitoring in a zone, good operability and repeatability are achieved, and efficiency and accuracy of forest remote sensing monitoring in the zone can be effectively improved.

The present invention discloses a tree species classification method based on multi-source simultaneous high-resolution remote sensing data. High-resolution and hyperspectral data simultaneously acquired by an integrated sensor is utilized; firstly, coronal breadth identification is carried out based on the high-resolution data and an object-oriented method, then tree species classification is carried out based on spatial details and spectral features, which are extracted by the hyperspectral data, by combining a BP neural network classifier, and finally, the accuracy is verified by a confusion matrix. According to the tree species classification method disclosed by the invention, based on an edge detection multi-scale segmentation method, segmentation grades with different scales are established from multiple levels and multiple patterns and segmentation and information extraction are carried out layer by layer, so that the classification accuracy of tree species and forest types of subtropical natural secondary forests are promoted.

A forecast method of forest fire danger of coupling climate and vegetation belongs to the computer digital image processing technical field. The forecast method of the forest fire danger of the coupling climate and vegetation comprises performing remote sensing classification on forest combustible in Zhejiang Province and performing grade division on forest fire combustible; generating a spatial forecast database of meteorological factors in a forest according to thin plate smoothing spline interpolation with meteorological data as the base; calculating to obtain a spatial database of climatic indexes of the forest fire danger in Zhejiang Province by utilizing a module of the climatic indexes of the forest fire danger and simultaneously performing the grade division on climate fire danger; performing coupling on the spatial database of the climatic indexes of the forest fire danger and a remote sensing classification database of the forest combustible, performing analysis and process by utilizing an ArcGIS ( Arc Geographic Information System) software, combining the occurrence intensity, spatial distribution pattern and regional characteristics of the forest fire disasters in Zhejiang Province, performing comprehensive analysis on fire occurrence and harm and combining with the media to establish an early warning and forecast system of the forest fire danger in Zhejiang Province to achieve forest fire danger forecast of different regions and different forest types.

The invention discloses a tree species classification method based on a LiDAR (Light Detection and Ranging) false-vertical waveform model. The method comprises a step 1 of collecting data using an airborne small-light spot full-waveform LiDAR sensor, in order to obtain complete waveform information, wherein the sampling interval is 1ns; a step 2 of preprocessing LiDAR waveform data; a step 3 of establishing a false vertical wave through waveform structural decomposition and comprehensive waveform recombination; a step 4 of carrying out single tree segmentation and information integration; a step 5 of gathering point cloud attributes within in a single tree range and calculating a comprehensive waveform characteristic parameter; and a step 6 of carrying tree species classification with a support vector machine classifier. With the tree species classification method, acquired LiDAR energy signals can be enhanced; on the basis of single tree segmentation, comprehensive waveform characteristic variables are extracted from multiple dimensions, and thus classification accuracy of tree species in the subtropical forest is obtained and improved by a single data source; spatial and temporal variation of main tree species of a forest type can be well embodied; through experimental verification results, the method provided by the invention is improved by 10% in terms of overall precision compared with other LiDAR-employing tree species classification methods; and the Kappa coefficient is improved by 0.1.

The invention discloses a forest type identification method based on texture information, belongs to the technical field of remote sensing, and particularly relates to a remote sensing method for classifying forest type on the basis of TM (thematic map) data of the texture information. The method includes the following six steps of S1, confirming TM remote sensing data of an appropriate time as a data source according to bio-meteorology information, preprocessing the TM data; S2, extracting a forest land portion of a researched area; S3, confirming size of a window or a step when the texture information is extracted; S4, extracting video texture information by means of the principle of geostatistics; S5, classifying TM video by means of the maximum likelihood method; and S6, verifying classified result of the maximum likelihood method by means of geoclimatic verifying data. By the aid of the forest type identification method based on the texture information, precision of remote sensing for the forest type, especially the precision for identifying large-area forest type, is improved.

The invention discloses a method for preparing a self-repairing transducer based on a forest-type graphene interwoven network and relates to the technical field of graphene. A forest-type nickel dendritic crystal interwoven network is adopted as a growth template, graphene is deposited by a chemical vapor deposition process, and after the graphene is transferred, encapsulation is performed by utilizing flexible colloid to obtain a graphene network having a special forest-type interwoven structure. After the graphene network is utilized to prepare an electrode lead, the strain transducer having a self-healing property is formed. The strain transducer has characteristics of high sensitivity, resistance to large strain, and capability of being repaired after the strain transducer is broken by overloads, and has an application prospect in medical diagnostics, artificial electronic skin, biomorphic robots, flexible touch screens, and the like.

The invention discloses a method for performing forest tree multiple-trait pyramiding breeding based on multiple-trait genomic selection. The method sequentially comprises the following steps: clonal individual phenotypic data acquisition; clonal pedigree information and A matrix construction or SNP marker typing data and G matrix construction; multiple-trait model establishment and data analysis. The method disclosed by the invention has the benefits that the workload of forest tree trait phenotype determination can be reduced, and no lamination by the correlation degree between traits and markers exists; compared with a conventional traditional progeny determination technology, multiple-trait orientation and accurate breeding can be realized, an obtained hybrid offspring have reliable productivity and genetic background, the forest tree multiple-trait pyramiding breeding period is significantly shortened, and no specific limitation on tree species and test forest types exists; the forest tree multiple-trait pyramiding breeding purpose can be quickly achieved; therefore, the application prospect is wide.

The invention relates to a four-component optical physical model based inversion method of leaf area indexes, relating to the inversion method of the leaf area indexes and aiming at solving the problem that a simulated result of a physical model is inaccurate because the traditional two-component physical model can not accurately describe the real composition of a vegetation coverage pixel of a remote sensing image. The inversion method particularly comprises the following steps of: 1, determining the input parameters of the four-component optical physical model through the imaging conditions of the remote sensing image and the characteristics of a forest type; 2, setting an L value to be gradually increased according to 0.1 step length, and establishing a lookup table according to the L value and R4 corresponding to the L value so as to establish the one-to-one corresponding relationship of the leaf area indexes and the reflectivity data of a vegetation canopy; 3, traveling the lookup table by adopting a mean square root error optimization technology to ensure that the leaf area index corresponding to minimum mean square root error is an optimal leaf area index, and taking the optimal leaf area index as an inversion result, wherein the four-component optical physical model is shown as a formula. The invention is suitable for accurately describing the vegetation coverage of the remote sensing image.

The invention discloses a method for measuring a canopy gap three-dimensional structure based on laser radar. The method includes a first step of obtaining and pre-treating laser radar data which is remote sensing data obtained by laser radar loaded on a fixed wing aircraft or an unmanned aerial vehicle, the data of each laser point including X, Y, Z coordinate data and echo intensity data, a second step of processing canopy layer data, a third step of identifying canopy gaps, and a fourth step of measuring a three-dimensional canopy gap structure. The method is easy to realize, and is not affected by surveyed land and forest types and ground situations. Rapid, accurate, large-scale and multi-dimensional canopy structure measurement can be realized.

The invention discloses a three-dimensional forest fire spreading simulation method and system. The method comprises steps of S1, three-dimensional landform factor generation; S2, three-dimensional wind field stimulation, on the basis of the data of a digital elevation model, wind speed and wind direction simulation is carried out, and the wind field data of a three-dimensional landform is acquired; S3, temperature field and humidity field stimulation, on the basis of the discrete temperature, humidity and digital elevation model data, interpolation processing is carried out; S4, combustible coefficient division, three-dimensional combustible coefficient data distribution is generated on the basis of the forest type distribution data; S5, three-dimensional fire field spreading simulation, a three-dimensional fire field spreading model is generated on the basis of a three-dimensional gradient, a slope direction, a wind speed, a wind direction, temperature, humidity and a combustible coefficient factor; and S6, three-dimensional fire field spreading information acquisition. The method is advantaged in that the method is applicable to forest fire spreading simulation, and a more precise simulation result can be acquired.

The invention relates to a visible light unmanned aerial vehicle remote sensing image forest tree species classification method based on multi-feature optimization, and the method comprises the steps:obtaining a visible light remote sensing image of a needed forest region through unmanned aerial vehicle aerial photography, and generating a digital surface model and a digital orthographic image map through preprocessing; establishing a visible light unmanned aerial vehicle image tree species classification system; extracting multiple types of features from the digital surface model and the digital orthographic image map to construct a feature space; selecting an optimal classification feature subset by using a recursion elimination random forest algorithm; using a random forest algorithm on the feature subset to realize tree species classification, and extracting a tree species distribution diagram; and performing precision evaluation on the classification result. The method is beneficial to popularization and application of the unmanned aerial vehicle visible light remote sensing image in forest type and tree species identification in a forest region.

The invention relates to a method for measuring forest disturbance degree, which is characterized by comprising the following steps: defining target forest as pre-disturbance forest, potential forest cover or zonality forest; establishing a forest disturbance degree evaluating indicator system, and determining the specific evaluating indicator or a relative value; and applying a forest disturbance degree computing formula to evaluate the disturbance degree of the existing forests. The method achieves the quantitive evaluation of the forest disturbance degree, provides a specific method for researching the forest disturbance degree in the future, is not restricted by areas, forest types and disturbance types, has flexible choice indicators, can evaluate the disturbance degree of the existing forests with treatments in accordance with local conditions, and is easy to operate.

The invention provides an infrared ray camouflage coating, a camouflage production method utilizing the same and camouflage overcloth. The infrared ray camouflage coating has a good camouflage effect and further can achieve an infrared ray camouflage effect. According to the technical scheme, the infrared ray camouflage coating comprises a south forest type coating, a north forest type coating, a steppe type coating and a desert type coating. The camouflage overcloth comprises a shielding surface, wherein the shielding surface comprises an outer surface layer, an inner surface layer and base cloth.

The invention discloses a method of researching forest soil carbon storage. The method comprises following steps of s1, investigating coastal platforms in multiple regions, and establishing a sample platform; s2, digging soil profiles of the sample platform inwards and outwards and collecting soil samples, and analyzing and measuring physical properties, chemical properties and biological properties of the forest soil; s3, researching a litterfall contemporary carbon characteristic, a root biomass stock carbon characteristic and microbial morphology carbon of each layer of the soil; s4, researching a distribution state and a stock characteristic of soil carbon of a main forest type in the coastal platform, and obtaining an accumulation characteristic of the forest carbon; and s5, estimating and researching storage of the soil carbon of the main forest type in the region. The method acquires accurate data through measurement of the sample platform so as to analyze a distribution law anda relationship between the distribution law and environment variables, thereby being conducive to ecological functions of artificial forest soil in the coasting platform and being significant for sustainable running of coasting platform forest and recovery of forest productivity.

The invention relates to a mode of cultivating paris polyphylla var.yunnanensis under a purple soil coniferous forest and belongs to the field of traditional Chinese medicinal material cultivation.According to the actual conditions that purple soil is the soil type which is great in distribution area in Yunnan province, soil physical properties are poor, water and soil loss is serious, and the purple soil-Yunnan pine forest is one of the main forest types, terraced fields are built according to the terrain, and ridges are protected through vetiveria zizanioides L.so as to retain soil moisture and prevent water and soil loss; under-forest soil improvement is conducted through organic fertilizer and lime; fertilizing is conducted in a balanced mode according to the nutrient features of soil under the purple soil coniferous forest and rules formed by the fertilizer-demanding characteristics and quality of paris polyphylla var.yunnanensis; the ecological environment is protected, water and soil loss is prevented, income of farmers is increased, and sustainable development of regional economy is promoted.

The invention relates to a forest type identification method based on high-score remote sensing images, which comprises the following steps: preprocessing the high-score remote sensing images to obtain panchromatic spectral images and multi-spectral images; performing gray scale extraction from panchromatic spectral image and vegetation index extraction from multi-spectral image; according to theforest resources survey data, analyzing the dominant tree species, and obtaining the gray value and vegetation index value of the dominant tree species. According to the classification standard of woodland, the research is divided into woodland and non-woodland. According to the correlation between grey value and vegetation index value, the recognition function models of related forest types are established respectively. The correlation function model is tested and evaluated; the correlation function model is applied to forest type recognition. The gray scale and vegetation index can be obtained by using the texture features of the high-score No. 2 image, which can identify the forest types, reasonably reduce the field investigation work of forest resources, and save the cost and resources.

The invention discloses a model system and a prediction method for forest growth. The system comprises an age implicit single tree model Yk equal to y(y0, k) equal to a. exp(minus b divided by (k minus (b divided by ln(y0) minus ln(a)))), an age retention rate model P<1> equal to p(D, 1) equal to Alpha. exp(Beta. ln(D)), an ingrowth model Nyr equal to N(A) equal to A<2> divided by (C0A<3> plus C1A<2> plus C2A plus C3), and the like. Forest resources are predicted through the models, forest aspect is simple and complete, and the prediction is accurate. The invention is suitable for various forest types, tree species and stumpage types.

The invention provides a processing method and device for a forest carbon sink management plan, belonging to the field of forest management, including: obtaining regional forest type distribution data, and classifying the obtained regional forest type distribution data; selecting from the classification results Stands of typical forest types are used as sample plots, and the sample plot data of the sample plots are obtained; based on the sample plot data, the initial forest stock and carbon storage estimation model is obtained, and based on the initial forest stock and carbon storage estimation model Get the initial forest carbon sink management plan; optimize the initial forest carbon sink management plan through simulated annealing algorithm; display the optimized forest carbon sink management plan. Compared with customizing forest management plans based on human experience in the prior art, it is possible to formulate forest carbon sink management plans more objectively, to ensure that forest management plans are accurate and efficient, and to ensure the reasonable distribution of forest management measures in space and time, thereby ensuring Rational management of forests.

The invention provides a forest type identification method and device, and relates to the technical field of forest type classification and identification. The method comprises the following steps: obtaining an identifier of a to-be-identified area and a target time interval corresponding to the to-be-identified area, obtaining target remote sensing data of the to-be-identified area in the target time interval from a cloud platform, and analyzing and processing the target remote sensing data by using a random forest model to obtain a forest type of the to-be-identified area. Through the above steps, the target remote sensing data of the to-be-identified area can be acquired from the cloud platform, and the forest type of the to-be-identified area can be identified. Therefore, the problems that in the prior art, when stand-alone software is adopted to apply a machine learning algorithm to recognize the type of a large-area forest through remote sensing data, a large amount of remote sensing data is preprocessed, the operation classification speed of the machine learning algorithm is low, and the storage cost of TB-level remote sensing image data is high are solved.

The invention provides a different-forest-type environmental factor observation data acquiring device, relates to a forest environment factor observation data acquiring device and aims at solving the problem that existing fixed meteorological stations are low in data distribution, data acquiring procedures are tedious, handheld meteorological stations have no long-term automatic observation capacity, building of data bases of different needs a large number of manpower, and acquired data in existing modes are various in format and not easy to dispose. A casing upper cover of the data acquiring device is transparent, and a single chip microcomputer, an output module and a time module are installed in a casing. The signal output ends of a temperature sensor, a humidity sensor and an illumination intensity sensor are connected with the signal input end of the single chip microcomputer, a secure digital (SD) memory card recording module and a display module are connected with the signal output end of the single chip microcomputer, the time module is connected to a serial clock line (SCL) of the single chip microcomputer through an SCL in series, and a power supply module is connected to a power interface. The different-forest-type environmental factor observation data acquiring device is used for acquisition of forest-type environmental factor observation data.