CN101699315A - Monitoring device and method for crop growth uniformity - Google Patents

Abstract

The invention discloses a monitoring device and a method for crop growth uniformity degree, aiming at the problem of low efficiency for manually investigating data. The device comprises a remote sensing image processing module, a plot vector data processing module, a vegetation index processing module and a growth uniformity processing module. The method comprises: obtaining the remote sensing images of a satellite; carrying out radiometric ratification, atmospheric correction and geometric correction according to the remote sensing images of the satellite; classifying crops in the remote sensing images of a satellite to obtain a spatial distribution map; converting a grid classification result in the spatial distribution map into facet vector data; processing the spatial distribution map to correct the plot boundary of the crops; according to the spectral characteristics of the plot in the remote sensing image, calculating the vegetation index of the plot; and calculating the growth uniformity degree index according to the vegetation index of each plot. The invention uses the integration of the grid and the vector data to monitor the growth uniformity degree of crops in natural plots and performs the advantages of remote sensing data.

Description

Device and method for monitoring growth uniformity of crops

Technical Field

The invention relates to a device and a method for monitoring the growth uniformity of crops.

Background

The crop growth monitoring refers to the macroscopic monitoring of the seedling condition, the growth condition and the change of the crop, and the crop growth monitoring is required to be capable of comprehensively reflecting the crop condition in time. The growth uniformity of crops is an important index for evaluating the growth condition of crops, and the index not only can reflect the difference of foundation soil fertility and terrain of different farmland plots, but also can reflect the management levels of different cultivators.

The growth uniformity of the traditional crops is mostly determined by field sampling investigation. For example, the winter wheat basic seedling uniformity index P proposed in Maerlia et al 2001 adopts a random sampling method. The method determines the number of survey points according to the area of each land, and then arranges the seedling quantity of each survey point of each land from large to small. Setting the sum of the seedling quantities of the first half of investigation points as sigma a and the sum of the seedling quantities of the second half of investigation points as sigma b, and then substituting the sum into a formula (1-1) to calculate and obtain the uniformity degree P of the basic seedlings corresponding to each plot:

<math><mrow><mi>P</mi><mo>=</mo><mo>[</mo><mn>1</mn><mo>-</mo><mfrac><mrow><mi>&Sigma;a</mi><mo>&divide;</mo><mfrac><mn>1</mn><mn>2</mn></mfrac><mi>n</mi><mo>-</mo><mi>&Sigma;b</mi><mo>&divide;</mo><mfrac><mn>1</mn><mn>2</mn></mfrac><mi>n</mi></mrow><mrow><mo>(</mo><mfrac><mrow><mi>&Sigma;a</mi><mo>+</mo><mi>&Sigma;b</mi></mrow><mi>n</mi></mfrac><mo>)</mo></mrow></mfrac><mo>]</mo><mo>&times;</mo><mn>100</mn><mo>%</mo><mo>-</mo><mo>-</mo><mo>-</mo><mrow><mo>(</mo><mn>1</mn><mo>-</mo><mn>1</mn><mo>)</mo></mrow></mrow></math>

wherein n is the number of survey points of the plot.

In addition, the indexes of cotton uniformity are also provided in 2001, such as Zhongzhuang. A series of evaluation indexes including emergence uniformity, seedling retention uniformity, bud uniformity, flowering uniformity, boll opening uniformity, plant height uniformity and the like are defined according to growth and development, appearance characteristics and cultivation management measures of cotton, and technical measures for improving uniformity are provided according to the indexes.

The definition of the field distribution uniformity of plants is proposed in 2006 by Yanglihua and the like, the degree of the plants which are similar to a honeycomb in the field layout is defined as the field distribution uniformity of the plants, and a calculation formula (1-2) of the field distribution uniformity of the plants is given:

<math><mrow><mi>UD</mi><mo>=</mo><mfrac><mn>1</mn><mrow><mrow><mo>(</mo><mn>1</mn><mo>+</mo><mi>&Sigma;&eta;</mi><mo>/</mo><mi>m</mi><mo>)</mo></mrow><mi>N</mi></mrow></mfrac><mo>-</mo><mo>-</mo><mo>-</mo><mrow><mo>(</mo><mn>1</mn><mo>-</mo><mn>2</mn><mo>)</mo></mrow></mrow></math>

UD is the field distribution uniformity of the plants, UD is more than 0 and less than or equal to 1, the closer the value is to 1, the more uniform the distribution of the plants is; m is the number of observation points; n is the average hole plant number; η (i ═ 1, 2.... m) is a dispersion coefficient for each observation point, and can be calculated by the formula (1-3):

<math><mrow><mi>&eta;</mi><mo>=</mo><msqrt><mfrac><mrow><mi>&Sigma;</mi><msup><mrow><mo>(</mo><msub><mi>P</mi><mi>i</mi></msub><mo>-</mo><mi>P</mi><mo>)</mo></mrow><mn>2</mn></msup></mrow><mn>12</mn></mfrac></msqrt><mo>/</mo><mi>p</mi><mo>-</mo><mo>-</mo><mo>-</mo><mrow><mo>(</mo><mn>1</mn><mo>-</mo><mn>3</mn><mo>)</mo></mrow></mrow></math>

in the formula P_iThe 12 plant observations are for one honeycomb observation point, and p is the standard row spacing.

In practical application, a large amount of manpower and material resources are consumed for monitoring the growth uniformity of crops and acquiring related indexes. The field investigation of the growth difference of crops is needed, so the method has the defects of large workload, low automation degree, long update period and the like. And when a large-range crop growth uniformity survey needs to be carried out, the measurement difficulty is increased. Meanwhile, the growth differences of crops in different farmland plots are ubiquitous, and the requirements of modern agricultural management cannot be met simply by relying on manual survey data.

Disclosure of Invention

Aiming at the defects and shortcomings in the prior art, the invention aims to provide a device and a method for monitoring the growth uniformity of crops, which can obtain the growth uniformity through satellite remote sensing.

In order to achieve the above object, the present invention provides a device for monitoring the uniformity of growth of crops, comprising:

the remote sensing image processing module is used for carrying out radiation correction, atmospheric correction and geometric correction on the remote sensing image according to the obtained remote sensing image;

the plot vector data processing module classifies crops in the remote sensing image to obtain a spatial distribution map of a target crop; converting the classified grid classification result in the remote sensing image into planar vector data; then correcting the boundary of the land parcel of the space distribution map;

a Vegetation index processing module, wherein the Vegetation parameter processing module calculates the Vegetation index NDVI (normalized difference Vegetation index) of the land block according to the spectral features in the land block in the remote sensing image:

$\mathrm{NDVI}=\frac{(R_{\mathrm{nir}}-R_{\mathrm{red}})}{(R_{\mathrm{nir}}+R_{\mathrm{red}})}$

wherein R is_nirThe reflectivity of a near infrared band of a remote sensing image is referred to; r_redThe reflectivity of a red light wave band of a remote sensing image is referred to;

the growth Uniformity processing module is used for calculating a growth Uniformity index GUI (growth Uniformity index) of the plot according to the vegetation index NDVI;

$\mathrm{GUI}=1-\frac{{\mathrm{NDVI}}_{\mathrm{CV}}}{({\mathrm{NDVI}}_{\mathrm{CV}\min }+{\mathrm{NDVI}}_{\mathrm{CV}\max })}$

wherein:

NDVI_CVis the coefficient of variation of the corresponding NDVI for each plot;

NDVI_{CV min}the minimum value of the NDVI (normalized difference of absolute difference) coefficients of all the plots in the same time phase;

NDVI_{CV max}the maximum value of the NDVI variation coefficients of all the plots in the same time phase.

Wherein, the block vector data processing module comprises:

the spatial distribution map processing submodule classifies crops in the remote sensing image to obtain a spatial distribution map of a target crop; and converting the grid classification of the space distribution map into planar vector data;

a land utilization data processing submodule; the land utilization data processing submodule performs visual interpretation on the remote sensing image according to the satellite remote sensing image to obtain reference time phase land utilization thematic data of the past year;

the land parcel boundary processing submodule is used for superposing the planar vector data and the reference time phase land utilization thematic data and cutting the data through a vector layer Intersect algorithm to extract a land parcel boundary; and correcting the plot boundary by visual interpretation by using the satellite remote sensing image of the year to obtain final crop plot boundary data.

Wherein the vegetation index processing module comprises:

the spectral feature processing submodule extracts remote sensing data corresponding to the plot and obtains spectral feature information of different time phases and different wave bands of crops according to the remote sensing data;

the vegetation parameter processing submodule carries out band operation on the spectral characteristic information to obtain a vegetation parameter NDVI;

$\mathrm{NDVI}=\frac{(R_{\mathrm{nir}}-R_{\mathrm{red}})}{(R_{\mathrm{nir}}+R_{\mathrm{red}})}$

wherein R is_nirThe reflectivity of a near infrared band of a remote sensing image is referred to; r_redThe reflectivity of a red light wave band of a remote sensing image is referred to;

the waveband calculation sub-module carries out waveband calculation on the spectral characteristic information of different time phases and different wavebands to obtain NDVI minimum value, maximum value, mean value, standard deviation and variation coefficient NDVI_CV。

Wherein the apparatus further comprises:

the crop spectrum information module extracts all pixel NDVI values corresponding to each plot aiming at each plot, and calculates the minimum value, the maximum value, the mean value, the standard deviation and the variation coefficient NDVI of the plot in different time phases_CVAnd adding the information into a record corresponding to the plot vector file by a new field to generate a crop growth spectrum information knowledge base.

Wherein the apparatus further comprises:

and the thematic map making module is used for establishing a thematic map for the growth uniformity of the target crops in all plots according to the crop growth spectrum information knowledge base generated by the crop spectrum information module.

Meanwhile, the invention also provides a method for monitoring the growth uniformity of crops, which comprises the following steps:

step 1, obtaining a satellite remote sensing image, and performing radiation correction, atmospheric correction and geometric correction on the satellite remote sensing image;

step 2, classifying crops in the satellite remote sensing image to obtain a spatial distribution map of the target crops;

step 3, converting the grid classification result in the spatial distribution map into planar vector data; processing the spatial distribution map to correct the plot boundary of the crops;

step 4, calculating the vegetation index NDVI of the plot according to the spectral characteristics in the plot in the remote sensing image:

$\mathrm{NDVI}=\frac{(R_{\mathrm{nir}}-R_{\mathrm{red}})}{(R_{\mathrm{nir}}+R_{\mathrm{red}})}$

wherein R is_nirThe reflectivity of a near infrared band of a remote sensing image is referred to; r_redThe reflectivity of a red light wave band of a remote sensing image is referred to;

step 5, calculating a growth uniformity index GUI according to the vegetation index NDVI of each plot;

$\mathrm{GUI}=1-\frac{{\mathrm{NDVI}}_{\mathrm{CV}}}{({\mathrm{NDVI}}_{\mathrm{CV}\min }+{\mathrm{NDVI}}_{\mathrm{CV}\max })}$

wherein:

NDVI_CVis the coefficient of variation of the corresponding NDVI for each plot;

NDVI_{CV min}the minimum value of the NDVI (normalized difference of absolute difference) coefficients of all the plots in the same time phase;

NDVI_{CV max}are at the same time phase placeThere is a maximum in the NDVI coefficient of variation of the plot.

Wherein, the step 3 specifically comprises the following steps:

step 31, converting the grid classification of the spatial distribution map into planar vector data;

step 32, carrying out visual interpretation on the remote sensing images of the past year through the high-resolution satellite remote sensing image to obtain reference time phase land utilization thematic data;

step 33, after the data obtained in the step 31 and the step 32 are superposed, cutting the two layers of vector data obtained in the step 31 and the step 32 through a vector layer Intersect algorithm, and then extracting a land boundary; and correcting the plot boundary by visual interpretation by using the satellite remote sensing image of the year to obtain final crop plot boundary data.

Wherein, the step 4 specifically comprises the following steps:

step 41, extracting remote sensing data corresponding to the plots, and obtaining spectral feature information of different time phases and different wave bands of crops according to the remote sensing data;

step 42, performing band operation on the spectral characteristic information to obtain a vegetation parameter NDVI;

$\mathrm{NDVI}=\frac{(R_{\mathrm{nir}}-R_{\mathrm{red}})}{(R_{\mathrm{nir}}+R_{\mathrm{red}})}$

wherein R is_nirThe reflectivity of a near infrared band of a remote sensing image is referred to; r_redThe reflectivity of a red light wave band of a remote sensing image is referred to;

step 43, performing band operation on the spectral characteristic information of different time phases and different bands to obtain the minimum value, the maximum value, the mean value and the standard of the NDVIQuasi-deviation, coefficient of variation NDVI_CV。

Wherein the method further comprises:

step 6, aiming at each land block, extracting all pixel NDVI values corresponding to the land block, and calculating the NDVI minimum value, maximum value, mean value, standard deviation and variation coefficient NDVI of the land block at different time phases_CVAnd adding the information into a record corresponding to the plot vector file by a new field to generate a crop growth spectrum information knowledge base.

Wherein the method further comprises:

and 7, establishing thematic maps for the growth uniformity of the target crops in all plots according to the crop growth spectrum information knowledge base generated by the crop spectrum information module.

The technical scheme has the following advantages: the invention can obtain the remote sensing image through the remote sensing technology, obtain the vector data of the plot through the plot boundary, and then obtain the vegetation parameter according to the vector data of the plot, thereby calculating and calculating the crop growth uniformity degree index. The invention can solve the defects of large workload, low automation degree, long update period and the like in the prior art and improve the working efficiency. The invention integrates remote sensing and GIS technology, realizes the monitoring of the growth uniformity of crops aiming at natural plots by utilizing the technology of integrating grid and vector data, fully exerts the advantages of remote sensing data in the monitoring of the growth uniformity of crops, provides an evaluation index of the growth uniformity of crops, constructs a knowledge base of the growth uniformity of crops based on remote sensing characteristic information, realizes the remote sensing monitoring of the growth uniformity of crops in real time, rapidly and accurately, and improves the investigation precision of the growth uniformity of crops.

Drawings

FIG. 1 is a schematic structural diagram of a device for monitoring uniformity of growth of crops according to the present invention;

FIG. 2 is a schematic flow chart of a method for monitoring uniformity of growth of crops according to the present invention;

FIG. 3 is a spatial distribution map of an extracted target crop in an embodiment of the present invention;

FIG. 4 is a plot image after the raster data of FIG. 3 is converted into vector data;

fig. 5 is the corrected image of the land of fig. 4.

Detailed Description

The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.

Example 1

The first preferred embodiment of the present invention provides a device for monitoring the uniformity of growth of crops, the structure of which is shown in fig. 1, comprising:

the remote sensing image processing module is used for carrying out radiation correction, atmospheric correction and geometric correction on the remote sensing image according to the obtained remote sensing image;

the plot vector data processing module classifies crops in the remote sensing image to obtain a spatial distribution map of a target crop; converting the classified grid classification result in the remote sensing image into planar vector data; then correcting the boundary of the land parcel of the space distribution map;

the vegetation index processing module is used for calculating the vegetation index NDVI of the plot according to the spectral characteristics in the plot in the remote sensing image:

$\mathrm{NDVI}=\frac{(R_{\mathrm{nir}}-R_{\mathrm{red}})}{(R_{\mathrm{nir}}+R_{\mathrm{red}})}$

wherein R is_nirThe reflectivity of a near infrared band of a remote sensing image is referred to; r_redThe reflectivity of a red light wave band of a remote sensing image is referred to;

the growth uniformity processing module is used for calculating a growth uniformity index GUI of the plot according to the vegetation index NDVI;

$\mathrm{GUI}=1-\frac{{\mathrm{NDVI}}_{\mathrm{CV}}}{({\mathrm{NDVI}}_{\mathrm{CV}\min }+{\mathrm{NDVI}}_{\mathrm{CV}\max })}$

wherein:

NDVI_CVis the coefficient of variation of the corresponding NDVI for each plot;

NDVI_{CV min}the minimum value of the NDVI (normalized difference of absolute difference) coefficients of all the plots in the same time phase;

NDVI_{CV max}the maximum value of the NDVI variation coefficients of all the plots in the same time phase. According to the first preferred embodiment of the invention, the plot of the target crop can be determined through the remote sensing image, and the vegetation index NDVI and the growth uniformity index GUI of the plot are calculated through the spectral characteristics in the plot in the remote sensing image in the plot. The first preferred embodiment of the invention realizes the monitoring of the crop growth uniformity aiming at the natural plot by utilizing the grid and vector data integrated technology, and fully exerts the advantages of the remote sensing data in the crop growth monitoring. The invention realizes real-time, rapid and accurate growth of cropsThe remote sensing monitoring of uniformity improves the investigation precision of the uniformity of the growth of crops.

Example 2

The second preferred embodiment of the present invention is improved on the basis of the first preferred embodiment, that is, the block vector data processing module includes:

the spatial distribution map processing submodule classifies crops in the remote sensing image to obtain a spatial distribution map of a target crop; and converting the grid classification of the space distribution map into planar vector data;

a land utilization data processing submodule; the land utilization data processing submodule performs visual interpretation on the remote sensing image according to the satellite remote sensing image to obtain reference time phase land utilization thematic data of the past year;

the land parcel boundary processing submodule is used for superposing the planar vector data and the reference time phase land utilization thematic data and cutting the data through a vector layer Intersect algorithm to extract a land parcel boundary; and correcting the plot boundary by visual interpretation by using the satellite remote sensing image of the year to obtain final crop plot boundary data.

In the second preferred embodiment of the invention, the plot boundary is cut and extracted after the superposition of the reference time phase land utilization thematic data so as to correct the plot boundary more accurately and improve the accuracy of the monitoring data.

The 'reference time phase land utilization thematic data' refers to farmland land block data obtained by utilizing high-resolution remote sensing images of the past year or historical land utilization data of a research area. Since the historical data and the latest land use condition may slightly come in and go out, the accuracy of the land boundary data can be improved by correcting the land boundary by combining the historical data with the current data.

Example 3

The third preferred embodiment of the present invention is improved on the basis of the first preferred embodiment and the second preferred embodiment, that is, the vegetation index processing module includes:

the spectral feature processing submodule extracts remote sensing data corresponding to the plot and obtains spectral feature information of different time phases and different wave bands of crops according to the remote sensing data;

the vegetation parameter processing submodule carries out band operation on the spectral characteristic information to obtain a vegetation parameter NDVI;

$\mathrm{NDVI}=\frac{(R_{\mathrm{nir}}-R_{\mathrm{red}})}{(R_{\mathrm{nir}}+R_{\mathrm{red}})}$

wherein R is_nirThe reflectivity of a near infrared band of a remote sensing image is referred to; r_redThe reflectivity of a red light wave band of a remote sensing image is referred to;

the waveband calculation sub-module carries out waveband calculation on the spectral characteristic information of different time phases and different wavebands to obtain NDVI minimum value, maximum value, mean value, standard deviation and variation coefficient NDVI_CV。

The vegetation parameter NDVI is a normalized ratio of a visible light red waveband and a near infrared waveband, can reflect the effective radiation absorption condition of vegetation photosynthesis on one hand, can reflect crop growth, leaf area index LAI and the like on the other hand, and is the most widely applied vegetation index at present. In the third preferred embodiment of the invention, the remote sensing image and the precisely corrected block boundary are applied to obtain the spectral characteristic information in the block, and the vegetation parameter NDVI is calculated according to the spectral characteristic information, so that the accuracy of the NDVI can be improved.

Example 4

The fourth preferred embodiment of the present invention is improved on the basis of the above three preferred embodiments, that is, the apparatus further comprises:

the crop spectrum information module extracts all pixel NDVI values corresponding to each plot aiming at each plot, and calculates the minimum value, the maximum value, the mean value, the standard deviation and the variation coefficient NDVI of the plot in different time phases_CVAnd adding the information into a record corresponding to the plot vector file by a new field to generate a crop growth spectrum information knowledge base.

The fourth preferred embodiment of the present invention utilizes NDVI to build a knowledge base of crop growth spectrum information to improve long-term, efficient monitoring of crop growth spectrum information.

Example 5

The fifth preferred embodiment of the present invention is improved on the basis of the above four preferred embodiments, that is, the apparatus further comprises:

and the thematic map making module is used for establishing a thematic map for the growth uniformity of the target crops in all plots according to the crop growth spectrum information knowledge base generated by the crop spectrum information module.

The thematic map can obtain more visual imaging data according to the crop growth spectrum information knowledge base.

Example 6

The flow of a preferred embodiment of the method for monitoring the uniformity of the growth of crops provided by the invention is shown in fig. 2, and the method comprises the following steps:

step 1, obtaining a satellite remote sensing image, and performing radiation correction, atmospheric correction and geometric correction on the satellite remote sensing image;

step 2, classifying crops in the satellite remote sensing image to obtain a spatial distribution map of the target crops;

step 3, processing the spatial distribution map to correct the plot boundary of the crops to obtain vector data of the plot;

step 4, calculating the vegetation index NDVI of the plot according to the spectral characteristics in the plot in the remote sensing image:

$\mathrm{NDVI}=\frac{(R_{\mathrm{nir}}-R_{\mathrm{red}})}{(R_{\mathrm{nir}}+R_{\mathrm{red}})}$

wherein R is_nirThe reflectivity of a near infrared band of a remote sensing image is referred to; r_redThe reflectivity of a red light wave band of a remote sensing image is referred to;

step 5, calculating a growth uniformity degree index GUI according to the vegetation index NDVI of each land;

$\mathrm{GUI}=1-\frac{{\mathrm{NDVI}}_{\mathrm{CV}}}{({\mathrm{NDVI}}_{\mathrm{CV}\min }+{\mathrm{NDVI}}_{\mathrm{CV}\max })}$

wherein:

NDVI_CVis the coefficient of variation of the corresponding NDVI for each plot;

NDVI_{CV min}the minimum value of the NDVI (normalized difference of absolute difference) coefficients of all the plots in the same time phase;

NDVI_{CV max}all at the same time phaseThe maximum value in the block NDVI coefficient of variation.

According to the sixth preferred embodiment of the invention, the plot of the target crop can be determined through the remote sensing image, and the vegetation index NDVI and the growth uniformity GUI of the plot are calculated through the spectral characteristics in the plot in the remote sensing image in the plot. The sixth preferred embodiment of the invention realizes the monitoring of the crop growth uniformity aiming at the natural plot by utilizing the grid and vector data integrated technology, and fully exerts the advantages of the remote sensing data in the crop growth monitoring. The invention realizes real-time, rapid and accurate remote sensing monitoring of the uniformity of the growth vigor of the crops and improves the precision of investigation of the uniformity of the growth vigor of the crops.

Example 7

The seventh preferred embodiment of the present invention is improved on the basis of the above sixth preferred embodiment, that is, the step 3 specifically is:

step 31, converting the grid classification of the spatial distribution map into planar vector data;

step 32, carrying out visual interpretation on the remote sensing images of the past year through the high-resolution satellite remote sensing image to obtain reference time phase land utilization thematic data;

step 33, after the data obtained in the step 31 and the step 32 are superposed, cutting the two layers of vector data obtained in the step 31 and the step 32 through a vector layer Intersect algorithm, and then extracting a land boundary; and correcting the plot boundary by visual interpretation by using the satellite remote sensing image of the year to obtain final crop plot boundary data.

In the seventh preferred embodiment of the invention, the plot boundary is cut and extracted after the superposition of the reference time phase land utilization thematic data so as to correct the plot boundary more accurately and improve the accuracy of the monitoring data.

Example 8

The eighth preferred embodiment of the present invention is improved on the basis of the sixth or seventh preferred embodiment, that is, the step 4 specifically is:

step 41, extracting remote sensing data corresponding to the plots, and obtaining spectral feature information of different time phases and different wave bands of crops according to the remote sensing data;

step 42, performing band operation on the spectral characteristic information to obtain a vegetation parameter NDVI;

$\mathrm{NDVI}=\frac{(R_{\mathrm{nir}}-R_{\mathrm{red}})}{(R_{\mathrm{nir}}+R_{\mathrm{red}})}$

wherein R is_nirThe reflectivity of a near infrared band of a remote sensing image is referred to; r_redThe reflectivity of a red light wave band of a remote sensing image is referred to;

step 43, performing band operation on the spectral characteristic information of different time phases and different bands to obtain NDVI minimum value, maximum value, mean value, standard deviation and variation coefficient NDVI_CV。

The vegetation parameter NDVI is a normalized ratio of a visible light red waveband and a near infrared waveband, can reflect the effective radiation absorption condition of vegetation photosynthesis on one hand, can reflect crop growth, leaf area index LAI and the like on the other hand, and is the most widely applied vegetation index at present. In a sixth preferred embodiment of the present invention, the remote sensing image and the accurately corrected boundary of the plot are used to obtain spectral feature information in the plot, and the vegetation parameter NDVI is calculated according to the spectral feature information, so that the accuracy of the NDVI can be improved.

Example 9

An eighth preferred embodiment of the present invention is improved on the basis of the sixth, seventh or eighth preferred embodiment described above, that is, the method further comprises:

step 6, aiming at each land block, extracting all pixel NDVI values corresponding to the land block, and calculating the NDVI minimum value, maximum value, mean value, standard deviation and variation coefficient NDVI of the land block at different time phases_CVAnd adding the information into a record corresponding to the plot vector file by a new field to generate a crop growth spectrum information knowledge base.

The fourth preferred embodiment of the present invention utilizes NDVI to build a knowledge base of crop growth spectrum information to improve long-term, effective crop growth spectrum information.

Example 10

An eighth preferred embodiment of the present invention is improved on the basis of the sixth or seventh or eighth or ninth preferred embodiment described above, that is, the method further comprises:

and 7, establishing thematic maps for the growth uniformity of the target crops in all plots according to the crop growth spectrum information knowledge base generated by the crop spectrum information module.

The thematic map can obtain more visual imaging data according to the crop growth spectrum information knowledge base.

The invention is illustrated below by means of a specific example.

(1) Remote sensing image acquisition and processing

In 2008, 3 scenes of Landsat (TM) remote sensing images in a research area are acquired together in the growing season of the winter wheat, the acquisition dates are respectively 3 months and 27 days, 4 months and 28 days and 5 months and 30 days, and the acquisition dates respectively correspond to the rising period, the booting period and the milk ripening period of the winter wheat. In addition, a scene of an indian star ISP6 image of the research area was also acquired on day 7, month 12 of 08. And (3) performing atmospheric correction on all Landsat images by adopting a dark target method supported by a 6S model, and performing atmospheric correction on ISP6 images by adopting an ENVI software FLAASH module to obtain the earth surface reflectivity of all the images. The geometric correction of the image adopts a method of selecting ground control points for the image, more than 300 ground control points are selected for each scene image, in addition, the whole image is corrected according to the satellite differential GPS control point acquired during the actual investigation, and the image precision after the geometric correction is controlled within one pixel.

(2) Extraction of target crop

A decision tree classification method is adopted to extract winter wheat growing areas in Tongzhou areas by using Landsat5 TM winter wheat growing season remote sensing images of 27 days in 3 months, 28 days in 4 months and 30 days in 5 months in 2008, and remote sensing images obtained after harvesting winter wheat in 12 days in 7 months in 2008. The extraction results are shown in fig. 3.

The raster data is converted into vector data using the ENVI software classification post-processing function, the result of which is shown in FIG. 4. Among them, ENVI (the environmental for visualization images) is remote sensing image processing software of ITT Visual Information Solutions, Inc. in the United states.

(3) Acquisition of target crop plot vector data

In ARCVIEW3.3 software, overlay cutting operation is carried out through 2006 Beijing region farmland division type vector diagrams obtained by visual interpretation of high-resolution satellite remote sensing images, finer farmland plot vector boundary data are obtained, and 2008 winter wheat remote sensing images are overlaid to extract information. Through visual interpretation, the border of the winter wheat planting plot in the Tongzhou area of Beijing in 2008 is determined. The winter wheat block data is corrected as shown in fig. 5.

In 2008, the total number of winter wheat planting plots in the Tongzhou area is 1105, the total area is 17791 hectare (26.7 ten thousand mu), wherein 526 wheat plots with the area less than 10 hectare (150 mu), 394 wheat plots with the area between 10 and 30 hectare (150 and 450 mu), 130 wheat plots with the area between 30 and 70 hectare (450 and 1050 mu) and 2 wheat plots with the area more than 70 hectare (1050 mu) are provided.

(4) For each plot, VB (visual basic) is combined with the MO (modeling information system) of the GIS secondary development control to program and extract NDVI values of all pixels corresponding to the plot, and the minimum value, the maximum value, the mean value, the standard deviation and the variation of the NDVI of the plot in different time phases are calculatedDifferential coefficient NDVI_CVAnd adding the information into a record corresponding to the plot vector file by a new field to generate a crop spectrum information knowledge base.

(5) Determination of crop growth uniformity index based on plot

In order to comprehensively examine the growth conditions of all plots, the research is based on the variable coefficients NDVI of all plots at the same time phase_CVA winter wheat growth Uniformity index GUI (growth Uniformity index) is constructed, the growth of winter wheat in different plots is evaluated according to the size of the index, the GUI is defined as follows, the value of the GUI is between 0 and 1, the larger the value of the GUI is, the better the growth of the plot is, and the higher the NDVI value is and the more uniform the growth is.

$\mathrm{GUI}=1-\frac{{\mathrm{NDVI}}_{\mathrm{CV}}}{({\mathrm{NDVI}}_{\mathrm{CV}\min }+{\mathrm{NDVI}}_{\mathrm{CV}\max })}$

Wherein,

NDVI_CVis the coefficient of variation of the corresponding NDVI for each plot;

NDVI_{CV min}the minimum value of the NDVI (normalized difference of absolute difference) coefficients of all the plots in the same time phase;

NDVI_{CV max}the maximum value of the NDVI (normalized difference of absolute difference) coefficients of all the plots in the same time phase;

(6) generating a crop spectrum information knowledge base;

for each plot, VB (visual basic) is combined with the MO (modeling information system) of the GIS secondary development control to program and extract NDVI values of all pixels corresponding to the plot, and the minimum value, the maximum value, the mean value, the standard deviation and the NDVI coefficient of variation of the plot in different time phases are calculated_CVAnd winter wheat growth uniformity index GUI, and add this information in new fieldsAnd generating a crop spectrum information knowledge base in the records corresponding to the land parcel vector files.

(7) Generating a thematic map;

according to the thematic map of the growing uniformity of crops in different plots and different time phases in the area.

The method provided by the invention is utilized to realize the remote sensing monitoring of the crop growth uniformity based on the farmland plots, the technical scheme provided by the invention fully utilizes the characteristic that the remote sensing image data can acquire the spectral information of a large-range 'planar' surface feature for multiple times, instantly and nondestructively, the evaluation of the crop growth uniformity is carried out aiming at the natural plots, the defects of time and labor waste and low efficiency of the conventional investigation of the crop growth uniformity are overcome, the working efficiency is improved, the working intensity is reduced, and the accuracy and precision of the monitoring of the crop growth uniformity in a large range are effectively improved.

The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.

Claims (10)

1. A monitoring device for the uniformity of crop growth, comprising:

the remote sensing image processing module is used for carrying out radiation correction, atmospheric correction and geometric correction on the remote sensing image according to the obtained remote sensing image;

the plot vector data processing module classifies crops in the remote sensing image to obtain a spatial distribution map of a target crop; converting the classified grid classification result in the remote sensing image into planar vector data; then correcting the boundary of the land parcel of the space distribution map;

the vegetation index processing module is used for calculating the vegetation index NDVI of the plot according to the spectral characteristics in the plot in the remote sensing image:

$\mathrm{NDVI}=\frac{(R_{\mathrm{nir}}-R_{\mathrm{red}})}{(R_{\mathrm{nir}}+R_{\mathrm{red}})}$

wherein R is_nirThe reflectivity of a near infrared band of a remote sensing image is referred to; r_redThe reflectivity of a red light wave band of a remote sensing image is referred to;

the growth uniformity processing module is used for calculating a growth uniformity index GUI of the plot according to the vegetation index NDVI;

$\mathrm{GUI}=1-\frac{{\mathrm{NDVI}}_{\mathrm{CV}}}{({\mathrm{NDVI}}_{\mathrm{CV}\min }+{\mathrm{NDVI}}_{\mathrm{CV}\max })}$

wherein:

NDVI_CVis the coefficient of variation of the corresponding NDVI for each plot;

NDVI_CVminthe minimum value of the NDVI (normalized difference of absolute difference) coefficients of all the plots in the same time phase;

NDVI_CVmaxthe maximum value of the NDVI variation coefficients of all the plots in the same time phase.

2. The device for monitoring the uniformity of crop growth as claimed in claim 1, wherein said block vector data processing module comprises:

the spatial distribution map processing submodule classifies crops in the remote sensing image to obtain a spatial distribution map of a target crop; and converting the grid classification of the space distribution map into planar vector data;

a land utilization data processing submodule; the land utilization data processing submodule performs visual interpretation on the remote sensing image according to the satellite remote sensing image to obtain reference time phase land utilization thematic data based on the data of the past year;

the land parcel boundary processing submodule is used for superposing the planar vector data and the reference time phase land utilization thematic data and cutting the data through a vector layer Intersect algorithm to extract a land parcel boundary; and correcting the plot boundary by visual interpretation by using the satellite remote sensing image of the year to obtain final crop plot boundary data.

3. The device for monitoring the uniformity of crop growth according to claim 1 or 2, wherein the vegetation index processing module comprises:

the spectral feature processing submodule extracts remote sensing data corresponding to the plot and obtains spectral feature information of different time phases and different wave bands of crops according to the remote sensing data;

the vegetation parameter processing submodule carries out band operation on the spectral characteristic information to obtain a vegetation parameter NDVI;

$\mathrm{NDVI}=\frac{(R_{\mathrm{nir}}-R_{\mathrm{red}})}{(R_{\mathrm{nir}}+R_{\mathrm{red}})}$

wherein R is_nirThe reflectivity of a near infrared band of a remote sensing image is referred to; r_redThe reflectivity of a red light wave band of a remote sensing image is referred to;

the waveband calculation sub-module carries out waveband calculation on the spectral characteristic information of different time phases and different wavebands to obtain the NDVI minimum value, the maximum value, the mean value, the standard deviation and the variation coefficient NDVI of the remote sensing image_CV。

4. The apparatus for monitoring the uniformity of growth of a crop as claimed in claim 1, further comprising:

the crop spectrum information module extracts all pixel NDVI values corresponding to each plot aiming at each plot, and calculates the minimum value, the maximum value, the mean value, the standard deviation and the variation coefficient NDVI of the plot in different time phases_CVAnd adding the information into a record corresponding to the plot vector file by a new field to generate a crop growth spectrum information knowledge base.

5. The apparatus for monitoring the uniformity of growth of a crop as claimed in claim 4, further comprising:

and the thematic map making module is used for establishing a thematic map for the growth uniformity of the target crops in all plots according to the crop growth spectrum information knowledge base generated by the crop spectrum information module.

6. A method for monitoring the growth uniformity of crops comprises the following steps:

step 1, obtaining a satellite remote sensing image, and performing radiation correction, atmospheric correction and geometric correction on the satellite remote sensing image;

step 2, classifying crops in the satellite remote sensing image to obtain a spatial distribution map of the target crops;

step 3, converting the grid classification result in the spatial distribution map into planar vector data; processing the spatial distribution map to correct the plot boundary of the crops;

step 4, calculating the vegetation index NDVI of the plot according to the spectral characteristics in the plot in the remote sensing image:

$\mathrm{NDVI}=\frac{(R_{\mathrm{nir}}-R_{\mathrm{red}})}{(R_{\mathrm{nir}}+R_{\mathrm{red}})}$

wherein R is_nirThe reflectivity of a near infrared band of a remote sensing image is referred to; r_redThe reflectivity of a red light wave band of a remote sensing image is referred to;

step 5, calculating a growth uniformity index GUI according to the vegetation index NDVI of each plot,

$\mathrm{GUI}=1-\frac{{\mathrm{NDVI}}_{\mathrm{CV}}}{({\mathrm{NDVI}}_{\mathrm{CV}\min }+{\mathrm{NDVI}}_{\mathrm{CV}\max })}$

wherein:

NDVI_CVis the coefficient of variation of the corresponding NDVI for each plot;

NDVI_CVminthe minimum value of the NDVI (normalized difference of absolute difference) coefficients of all the plots in the same time phase;

NDVI_CVmaxthe maximum value of the NDVI variation coefficients of all the plots in the same time phase.

7. The method for monitoring the uniformity of crop growth according to claim 6, wherein the step 3 is specifically as follows:

step 31, converting the grid classification of the spatial distribution map into planar vector data;

step 32, carrying out visual interpretation on the remote sensing images of the past year through the high-resolution satellite remote sensing image to obtain reference time phase land utilization thematic data;

step 33, after the data obtained in the step 31 and the step 32 are superposed, cutting the two layers of vector data obtained in the step 31 and the step 32 through a vector layer Intersect algorithm, and then extracting a land boundary; and correcting the plot boundary by visual interpretation by using the satellite remote sensing image of the year to obtain final crop plot boundary data.

8. The method for monitoring the uniformity of crop growth according to claim 6 or 7, wherein the step 4 is specifically as follows:

step 41, extracting remote sensing data corresponding to the plots, and obtaining spectral feature information of different time phases and different wave bands of crops according to the remote sensing data;

step 42, performing band operation on the spectral characteristic information to obtain a vegetation parameter NDVI;

$\mathrm{NDVI}=\frac{(R_{\mathrm{nir}}-R_{\mathrm{red}})}{(R_{\mathrm{nir}}+R_{\mathrm{red}})}$

wherein R is_nirThe reflectivity of a near infrared band of a remote sensing image is referred to; r_redThe reflectivity of a red light wave band of a remote sensing image is referred to;

step 43, performing band operation on the spectral characteristic information of different time phases and different bands to obtain NDVI minimum value, maximum value, mean value, standard deviation and variation coefficient NDVI_CV。

9. The method for monitoring the uniformity of crop growth as claimed in claim 6, further comprising:

step 6, aiming at each land block, extracting all pixel NDVI values corresponding to the land block, and calculating the NDVI minimum value, maximum value, mean value, standard deviation and variation coefficient NDVI of the land block at different time phases_CVAnd winter wheat growth uniformity index GUIAnd adding the information into a record corresponding to the block vector file by a new field to generate a crop growth spectrum information knowledge base.

10. The method for monitoring the uniformity of crop growth as claimed in claim 9, further comprising:

and 7, establishing thematic maps for the growth uniformity of the target crops in all plots according to the crop growth spectrum information knowledge base generated by the crop spectrum information module.

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