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 invention discloses a regional scale forest canopy height remote sensing retrieval method. The regional scale forest canopy height remote sensing retrieval method includes the following steps: (1) setting a field sampling plot, and surveying parameters, (2) extracting forest type information based on an object-oriented classification method, (3) carrying out remote sensing estimation on the leaf area index, (4) carrying out remote sensing retrieval on the canopy density, (5) extracting and standardizing laser radar complete-waveform data and corresponding geographic position and elevation information, (6) carrying out Fourier transformation and low-pass filtering on the waveform data, (7) estimating noise of the waveform data, (8) judging the beginning position and the end position of waveform data signals, (9) determining waveform data peak value positions which include the ground echo position, the canopy top position and the centroid position, (10) computing the forest canopy height in a flat area with the gradient smaller than 5 degrees, (11) building a GLAS forest canopy height extracting model under the slopping-field terrain condition, and (12) fusing the laser radar canopy height data with multi-spectral information to carry out regional retrieval.

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 provides an MODIS mixed pixels decomposition forest information extraction method, being integrated with the standard images of an MOD09A1 surface reflectance product and the standard images of an MOD13Q1 vegetation index product. The MODIS mixed pixels decomposition forest information extraction method is characterized in that the images of different MODIS products are provided with different forest information; and however, as the resolution ratio is low and large amount of mixed pixels exist in the images, the forest information is extracted through an improved end-member purification method and adoption of a linear mixed pixel decomposition model. The MODIS mixed pixels decomposition forest information extraction method of the invention has the advantages of being able to quickly extract the forest cover information in wide range, improving the forest mapping precision and the forest type identification precision, wherein only the MOD09A1 surface reflectance product and the MOD13Q1 vegetation index product are used during the information extraction process, thus obtaining ideal effect and being simple and practicable.

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.

A microwave remote sensing image based forest type identification method relates to a forest type identification method. The invention aims to solve the problem that the conventional method cannot identify the forest type precisely and has a complicated operation process. The method comprises a first step of inputting an image and preprocessing the image; a second step of extracting a coherence matrix of the preprocessed complete polarization SAR image; a third step of performing unsupervised classification on the whole remote sensing image by use of an H-alpha / Wishart classification method and an H-A-alpha / Wishart classification method so as to extract an experimental region forest part; and a fourth step of using a result of the unsupervised classification of the third step as an original input data, and performing complex Wishart supervised classification by use of a maximum likelihood classifier, so as to realize identification of the forest type. According to the method, the classification method is simple and the classification accuracy is high.

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 forest information remote sensing and automatic extracting method based on vegetation index time series data dispersion measures. According to the forest information remote sensing and automatic extracting method, based on vegetation index time series data of each day in a year of each grid pixel element in a research area, according to the overall distribution condition and the distribution condition in different value ranges of the index time series data, an overall dispersion measure index, an intermediate-high dispersion measure index, a growth peak period dispersion measure index and a high value continuity index are designed; based on the principle the vegetation index time series data dispersion of a forest is small, forest classification flow chart is established, forest information is remotely sensed and automatically extracted, and finally a forest distribution map of the research area is obtained. By the adoption of the forest information remote sensing and automatic extracting method, based on the process of fully extracting the changes of vegetation index data dispersion measures of different forest types on the whole within different value ranges and different time periods, multiple dispersion indexes are established and used for forest information remote sensing and automatic extracting, and the forest information remote sensing and automatic extracting method has the advantages that the robustness is good, the classification accuracy is high, the automation degree is high, and the disturbance resistance is high.

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 system comprises a resource layer, an information provider layer and an information service layer. The resource layer is all resources contained in the grid. The information provider layer is an aggregation of application programs providing all kind of information, and takes charge in collecting information relating to resources from lower layer, and provides the collected information to upper layer. The information service layer is the core of whole grid information management system, and is composed of multi information service nodes. Each information service node is located at each grid node of the information service layer, and is organized based on a tree type hierarchical structure to form multi information service trees, which construct the forest type grid information management system. The invention is used for providing resource information service for the service grid platform based on OGSA architecture.

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 provides a method for acquiring a basin coverage rate for water conversation. The method includes the steps: 1) according to four stand types, including coniferous forest, broad-leaved forest, mixed broadleaf-conifer forest and shrubbery, of a basin forest, measuring area, canopy interception rate, saturated soil water-holding capacity and the like of each stand type; 2) superposing according to respective weights to acquire coverage area, capable of realizing water conservation, of one stand type, and determining a coverage rate of each stand for water conservation; 3) fitting to acquire a basin coverage rate used for water conservation. According to the method, the forest coverage rate of a basin for water conservation is obtained by combination of years' precipitation data of the studied basin according to forest area and forest types of a surface water basin, under the guidance of the coverage rate, reasonable planning and purposeful afforestation or deforestation are performed, a water conservation function of forests is developed fully, suitable forest area of the basin is regulated in real time, blind afforestation is avoided, the contradiction of the forests and farmland is alleviated, and the method has great economic significance.

The invention provides a forest fire combustible load capacity estimation method. The method includes that combustible types are divided; a sample plot with representative forest types and site conditions in forest areas is selected, and different areas are selected in the sample plot according to diagonal directions and load capacities of different combustible types are measured respectively; a calculation model of forest surface combustible load capacities is determined through clustering analysis according to the measured load capacities of the different combustible types and the forest types and the forest site conditions in the areas so that ground surface combustible load capacity in a to-be-measured forest area can be determined. The invention further discloses a forest fire combustible load capacity estimation device which comprises a dividing module, a measuring module and a calculating module. By means of the forest fire combustible load capacity estimation method and device, load capacities of various ground surface combustibles can be comprehensively estimated, and predicted negative values are avoided.

The invention discloses a forest type storage structure and a method for a distributed erasure code hybrid storage based on multiple storage media, which are characterized in that data block data of erasure codes are placed in a solid state disk for storage in a distributed storage system, and data check block data of the erasure codes are placed in a mechanical hard disk for storage. The method comprises the following steps: (1) classifying data storage media in a distributed storage system, and establishing a forest type hybrid storage structure; (2) classifying erasure code data in the distributed storage system into data block data and check block data, and marking the data block data and the check block data; and (3) placing the classified erasure code data on a specific tree of a forest type storage structure for distribution and disk falling. In this way, hybrid architecture storage of erasure code data on distributed storage based on multiple storage media is achieved. According to the invention, excessive wear of SSD caused by erasure code write amplification can be solved, the system performance is improved with lower cost, the service life is prolonged, and the reliability is enhanced.

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 relates to a large-scale forest height remote sensing inversion method considering ecological partition. The method comprises the following steps: firstly, combining forest ecological partition data with a spaceborne photon counting laser radar, an optical image, topographic data, meteorological data and latitude and longitude data, then creating a multi-source remote sensing data nonparametric tree height model considering different ecological partitions and different forest types, and estimating the continuous forest height of the whole research area space by using the model. Ecological factors related to forest growth are comprehensively considered, the remote sensing-ecological coupling forest height estimation model is obtained by incorporating ecological partition factors into forest height modeling, and help can be provided for generating forest height space information from global and regional scales to more local scales.

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.

The invention discloses a root restriction cultivation method of polygonatum multiflorum L. under high-slope forests. The method comprises seven aspects of seedling reproduction, forest selection, soil preparation and placement of root control devices, seedling transplanting, under-forest management, prevention and control over diseases and insect pests, harvesting and primary processing; according to the method, forests such as mixed forests, bamboo forests and broad-leaved forests where slopes are greater than 25 degrees, and ridging and formation of rectangular pieces of land are not easilyconducted are mainly selected, seedlings or seed stems (bud heads face upward) are planted in the root control devices, and a base fertilizer prepared by adopting a special formula and a planting substrate special for traditional Chinese medicinal materials are applied for planting. By adopting the method of cultivation of the polygonatum multiflorum L. under the high-slope forests, the problemscan be solved that at present, the utilization efficiency of the forest type is low, the impurity removal workload is high, according to traditional under-forest ridging cultivation of the polygonatummultiflorum L., the water and fertilizer utilization efficiency is low, soil-borne diseases are severe, and the yield and quality of the polygonatum multiflorum L. can be influenced; effective components of the obtained medicinal materials are consistent in content, stable and reliable, and can meet the standards of the 'polygonatum multiflorum L.' item of the 2015-version 'Chinese pharmacopoeia', and the method has a good popularization prospect.