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

A monitoring device and method for crop growth uniformity

technical field

The invention relates to a monitoring device and method for crop growth uniformity.

Background technique

Crop growth monitoring refers to the macro-monitoring of crop seedling condition, growth status and changes, which requires timely and comprehensive reflection of agricultural conditions. The uniformity of crop growth has always been an important index to evaluate the quality of crop growth. This index can not only reflect the differences in soil fertility and topography of different farmland plots, but also reflect the management level of different cultivators.

The growth uniformity of traditional crops is mostly determined through field sampling surveys. For example, the basic seedling uniformity index P of winter wheat proposed by Ma Aiguo et al. in 2001 is a random sampling method. This method determines the number of survey points according to the area of each plot, and then arranges the seedlings of each survey point in each plot from large to small. Assume that the sum of the seedling quantity of the first half of the survey points is ∑a, and the sum of the seedling quantity of the second half of the survey points is ∑b, and then substitute it into the formula (1-1) to calculate the basic seedling uniformity P corresponding to each plot:

$\mathrm{<span\; class="notranslate">\; P</span>}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>\frac{\mathrm{<span\; class="notranslate">\; \&Sigma;a</span>}<span\; class="notranslate">\; \&divide;</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 2</span>}}\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; \&Sigma;b</span>}<span\; class="notranslate">\; \&divide;</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 2</span>}}\mathrm{<span\; class="notranslate">\; no</span>}}{<span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; \&Sigma;a</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; \&Sigma;b</span>}}{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; ]</span><span\; class="notranslate">\; \&times;</span>\mathrm{<span\; class="notranslate">\; 100</span>}<span\; class="notranslate">\; \%</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>$

Among them, n is the number of plot survey points.

In addition, Zhong Qizhuang et al. 2001 also proposed cotton uniformity index. According to the growth and development, external morphological characteristics and cultivation management measures of cotton, a series of evaluation indexes including uniformity of seedling emergence, uniformity of seedling retention, budding uniformity, flowering uniformity, boll opening uniformity and plant height uniformity were defined. These indicators suggest technical measures to improve uniformity.

Yang Lihua et al. proposed the definition of plant field distribution uniformity in 2006, defined the degree of plant field distribution similar to honeycomb as plant field distribution uniformity, and gave the calculation formula of plant field distribution uniformity (1-2):

$\mathrm{<span\; class="notranslate">\; UD</span>}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; \&Sigma;\&eta;</span>}<span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; N</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>$

Among them, UD is the uniformity of plant distribution in the field, 0<UD≤1, the closer the value is to 1, the more uniform the plant distribution; m is the number of observation points; N is the average number of plants in holes; η(i=1,2,... .m) is the deviation coefficient of each observation point, which can be calculated by formula (1-3):

$\mathrm{<span\; class="notranslate">\; \&eta;</span>}<span\; class="notranslate">\; =</span>\sqrt{\frac{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; P</span>}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{\mathrm{<span\; class="notranslate">\; 12</span>}}}<span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; p</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 3</span>}<span\; class="notranslate">\; )</span>$

In the formula, P _i is the observed value of 12 plants at one honeycomb observation point, and p is the standard row spacing.

In practical applications, the monitoring of crop growth uniformity and the acquisition of related indicators require a lot of manpower and material resources. Due to the need to go to the field to investigate the differences in the growth of crops, there are disadvantages such as heavy workload, low degree of automation, and long update cycle. Moreover, when a large-scale survey of crop growth uniformity is required, the measurement difficulty will also increase. At the same time, differences in crop growth in different farmland plots are common, and relying solely on manual survey data cannot meet the needs of modern agricultural management.

Contents of the invention

Aiming at the defects and deficiencies in the prior art, the object of the present invention is to provide a monitoring device and method for crop growth uniformity, which can obtain growth uniformity through satellite remote sensing.

In order to achieve the above object, the present invention proposes a monitoring device for crop growth uniformity, comprising:

A remote sensing image processing module, the remote sensing image processing module performs radiometric correction, atmospheric correction and geometric correction on the remote sensing image according to the obtained remote sensing image;

A plot vector data processing module, the plot vector data processing module classifies the crops in the remote sensing images to obtain the spatial distribution map of the target crops; and transforms the raster classification results in the classified remote sensing images It is area vector data; then the plot boundaries of the spatial distribution map are corrected;

Vegetation index processing module, the vegetation parameter processing module calculates the vegetation index NDVI (Normalized Difference Vegetation Index) of this plot according to the spectral features in the plot in the remote sensing image:

$\mathrm{<span\; class="notranslate">\; NDVI</span>}<span\; class="notranslate">\; =</span>\frac{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; nir</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; red</span>}}<span\; class="notranslate">\; )</span>}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; nir</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; red</span>}}<span\; class="notranslate">\; )</span>}$

Among them, R _nir refers to the reflectance of the near-infrared band of the remote sensing image; R _red refers to the reflectance of the red band of the remote sensing image;

Growth uniformity processing module, said growth uniformity processing module calculates the growth uniformity index GUI (Growth Uniformity Index) of this plot according to vegetation index NDVI;

$\mathrm{<span\; class="notranslate">\; GUI</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>\frac{{\mathrm{<span\; class="notranslate">\; NDVI</span>}}_{\mathrm{<span\; class="notranslate">\; cv</span>}}}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; NDVI</span>}}_{\mathrm{<span\; class="notranslate">\; cv</span>}\mathrm{<span\; class="notranslate">\; min</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; NDVI</span>}}_{\mathrm{<span\; class="notranslate">\; cv</span>}\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; )</span>}$

in:

NDVI _CV is the coefficient of variation of NDVI corresponding to each plot;

NDVI _{CV min} is the minimum value of the NDVI coefficient of variation of all plots in the same phase;

NDVI _{CV max} is the maximum value of the NDVI coefficient of variation of all plots in the same period.

Wherein, the block vector data processing module includes:

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

The land use data processing submodule; the land use data processing submodule performs visual interpretation on the remote sensing image according to the satellite remote sensing image to obtain the reference time phase land use special data of the historical data;

A plot boundary processing sub-module, the plot boundary processing sub-module superimposes the surface vector data and the reference phase land use thematic data, and extracts the plot boundary after cutting through the vector layer Intersect algorithm; and Using the satellite remote sensing images of this year, the land boundary correction is carried out through visual interpretation, and the final crop land boundary data are obtained.

Wherein, the vegetation index processing module includes:

The spectral feature processing sub-module, the spectral feature processing sub-module extracts the remote sensing data corresponding to the plot, and obtains the spectral feature information of different time phases and different bands of crops according to the remote sensing data;

Vegetation parameter processing sub-module, the vegetation parameter processing sub-module performs band operation on spectral feature information to obtain vegetation parameter NDVI;

$\mathrm{<span\; class="notranslate">\; NDVI</span>}<span\; class="notranslate">\; =</span>\frac{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; nir</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; red</span>}}<span\; class="notranslate">\; )</span>}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; nir</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; red</span>}}<span\; class="notranslate">\; )</span>}$

Among them, R _nir refers to the reflectance of the near-infrared band of the remote sensing image; R _red refers to the reflectance of the red band of the remote sensing image;

The band calculation sub-module, the band calculation sub-module performs band calculation on the spectral feature information of different time phases and different bands to obtain the minimum value, maximum value, mean value, standard deviation, and coefficient of variation NDVI _CV of NDVI.

Wherein, the device also includes:

Crop spectral information module, the crop spectral information module extracts the NDVI values of all pixels corresponding to the plot for each plot, and calculates the minimum, maximum, mean, standard deviation and variation of NDVI in different phases of the plot Coefficient NDVI _CV and winter wheat growth uniformity index GUI, and add this information as a new field to the corresponding record of the plot vector file to generate a knowledge base of crop growth spectrum information.

Wherein, the device also includes:

A thematic map making module, the thematic map making module builds a thematic map for the growth uniformity of target crops in all plots according to the crop growth spectrum information knowledge base generated by the crop spectral information module.

Simultaneously, the present invention also proposes a monitoring method of crop growth uniformity, comprising:

Step 1, obtaining satellite remote sensing images, and performing radiation correction, atmospheric correction and geometric correction on the satellite remote sensing images;

Step 2, classify the crops in the satellite remote sensing images to obtain the spatial distribution map of the target crops;

Step 3, transforming the grid classification result in the spatial distribution map into area vector data; and processing the spatial distribution map to correct the plot boundaries of the crops;

Step 4, calculate the vegetation index NDVI of this plot according to the spectral feature in the plot in the remote sensing image:

$\mathrm{<span\; class="notranslate">\; NDVI</span>}<span\; class="notranslate">\; =</span>\frac{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; nir</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; red</span>}}<span\; class="notranslate">\; )</span>}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; nir</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; red</span>}}<span\; class="notranslate">\; )</span>}$

Among them, R _nir refers to the reflectance of the near-infrared band of the remote sensing image; R _red refers to the reflectance of the red band of the remote sensing image;

Step 5, calculate the growth uniformity index GUI according to the vegetation index NDVI of each plot;

$\mathrm{<span\; class="notranslate">\; GUI</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>\frac{{\mathrm{<span\; class="notranslate">\; NDVI</span>}}_{\mathrm{<span\; class="notranslate">\; cv</span>}}}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; NDVI</span>}}_{\mathrm{<span\; class="notranslate">\; cv</span>}\mathrm{<span\; class="notranslate">\; min</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; NDVI</span>}}_{\mathrm{<span\; class="notranslate">\; cv</span>}\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; )</span>}$

in:

NDVI _CV is the coefficient of variation of NDVI corresponding to each plot;

NDVI _{CV min} is the minimum value of the NDVI coefficient of variation of all plots in the same phase;

NDVI _{CV max} is the maximum value of the NDVI coefficient of variation of all plots in the same period.

Wherein, the step 3 is specifically:

Step 31, converting the raster classification of the spatial distribution map into area vector data;

Step 32, through high-resolution satellite remote sensing images, visually interpret the remote sensing images over the years to obtain reference time-phase land use thematic data;

Step 33, after superimposing the data obtained in step 31 and step 32, the two-layer vector data obtained in step 31 and step 32 is cut through the vector layer Intersect algorithm to extract the plot boundary; and using the satellite remote sensing image of this year, through Visual interpretation is used to correct the plot boundaries to obtain the final crop plot boundary data.

Wherein, the step 4 is specifically:

Step 41, extract the remote sensing data corresponding to the plot, and obtain the spectral characteristic information of different time phases and different bands of crops according to the remote sensing data;

Step 42, performing band calculation on the spectral feature information to obtain the vegetation parameter NDVI;

$\mathrm{<span\; class="notranslate">\; NDVI</span>}<span\; class="notranslate">\; =</span>\frac{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; nir</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; red</span>}}<span\; class="notranslate">\; )</span>}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; nir</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; red</span>}}<span\; class="notranslate">\; )</span>}$

Among them, R _nir refers to the reflectance of the near-infrared band of the remote sensing image; R _red refers to the reflectance of the red band of the remote sensing image;

Step 43 , perform band calculation on the spectral characteristic information of different time phases and different bands, and obtain the minimum value, maximum value, mean value, standard deviation, and coefficient of variation NDVI _{CV of} NDVI.

Wherein, the method also includes:

Step 6. For each plot, extract the NDVI values of all pixels corresponding to the plot, and calculate the minimum, maximum, mean, standard deviation, NDVI _CV and winter wheat growth uniformity of the plot in different phases Index GUI, and add this information as a new field to the record corresponding to the plot vector file to generate a knowledge base of crop growth spectrum information.

Wherein, the method also includes:

Step 7. According to the crop growth spectrum information knowledge base generated by the crop spectrum information module, a thematic map is established for the growth uniformity of the target crops in all plots.

The above technical solution has the following advantages: the present invention can obtain remote sensing images through remote sensing technology, and obtain the vector data of the plot through the plot boundary, and then obtain the vegetation parameters according to the vector data of the plot, so as to calculate the uniform growth of crops. degree index. The invention can solve the disadvantages of the prior art, such as heavy workload, low degree of automation, long update cycle, etc., and improve work efficiency. The invention integrates remote sensing and GIS technology, utilizes grid and vector data integration technology to realize the monitoring of crop growth uniformity for natural plots, gives full play to the advantages of remote sensing data in crop growth monitoring, and proposes the evaluation of crop growth uniformity Indexes, constructing a crop growth uniformity knowledge base based on remote sensing feature information, and realizing real-time, fast, and accurate remote sensing monitoring of crop growth uniformity, improving the accuracy of crop growth uniformity investigations.

Description of drawings

Fig. 1 is the structural representation of the monitoring device of crop growth uniformity that the present invention proposes;

Fig. 2 is the schematic flow sheet of the monitoring method of crop growth uniformity that the present invention proposes;

Fig. 3 is a spatial distribution diagram of the extracted target crops in a specific embodiment of the present invention;

Fig. 4 is the plot image after converting the raster data in Fig. 3 into vector data;

Figure 5 is the corrected plot image of Figure 4.

Detailed ways

The specific implementation manners of the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

Example 1

The first preferred embodiment of the present invention proposes a monitoring device for crop growth uniformity, its structure as shown in Figure 1, comprising:

A remote sensing image processing module, the remote sensing image processing module performs radiometric correction, atmospheric correction and geometric correction on the remote sensing image according to the obtained remote sensing image;

A plot vector data processing module, the plot vector data processing module classifies the crops in the remote sensing images to obtain the spatial distribution map of the target crops; and transforms the raster classification results in the classified remote sensing images It is area vector data; then the plot boundaries of the spatial distribution map are corrected;

Vegetation index processing module, the vegetation parameter processing module calculates the vegetation index NDVI of the plot according to the spectral features in the plot in the remote sensing image:

$\mathrm{<span\; class="notranslate">\; NDVI</span>}<span\; class="notranslate">\; =</span>\frac{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; nir</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; red</span>}}<span\; class="notranslate">\; )</span>}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; nir</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; red</span>}}<span\; class="notranslate">\; )</span>}$

Among them, R _nir refers to the reflectance of the near-infrared band of the remote sensing image; R _red refers to the reflectance of the red band of the remote sensing image;

The growth uniformity processing module, the growth uniformity processing module calculates the growth uniformity index GUI of the plot according to the vegetation index NDVI;

$\mathrm{<span\; class="notranslate">\; GUI</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>\frac{{\mathrm{<span\; class="notranslate">\; NDVI</span>}}_{\mathrm{<span\; class="notranslate">\; cv</span>}}}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; NDVI</span>}}_{\mathrm{<span\; class="notranslate">\; cv</span>}\mathrm{<span\; class="notranslate">\; min</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; NDVI</span>}}_{\mathrm{<span\; class="notranslate">\; cv</span>}\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; )</span>}$

in:

NDVI _CV is the coefficient of variation of NDVI corresponding to each plot;

NDVI _{CV min} is the minimum value of the NDVI coefficient of variation of all plots in the same phase;

NDVI _{CV max} is the maximum value of the NDVI coefficient of variation of all plots in the same period. In the first preferred embodiment of the present 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 can be calculated through the spectral characteristics in the plot in the remote sensing image. The first preferred embodiment of the present invention utilizes grid and vector data integration technology to realize crop growth uniformity monitoring for natural plots, and fully utilizes the advantages of remote sensing data in crop growth monitoring. The invention realizes real-time, rapid and accurate remote sensing monitoring of the uniformity of crop growth, and improves the precision of investigation of the uniformity of crop growth.

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:

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

The land use data processing submodule; the land use data processing submodule performs visual interpretation on the remote sensing image according to the satellite remote sensing image to obtain the reference time phase land use special data of the historical data;

A plot boundary processing sub-module, the plot boundary processing sub-module superimposes the surface vector data and the reference phase land use thematic data, and extracts the plot boundary after cutting through the vector layer Intersect algorithm; and Using the satellite remote sensing images of this year, the land boundary correction is carried out through visual interpretation, and the final crop land boundary data are obtained.

In the second preferred embodiment of the present invention, by referring to the time-phase land use thematic data to superimpose and then cut and extract the plot boundary, the plot boundary can be corrected more accurately and the accuracy of the monitoring data can be improved.

Among them, the "reference time-phase land use thematic data" refers to the farmland plot data obtained by using the high-resolution remote sensing images over the years or the historical land use data of the research area. Since the historical data may be slightly different from the latest land use conditions, using historical data combined with current data to correct the plot boundary can improve the accuracy of the plot boundary 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 sub-module, the spectral feature processing sub-module extracts the remote sensing data corresponding to the plot, and obtains the spectral feature information of different time phases and different bands of crops according to the remote sensing data;

Vegetation parameter processing sub-module, the vegetation parameter processing sub-module performs band operation on spectral feature information to obtain vegetation parameter NDVI;

$\mathrm{<span\; class="notranslate">\; NDVI</span>}<span\; class="notranslate">\; =</span>\frac{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; nir</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; red</span>}}<span\; class="notranslate">\; )</span>}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; nir</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; red</span>}}<span\; class="notranslate">\; )</span>}$

Among them, R _nir refers to the reflectance of the near-infrared band of the remote sensing image; R _red refers to the reflectance of the red band of the remote sensing image;

The band calculation sub-module, the band calculation sub-module performs band calculation on the spectral feature information of different time phases and different bands to obtain the minimum value, maximum value, mean value, standard deviation, and coefficient of variation NDVI _CV of NDVI.

Among them, the vegetation parameter NDVI is the vegetation index NDVI is the normalized ratio of the visible red band and the near-infrared band. On the one hand, it can reflect the effective radiation absorption of vegetation photosynthesis, and on the other hand, it can reflect crop growth and leaf area index LAI It is the most widely used vegetation index at present. In the third preferred embodiment of the present invention, the spectral feature information in the plot is obtained by using the remote sensing image and the accurately corrected plot boundary, and the vegetation parameter NDVI is calculated according to the spectral feature information, which can improve the accuracy of NDVI.

Example 4

The fourth preferred embodiment of the present invention is improved on the basis of the above three preferred embodiments, that is, the device also includes:

Crop spectral information module, the crop spectral information module extracts the NDVI values of all pixels corresponding to the plot for each plot, and calculates the minimum, maximum, mean, standard deviation and variation of NDVI in different phases of the plot Coefficient NDVI _CV and winter wheat growth uniformity index GUI, and add this information as a new field to the corresponding record of the plot vector file to generate a knowledge base of crop growth spectrum information.

The fourth preferred embodiment of the present invention utilizes NDVI to establish a knowledge base of crop growth spectrum information to improve long-term and effective 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 device also includes:

A thematic map making module, the thematic map making module builds a thematic map for the growth uniformity of target crops in all plots according to the crop growth spectrum information knowledge base generated by the crop spectral information module.

Thematic maps can obtain more intuitive image data based on the knowledge base of crop growth spectrum information.

Example 6

A kind of monitoring method of crop growth uniformity that the present invention proposes, its preferred embodiment process flow as shown in Figure 2, comprises:

Step 1, obtaining satellite remote sensing images, and performing radiation correction, atmospheric correction and geometric correction on the satellite remote sensing images;

Step 2, classify the crops in the satellite remote sensing images to obtain the spatial distribution map of the target crops;

Step 3, processing the spatial distribution map to correct the plot boundary of the crops, and obtaining the vector data of the plot;

Step 4, calculate the vegetation index NDVI of this plot according to the spectral feature in the plot in the remote sensing image:

$\mathrm{<span\; class="notranslate">\; NDVI</span>}<span\; class="notranslate">\; =</span>\frac{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; nir</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; red</span>}}<span\; class="notranslate">\; )</span>}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; nir</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; red</span>}}<span\; class="notranslate">\; )</span>}$

Among them, R _nir refers to the reflectance of the near-infrared band of the remote sensing image; R _red refers to the reflectance of the red band of the remote sensing image;

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

$\mathrm{<span\; class="notranslate">\; GUI</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>\frac{{\mathrm{<span\; class="notranslate">\; NDVI</span>}}_{\mathrm{<span\; class="notranslate">\; cv</span>}}}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; NDVI</span>}}_{\mathrm{<span\; class="notranslate">\; cv</span>}\mathrm{<span\; class="notranslate">\; min</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; NDVI</span>}}_{\mathrm{<span\; class="notranslate">\; cv</span>}\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; )</span>}$

in:

NDVI _CV is the coefficient of variation of NDVI corresponding to each plot;

NDVI _{CV min} is the minimum value of the NDVI coefficient of variation of all plots in the same phase;

NDVI _{CV max} is the maximum value of the NDVI coefficient of variation of all plots in the same period.

In the sixth preferred embodiment of the present invention, the plot of the target crop can be determined through remote sensing images, and the vegetation index NDVI and growth uniformity GUI of the plot can be calculated through the spectral features in the plot in the remote sensing image. The sixth preferred embodiment of the present invention utilizes grid and vector data integration technology to realize crop growth uniformity monitoring for natural plots, and fully utilizes the advantages of remote sensing data in crop growth monitoring. The invention realizes real-time, rapid and accurate remote sensing monitoring of the uniformity of crop growth, and improves the precision of investigation of the uniformity of crop growth.

Example 7

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

Step 31, converting the raster classification of the spatial distribution map into area vector data;

Step 32, through high-resolution satellite remote sensing images, visually interpret the remote sensing images over the years to obtain reference time-phase land use thematic data;

Step 33, after superimposing the data obtained in step 31 and step 32, the two-layer vector data obtained in step 31 and step 32 is cut through the vector layer Intersect algorithm to extract the plot boundary; and using the satellite remote sensing image of this year, through Visual interpretation is used to correct the plot boundaries to obtain the final crop plot boundary data.

In the seventh preferred embodiment of the present invention, by superimposing with reference to the thematic data of time-phase land use and then cutting and extracting the boundary of the plot, the boundary of the plot can be corrected more accurately and the accuracy of the monitoring data can be improved.

Example 8

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

Step 41, extract the remote sensing data corresponding to the plot, and obtain the spectral characteristic information of different time phases and different bands of crops according to the remote sensing data;

Step 42, performing band calculation on the spectral feature information to obtain the vegetation parameter NDVI;

$\mathrm{<span\; class="notranslate">\; NDVI</span>}<span\; class="notranslate">\; =</span>\frac{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; nir</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; red</span>}}<span\; class="notranslate">\; )</span>}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; nir</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; red</span>}}<span\; class="notranslate">\; )</span>}$

Among them, R _nir refers to the reflectance of the near-infrared band of the remote sensing image; R _red refers to the reflectance of the red band of the remote sensing image;

Step 43 , perform band calculation on the spectral characteristic information of different time phases and different bands, and obtain the minimum value, maximum value, mean value, standard deviation, and coefficient of variation NDVI _{CV of} NDVI.

Among them, the vegetation parameter NDVI is the vegetation index NDVI is the normalized ratio of the visible red band and the near-infrared band. On the one hand, it can reflect the effective radiation absorption of vegetation photosynthesis, and on the other hand, it can reflect crop growth and leaf area index LAI It is the most widely used vegetation index at present. In the sixth preferred embodiment of the present invention, the spectral feature information in the plot is obtained by using the remote sensing image and the accurately corrected plot boundary, and the vegetation parameter NDVI is calculated according to the spectral feature information, which can improve the accuracy of NDVI.

Example 9

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

Step 6. For each plot, extract the NDVI values of all pixels corresponding to the plot, and calculate the minimum, maximum, mean, standard deviation, NDVI _CV and winter wheat growth uniformity of the plot in different phases Index GUI, and add this information as a new field to the record corresponding to the plot vector file to generate a knowledge base of crop growth spectrum information.

The fourth preferred embodiment of the present invention uses NDVI to establish a knowledge base of crop growth spectrum information to improve long-term and effective crop growth spectrum information.

Example 10

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

Step 7. According to the crop growth spectrum information knowledge base generated by the crop spectrum information module, a thematic map is established for the growth uniformity of the target crops in all plots.

Thematic maps can obtain more intuitive image data based on the knowledge base of crop growth spectrum information.

The present invention will be described below through a specific embodiment.

(1) Remote sensing image acquisition and processing

In 2008, a total of 3 scenes of Landsat TM remote sensing images in the research area were obtained during the winter wheat growing season. The acquisition dates were March 27, April 28 and May 30, corresponding to the rising stage, booting stage and milky stage of winter wheat respectively. . In addition, on July 12, 2008, a scene of the ISP6 image of the Indian star in the research area was obtained. All Landsat images were atmospherically corrected using the dark target method supported by the 6S model, and the ISP6 images were atmospherically corrected using the FLAASH module of ENVI software to obtain the surface reflectance of all images. The geometric correction of the image adopts the method of selecting ground control points from image to image. More than 300 ground control points are selected for each image. In addition, the entire image is corrected according to the satellite differential GPS control points obtained during the actual survey. After geometric correction The image precision is controlled within one pixel.

(2) Target crop extraction

Using the remote sensing images of Landsat5 TM winter wheat growing season on March 27, April 28 and May 30, 2008, and the remote sensing images of winter wheat harvested on July 12, 2008, the winter wheat planting in Tongzhou area was classified by decision tree classification method. area was extracted. The extraction results are shown in Figure 3.

The raster data is converted into vector data by using the classification post-processing function of ENVI software, and the result is shown in Figure 4. Among them, ENVI (The Environment for Visualizing Images) is a remote sensing image processing software of ITT Visual Information Solutions in the United States.

(3) Acquisition of target crop plot vector data

In the ARCVIEW3.3 software, by superimposing and cutting the 2006 Beijing area farmland division type vector map obtained by visual interpretation of high-resolution satellite remote sensing images, a relatively fine vector boundary data of farmland plots was obtained, and at the same time superimposed the 2008 Information extraction from remote sensing images of winter wheat. Determination of the boundaries of winter wheat planting plots in Tongzhou, Beijing in 2008 by visual interpretation. Figure 5 shows the data of winter wheat plots after correction.

There were 1,105 winter wheat plots in Tongzhou in 2008, with a total area of 17,791 hectares (267,000 mu), of which there were 526 wheat plots with an area of less than 10 hectares (150 mu) ), there are 394 wheat plots, 130 wheat plots of 30-70 hectares (450-1050 mu), and 2 wheat plots larger than 70 hectares (1050 mu).

(4) For each plot, use VB combined with GIS secondary development control MO programming to extract the NDVI values of all pixels corresponding to the plot, and calculate the minimum, maximum, mean, standard deviation and Variation coefficient NDVI _CV , and this information is added to the record corresponding to the plot vector file as a new field to generate a knowledge base of crop spectral information.

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

In order to comprehensively examine the growth conditions of all plots, this study constructed the growth uniformity index GUI (Growth Uniformity Index) of winter wheat based on the variation coefficient NDVI _CV of all plots in the same phase. The growth trend of , the definition of GUI is as follows, the value of GUI is between 0-1, the larger the GUI value, the better the growth of the plot, the higher the NDVI value and the more uniform the growth.

$\mathrm{<span\; class="notranslate">\; GUI</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>\frac{{\mathrm{<span\; class="notranslate">\; NDVI</span>}}_{\mathrm{<span\; class="notranslate">\; cv</span>}}}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; NDVI</span>}}_{\mathrm{<span\; class="notranslate">\; cv</span>}\mathrm{<span\; class="notranslate">\; min</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; NDVI</span>}}_{\mathrm{<span\; class="notranslate">\; cv</span>}\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; )</span>}$

in,

NDVI _CV is the coefficient of variation of NDVI corresponding to each plot;

NDVI _{CV min} is the minimum value of the NDVI coefficient of variation of all plots in the same phase;

NDVI _{CV max} is the maximum value of the NDVI coefficient of variation of all plots in the same phase;

(6) Generate crop spectral information knowledge base;

For each plot, use VB combined with GIS secondary development control MO programming to extract the NDVI values of all pixels corresponding to the plot, and calculate the minimum, maximum, mean, standard deviation and variation coefficient NDVI of the plot in different phases of NDVI _CV and winter wheat growth uniformity index GUI, and this information is added as a new field to the record corresponding to the plot vector file to generate a knowledge base of crop spectral information.

(7) generate thematic maps;

According to the thematic map of crop growth uniformity in different plots and different phases in the region.

This example uses the method proposed by the present invention to realize the remote sensing monitoring of the uniformity of crop growth based on farmland plots. The technical solution proposed by the present invention makes full use of the remote sensing image data to obtain multiple, instantaneous and non-destructive acquisitions of large-scale "area-shaped "The characteristics of ground object spectral information, the evaluation of crop growth uniformity for natural plots, overcomes the shortcomings of time-consuming, laborious and low-efficiency investigations of crop growth uniformity in the past, and effectively improves work efficiency and reduces work intensity. The accuracy and precision of monitoring the uniformity of crop growth in a wide range are improved.

The above is only a preferred embodiment of the present invention, it should be pointed out that for those of ordinary skill in the art, without departing from the technical principle of the present invention, some improvements and modifications can also be made, these improvements and modifications It 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.

Images