CN101699315B - 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 kind of monitoring device of crop growth uniformity and method

Technical field

The present invention relates to a kind of monitoring device and method of crop growth uniformity.

Background technology

Crop growth monitoring refers to the macroscopic view monitoring of the growth of cereal crop seedlings, upgrowth situation and variation thereof to crop, and requirement can reflect agricultural feelings in time comprehensively.The growth uniformity of crop is an important indicator estimating the crop growing state quality always, and this index not only can embody the difference of different farmland massifs basis soil fertility, landform, also can reflect different tillers' management level.

How the tradition crop growth uniformity is confirmed through the field sample survey.Like the patriotic basic seedling uniformity coefficient of the winter wheat index P that waits the calendar year 2001 proposition of horse, adopt the method for grab sample exactly.This method is counted according to definite investigation of each block area, then the seedling amount of every each points for investigation of ground is arranged from big to small.If the first half points for investigation seedling amount sum is ∑ a, half points for investigation seedling amount sum of back is ∑ b, calculates in the substitution formula (1-1) then and obtains the corresponding basic seedling uniformity coefficient P in each plot:

$P=[1-\frac{\mathrm{\Σa}\÷\frac{1}{2}n-\mathrm{\Σb}\÷\frac{1}{2}n}{\left(\frac{\mathrm{\Σa}+\mathrm{\Σb}}{n}\right)}]\×100\%---(1-1)$

Wherein, n is that the plot investigation is counted.

In addition, calendar year 2001 such as Zhong Qizhuan has also proposed cotton uniformity coefficient index.Defined according to the growing of cotton, formalness characteristic and cultivation management measure comprise the regularity of emerging, stay the seedling uniformity coefficient, a series of evaluation indexes such as the regularity of buddingging, the regularity of blooming, blow-of-cottons regularity and plant height uniformity coefficient, and proposed to improve the technical measures of uniformity coefficient to these indexs.

Yang Lihua etc. proposed the definition of plant field distribution consistency degree in 2006, the degree of plant approximate honeycomb of layout in the field is defined as plant field distribution consistency degree, and has provided the computing formula (1-2) of plant field distribution consistency degree:

$\mathrm{UD}=\frac{1}{(1+\mathrm{\Σ\η}/m)N}---(1-2)$

Wherein, UD is a plant field distribution consistency degree, 0＜UD≤1, and it is worth more near 1, and plant distributes even more; M counts for observation; N is average cave strain number; η (i=1,2 ... be the equal coefficient that leaves of each observation station ..m), can calculate through formula (1-3):

$\mathrm{\η}=\sqrt{\frac{\mathrm{\Σ}{(P_i-P)}^2}{12}}/P---(1-3)$

P in the formula _iBe 12 plant observed readings of a honeycomb observation station, p is a nominal linewidth.

In practical application, the monitoring of crop growth uniformity and index of correlation obtain the manpower and materials that need labor.Owing to need growing way difference, so there are shortcomings such as workload is big, automaticity is low, the update cycle is long to open-air on-site inspection crops.And when needs carried out the investigation of large-scale crop growth uniformity, it was measured difficulty and also will strengthen.Simultaneously, the difference of different farmland massif crop growing states is again ubiquitous, relies on the manual research data merely, can not satisfy the needs of modern agriculture management.

Summary of the invention

To defective that exists in the prior art and deficiency, the purpose of this invention is to provide a kind of monitoring device and method of crop growth uniformity, remote sensing via satellite obtains growth uniformity.

For achieving the above object, the present invention proposes a kind of monitoring device of crop growth uniformity, comprising:

Remote sensing image processing module, said remote sensing image processing module are carried out radiation correcting, atmosphere correction and geometric correction according to the satellite remote sensing images that obtains to satellite remote sensing images;

Plot vector data processing module, said plot vector data processing module is according to classifying to the crops in the said satellite remote sensing images, to obtain the spatial distribution map of target crops; And the grid classification result in the sorted satellite remote sensing images is converted into facet vector data; Then the ground block boundary of said spatial distribution map is revised;

Vegetation index processing module, said vegetation index processing module are according to the vegetation index NDVI (Normalized Difference Vegetation Index) in this plot of intramassif spectral signature information calculations in the said satellite remote sensing images:

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

Wherein, R _NirThe reflectivity that refers to the near-infrared band of satellite remote sensing images; R _RedThe reflectivity that refers to the red spectral band of satellite remote sensing images;

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

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

Wherein:

NDVI _CVIt is the coefficient of variation of each pairing NDVI in plot;

NDVI _CVminFor with the minimum value in all plot NDVI coefficient of variation mutually for the moment;

NDVI _CVmaxFor with the maximal value in all plot NDVI coefficient of variation mutually for the moment.

Wherein, said plot vector data processing module comprises:

The spatial distribution map processing sub, said spatial distribution map processing sub is classified to the crops in the said satellite remote sensing images, to obtain the spatial distribution map of target crops; And the classification of the grid of said spatial distribution map is converted into facet vector data;

The land use data processing sub; Said land use data processing sub is according to satellite remote sensing images, and the phase soil utilized thematic data when said satellite remote sensing images was carried out reference that visual interpretation obtains data over the years;

Plot boundary treatment submodule, said plot boundary treatment submodule with said facet vector data with said with reference to the time after the soil utilizes thematic data stack mutually, cut the back through polar plot layer Intersect algorithm and extract the ground block boundary; And utilize the satellite remote sensing images in this year, and carry out ground block boundary correction through visual interpretation, obtain final crops plot data boundary.

Wherein, said vegetation index processing module comprises:

Spectral signature processing sub, said spectral signature processing sub are extracted the corresponding remotely-sensed data in said plot, and obtain crops not phase, different-waveband spectral signature information simultaneously according to remotely-sensed data;

The vegetation parameter processing sub, said vegetation parameter processing sub is carried out the wave band computing to spectral signature information, obtains vegetation index NDVI;

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

Wherein, R _NirThe reflectivity that refers to the near-infrared band of satellite remote sensing images; R _RedThe reflectivity that refers to the red spectral band of satellite remote sensing images;

The wave band calculating sub module, said wave band calculating sub module to not simultaneously the spectral signature information of phase, different-waveband carry out the wave band computing, obtain the NDVI minimum value, maximal value, average, standard deviation, coefficient of variation NDVI _CV

Wherein, said device also comprises:

Crop spectral information module, said crop spectral information module are extracted this corresponding all pixel NDVI values of plot institute to each plot, calculate this plot not phase NDVI minimum value, maximal value, average, standard deviation and coefficient of variation NDVI simultaneously _CVWith winter wheat growth uniformity index GUI, and this information added in the corresponding record of plot vector file with new field, generate crop growing state spectral information knowledge base.

Wherein, said device also comprises:

The thematic map manufacturing module, the crop growing state spectral information knowledge base that said thematic map manufacturing module generates according to said crop spectral information module is set up thematic map to the growth uniformity of the target crops in all plot.

Simultaneously, the invention allows for a kind of monitoring method of crop growth uniformity, comprising:

Step 1, obtain satellite remote sensing images, and to said satellite remote sensing images carry out radiation correcting, atmosphere is corrected and geometric correction;

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

Step 3, the grid classification result in the spatial distribution map is converted into facet vector data; And said spatial distribution map handled with the ground block boundary to crops revise;

Step 4, according to the vegetation index NDVI in this plot of intramassif spectral signature information calculations in the said satellite remote sensing images:

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

Wherein, R _NirThe reflectivity that refers to the near-infrared band of satellite remote sensing images; R _RedThe reflectivity that refers to the red spectral band of satellite remote sensing images;

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

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

Wherein:

NDVI _CVIt is the coefficient of variation of each pairing NDVI in plot;

NDVI _CVminFor with the minimum value in all plot NDVI coefficient of variation mutually for the moment;

NDVI _CVmaxFor with the maximal value in all plot NDVI coefficient of variation mutually for the moment.

Wherein, said step 3 is specially:

Step 31, the classification of the grid of said spatial distribution map is converted into facet vector data;

Step 32, through the high resolution ratio satellite remote-sensing image, to satellite remote sensing images over the years carry out visual interpretation obtain with reference to the time phase soil utilize thematic data;

Step 33, with after the stack of step 31 and step 32 gained data, through polar plot layer Intersect algorithm to the two-layer vector data of step 31 and step 32 gained cut the back extract block boundary; And utilize the satellite remote sensing images in this year, and carry out ground block boundary correction through visual interpretation, obtain final crops plot data boundary.

Wherein, said step 4 is specially:

Step 41, extract the corresponding remotely-sensed data in plot, and obtain crops not phase, different-waveband spectral signature information simultaneously according to remotely-sensed data;

Step 42, spectral signature information is carried out the wave band computing, obtain vegetation index NDVI;

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

Wherein, R _NirThe reflectivity that refers to the near-infrared band of satellite remote sensing images; R _RedThe reflectivity that refers to the red spectral band of satellite remote sensing images;

Step 43, to not simultaneously the spectral signature information of phase, different-waveband carry out the wave band computing, obtain the NDVI minimum value, maximal value, average, standard deviation, coefficient of variation NDVI _CV

Wherein, said method also comprises:

Step 6, to each plot, extract this corresponding all pixel NDVI values of plot institute, calculate this plot not phase NDVI minimum value, maximal value, average, standard deviation and coefficient of variation NDVI simultaneously _CVWith winter wheat growth uniformity index GUI, and this information added in the corresponding record of plot vector file with new field, generate crop growing state spectral information knowledge base.

Wherein, said method also comprises:

Step 7, the crop growing state spectral information knowledge base that generates according to said crop spectral information module are set up thematic map to the growth uniformity of the target crops in all plot.

Technique scheme has following advantage: the present invention can obtain satellite remote sensing images through remote sensing technology; And obtain the vector data in said plot through the ground block boundary; Vector data according to this plot obtains vegetation index again, calculates crops growth uniformity degree index thereby calculate.The present invention can solve shortcomings such as workload is big in the prior art, automaticity is low, the update cycle is long, increases work efficiency.Integrated remote sensing of the present invention and GIS technology; Utilize grid and vector data integrated technique to realize crops growth uniformity monitoring to the nature plot; Given full play to the advantage of remotely-sensed data in crop condition monitoring, proposed crops growth uniformity evaluation index, made up crop growth uniformity knowledge base based on remote sensing features information; And realized in real time, the remote sensing monitoring of crops growth uniformity fast and accurately, improved the precision of crop growth uniformity investigation.

Description of drawings

Fig. 1 is the structural representation of the monitoring device of the crop growth uniformity of the present invention's proposition;

Fig. 2 is the schematic flow sheet of the monitoring method of the crop growth uniformity of the present invention's proposition;

Fig. 3 is the spatial distribution map of the target crops of the extraction in specific embodiment of the present invention;

Fig. 4 is for converting this raster data among Fig. 3 into the plot image behind the vector data;

Fig. 5 is the revised plot of Fig. 4 process image.

Embodiment

Below in conjunction with accompanying drawing and embodiment, specific embodiments of the invention describes in further detail.Following examples are used to explain the present invention, but are not used for limiting scope of the present invention.

Embodiment 1

First preferred embodiment of the invention has proposed a kind of monitoring device of crop growth uniformity, and its structure is as shown in Figure 1, comprising:

Remote sensing image processing module, said remote sensing image processing module are carried out radiation correcting, atmosphere correction and geometric correction according to the satellite remote sensing images that obtains to satellite remote sensing images;

Plot vector data processing module, said plot vector data processing module is according to classifying to the crops in the said satellite remote sensing images, to obtain the spatial distribution map of target crops; And the grid classification result in the sorted satellite remote sensing images is converted into facet vector data; Then the ground block boundary of said spatial distribution map is revised;

Vegetation index processing module, said vegetation parameter processing module are according to the vegetation index NDVI in this plot of intramassif spectral signature information calculations in the said satellite remote sensing images:

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

Wherein, R _NirThe reflectivity that refers to the near-infrared band of satellite remote sensing images; R _RedThe reflectivity that refers to the red spectral band of satellite remote sensing images;

Growth uniformity processing module, said growth uniformity processing module are calculated the growth uniformity index GUI in this plot according to vegetation index NDVI;

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

Wherein:

NDVI _CVIt is the coefficient of variation of each pairing NDVI in plot;

NDVI _CVminFor with the minimum value in all plot NDVI coefficient of variation mutually for the moment;

NDVI _CVmaxFor with the maximal value in all plot NDVI coefficient of variation mutually for the moment.

First preferred embodiment of the invention, remote sensing images are confirmed the plot of target crops via satellite, and vegetation index NDVI and growth uniformity index GUI through this plot of intramassif spectral signature information calculations in the intramassif satellite remote sensing images.First preferred embodiment of the invention utilizes grid and vector data integrated technique to realize the crops growth uniformity monitoring to the nature plot, has given full play to the advantage of remotely-sensed data in crop condition monitoring.The present invention realized in real time, the remote sensing monitoring of crops growth uniformity fast and accurately, improved the precision of crop growth uniformity investigation.

Embodiment 2

Second preferred embodiment of the invention is on the first preferred embodiment basis, to improve, and promptly said plot vector data processing module comprises:

The spatial distribution map processing sub, said spatial distribution map processing sub is classified to the crops in the said satellite remote sensing images, to obtain the spatial distribution map of target crops; And the classification of the grid of said spatial distribution map is converted into facet vector data;

The land use data processing sub; Said land use data processing sub is according to satellite remote sensing images, and the phase soil utilized thematic data when said satellite remote sensing images was carried out reference that visual interpretation obtains data over the years;

Plot boundary treatment submodule, said plot boundary treatment submodule with said facet vector data with said with reference to the time after the soil utilizes thematic data stack mutually, cut the back through polar plot layer Intersect algorithm and extract the ground block boundary; And utilize the satellite remote sensing images in this year, and carry out ground block boundary correction through visual interpretation, obtain final crops plot data boundary.

In the second preferred embodiment of the invention, through with reference to the time phase soil utilize the thematic data back cutting that superposes to extract the ground block boundary, revise more accurately with block boundary over the ground, improve the accuracy of Monitoring Data.

Wherein " with reference to the time phase soil utilize thematic data " be meant and utilize high-resolution remote sensing image over the years or the historical land use data of study area, with the farmland massif data of obtaining.Because historical data and up-to-date soil utilize the situation may be slightly different, therefore utilize historical data combination current data over the ground block boundary revise, can improve the accuracy of plot data boundary.

Embodiment 3

Third preferred embodiment of the invention is on the basis of first preferred embodiment and second preferred embodiment, to improve, and promptly said vegetation index processing module comprises:

Spectral signature processing sub, said spectral signature processing sub are extracted the corresponding remotely-sensed data in said plot, and obtain crops not phase, different-waveband spectral signature information simultaneously according to remotely-sensed data;

The vegetation parameter processing sub, said vegetation parameter processing sub is carried out the wave band computing to spectral signature information, obtains vegetation index NDVI;

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

Wherein, R _NirThe reflectivity that refers to the near-infrared band of satellite remote sensing images; R _RedThe reflectivity that refers to the red spectral band of satellite remote sensing images;

The wave band calculating sub module, said wave band calculating sub module is to not phase, different-waveband simultaneously, and spectral signature information is carried out the wave band computing, obtains the NDVI minimum value, maximal value, average, standard deviation, coefficient of variation NDVI _CV

Wherein vegetation index NDVI is that vegetation index NDVI is the normalized ratio of visible red wave band and near-infrared band two wave bands; It can reflect the photosynthetic effective radiation absorbing state of vegetation on the one hand; Can reflect crop growing state, leaf area index LAI etc. on the other hand, be to use vegetation index the most widely at present.In the third preferred embodiment of the invention, applied satellite remote sensing images and accurate revised ground block boundary obtain intramassif spectral signature information, and according to spectral signature information calculations vegetation index NDVI, can improve the accuracy of NDVI.

Embodiment 4

Four preferred embodiment of the invention is on the basis of above-mentioned three preferred embodiments, to improve, and promptly said device also comprises:

Crop spectral information module, said crop spectral information module are extracted this corresponding all pixel NDVI values of plot institute to each plot, calculate this plot not phase NDVI minimum value, maximal value, average, standard deviation and coefficient of variation NDVI simultaneously _CVWith winter wheat growth uniformity index GUI, and this information added in the corresponding record of plot vector file with new field, generate crop growing state spectral information knowledge base.

Four preferred embodiment of the invention utilizes NDVI to set up crop growing state spectral information knowledge base,, the effectively monitoring of crop growing state spectral information long-term to improve.

Embodiment 5

Fifth preferred embodiment of the invention is on the basis of above-mentioned four preferred embodiments, to improve, and promptly said device also comprises:

The thematic map manufacturing module, the crop growing state spectral information knowledge base that said thematic map manufacturing module generates according to said crop spectral information module is set up thematic map to the growth uniformity of the target crops in all plot.

Thematic map can obtain more intuitive image data according to crop growing state spectral information knowledge base.

Embodiment 6

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

Step 1, obtain satellite remote sensing images, and to said satellite remote sensing images carry out radiation correcting, atmosphere is corrected and geometric correction;

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

Step 3, said spatial distribution map handled with the ground block boundary to crops revise, obtain the vector data in plot;

Step 4, according to the vegetation index NDVI in this plot of intramassif spectral signature information calculations in the said satellite remote sensing images:

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

Wherein, R _NirThe reflectivity that refers to the near-infrared band of satellite remote sensing images; R _RedThe reflectivity that refers to the red spectral band of satellite remote sensing images;

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

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

Wherein:

NDVI _CVIt is the coefficient of variation of each pairing NDVI in plot;

NDVI _CVminFor with the minimum value in all plot NDVI coefficient of variation mutually for the moment;

NDVI _CVmaxFor with the maximal value in all plot NDVI coefficient of variation mutually for the moment.

Sixth preferred embodiment of the invention, remote sensing images are confirmed the plot of target crops via satellite, and vegetation index NDVI and growth uniformity GUI through this plot of intramassif spectral signature information calculations in the intramassif satellite remote sensing images.Sixth preferred embodiment of the invention utilizes grid and vector data integrated technique to realize the crops growth uniformity monitoring to the nature plot, has given full play to the advantage of remotely-sensed data in crop condition monitoring.The present invention realized in real time, the remote sensing monitoring of crops growth uniformity fast and accurately, improved the precision of crop growth uniformity investigation.

Embodiment 7

Seventh preferred embodiment of the invention is on the basis of above-mentioned the 6th preferred embodiment, to improve, and promptly said step 3 is specially:

Step 31, the classification of the grid of said spatial distribution map is converted into facet vector data;

Step 32, through the high resolution ratio satellite remote-sensing image, to satellite remote sensing images over the years carry out visual interpretation obtain with reference to the time phase soil utilize thematic data;

Step 33, with after the stack of step 31 and step 32 gained data, through polar plot layer Intersect algorithm to the two-layer vector data of step 31 and step 32 gained cut the back extract block boundary; And utilize the satellite remote sensing images in this year, and carry out ground block boundary correction through visual interpretation, obtain final crops plot data boundary.

In the seventh preferred embodiment of the invention, through with reference to the time phase soil utilize the thematic data back cutting that superposes to extract the ground block boundary, revise more accurately with block boundary over the ground, improve the accuracy of Monitoring Data.

Embodiment 8

Eighth preferred embodiment of the invention be the above-mentioned the 6th or the basis of the 7th preferred embodiment on improve, promptly said step 4 is specially:

Step 41, extract the corresponding remotely-sensed data in plot, and obtain crops not phase, different-waveband spectral signature information simultaneously according to remotely-sensed data;

Step 42, spectral signature information is carried out the wave band computing, obtain vegetation index NDVI;

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

Wherein, R _NirThe reflectivity that refers to the near-infrared band of satellite remote sensing images; R _RedThe reflectivity that refers to the red spectral band of satellite remote sensing images;

Step 43, to not phase, different-waveband simultaneously, spectral signature information is carried out the wave band computing, obtains the NDVI minimum value, maximal value, average, standard deviation, coefficient of variation NDVI _CV

Wherein vegetation parameter NDVI is that vegetation index NDVI is the normalized ratio of visible red wave band and near-infrared band two wave bands; It can reflect the photosynthetic effective radiation absorbing state of vegetation on the one hand; Can reflect crop growing state, leaf area index LAI etc. on the other hand, be to use vegetation index the most widely at present.In the sixth preferred embodiment of the invention, applied satellite remote sensing images and accurate revised ground block boundary obtain intramassif spectral signature information, and according to spectral signature information calculations vegetation index NDVI, can improve the accuracy of NDVI.

Embodiment 9

Eighth preferred embodiment of the invention be the above-mentioned the 6th or the basis of the 7th or the 8th preferred embodiment on improve, promptly said method also comprises:

Step 6, to each plot, extract this corresponding all pixel NDVI values of plot institute, calculate this plot not phase NDVI minimum value, maximal value, average, standard deviation and coefficient of variation NDVI simultaneously _CVWith winter wheat growth uniformity index GUI, and this information added in the corresponding record of plot vector file with new field, generate crop growing state spectral information knowledge base.

Four preferred embodiment of the invention utilizes NDVI to set up crop growing state spectral information knowledge base, long-term to improve, effective crop growing state spectral information.

Embodiment 10

Eighth preferred embodiment of the invention be the above-mentioned the 6th or the basis of the 7th or the 8th or the 9th preferred embodiment on improve, promptly said method also comprises:

Step 7, the crop growing state spectral information knowledge base that generates according to said crop spectral information module are set up thematic map to the growth uniformity of the target crops in all plot.

Thematic map can obtain more intuitive image data according to crop growing state spectral information knowledge base.

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

(1) remote sensing images obtain and handle

2008 years, obtain study area Landsat TM satellite remote sensing images 3 scapes season altogether in winter wheat growth, to obtain the date to be respectively March 27, April 28 and May 30, corresponding respectively winter wheat is stood up phase, boot stage and winter wheat milk stage.In addition, also obtained study area India star ISP6 image one scape on July 12nd, 08.All Landsat images adopt the dark goal method under the 6S model supports to carry out the atmosphere correction, and the ISP6 image adopts ENVI software FLAASH module to carry out the atmosphere correction, has obtained the earth surface reflection rate of all images.The geometric correction of image adopts image image to be chosen the method for ground control point; Every scape image is chosen and is surpassed 300 ground control points; In addition; The satellite differential GPS reference mark that is obtained during according to factual survey is revised entire image, and the precision of images of process geometric correction is controlled within the pixel.

(2) the target crops are extracted

Utilize 2008 March 27 year, April 28 and Landsat5TM winter wheat growth on May 30 season satellite remote sensing images; And harvested satellite remote sensing images of winter wheat on July 12 in 2008; Adopt the decision tree classification method, area, Tongzhou winter wheat growing area is extracted.The extraction result is as shown in Figure 3.

Utilize ENVI software classification post-processing function to convert this raster data into vector data, the result is as shown in Figure 4.Wherein, ENVI (The Environment for Visualizing Images) is the remote sensing image processing software of American I TT Visual Information Solutions company.

(3) target crops plot vector data obtains

In ARCVIEW3.3 software; Divide the cutting computing that superposes of type polar plot through the farmland, Beijing area in 2006 that obtains with high resolution ratio satellite remote-sensing image visual interpretation; Obtained meticulousr farmland massif vector data boundary, winter wheat satellite remote sensing images in 2008 information extraction simultaneously superposes.Through visual interpretation, confirm area, 2008 Beijing Tongzhou winter wheat planting site block boundary.Winter wheat ground blocks of data is through after revising, and is as shown in Figure 5.

Totally 1105 in winter wheat plantation in 2008 plot, area, Tongzhou; The total area is 17791 hectares (26.7 ten thousand mus); Wherein, area has 526 less than the wheat plot of 10 hectares (150 mus), and the wheat plot of 10-30 hectare (150-450 mu) has 394; The wheat plot of 30-70 hectare (450-1050 mu) has 130, has 2 greater than the wheat plot of 70 hectares (1050 mus).

(4) for each plot, utilize VB to combine GIS secondary development control MO programming to extract corresponding all the pixel NDVI values of plot institute, calculate this plot not phase NDVI minimum value, maximal value, average, standard deviation and coefficient of variation NDVI simultaneously _CV, and this information added in the corresponding record of plot vector file with new field, generate crop spectral information knowledge base.

(5) based on the crop growth uniformity determination of index in plot

For the growing way situation to all plot is carried out integrated survey, this research is according to the coefficient of variation NDVI in all plot mutually for the moment _CVMade up the size of winter wheat growth uniformity index GUI (Growth Uniformity Index) according to this index; Estimate the growing way of different plot winter wheat; The definition of GUI is as follows, and the value of GUI is between 0-1, and the GUI value is big more; Explanatorily the block length gesture is good more, and NDVI value height and growing way are even more.

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

Wherein,

NDVI _CVIt is the coefficient of variation of each pairing NDVI in plot;

NDVI _CVminFor with the minimum value in all plot NDVI coefficient of variation mutually for the moment;

NDVI _CVmaxFor with the maximal value in all plot NDVI coefficient of variation mutually for the moment;

(6) generate crop spectral information knowledge base;

For each plot, utilize VB to combine GIS secondary development control MO programming to extract corresponding all the pixel NDVI values of plot institute, calculate this plot not phase NDVI minimum value, maximal value, average, standard deviation and coefficient of variation NDVI simultaneously _CVWith winter wheat growth uniformity index GUI, and this information added in the corresponding record of plot vector file with new field, generate crop spectral information knowledge base.

(7) generate thematic map;

According to the phase crop growth uniformity thematic map simultaneously not of different plot in the zone.

The method that this instance utilizes the present invention to propose; Realized remote sensing monitoring based on the crops growth uniformity of farmland massif; The technical scheme that the present invention proposes has made full use of the satellite remote sensing images data can be repeatedly, instantaneous, the harmless characteristics of obtaining on a large scale " planar " object spectrum information, carries out the evaluation of crop growth uniformity to natural plot, and having overcome the investigation of crop growth uniformity in the past wastes time and energy; Inefficient shortcoming; Increasing work efficiency, when alleviating working strength, effectively raising crop growth uniformity monitoring accuracy and precision on a large scale.

The above only is a preferred implementation of the present invention; Should be pointed out that for those skilled in the art, under the prerequisite that does not break away from know-why of the present invention; Can also make some improvement and modification, these improve and modification also should be regarded as protection scope of the present invention.

Claims (10)

1. the monitoring device of a crop growth uniformity is characterized in that, comprising:

Remote sensing image processing module, said remote sensing image processing module are carried out radiation correcting, atmosphere correction and geometric correction according to the satellite remote sensing images that obtains to satellite remote sensing images;

Plot vector data processing module, said plot vector data processing module is according to classifying to the crops in the said satellite remote sensing images, to obtain the spatial distribution map of target crops; And the grid classification result in the sorted satellite remote sensing images is converted into facet vector data; Then the ground block boundary of said spatial distribution map is revised;

Vegetation index processing module, said vegetation index processing module are according to the vegetation index NDVI in this plot of intramassif spectral signature information calculations in the said satellite remote sensing images:

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

Wherein, R _NirThe reflectivity that refers to the near-infrared band of satellite remote sensing images; R _RedThe reflectivity that refers to the red spectral band of satellite remote sensing images;

Growth uniformity processing module, said growth uniformity processing module are calculated the growth uniformity index GUI in this plot according to vegetation index NDVI;

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

Wherein:

NDVI _CVIt is the coefficient of variation of each pairing NDVI in plot;

NDVI _CVminFor with the minimum value in all plot NDVI coefficient of variation mutually for the moment;

NDVI _CVmaxFor with the maximal value in all plot NDVI coefficient of variation mutually for the moment.

2. the monitoring device of crop growth uniformity according to claim 1 is characterized in that, said plot vector data processing module comprises:

The spatial distribution map processing sub, said spatial distribution map processing sub is classified to the crops in the said satellite remote sensing images, to obtain the spatial distribution map of target crops; And the classification of the grid of said spatial distribution map is converted into facet vector data;

The land use data processing sub; Said land use data processing sub is according to satellite remote sensing images, said satellite remote sensing images carried out visual interpretation utilize thematic data in the phase soil when obtaining the reference based on data over the years;

Plot boundary treatment submodule, said plot boundary treatment submodule with said facet vector data with said with reference to the time after the soil utilizes thematic data stack mutually, cut the back through polar plot layer Intersect algorithm and extract the ground block boundary; And utilize the satellite remote sensing images in this year, and carry out ground block boundary correction through visual interpretation, obtain final crops plot data boundary.

3. the monitoring device of crop growth uniformity according to claim 1 and 2 is characterized in that, said vegetation index processing module comprises:

Spectral signature processing sub, said spectral signature processing sub are extracted the corresponding remotely-sensed data in said plot, and obtain crops not phase, different-waveband spectral signature information simultaneously according to remotely-sensed data;

The vegetation parameter processing sub, said vegetation parameter processing sub is carried out the wave band computing to spectral signature information, obtains vegetation index NDVI;

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

Wherein, R _NirThe reflectivity that refers to the near-infrared band of satellite remote sensing images; R _RedThe reflectivity that refers to the red spectral band of satellite remote sensing images;

The wave band calculating sub module, said wave band calculating sub module to not simultaneously the spectral signature information of phase, different-waveband carry out the wave band computing, obtain the NDVI minimum value of satellite remote sensing images, maximal value, average, standard deviation, coefficient of variation NDVI _CV

4. the monitoring device of crop growth uniformity according to claim 1 is characterized in that, said device also comprises:

Crop spectral information module, said crop spectral information module are extracted this corresponding all pixel NDVI values of plot institute to each plot, calculate this plot not phase NDVI minimum value, maximal value, average, standard deviation and coefficient of variation NDVI simultaneously _CVWith winter wheat growth uniformity index GUI, and this information added in the corresponding record of plot vector file with new field, generate crop growing state spectral information knowledge base.

5. the monitoring device of crop growth uniformity according to claim 4 is characterized in that, said device also comprises:

The thematic map manufacturing module, the crop growing state spectral information knowledge base that said thematic map manufacturing module generates according to said crop spectral information module is set up thematic map to the growth uniformity of the target crops in all plot.

6. the monitoring method of a crop growth uniformity comprises:

Step 1, obtain satellite remote sensing images, and to said satellite remote sensing images carry out radiation correcting, atmosphere is corrected and geometric correction;

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

Step 3, the grid classification result in the spatial distribution map is converted into facet vector data; And said spatial distribution map handled with the ground block boundary to crops revise;

Step 4, according to the vegetation index NDVI in this plot of intramassif spectral signature information calculations in the said satellite remote sensing images:

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

Wherein, R _NirThe reflectivity that refers to the near-infrared band of satellite remote sensing images; R _RedThe reflectivity that refers to the red spectral band of satellite remote sensing images;

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

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

Wherein:

NDVI _CVIt is the coefficient of variation of each pairing NDVI in plot;

NDVI _CVminFor with the minimum value in all plot NDVI coefficient of variation mutually for the moment;

NDVI _CVmaxFor with the maximal value in all plot NDVI coefficient of variation mutually for the moment.

7. the monitoring method of a kind of crop growth uniformity according to claim 6 is characterized in that, said step 3 is specially:

Step 31, the classification of the grid of said spatial distribution map is converted into facet vector data;

Step 32, through the high resolution ratio satellite remote-sensing image, to satellite remote sensing images over the years carry out visual interpretation obtain with reference to the time phase soil utilize thematic data;

Step 33, with after the stack of step 31 and step 32 gained data, through polar plot layer Intersect algorithm to the two-layer vector data of step 31 and step 32 gained cut the back extract block boundary; And utilize the satellite remote sensing images in this year, and carry out ground block boundary correction through visual interpretation, obtain final crops plot data boundary.

8. according to the monitoring method of claim 6 or 7 described a kind of crop growth uniformities, it is characterized in that said step 4 is specially:

Step 41, extract the corresponding remotely-sensed data in plot, and obtain crops not phase, different-waveband spectral signature information simultaneously according to remotely-sensed data;

Step 42, spectral signature information is carried out the wave band computing, obtain vegetation index NDVI;

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

Wherein, R _NirThe reflectivity that refers to the near-infrared band of satellite remote sensing images; R _RedThe reflectivity that refers to the red spectral band of satellite remote sensing images;

Step 43, to not simultaneously the spectral signature information of phase, different-waveband carry out the wave band computing, obtain the NDVI minimum value, maximal value, average, standard deviation, coefficient of variation NDVI _CV

9. the monitoring method of a kind of crop growth uniformity according to claim 6 is characterized in that, said method also comprises:

Step 6, to each plot, extract this corresponding all pixel NDVI values of plot institute, calculate this plot not phase NDVI minimum value, maximal value, average, standard deviation and coefficient of variation NDVI simultaneously _CVWith winter wheat growth uniformity index GUI, and this information added in the corresponding record of plot vector file with new field, generate crop growing state spectral information knowledge base.

10. the monitoring method of a kind of crop growth uniformity according to claim 9 is characterized in that, said method also comprises:

Step 7, the crop growing state spectral information knowledge base that generates according to crop spectral information module are set up thematic map to the growth uniformity of the target crops in all plot.

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