82 results about "Normalized Difference Vegetation Index" patented technology

The invention discloses an environmental satellite 1-based surface temperature single-window inversion method. The method comprises the following steps of: 1, acquiring remote sensing data of an environmental satellite 1B, namely an HJ-1B satellite, of the surface of a region to be tested, preprocessing the remote sensing data to acquire image data and converting a pixel brightness digital number (DN) value of the image data into radiance; 2, performing atmospheric correction on the image data by using an image-based continental offshore stratigraphic test (COST) atmospheric correction model; 3, estimating emissivity epsilon of the surface of the region to be tested by a normalized difference vegetation index (NDVI) threshold method; and 4, inverting the surface temperature by a single-window algorithm according to calculation results obtained in the steps 1 to 3. In the method, the image-based COST model atmospheric correction method and the surface temperature single-window algorithm with no need of an atmospheric water vapor content parameter are applied to the HJ-1B satellite remote sensing data, so that the inversion of the surface temperature is realized.

The invention discloses a land surface soil moisture downscaling method based on multisource remote sensing satellite merged data and is particularly applicable to cloudy and rainy areas. The method comprises the following steps: collecting and arranging a passive microwave soil moisture data set, an LST (land surface temperature) and NDVI (normalized difference vegetation index) data set and DEM(digital elevation model) data; performing spatial interpolation on LST images with serious pixel deletion affected by cloud and rain by adopting an NDVI and DEM data set as auxiliary data to obtain aday-by-day LST data set almost totally covering a research area; constructing a mathematic relation model for microwave soil moisture with optical remote sensing LST and NDVI by a geographical weighted regression model, and obtaining a land surface soil moisture data set with high spatial resolution with the mathematic relation model. Through the adoption of the method, reliability of a descalingresult and universality of descaling in the wide-range research area are improved effectively, and precision and efficiency of wide-range space mapping and monitoring for soil moisture content in thecloudy and rainy areas are improved.

The invention discloses a snow disaster remote sensing monitoring simulation evaluation method based on a disaster reduction small satellite, comprising the following steps: recognize accumulated snow according to the spectral characteristic of the accumulated snow and form accumulated snow remote sensing images data; create a snow disaster case database and a snow disaster statistical database, the snow disaster case database comprises a disaster time, a location hit by the disaster, an accumulated snow day number, a normalized difference vegetation index NDVI, a population number hit by the disaster and a accumulated snow area, the snow disaster statistical database comprises a total precipitation, a highest air temperature, a lowest air temperature, a average daily temperature, a wind speed and a surface temperature; carry out an accumulated snow depth inversion to a recognized accumulated snow, and respectively store the accumulated snow depth into the snow disaster case database and the snow disaster statistical database; carry out simulation evaluation to a snow disaster according to the data in the snow disaster case database and the snow disaster statistical database. A new and high applicability remote sensing data source is provided for snow disaster monitoring in the invention, and the snow disaster remote sensing monitoring simulation evaluation method has a great importance of disaster reduction and disaster relief works for China snow disaster.

The invention discloses a grassland dry matter mass remote sensing estimating method, which belongs to the field of remote sensing application. The method comprises the following steps of: collecting and processing field test data; downloading and processing Modis (Moderate Resolution Imaging Spectroradiometer) satellite data; analyzing and modeling the data and detecting the accuracy and the like. The method for establishing a ground spectrum model for predicating ground dry matter mass and correcting a Modis spectrum model through ground hyperspectral experiments is different from the method for directly predicating biomass liveweight by using NDVI (Normalized Difference Vegetation Index) data of the Modis remote sensing satellite in other like researches so that the grassland dry matter mass remote sensing estimating method is helpful for improving the predicating accuracy, is beneficial for scientific management and reasonable utilization of the grassland resources and has important practical significance in correct evaluation of the real productivity of the grassland and the sustainable use of the grass resources.

The invention discloses a point location data and remote sensing image data-based regional farmland quality monitoring method. The method comprises the following steps of: S1, acquiring a normalized difference vegetation index (NDVI) time sequence data, and performing smoothing processing; S2, extracting classification characteristics and classifying crop planting modes according to the classification characteristics; S3, performing principal component analysis on an NDVI time sequence in different planting modes, and extracting a plurality of principle components; S4, performing the principal component analysis on indexes which reflect farmland quality by utilizing point location data, extracting the principle components, calculating farmland quality index numbers, and performing quantitative evaluation on the farmland quality of a monitoring point; S5, respectively performing correlation analysis on the farmland quality index numbers and the principal components of the NDVI time sequence in different planting modes by utilizing the point location data; and S6, establishing a regression model by utilizing the point location data according to the farmland quality index numbers and a first principal component in different planting modes to obtain a farmland quality grade distribution graph. The method can completely reflect farmland quality grade distribution essentially and can acquire good differentiation.

The invention discloses a regional earth surface sensible heat/latent heat flux inversion method based on remote sensing data. The method comprises the following steps that a research area and the remote sensing data are determined; remote sensing earth surface and regional meteorological parameters are prepared according to the remote sensing data, wherein the remote sensing earth surface parameters comprise a normalized difference vegetation index NDVI, vegetation coverage f, albedo, earth surface emissivity Emiss, earth surface temperature Ts and a leaf area index LAI, and the regional meteorological parameters comprise air temperature Ta and relative humidity RH; net radiation flux Rn inversion is conducted according to the remote sensing earth surface and regional meteorological parameters; soil heat flux G inversion is conducted according to the leaf area index LAI, the air temperature Ta and net radiation flux Rn; radiation-convection impedance rae inversion is conducted according to theoretical two-dimensional space of the net radiation flux Rn, soil heat flux G, the vegetation coverage f and the earth surface temperature Ts and a temperature profile equation estimated through sensible heat flux; regional earth surface sensible heat H/latent heat LE flux inversion is achieved according to radiation-convection impedance rae.

A handheld sensor is disclosed. The sensor has a microcontroller, a current pulse control unit coupled to a light emitting diode (LED), and a photodiode. The microcontroller controls the current pulse control unit to provide a pulsed illumination of a target plant and the photodiode reads the magnitude of the reflectance from the target plant. The microcontroller accepts the reading from the photodiode and computes a normalized difference vegetative index (NDVI) based at least on the reading.

The invention discloses a method for predicting nitrogen content of cucumber leaf based on spectral image analysis. The method is characterized by comprising the following steps of: adding an infrared and red light filter with a characteristic wave length in front of the lens of a CCD (charge coupled device) camera, and firstly collecting a near-infrared and red light image of the cucumber leaf to obtain the gray value of the cucumber leaf; measuring the vegetation reflection range RIR at the characteristic wave length of the infrared light and the reflection rate RR of the cucumber leaf at the infrared characteristic wave length through an empirical formula; obtaining a normalized difference vegetation index (NDVI) according to the reflection rate values RIR and RR of the cucumber leaf at the infrared characteristic wave length and a red light wave segment; and judging the nitrogen content level of the cucumber leaf according to the NDVI so as to realize the prediction on the nitrogen content of the cucumber leaf. By the method, the nutrient state of crops can be obtained visually and effectively, and the nitrogen content of cucumber leaf can be predicted quickly and visually.

The invention discloses a multi-source remote sensing image radiometric normalization method. The method is characterized in that relative radiometric normalization of a multi-source remote sensing image is divided into a sensor radiation correction process and a radiometric normalization process specific to external factors such as illumination. The method comprises the following steps: S1, acquiring a sensor radiation correction coefficient in a categorical regression way based on a clear sky image; S2, implementing semi-automatic classification and sensor radiation deviation correction of the multi-source image by a sample transmitting and reclassifying method; and S3, implementing relative radiometric normalization of the image through a PIFs (Pseudo Invariant Features) automatic selection method based on an NDVI (Normalized Difference Vegetation Index) histogram of differences and a category constraint. Through adoption of the multi-source remote sensing image radiometric normalization method, radiation deviations among sensors are effectively corrected, and higher radiometric normalization accuracy is achieved as a whole than a conventional method. Meanwhile, radiation feature fluctuation among time-sequence images can be eliminated effectively through the method, so that aspect change information of land such as vegetative can be expressed more accurately, and a reference method is provided for cooperative utilization of the multi-source time-sequence images.

The invention discloses a method for building high-spatial resolution NDVI (normalized difference vegetation index) time series data. The high-spatial resolution NDVI time series data is predicted and built according to low-spatial resolution MODIS (moderate-resolution imaging spectroradiometer) pixels in known MODIS NDVI data and high-spatial resolution TM (thematic mapper) pixels in TMNDVI (thematic mapper normalized difference vegetation index) data. TM data is combined with MODIS data, and accordingly high-spatial resolution NDVI time series data with quite fine precision can be obtained effectively.

The invention relates to a method for remotely sensing and quantitatively monitoring steppe vegetation coverage space-time dynamic change based on normalized difference vegetation index (NDVI) data, and belongs to the technical field of vegetation coverage dynamic remote sensing and monitoring. In order to solve the problem that the quantitative research on remote sensing and monitoring of steppe vegetation coverage dynamic change is rare, the quantitative monitoring method provided by the invention comprises the following steps of: calculating to obtain six NDVI indexes, such as an NDVI yearly value, a yearly maximum value, a yearly minimum value, a yearly maximum value appearance date, a yearly minimum value appearance date and a season dynamic, which represent a steppe vegetation coverage situation, by using an NDVI time sequence file; and quantitatively monitoring a space-time dynamic change process, a phenological characteristic and a dynamic change tendency of steppe vegetation coverage by calculating a yearly dynamic change rate of the NDVI indexes within a certain time period and performing trend analysis by a Mann-Kendall method. The method is applicable to all vegetation NDVI data.

The invention discloses a spatial restoration analysis method for marsh wetland in a river basin. The method comprises the steps of firstly performing data collection: collecting TM remote sensing image data of the river basin, meteorological data, utilization data of land in the river basin, DEM (digital elevation model) data, NDVI (normalized difference vegetation index) data, LAI (leaf area index) data, and river and road vector data; secondly selecting a hydrology adjustment function and an aquatic bird habitat function of the marsh wetland in the river basin as marsh wetland restoration evaluation indexes; and finally performing marsh wetland restoration analysis. According to the method, function characteristics of the marsh wetland are fully considered, so that the functions of the marsh wetland can be brought into full play while the ecological environment of the marsh wetland is improved; and in addition, marsh wetland restoration of a large-range region can be realized, and a marsh wetland restoration region can be accurately determined, so that the convenience is brought for marsh wetland restoration project implementation.

The invention discloses a building shadow extraction processing method for a sentinel 2A image, and the method comprises the steps of S1, extracting the main ground feature component of an urban area,designing a shadow enhancement index based on a sentinel 2A original spectrum to enhance a building shadow; calculating a normalized difference water body index to enhance the water body; extractingan original spectral band Band 8 of the sentinel 2A to enhance other dark reflectivity ground objects; S2, based on the three extracted characteristic components, constructing a combined shadow indexto highlight a building shadow; and selecting an optimal threshold value by using an OTSU method to obtain a final urban building shadow distribution map. By the adoption of the technical scheme, thedifference between the shadow and other ground objects can be remarkably increased, the phenomenon that urban building shadow, water and dark reflectivity ground objects are mixed is effectively avoided, and the extraction precision of the urban building shadow is improved.

The invention relates to the field of cultivated land identification, and specifically relates to a method and a device for identifying effective cultivated land. According to the method and the device, Landsat TM remote sensing data of a long time sequence are used as a data source; Slope SLOPE data are used as auxiliary data. A normalized difference vegetation index NDVI is adopted as a classification feature. The phenological difference of crops on the agricultural cultivated land is taken as a basis; difference between crop interspecific planting periods is obtained; Characteristic differences such as spectral differences are reflected on a distribution change rule of time sequence NDVI data. An object-oriented decision classification rule combining a normalized difference vegetation index NDVI with other classification characteristics is designed. Spatial distribution information of part of crops on the agricultural cultivated land, especially the agricultural cultivated land in the western region, can be extracted. According to the method, the cultivation condition can be known, the influence of different crops in the complex planting period does not need to be considered, data sources needed by the method are easy to obtain, the data size needed to be processed is small, the classification rule is simple, the working efficiency is high, and the method is also suitable for regions with crushed cultivated land distribution.

The invention discloses a forestry disease monitoring method and device based on an unmanned aerial vehicle. By loading a dual-light camera integrated by a visible light camera and an infrared cameraon the unmanned aerial vehicle, aerial photography monitoring is carried out on a forest in a to-be-monitored area; by analyzing aerial photography data and fully combining with image information of visible light and orthophoto images of normalized difference vegetation indexes, obviously healthy or dead trees are screened out on the basis of the image information of the visible light; then, by combining with NDVI indexes for further judgment, information about the dead trees and ill trees in the to-be-monitored area is thus determined quickly, accurately and efficiently. The current situations that the judgment efficiency is not high and the accuracy is low since identification on forestry diseases only relies on utilization of visible light images or the NDVI indexes are overcome, and therefore the problem that when the forestry diseases and forestry pest disasters happen in the prior art, the ill trees and the dead trees cannot be quickly and accurately found out is solved.

The invention belongs to the technical field of remote sensing image processing, and relates to a green tide information extraction method. The method comprises: obtaining a sea area satellite remote sensing image with a conventional method, screening images of green tide regions, and performing preprocessing operations of geometric correction and image mosaicking; calculating and determining a green tide range by adopting a normalized difference vegetation index; cutting a region of interest in any shape to generate an irregular image file containing the green tide regions; extracting green tide information by adopting a split Bregman fast projection algorithm of a variational level set two-phase image segmentation based Chen-Wees model; and finally, quantitatively calculating the green tide regions by adopting a quantitative formula. The image segmentation based variational model accurately extracts and quantifies the green tide information on the satellite remote sensing image, so that a conventional manual threshold method can be completely replaced and operational application can be realized; and method is scientific and reasonable in principle, low in human factor quantity, high in calculation speed, accurate and stable in result, high in operability, friendly in application environment, high in practicality and easy to popularize.

The invention discloses a method for removing cloud noise effects in a normalized difference vegetation index (NDVI) time sequence image. The method comprises the following steps of: S1, generating an annual NDVI time sequence image according to NDVI data of every ten days; S2, carrying out least square fitting on all time sequence pixels in the NDVI time sequence image, and assigning same values to weights of all points relative to a curve; S3, comparing an observed value with a fitted value, and eliminating points under negative cloud action; S4, repeating the steps S2 and S3, eliminating all the points under the negative cloud action, and generating a new curve; and S5, post-processing the curve obtained in the S4 to obtain a finally fitted curve. In the method disclosed by the invention, by utilizing an NDVI time sequence file and a harmonic function analysis method based on the least square fitting, cloud shielding and atmospheric effects on a sensor in a data acquisition process are effectively removed, the time sequence image with cloud contamination removed can be generated, the obtained curve has the advantages of obvious trend, strong relative property among years and high precision, and the method has a wide applicable range.

The invention discloses a winter wheat extraction method based on NDVI (normalized difference vegetation index) time series curve integral. The method is based on a WFV (wide field of view) camera carried by a GF-1 satellite, an NDVI time series covering the winter wheat growth cycle is adopted, the characteristic that an NDVI curve in the winter wheat growth cycle is higher, that is, the integral value of an NDVI curve chart of winter wheat along with time is higher than those of other crops, is fully used, automatic and high-precision identification of the winter wheat is realized on the basis of a curve integral method, the operation process is simple, and the effect is significant.

The invention discloses a sophisticated crop classifying method based on combination of object oriented technology and NDVI (normalized difference vegetation index) time series. The characteristic that the NDVI time series can reflect crop phenology information is made into full use for realizing crop distinguishing. In addition, the salt-and-pepper effect can be eliminated on the basis of the object oriented technology, precision in sophisticated crop classification is effectively improved, and a new conception is provided for sophisticated crop classification.