32 results about "Vegetation remote sensing" patented technology

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 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.