146 results about "Vegetation type" patented technology

The invention provides for a vertical takeoff and landing capable aerial vehicle with multiple rotors that is designed to carry agricultural sensors and telemetry allowing for real time control of agricultural equipment in accord with sensor data. The ability to carry a suite of agricultural sensors combined with multiple rotors will allow the craft to operate quickly in hovering and longitudinal flight over rows of farm fields and other vegetation and use an NDVI imager and other sensors to take data readings and real time imagery which will allow farmers and other personnel to determine vegetation type, need for chemical applications, plant fertilization, irrigation requirements, and other vegetation features including types of vegetation present. This will allow for precision agricultural, vegetation, and crop management and for farmers it will increase the efficiency of precision agriculture operations.

The invention discloses a fine simulation test device and a fine simulation test method for slope water erosion. The fine simulation test device comprises an underframe provided with trundles and a test soil groove connected with the underframe, the test soil groove is arranged above the underframe, one end of the test soil groove is hinged to one end of the underframe, and a water tank matched with the test soil groove is arranged at the other end of the test soil groove. A hydraulic rod is arranged between the test soil groove and the underframe, an equalization pond is arranged between thetest soil groove and the water tank, the equalization pond is communicated with the water tank through a water conveying pipe, the water conveying pipe and a water pump are arranged on one side of the water tank, a water discharging pipe is communicated with the lower portion of the water tank, a three-dimensional laser scanner is arranged in front of the test soil groove, a railfall device is arranged right above the test soil groove, and a flow collecting opening connected with the test soil groove is arranged at the end of the test soil groove hinged to the underframe. The fine simulation test device and the fine simulation test method for the slope water erosion have the advantages of being capable of controlling slope soil bulk density, vegetation types and cover degree manually, simulating soil water erosion conditions under different site conditions, and studying influences and response theories of all factors on soil water erosion under different underlying surface conditions.

The invention provides a method for estimating global vegetation coverage. The method comprises the following steps: firstly, selecting a plurality of global land surface space sampling points according to distribution of global vegetation types; acquiring a first earth surface reflectivity data with a high space resolution and a second earth surface reflectivity data with a low space resolution in satellite remote sensing data at the sampling points, and then acquiring a vegetation coverage training sample by using the first earth surface reflectivity data through a dimidiate pixel model; subsequently extracting the second earth surface reflectivity data and the vegetation coverage which is calculated according to the first earth surface reflectivity data with the space corresponding to the space of the second earth surface reflectivity data at the sampling points, respectively taking the second earth surface reflectivity data and the vegetation coverage as input and output of the training sample so as to train a generalized regression neural network; and finally estimating the global land surface vegetation coverage according to the second earth surface reflectivity data by using the trained model. The method for estimating the global vegetation coverage makes full use of ground actual measurement data, remote sensing observation data with high space resolution and artificial intelligence learning algorithm, and has the advantages of good stability, high adaptability, high accuracy, easiness in operation and the like.

The invention provides a method for obtaining a leaf area index based on quantitative fusion and inversion of multi-angle remote sensing data and multi-spectral remote sensing data, which is characterized in that a coefficient of a bidirectional reflectance distribution function (BRDF) of a vegetation type of the best matching pixel level of the multi-angle remote sensing data and a surface reflectivity is adopted, a surface soil reflectivity profile is obtained based on best matching of the multi-spectral data, and a canopy radiation transmission model is driven to obtain the leaf area index with high accuracy and large-scope coverage based on the multi-spectral data. The invention has the advantages that: the ranges of wave bands of the multi-angle data and the multi-spectral data need not to be overlapped, and approximate treatment can be carried out by adopting the similarity of the bidirectional function of the available wave bands; the coefficient of the bidirectional reflectance function and the best matching vegetation type obtained based on the multi-angle data is relatively stable along with changes in the time and the space, time sequence data can be made into a background library to be used as input for inversion of the multi-spectral data, and thus the large-scale leaf area index with high time resolution can be obtained; and the best matching surface soil reflectivity profile obtained based on the multi-spectral data is relatively stable, and historical time sequence data can also be made into a background library. The method can be applied in crop growth monitoring, rapid estimation of crop yields and the like.

The invention discloses an annual regional vegetation coverage calculation method. The method comprises steps that accurate radiation calibration, atmospheric correction and geometric registration arecarried out for all remote sensing images of the same annual region, and a remote sensing image result set is acquired, based on the constructed cloud layer index, the shadow index, the background index and the vegetation index, the corresponding special information is extracted; remote sensing images of different time phases are processed through utilizing the de-cloud algorithm and the de-shadow algorithm respectively; spatial synthesis of the single phase vegetation index information is carried out; fusion of the annual multi-phase information is carried out, the spatial distribution map of the annual regional vegetation index information is calculated; and lastly, the spatial distribution map of the annual regional vegetation coverage information is calculated. The method is advantaged in that influence of weather, terrain, phase, vegetation type and environment factors on the vegetation coverage rate is eliminated, an annual regional vegetation coverage calculation problem is solved, accuracy and reliability of regional vegetation coverage calculation are improved, the operation process is simple and flexible, and the method is easy for popularization and application in the regional scale.

The invention relates to the field of ecological environment monitoring, and discloses a vegetation classification method based on a machine learning algorithm and multi-source remote sensing data fusion, which is used for efficiently realizing identification and classification of vegetation types in a target area. The method comprises the following steps: acquiring a low-altitude remote sensing image of terrestrial plants in a sample area by using an unmanned aerial vehicle, and acquiring a digital orthoimage and a digital surface model of the sample area based on the low-altitude remote sensing image; extracting elevation information of the digital surface model; acquiring an SAR image of a sample region corresponding to the aerial photography time of the unmanned aerial vehicle by utilizing satellite remote sensing; carrying out wave band and image fusion on the digital orthoimage, the elevation information and the SAR image; performing inversion model training and inversion model precision evaluation on the fused image through sample area actual measurement data and a machine learning algorithm to obtain an inversion model meeting requirements; and finally, classifying terrestrial plants in the target area based on the inversion model. The method is suitable for terrestrial plant ecological environment monitoring.

A turf protection utilization method is provided for turf protection utilization programming and planning, and comprises the following steps: delimiting a turf protection level according to nature vegetation types and coverage; delimiting a vegetation retrieval level according to rainfall, temperature and soil factors; carrying out field investigation to check two said levels; superposing and delimiting a plurality of ecology control sections; determining the turf excavating technology according to the vegetation types and coverage in each ecology control section; selecting three protection technologies according to road engineering construction progress and turf utilization time intervals, wherein the three technologies include piled stacking, layered stacking, and skeleton layered temporary planting; determining three planting modes according to wind erosion and water erosion characteristics and propagation coefficients, wherein the three planting modes include a chessboard type, a tic-tac-toe mode and a bar stripped mode. The method can scientifically unify turf protection and utilization planning, storage and utilization, thus providing best ecology benefits; the turf protection utilization method is practical in applications.

The invention discloses a water-keeping method for a backfill soil layer used in recovering green for a waste mine or a re-cultivating filling type land. The method comprises: determining bottom elevation of a seepage-proof layer according to the terrain of the areas to be recovered green in the waste mine, determining top elevation of a planting layer according to requirement of terrain design, and determining thicknesses of the planting layer and a water-keeping layer according to types of vegetation to be planted; and filling a solid waste foundation, a rigid seepage-proof layer, a plastic seepage-proof layer, the water-keeping layer and a planting soil layer from top down in turn. As the design of the water-keeping layer is adopted, after survival of the vegetation, only less water even no water is needed to meet the demand of moisture by the vegetation, so the method ensures long-term effect of biological recovery and is suitable for recovering green of waste mines in arid regions and semi-arid areas. Whether the solid waste foundation is filled stably or not does not influence the effect of water keeping so as to save time for the construction. The water-keeping method is simple and easy to construct, can increase water keeping space and effect, and can greatly reduce use of soil for planting.

The invention discloses a vegetation coverage extraction method and system. The method comprises the steps of obtaining an aerial image collected by an unmanned aerial vehicle; De-noising processing is carried out on the aerial image; Carrying out image enhancement processing on the aerial image after noise removal; Calculating a vegetation index of the aerial image after image enhancement processing to obtain a vegetation index image; And taking the discrimination function of the information entropy of each surface feature category as a fitness function, and segmenting the vegetation type image from the vegetation index image by using a genetic algorithm, thereby obtaining the vegetation coverage. According to the vegetation coverage extraction method and system, the stability of image shooting and the precision of vegetation coverage extraction can be improved.

The invention relates to forest fire prevention, in particular to a forest fire risk assessment method based on space Logistic. The method comprises: 1) collecting a surface temperature data set; 2) collecting a normalized vegetation index data set; 3) collecting a GDP spatial distribution data set and a vegetation type spatial distribution data set; 4) collecting a DEM digital elevation data setand a ground climate data monthly value data set; 5) calculating a vegetation drought index through a formula; 6) establishing a spatial Logistic forest fire risk probability model; and 7) evaluatingthe forest fire risk of the to-be-detected area. According to the invention, forest fire monitoring data of the Chinese forest grassland fireproof net is used as basic data; data such as elevation (GC), gradient (PD), temperature (TEM), rainfall (RAIN), temperature vegetation drought index (TVDI), gross domestic product (GDP), vegetation type (ZB) and the like are used as forest fire danger influence factors. A spatial Logistic forest fire risk probability model is constructed, forest fire risk division can be carried out in a provincial domain range, and a scientific basis is provided for forest fire risk prevention and risk decision making.

The invention discloses a remote-sensing-based method for estimating influences of area vegetation cover on the earth surface air temperature, and relates to a method for simulating the influences of area vegetation cover on the earth surface air temperature. The method aims at solving the problem that existing research related to the feedback of vegetation to climate can not be combined with changes of vegetation cover along with time, and includes the steps of 1, obtaining data of a cover research area, and carrying out preprocessing; 2, extracting the distribution of a certain vegetation type without land use changes within a research time period; 3, achieving anomaly processing of data of earth-surface air temperature observing lattice points; 4, obtaining OMR air temperature values of the lattice points; 5, rasterizing OMR air temperature data; 6, calculating pixel NDVI change trend values and pixel OMR air temperature change trend values; 7, extracting NDVI annual average values, the NDVI change trend values and the OMR air temperature change trend values of all pixels within the whole research time period; 8, building an OMR air temperature forecasting model. The remote-sensing-based method is applied to the field of eco-climate researching.

The invention provides a construction method of a regional scale double-source evapotranspiration model considering dynamic vegetation change. The construction method comprises the following steps: S1, performing regional evapotranspiration estimation of the double-source evapotranspiration model: calculating net radiation data of a required regional scale input by a model; calculating net radiation components Rns and Rnc; calculating each influence factor; performing simulated estimation on evapotranspiration ET; s2, carrying out parameter optimization under different vegetation types; s3, performing simulation effect verification of the regional scale double-source evapotranspiration model considering dynamic vegetation change: performing site scale verification; performing regional scale verification; and performing basin scale verification. According to the model disclosed by the invention, the dynamic change of the vegetation is incorporated into evapotranspiration calculation andanalysis, and meanwhile, the model is expanded to a regional scale, has a multi-scale analysis function and has the advantage of few aerodynamic parameters of the PT-JPL original model, has low requirements on ground data and aerodynamics, and plays a role in promoting the accuracy improvement of regional scale evapotranspiration estimation.

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 embodiment of the invention discloses a basin vegetation drought occurrence remote sensing early warning and water shortage estimation method, which comprises the following steps: step 1, spatialdata processing and information extraction: extracting multi-scale basin information, vegetation type information, runoff information, rainfall information and soil moisture information; step 2, carrying out drought stress early warning, calculating a water resource quantity variable quantity and a same-period vegetation variable quantity, and carrying out drought early warning according to the instantaneous evapotranspiration index; and step 3, estimating the drought and water shortage of the vegetation, estimating the water consumption in the specific time period of the monitoring year according to the historical average water consumption for many years in the specific time period, and estimating the water shortage in the specific time period of the monitoring year. According to the method, the problems that stress early warning cannot be carried out on existing watershed vegetation drought, and the multi-scale watershed water shortage estimation precision is not high are solved.

The invention discloses a vegetation type cement building block prefabricating and using method for water bank economic protection. The building block comprises a hollow lump hexahedron of which one side surface is at least provided with one cross through slot; the other five surfaces are provided with holes having different apertures and communicated with the inner cavity of the hexahedron; the top of the main body is an openable movable top cover; the top corner of every three surfaces is provided with a round ring buckle; when the building blocks are spliced up and down and left and right, the ring buckles are serially connected and locked together by a large ring buckle with an opening and closing device, thus forming the whole water bank into a whole and achieving considerable flexibility. The building block is formed by mixing vegetation type cement, crushed stone, sand and water through prefabrication and pouring. A composite medium is injected in the hollow inner cavity of the building block which is built at the water bank, the side surface, provided with the cross through slot, of the building block is of a waterward surface and can be planted with plants, and the medium in the inner cavity can provide fertilizer and purify water quality.

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 method for evaluating the spatial representativeness of station LAI (Leaf Area Index) observation in a remote sensing product pixel scale. The method includes the following step that: (1) corresponding high-spatial resolution images in station observation are obtained, and reflectivity data are obtained; (2) an LAI and reflectivity lookup table is generated by using a plant canopy physical model, a corresponding LAI high-resolution image corresponding to the station observation is generated based on the reflectivity data, and the LAI high-resolution image is evaluated, so that an LAI high-resolution image with a certain precision can be obtained; (3) a station observation vegetation type representative evaluation index DVTP (Dominant Vegetation Type Percent) and the representative evaluation indexes RSSE (Relative Spatial Sampling Error) and CS (Coefficient of Sill) of the station observation LAI (Leaf Area Index) for vegetation growth in the product pixel scale are calculated; and (4) the levels of the spatial representativeness of the station LAI observation in the product pixel scale are divided into L0 to L4 according to the maximum dividability principle of representative errors of different levels. The method of the invention has the advantages of high verification precision, high reliability, simple evaluation index calculation mode, and easiness in realization, and can provide references for LAI products in climate change research, agricultural production estimation, environmental monitoring and the like.

The invention discloses a regional carbon flux estimation method based on remote sensing data. A mesoscale meteorological model (WRF) is used to output results, to provide spatial information within atarget region range and required meteorological field data for a vegetation photosynthetic respiration model (VPRM). High-precision vegetation type data (SYNMAP with a resolution of 1 km) is extracted, to classify land use types in a target area; an enhanced vegetation index (EVI) and a land surface water index (LSWI) are obtained by inversion of medium-high-resolution remote sensing data (MODIS09A1); parameters such as the EVI and the LSWI are placed in a target area grid by a spatial difference method; for different regional characteristics, more than one year of historical observation datais used, and these parameters are optimized by using a light response equation (Michaelis-Menten); and finally, based on the data, the carbon flux calculation is performed on the target area using the VPRM model.

The invention provides a desertificated land remote sensing rapid identification method and belongs to the technical field of remote sensing monitoring. The method comprises the following steps: obtaining data and carrying out data pretreatment; extracting non-vegetation type information from the pre-processed data; carrying out processing on the extracted non-vegetation type information, including extracting water body and building land type information; extracting naked land type information; and analyzing and identifying desertificated land. The method is high in data source time series andhigh in time resolution, is short in monitoring period with one year being a cycle, and can master the development or reversal of the desertificated land every year.

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 method and a system for analyzing a landslide disaster of a transmission pole tower based on an analogy method, The method comprises the following steps: obtaining the evaluation data in the region to be used for the landslide hazard early warning of transmission poles and towers. The evaluation data include: geological structure data, rock and soil type data, topographyand geomorphology data, surface vegetation type data, human activity data, landslide potential hazard point data confirmed by investigation and landslide hazard point data in recent 10 years; According to the evaluation data, the evaluation grade of landslide disaster risk degree is divided into gridded grades. Selecting the mesh of the evaluation grade of landslide hazard susceptibility, extracting the characteristic information data of hazard points and the characteristic information data around the transmission tower and comparing them one by one. According to the comparison results, the possibility grade of landslide hazard around the transmission tower is divided.

The invention discloses a method for detecting vegetation canopy sun leaves and shade leaves based on fluorescence remote sensing. The method comprises the steps that precise geometric correction andradiation calibration are conducted on a target remote sensing image; a fluorescence remote sensing vegetation identification index is established ad calculated; a vegetation spatial distribution mapis obtained by calculation through a K-means method; a remote sensing image map only containing vegetation information is obtained by adopting a spatial operation method; a vegetation sun-shade leaf fluorescence index is established ad calculated; and vegetation canopy sun-shade leaf information is extracted by adopting a remote sensing unsupervised classification method to obtain a vegetation sun-shade leaf spatial distribution map. The method for detecting vegetation canopy sun leaves and shade leaves based on the fluorescence remote sensing has the advantages that the influence of non-vegetation background information on a vegetation canopy sun-shade leaf detection result is eliminated furthest, and the accuracy and reliability of sun-shade leaf detection are improved; the problem of vegetation canopy sun leaves and shade leaves distinction is effectively solved, the detection precision is high, the operation processes are simple and flexible, the vegetation types are not limited, the manual interference is little, the automation degree is high, the universality is higher, and the promotion and application are easy.

The invention provides a protection method of vegetation in occupied land in the road construction period. The protection method includes the steps that firstly, the types of main vegetation in the area where a road passing through are recognized through interpretation and analysis of a remote-sensing image of a road route range; secondly, GPS point positions for spot plant sample investigation are arranged according to the types and the amount of the vegetation which each roadbed construction section passes through; thirdly, spot paying-off work is conducted, significant protection zones of trees and shrubs in the land occupied by a road are defined, and meanwhile spot plant sample investigation is conducted according to the set GPS point positions; fourthly, sample investigation data are counted, and relevant parameters are calculated; finally, a plant protection method is formulated according to calculation results. The method is based on macroscopic analysis, and overcomes the defect that understanding of the overall situation of the vegetation along the road in the early stage is not thorough enough, the situations of the detailed vegetation types, detailed vegetation distribution and the like can be known clearly through the spot sample investigation, therefore, the basis is provided for plant protection measures formulation, and it is guaranteed that the vegetation in the occupied land of the road can be protected and retained to the maximum limit.

The invention relates to an unmanned aerial vehicle remote sensing technology-based surface vegetation identification method and system, and a readable storage medium. The method comprises the steps of establishing an observation point location, generating an acquisition mode, generating an unmanned aerial vehicle formation according to the acquisition mode, and obtaining formation information; generating scheduling information according to the formation information, and performing position management and control on the unmanned aerial vehicle according to the scheduling information to obtainunmanned aerial vehicle position information; collecting a multi-angle vegetation remote sensing image according to the unmanned aerial vehicle position information, receiving an electromagnetic wavereflection 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 result information; judging whether a difference value between the result information and preset information is greater than a preset threshold value; and if yes, generating correction information, collecting the position information of the unmanned aerial vehicle through the correction information, and transmitting a correction result to the terminal.

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.

The invention relates to the field of grassland construction, and provides a steppe area derelict land vegetation maintenance method and a steppe area ecological restoration method. The maintenance method includes the steps: performing background survey on a derelict land; surveying the vegetation type of an unbroken section and building an indigenous plant bank; determining vegetation restorationapplications; turning over the land; applying basic fertilizers before tillage or in the tillage process; laying irrigation pipelines at the bottom of the derelict land; building fences on the periphery of the derelict land; selecting vegetation seeds from the indigenous plant bank and sowing the vegetation seeds on the derelict land; covering the seeds with soil and compacting the soil; loosening the soil of a hardened ground after sowing; applying mixed fertilizers to the derelict land after seedling emergence; irrigating the derelict land by the irrigation pipelines. According to the maintenance method, a damaged steppe ecosystem can be restored, and ecosystem functions are restored. The steppe area ecological restoration method includes the steppe area derelict land vegetation maintenance method.

The invention provides a method and a system for estimating annual GPP based on an EVI2 season change curve. The method comprises the following steps: acquiring an annual complete time sequence remotesensing image, quantitatively processing the remote sensing image to obtain time sequence ground surface reflectivity data, cutting the time sequence ground surface reflectivity data, extracting ground surface reflectivity data, performing waveband operating on near-infrared wavebands and red wavebands in the ground surface reflectivity data, calculating the EVI2 time sequence data of remote sensing images in every studying area pixel by pixel, filtering the EVI2 time sequence data, calculating a curve integral pixel by pixel according to the filtered EVI2 time sequence data in order to obtain sigma EVI2, extracting a pre-established estimation model between the sigma EVI2 and a GPP sample based on vegetation types, and estimating the annual GPP in the studying area based on the sigma EVI2 and the estimation model. The method and the system can achieve the effective estimation of the regional total GPP for different vegetation types based on the vegetation EVI2 annual time sequence curve integral.

The invention provides a forest grassland fire behavior discrimination method based on a remote sensing image and a vegetation type graph. The method is used for solving the problem that the combustion situation of a fire scene is not quantitatively evaluated in the prior art. According to the forest grassland fire behavior discrimination method based on the remote sensing image and the vegetationtype graph, GF-4 PMI data are utilized, a visible light near-infrared image and a medium wave infrared image are combined and then combined with the vegetation type graph, specific emissivity corresponding to the vegetation type is obtained to serve as specific emissivity of fire, the proportion of the burning vegetation, the area of the burning vegetation and the temperature of the fire are quantitatively calculated, the radiation intensity, the live wire intensity, the flame height and other parameters representing the fire behavior of the forest grassland fire are further calculated, and the fire behavior grade is judged based on the quantitative parameters. Quantitative, reliable and accurate data support of fire scene radiation energy is provided for fire fighting decision making offorest grassland fire disasters.