164 results about "Vegetation types" patented technology

The invention relates to the field of ecological environment monitoring, and discloses a terrestrial plant ecological environment monitoring method based on multi-source remote sensing data fusion, which is used for realizing identification, classification and dynamic monitoring of vegetation types in a target area more efficiently. An unmanned aerial vehicle is used for vertically shooting a target area, and unmanned aerial vehicle remote sensing three-dimensional modeling is generated; Obtaining a spectral remote sensing image of a target area through satellite remote sensing, and fusing thesatellite remote sensing image with a model generated by unmanned aerial vehicle remote sensing three-dimensional modeling; Calculating and segmenting the fused image; Determining characteristic parameter indexes participating in object-oriented classification, and classifying and extracting monitored vegetation types; Constructing a confusion matrix, evaluating the classification precision by using the confusion matrix, and extracting the area size and distribution condition of each type of vegetation; Finally, the classification results of the two stages are subjected to superposition comparison analysis, and the dynamic change ranges and areas of different vegetation types are calculated. The method is suitable for terrestrial plant ecological environment monitoring.

The invention discloses a large-scale soil organic carbon spatial distribution simulation method involving environmental factors, relating to the field of digital soil cartography. The method includes the following steps of: 1, overlapping a vegetation type map with a soil type map to generate a combined spatial distribution map of vegetation-soil units, assigning a corresponding pattern spot in the map with the mean value of the soil organic carbon content of each vegetation-soil unit, and thereby obtaining the soil organic carbon mean value spatial distribution curve m(k) with the resolution ratio of 1km; 2, expressing relation between vegetation-soil mean value residuals and the quantitative environmental factors by a neural network model, and thereby obtaining the vegetation-soil mean value residual spatial distribution curve f'(xi, k, yj, k); 3, stimulating the spatial distribution curves of the remained residuals r'(xi, k, yj, k) by an HASM model; 4, at last, adding the m(k) with the f'(xi, k, yj, k) and the r'(xi, k, yj, k) to obtain the stimulation result of the soil organic carbon spatial distribution by the HASM_EF method. According to the large-scale soil organic carbon spatial distribution simulation method involving environmental factors, reference is provided for accurately estimating the national-scale soil organic carbon reserve and researching spatiotemporal changes of large-scale soil property.

The invention provides a method for evaluating the forest fire spreading risk of a power transmission channel. The method for evaluating the forest fire spreading risk of the power transmission channel is characterized by comprising the following steps that S101, a remote sensing image is preprocessed; S102, fire points and obstruction objective image elements are extracted based on the corrected remote sensing image; S103, fire outbreak point calculation is carried out; S104, the forest fire spreading speed is calculated; S105, burning range estimation is performed within predefined time duration; S106, a power transmission tower set affected by a fire is searched; S107, the forest fire spreading risk of the power transmission channel is evaluated. By the adoption of the method, various data of the wind speed and direction, the topographic slope, the vegetation type, ground barriers and the like are collected, so that a calculation method including forest fire spreading conditions and relevant factors for causing power transmission line tripping by the fire is established, hence, evaluating calculation of the forest fire spreading risk of the power transmission channel is achieved, and the national economy loss caused by large-area outage of a power grid can be effectively prevented and reduced.

The invention provides a riverbed base vegetation type ecological blanket repair method. The method comprises the steps of: preparing vegetation ground substance by mixing sand and stones, raw soil, substrate sludge, water-retaining agents and water by proportion; uniformly adding aquatic plant seeds or interplanting aquatic plant branches in the vegetation ground substance; covering the vegetation ground substance by nonwoven fabric, and forming a blanket by a pressing machine and a forming machine; and covering a layer of nylon mesh outside to form the vegetation type ecological blanket. When in use, the vegetation type ecological blanket is smoothly laid at riverbed bottom, and bamboo fillers are drilled at the four corners, and the centers of the upper edge and the lower edge of the vegetation type ecological blanket to fix the vegetation type ecological blanket at the riverbed bottom. The invention solves the problems of the difficulty of aquatic plant repair at the riverbed with deeper silt, and the damage to riverbed microecosystem caused by desilting and dredging. Inner source pollution caused by the release of infectant in substrate sludge to water can be effectively restricted, and all of the used materials are environmental friendly. When in use, the vegetation type ecological blanket can be installed and laid conveniently on site, and have low cost and obvious effect.

The invention discloses a line mountain fire disaster assessment method without the real-time acquisition of field information. The method comprises the steps of determining a vegetation type and a vegetation height, judging fire scene fire behavior type, obtaining alarm transmission line information, obtaining meteorological data of a mountain fire position, calculating mountain fire flame zone, ion zone and smoke zone heights, calculating flame zone breakdown voltage, calculating ion zone breakdown voltage, calculating smoke zone breakdown voltage, calculating transmission line trip probability, and evaluating a mountain fire level. The invention also provides a line mountain fire disaster assessment system without the real-time acquisition of field information. The method and the system have the advantages that a bearing body transmission line parameter, a mountain fire internal fire behavior characteristic and external environment conditions are considered, an accurate line fault probability model is established, use data is obtained based on a forestry database and a meteorological data value calculation result, the field acquisition is not needed, the probability quantitative calculation of transmission line trip caused by mountain fire is realized, the adaptability is high, and the mountain fire disaster resistance ability of a grid is improved.

The invention relates to a satellite-aviation-ground combined forest fire trend early warning method, device and system of a power transmission line channel, and belongs to forest fire trend early warning of the power grid. According to the method, a remote sensing satellite, an unmanned aerial vehicle/helicopter and a power grid online monitoring device are used for monitoring the fire point position, the landform, the vegetation type and the microclimate; a set value of each parameter is obtained through a data receiving and processing module, a forest fire development trend vector diagram is obtained through calculation and deduction via a Rothermel model and a Huygens model, early warning information is obtained through power grid GIS superposition analysis, and the early warning information is sent to a mobile terminal of a manager of an equipment operation and maintenance department. By constructing the satellite-aviation-ground combined forest fire trend early warning system, investment of field operation and maintenance personnel can be reduced, the forest fire monitoring area can be expanded, the forest fire development trend can be quickly predicted, and an objective, accurate and stable data source can be provided for the power grid forest fire development trend.

The invention discloses the classification method of tropical natural forest vegetation type groups in Hainan. In the invention, multi-temporal SAR data and mono-temporal less-cloud-covered optical remote sensing data are used to analyze a typical tropical forest land spectrum and scattering characteristic difference and dry and rainy season change information. A Hainan tropical natural forest vegetation type group type and a Hainan tropical natural forest growth distribution rule are combined and a remote sensing vegetation type group classification system suitable for a Hainan tropical natural forest is provided. Based on that, a Hainan tropical natural forest classification method based on machine learning is established. The method provides a solution for the remote sensing classification problem of a natural forest vegetation type group (three-level class) in the Hainan Island, and classification precision is higher than 80%.

The invention discloses a mangrove extraction method and system based on remote sensing images. The method comprises the following steps: acquiring remote sensing images of an area to be studied; using remote sensing images to identify the water area of the area to be studied; taking the edge of each water body region as the starting point to extend a certain distance to the outside of each waterbody region, and determining the water-land boundary region; obtaining the pseudocolor image of a land-water interface by pseudocolor synthesis technology; segmenting the false color image according to pixel similarity to obtain a plurality of segmented images; dividing the vegetation types and the non-vegetation types into several segmented regions, and removing the regions belonging to the non-vegetation types to obtain a plurality of vegetation region images; based on the texture and topological features of mangroves, classifying the mangrove images into mangrove images and non-mangrove images. The method and the system of the invention can improve the accuracy of mangrove spatial extraction.

The invention discloses a type prediction method of vegetation in a high-altitude permafrost region, and relates to the field of geography. The method comprises the steps that the survey data of characteristics of the vegetation in the permafrost region of Qinghai-Tibet Plateau is obtained; bio-climatic parameters are obtained; according to an NDVI data set, NDVI parameters are obtained; according to a digital elevation model (DEM), the slope, the slope direction and the profile curvature at each grid pixel element point in the permafrost region of Qinghai-Tibet Plateau are obtained; the elevations, the slopes, the slope directions and the profile curvatures are used as topography parameters; the parameters with the correlation coefficients larger than 0.8 are selected in the bio-climatic parameters, the NDVI parameters and the topography parameters through a principal component analysis method, and vegetation classification parameters are obtained; according to the survey data of the characteristics of the vegetation, the vegetation classification parameters, climate scenarios data and a climatic system mode, the types of the vegetation in the permafrost region of Qinghai-Tibet Plateau are obtained through a decision tree classification method. By means of the method, the type distribution prediction, in the mode of ten types of ten categories of climatic systems and four types of climate scenarios in 2050 and 2070, of the vegetation in the permafrost region of Qinghai-Tibet Plateau can be achieved.

Provided is a vegetation restorability evaluation method based on investigation and factor correction. The method includes that according to the natural vegetation types and the main environmental factor spatial distribution, the regional vegetation factor level and the main environmental factor level are defined, the points selected from the mutual uncoordinated parts of the two levels are subjected to uncoordinated point screening, on-site reconnaissance and reexamination, the environmental factors are developed and are combined with the main environmental factor evaluation result to define the serial environmental factor levels, the consistency of the levels with the actual site is evaluated, the vegetation factors and the environmental factors finally for the vegetation restorability evaluation are determined, and the final vegetation restorability level is evaluated. The evaluation method is scientific, practical, and is suitable for application.

The invention relates to a novel method for greening a roof. Tests determine that the maximum water-holding capacity of a substrate in the method is 750mg/ml, and is higher than that of common soil by37 percent; and the proportion of the substrate in the method is 490mg/ml, and is lighter than that of the common soil by 52 percent. The determination is clear that the substrate in the method has strong water-holding capacity and light weight, and is the proper substrate for greening the roof. The test results prove that the substrate in the method is the environment-friendly substrate with light weight and strong water-holding capacity, and can meet the requirement of greening the roof. A novel technical thought is the key point in the method, can greatly reduce the construction cost and maintenance cost, and simultaneously can flexibly change scenery patterns and select different vegetation types, so the method has good operability.

The invention discloses a mangrove extracting method and system in water and land regions of a remote sensing image. The method comprises the following steps of: acquiring remote sensing images of anarea to be studied; using remote sensing images to identify the water area of the area to be studied; using pseudocolor synthesis and image segmentation techniques to process the image outside the water body region to obtain multiple segmented images; removing images belonging to non-vegetation types from the segmented images to obtain multiple vegetation region images; using the NDVI arithmetic features, LSWI arithmetic features, texture features and topological features of the mangrove, classifying a plurality of the vegetation region images into a land mangrove image and a land non-mangroveimage. At the inner side of the water body region, the mangrove index is calculated by using the spectral response values of the pixel points at the fourth wave band and the spectral response valuesat the fifth wave band, and the mangrove pixels in the water are determined by comparing the calculated mangrove index values with the mangrove index threshold values. The method and the system of theinvention can improve the accuracy of mangrove spatial extraction.

The invention relates to the field of ecological environment monitoring, and discloses an arbor biomass measuring and calculating method based on an unmanned aerial vehicle hyperspectrum and a machinelearning algorithm, which is used for better realizing biomass monitoring of arbor of a target area type. The method comprises the following steps: acquiring a hyperspectral image of terrestrial plants in a target area by using an unmanned aerial vehicle, modeling based on the hyperspectral image, and extracting elevation information of a digital surface model; extracting spectral information from the original image photos, and performing quantitative inversion model training by adopting a machine learning algorithm according to types of terrestrial plant ecological environment monitoring vegetation classifications in combination with high-level information, characteristic wave bands and vegetation indexes of various plants in the target area to obtain an inversion model; classifying thevegetation types of the target area by using an inversion model so as to extract arbor classification data; and finally, calculating to obtain the arbor biomass by utilizing the extracted arbor classification data and combining with an aboveground biomass formula. The invention is suitable for arbor biomass measurement and calculation.

The invention discloses an optical remote sensing image vegetation and water body information automatic extraction method comprising the following steps: obtaining an optical remote sensing data sample, and randomly extracting 10% of the optical remote sensing data sample as a sample subset; calculating normalized vegetation indexes and normalized water indexes of all the samples in the sample subset, obtaining a general characteristic spectrum to execute supervised classification based on a minimum spectral angle on all the samples in the sample subset, and recording vegetation types, water body types and the sizes of the minimum spectral angles corresponding to other types at the same time; taking the minimum first 50% of samples in the minimum spectral angles, performing k-means unsupervised classification based on the minimum Euclidean distance on the first 50% of samples, obtaining 10 characteristic spectrums for each type, totally 30 characteristic spectrums, and performing supervised classification based on the minimum Euclidean distance pixel by pixel on the global image to obtain an extraction result of vegetation and water. Any prior sample is not needed for supporting from beginning to end, manual intervention is avoided, and full-automatic extraction of vegetation and water body information is achieved.

The invention belongs to the technical field of bursaphelenchus xylophilus invasion risk prediction, and discloses a bursaphelenchus xylophilus invasion risk prediction method based on an ecological niche factor model, and the method comprises the steps: obtaining distribution point data and environment data of an epidemic area of bursaphelenchus xylophilus; performing Kriging interpolation processing on the climate data, calculating the gradient and the slope direction in the topographic data, and obtaining vegetation types and human activity interference data; importing the distribution point data and various environmental data into an ecological niche factor model, and calculating pine wood nematode disease risk early warning; and evaluating the pine wood nematode disease invasion riskbased on the feature matrix and the risk map obtained by calculation. According to the invention, main environmental influence factors influencing pine wood nematode propagation are used; the method is suitable for local conditions, lays a foundation for early warning, monitoring and control work of pine wood nematode diseases and insect pests, provides a scientific basis for measures of forest biodiversity protection and ecological environment protection, and provides a basis and a reference for early warning and control work of forest diseases and insect pests.

The invention discloses a semi-supervised polarimetric SAR terrain classification method based on characteristic mixup. The method comprises the following steps: preparing a data set; making a data set; preprocessing the data; designing a semi-supervised network structure; extracting double-branch features; carrying out feature mixup fusion; predicting a classification result by multiple branches;carrying out network training; predicting a ground object category; and evaluating the network performance. According to the method, the problems caused by mixup and single-branch prediction on the input in the prior art are effectively solved, the performance of the network is greatly improved, and the robustness of the network is enhanced. The method can be used for land cover type discrimination, and can also be used as an intermediate link of target detection, geological exploration and vegetation type discrimination.

The invention discloses a mixed effect model for predicting large-area subtropical forest biomass, belonging to the technical field of forest management and related to an extraction technology of forest biomass. By relatively convenient data acquisition approaches and relatively less capital and personnel investment, an extraction technology aiming at large-area subtropical forest biomass is designed. The technology is mainly composed of five stages including extraction of laser radar data feature variables, ground sample plot data stem volume estimation, model construction and model verification based on vegetational forms and biomass calculation based on the stem volume and a biomass relative growth model. The technology improves biomass estimation precision of a laser radar method in alarge-scale forest, and provides a simplified technical solution for an airborne laser radar in forestry reconnaissance application of a large-area subtropical forest.

The invention relates to a multi-source data synergetic refined forest vegetation type information remote sensing updating technical method, and relates to the technical field of technologies of remote sensing and geographic information systems. The method realizes the key technology of multi-source data synergetic updating of formation level forest vegetation type information. By the aid of the multi-source data synergetic refined forest vegetation type information remote sensing updating technical method, on the basis of later-stage forest data, multi-time-phase MODIS (moderate resolution imaging spectroradiometer) data and earlier-stage forest vegetation type data, later-stage refined forest vegetation type information is updated and obtained. The method has advantages of automation, rapidness, low cost and high precision. After the method is applied in Szechwan, updating precisions of formation and vegetation types respectively can achieve 88% and 90%. The multi-source data synergetic refined forest vegetation type information remote sensing updating technical method is applicable to updating forest vegetation type information in other areas or at other time intervals.

The invention discloses a watershed scale vegetation net primary productivity remote sensing fine inversion method. The method comprises the following steps: acquiring a vegetation type distribution diagram, remote sensing image data and site meteorological data of a measured area; interpreting the vegetation type distribution diagram; calculating the remote sensing image data to obtain normalized vegetation index time sequence data; carrying out spatial interpolation on the station meteorological data to obtain raster data with the same resolution as the normalized vegetation index time sequence data; improving the resolution of the data by using a fully constrained least square mixed pixel decomposition and sub-pixel spatial gravitation model; configuring static parameters; and using the improved NPP estimation model to invert the vegetation net primary productivity NPP. On the basis of existing low-resolution remote sensing data, the method for improving the product resolution by means of sub-pixel mapping is provided, the vegetation net primary productivity is accurately estimated, the estimation precision of the net primary productivity is improved, and remote sensing fine inversion of the vegetation net primary productivity in the watershed scale is achieved.

The invention discloses a vegetation-type ecological concrete micro-plant cultivation method. The method is characterized by including the following steps: step 1, uniformly pasting slurry on vegetation-type ecological concrete; step 2, collecting rock moss in autumn, removing impurities, adding water to mash the moss into juice, smearing the juice on the vegetation-type ecological concrete with the slurry, sprinkling water on the concrete to enable the concrete to maintain moist, and allowing the moss to grow and form a moss cluster of a cluster-cushion-shaped structure; step 3, scattering seeds in the moss cluster formed in step 2, continuing sprinkling water on the concrete to enable the same to maintain moist, when the seeds germinate and grow to the plant height of 10-15cm, spraying growth regulator to control the plant height to be within 25cm, and hereafter spraying the growth regulator for one time in each growth season to maintain the plant height to be within 25cm.

The invention relates to a canopy interception model, particularly a GIS-based canopy interception model. According to basin reality and in combination with distribution and percentage of different vegetation types extracted through GIS, after weight average, a parameter forest crown closure Sigma, a leaf area index and a branches and leaves wetted water layer thickness hw are obtained. <{EN2}>According to the GIS-based canopy interception model, from the physical process of crown interception, a canopy interception model in heavy rain is established; the number of the parameters is small; GIS technology is combined; nonparametric solve of canopy interception is realized; and application scope and calculating accuracy of the model are improved.

The invention relates to a vegetation distribution identification method and system based on an unmanned aerial vehicle, and a readable storage medium. The method comprises the steps of collecting vegetation remote sensing image information, receiving an electromagnetic wave reflection signal, and establishing a vegetation region space through spectral reflectivity characteristics; extracting vegetation space characteristic values, obtaining classification rules, and performing classification processing on the space characteristic values through the classification rules to obtain vegetation information; carrying out geometric decomposition and remote sensing interpretation on the vegetation information to obtain a vegetation type, and obtaining a first identification result; multiplying the proportion scale, obtaining a vegetation remote sensing image again, and performing vegetation type identification to obtain a second identification result; comparing the first identification resultwith the second identification result to obtain a deviation ratio; and judging whether the deviation rate is greater than a preset deviation rate threshold, if so, generating correction information,correcting the remote sensing image information through the correction information, and transmitting a correction result to the terminal.