32 results about "Vegetation remote sensing" patented technology

The invention provides a regional crop yield estimation method based on ensemble Kalman filter assimilation. The advantages of remote sensing data and crop models are combined, EVIs generally used in vegetation remote sensing are used as observational variables, LAIs are used as assimilation variables, optimization adjustment is carried out on the model LAIs through an ensemble Kalman filter algorithm, a PROSAIL model is used as an observational operator, the problem that the observational variables and state variables are inconsistent is solved, assimilation of remote sensing information and the models is achieved, and errors due to the fact that inversion is carried out on the LAIs through reflectivity are avoided. Compared with the yield of crop with unassimilated EVIs, the RMSE of the yield of crop with the assimilated EVIs is reduced, the determination coefficient R2 is improved obviously, estimation precision of the crop model yield is obviously improved after assimilation, and the yield space distribution trend is consistent with the statistical yield.

The invention relates to the field of agricultural vegetation remote sensing, particularly to a wheat LAI (leaf area index) estimation method coupled with satellite-ground remote sensing. The method comprises steps as follows: 1) acquiring two different scales of canopy spectrum information, namely, a wheat ground high-spectrum and an SPOT-5 image; 2) fitting a wheat canopy high-spectrum and a ridge high-spectrum with a satellite sensor wave spectrum response function; 3) extracting a pure satellite pixel spectrum on the basis of a mixed pixel linear decomposition model; 4) meanwhile, verifying the pure satellite pixel spectrum by the aid of a synchronous wheat simulation pixel spectrum; 5) constructing a wheat LAI monitoring model coupled with satellite-ground remote sensing with related statistical methods. The method overcomes defects of existing ground remote sensing point scales and satellite remote sensing mixed pixels and is higher in precision and accuracy of estimation of wheat LALs at different nitrogen application levels in different ecological regions in particular, wheat LAI growth information is acquired in real time, and wide application of remote sensing based crop growth remote sensing monitoring technologies is facilitated.

The invention provides a method of utilizing a multi-angle observation system to obtain the vegetation canopy spectroscopic data to precisely inverting sunlight induced chlorophyll fluorescence of shade / sun leaves of a canopy, and belongs to the research field of vegetation remote sensing inversion parameter obtaining methods. The method comprises following steps: establishing a multi-angle super-hyperspectral observing system; obtaining multi-angle super-hyperspectral data; calculating the solar incident angle and canopy reflection brightness; calculating the reflection rate and inverted chlorophyll fluorescence; using a leaf clamp to observe the leaf reflection rate; utilizing the ratio of canopy reflection rate to leaf reflection rate, under the assistance of a geometrical optical model, calculating the ratio of shade / sun leaves from different observation angles, and obtaining the fluorescence of the sun leaves and shade leaves through fitting of least square method. The provided method can obtain continuous multi-angle vegetation canopy super-hyperspectral data, is used to invert chlorophyll fluorescence, can simply and effectively calculate the ratio of sun leaves and shade leaves of a canopy from different observation angles and solar incident angles based on the leaf reflection rate and a geometrical optical model, calculates the fluorescence of the sun leaves and shadeleaves, and improves the precision of monitoring the primary productivity of a land.

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 provides a landslide disaster monitoring device, which comprises a vegetation remote sensing monitoring device, a seismic wave monitoring device, a groundwater monitoring device, a rainfall monitoring device, a determining device, a slope monitoring device, a data analysis device, a data transmitting device and an early warning device. The invention also provides a monitoring methodfor the landslide disaster monitoring device. The method comprises the steps of: acquiring the geological data of a landslide monitoring area, the geological data including vegetation image data, seismic wave data, groundwater data and rainfall data; determining whether a landslide may occur according to the geological data, if so, using the slope monitoring device to monitor the displacement of the slope that may cause the landslide, using the data analysis device to analyze the displacement of the slope to determine whether a landslide occurs, and if a landslide occurs, enabling the data analysis device to give an early warning signal; and receiving the early warning signal by the data transmitting device and converting the early warning signal into an intervention processing alarm and transmitting the intervention processing alarm to the early warning device. The landslide disaster monitoring device has high monitoring efficiency.

The invention provides an eutrophic water aquatic vegetation remote sensing extraction method based on an alga index frequency method. The eutrophic water aquatic vegetation remote sensing extraction method includes S1, selecting MODIS (moderate resolution imaging spectroradiometer) images of eutrophic water with aquatic vegetation luxuriant and area stable at different periods; S2, subjecting the MODIS images selected to FAI (floating alga index) calculation; S3, according to the FAI, calculating VPF (vegetation signal appearance frequency) of pixels j at the periods; S4, defining VPF threshold values of all periods and calculating the aquatic vegetation area Aav. The eutrophic water aquatic vegetation remote sensing extraction method has the advantages that the influence of floating alga to aquatic vegetation remote sensing inversion is eliminated, and by integrating typical period dividing, frequency judgment and satellite image judgment, the influence of the floating alga and perennial alga bloom areas to the accurate aquatic vegetation area remote sensing inversion is eliminated.

The invention provides a green healthy vegetation remote sensing recognition index construction method, which comprises the following steps of: acquiring a surface reflectance image of an optical satellite image through a cloud platform, and acquiring spectral information of green healthy vegetation, withered and fallen vegetation and other ground feature samples by utilizing spectral acquisition software; the spectral difference between the green healthy vegetation and the withered and fallen vegetation and other ground objects in the satellite image is analyzed to obtain that the green healthy vegetation shows high reflectivity in a near-infrared band and shows low reflectivity in a red band and a short-wave infrared band, and accordingly, a green healthy vegetation remote sensing index algorithm is constructed, and the green healthy vegetation remote sensing index is calculated. And determining a green healthy vegetation index to identify a threshold value of the green healthy vegetation. According to the method, the technical problem that the green and healthy vegetation and the withered and deciduous vegetation are difficult to accurately distinguish is solved, and a technical scheme and theoretical support are provided for realizing green and healthy vegetation spatial distribution drawing, disease and pest monitoring and the like.

The invention provides a crop planting pattern classification method and system considering phenological characteristics, and relates to the field of vegetation remote sensing. By making full use of the difference of EVI time series curves in key phenological periods in the identification of planting pattern types, and by enhancing the weight of key phenological periods, the information of key phenological periods can be effectively used, and the information of non-critical phenological periods can be suppressed to improve the quality of crops. Accuracy of planting pattern classification.

The invention discloses a method for obtaining spatial distribution of desert riparian forests based on GIS buffer analysis, and belongs to the technical field of vegetation remote sensing monitoring in arid regions. It includes the following steps: Step 1: Download the TM remote sensing image of the river in the arid area, perform image preprocessing, remote sensing interpretation, and NDVI calculation on it, and obtain the NDVI remote sensing data of the desert riparian forest; Step 2: Extract the river bank boundary vector line, and remote sensing The images are uniformly registered and corrected under the GIS platform; Step 3: Generate multiple buffer vector maps around the river according to the research requirements; Step 4: Overlay the multiple buffer vector maps with the NDVI remote sensing data of the desert riparian forest, and extract each The mean value of NDVI in the buffer zone, with the vertical channel distance as the independent variable and the mean NDVI as the dependent variable, was used to analyze the horizontal spatial distribution structure of desert riparian forest by curve fitting. The invention provides a beneficial reference for determining the spatial continuous gradient distribution of the desert riparian forest and its protection range.

The invention provides a method of utilizing a multi-angle observation system to obtain the vegetation canopy spectroscopic data to precisely inverting sunlight induced chlorophyll fluorescence of shade / sun leaves of a canopy, and belongs to the research field of vegetation remote sensing inversion parameter obtaining methods. The method comprises following steps: establishing a multi-angle super-hyperspectral observing system; obtaining multi-angle super-hyperspectral data; calculating the solar incident angle and canopy reflection brightness; calculating the reflection rate and inverted chlorophyll fluorescence; using a leaf clamp to observe the leaf reflection rate; utilizing the ratio of canopy reflection rate to leaf reflection rate, under the assistance of a geometrical optical model, calculating the ratio of shade / sun leaves from different observation angles, and obtaining the fluorescence of the sun leaves and shade leaves through fitting of least square method. The provided method can obtain continuous multi-angle vegetation canopy super-hyperspectral data, is used to invert chlorophyll fluorescence, can simply and effectively calculate the ratio of sun leaves and shade leaves of a canopy from different observation angles and solar incident angles based on the leaf reflection rate and a geometrical optical model, calculates the fluorescence of the sun leaves and shadeleaves, and improves the precision of monitoring the primary productivity of a land.

The invention discloses a vegetation parameter fitting method based on middle-high resolution remote sensing. Due to the facts that used coarse resolution remote sensing data can be acquired easily, time resolution is very high, free shared data and products can be obtained, and the growth and development law difference of different vegetation types and the growth and development law difference in the vegetation of the same type are also used, when a study urges for the vegetation parameter in certain time, however, only the remote sensing data in another time can be acquired, through the method, the vegetation remote sensing parameter in the needed time can be simulated, and remote sensing study methods are enriched. For the reason that the middle-high resolution remote sensing is used for regional scale study, through the method, remote sensing data acquired at the study area in different time can be unified to the time which is needed by the study, and therefore the remote sensing data covering the entire study area in the same time can be acquired. Cloudless middle-high resolution remote sensing data in the needed time can reappear, and thus necessary data support can be provided for vegetation remote sensing correlation study work such as ecological remote sensing, environmental remote sensing and agricultural remote sensing.

The invention belongs to the technical field of vegetation remote sensing, and discloses a method model based on Bayesian theory to obtain high time-series EVI index, comprising the following generation steps: (1) constructing MODIS EVI prior information: combining MODIS surface classification data and land use data in Superimpose in space, judge and extract MODIS pixels of each category; combine MODIS reflectance band data and its QC quality control band to obtain EVI mean time series; carry out filter reconstruction; (2) generate EVI initial value: use Bay The Yasian theory introduces the MODIS EVI prior information; combined with the land use map, the MODIS EVI prior information and the MODIS EVI observation value are decomposed into mixed pixels; (3) Generate Landsat EVI prediction value and use EVI data to predict The model uses the Landsat EVI observations at paired moments to reconstruct the initial value of EVI. Based on Bayesian theory, the present invention fuses Landsat high spatial resolution data and MODIS high temporal resolution data, and finally obtains EVI data with high temporal and spatial resolution.

The invention discloses a method for extracting vegetation information from aerial photographs of cooperative remote sensing images, and relates to the technical field of image processing. An effective method for vegetation remote sensing monitoring is established by using aerial photographs and remote sensing images containing visible light bands to extract vegetation information. , to be applied to the plant identification research of destination aerial photos, to extract vegetation information quickly and relatively accurately.

The invention provides a method for extracting under-forest vegetation coverage under a complex terrain condition by using ground laser radar data, belongs to the research field of a vegetation remote sensing inversion parameter method, and aims to solve the problem that the under-forest vegetation coverage cannot be rapidly and effectively extracted under the complex terrain condition at present. According to the method, a ground point is extracted via height information, and on the basis of the ground point, the coverage of the vegetation under the forest is estimated by using a directional slicing method. The method can be used to accurately extract the coverage of the vegetation under the forest under the complex terrain condition, is high in precision, and is has great significance in recognition of erosion of soil under the forest and research of forest carbon cycle.

The invention provides a leaf reflectance satellite remote sensing extraction method for eliminating influence of vegetation canopy structures and earth surface backgrounds and belongs to the field ofresearch on vegetation remote sensing retrieval parameter methods. The method comprises the following steps: by using a 4-Scale model, confirming visual probability (PT) of sun leaves corresponding to a remote sensing image pixel spectrum and visual probability (PG) of a lighting background; calculating an angle index (AI) of the spectrum, performing related analysis on PT and PG, and establishing an estimation model based on Al, PT and PG; by taking a leaf area index (LAI), PT and PT as three retrieval items, establishing a checking table (related to wavelengths) for multi-time scattering factors M in a simulation manner by using the 4-Scale model; finally, by using the established estimation model and the checking table, calculating average leaf reflectance of remote sensing image vegetation pixels. By adopting the method provided by the invention, reflection spectrums of leaves can be extracted through remote sensing images; compared with an optimal iterative computation method, the method is high in calculation efficiency, and compared with a table checking method with multiple steps of checking, the method is relatively simple in calculation process and relatively high in efficiency.

The invention provides an eutrophic water aquatic vegetation remote sensing extraction method based on an alga index frequency method. The eutrophic water aquatic vegetation remote sensing extraction method includes S1, selecting MODIS (moderate resolution imaging spectroradiometer) images of eutrophic water with aquatic vegetation luxuriant and area stable at different periods; S2, subjecting the MODIS images selected to FAI (floating alga index) calculation; S3, according to the FAI, calculating VPF (vegetation signal appearance frequency) of pixels j at the periods; S4, defining VPF threshold values of all periods and calculating the aquatic vegetation area Aav. The eutrophic water aquatic vegetation remote sensing extraction method has the advantages that the influence of floating alga to aquatic vegetation remote sensing inversion is eliminated, and by integrating typical period dividing, frequency judgment and satellite image judgment, the influence of the floating alga and perennial alga bloom areas to the accurate aquatic vegetation area remote sensing inversion is eliminated.