CN102013047A - Method for monitoring yield variation degree of crops - Google Patents
Method for monitoring yield variation degree of crops Download PDFInfo
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- CN102013047A CN102013047A CN2010102796455A CN201010279645A CN102013047A CN 102013047 A CN102013047 A CN 102013047A CN 2010102796455 A CN2010102796455 A CN 2010102796455A CN 201010279645 A CN201010279645 A CN 201010279645A CN 102013047 A CN102013047 A CN 102013047A
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Abstract
The invention discloses a method for monitoring yield variation degree of crops, which comprises the steps of: 1, processing obtained remote sensing image data of target crops to obtain raster data, and calculating according to the raster data to obtain remote sensing yield estimation data of different land parcels; 2, obtaining crop yield data on image element scales according to the raster data and obtaining facet vector data of different land parcels; 3, establishing a crop yield database of different land parcels; and 4, formulating the yield variation degree of the target crops of all land parcels into a thematic map according to the crop yield database. The invention overcomes the defects of time and labor saving and low efficiency of the traditional monitoring method, and effectively improves the accuracy and the precision of monitoring the yield variation degree of the crops in a large range while improving the working efficiency and lightening the working intensity.
Description
Technical field
The present invention relates to crop yield monitoring field, particularly a kind of crop yield degree of variation monitoring method based on remote sensing technology.
Background technology
Under the present agricultural production conditions of China, peasant's plot is relatively dispersion, scrappy usually, and different plot there are differences at aspects such as soil, weathers in the same area, simultaneously, peasant household's control measures are various, thereby make different plot also have bigger difference on crop growing state and per mu yield level.Even if owing to the difference of its basic soil fertility, landform and management of water equality factor, also there is the difference of crop yield in inside, same plot.The degree of variation of crop yield is to weigh a standard of crop growing state, also is to estimate a different plot important indicator of power fundamentally.
In the prior art, obtain the inner crop yield data in plot, the one, by traditional methods of sampling, this method generally all is to carry out at big zone, adopts multiphase sampling, as being overall five-stage sampling to economize, promptly economize and take out counties and cities, the small towns is taken out by counties and cities, and villager group is taken out in the small towns, the plot is taken out by villager group, ground this point of block sampling; And three stage samplings of county authorities assume main responsibilities body and based on the sampling in village.Perhaps strengthen sampling density, in the hope of obtaining yield data comparatively accurately in inside, plot.But, this mode by manual research step by step one by one the method for block sampling often can't guarantee the representativeness of sample, the inside, concrete plot that also can't implement the monitoring of yield variation; And the crop yield data to obtain workload big and cost is high, speed is slow, can't large tracts of land promote.
Along with the development of precision agriculture technology, the crop yield data can rely on the united reaper of band system for measuring yield to obtain in the plot, also can generate the output figure at the plot simultaneously.But because it is higher to survey product harvester cost, can't large tracts of land promote, therefore, also can't in big zone, obtain the yield data in different plot, can not satisfy the needs of modern agriculture management.
Summary of the invention
(1) technical matters that will solve
The technical problem to be solved in the present invention is the crop yield variation data that how obtain fast more accurately based on the plot, and reduces cost, uses manpower and material resources sparingly.
(2) technical scheme
For this reason, the invention provides a kind of crop yield degree of variation monitoring method, may further comprise the steps:
Step 1, the remote sensing image data of the target crops that obtain handled obtain raster data, calculate the Remote Sensing Yield Estimation data in different plot according to described raster data;
Step 2, obtain the crop yield data on the pixel yardstick and obtain the facet vector data in different plot according to described raster data;
Wherein, described step 1 specifically comprises:
Described step 3 specifically comprises:
The described facet vector data and the Remote Sensing Yield Estimation data in different plot are superposeed, obtain crop yield database based on the plot object element.
Described step 3 also comprises: obtain the polar plot based on the plot object element.
Described step 3 specifically comprises:
Facet vector data with the plot is a computing unit, and the crop yield information that different plot and its comprise is corresponding, calculates the feature of the yield data that each plot comprises one by one;
Result of calculation as attribute information, is added in the facet vector data of described plot, make up crop yield database based on different plot.
Described yield data comprises: output maximal value, minimum value, average, standard deviation and the coefficient of variation.
(3) beneficial effect
Technique scheme has following advantage: by obtaining the yield data in plot, help management unit and agribusiness and monitor the information of grasping crop yield in real time on a macro scale when producing regulation and control, shorten the sample time, reduce cost, raise the efficiency; Also help by giving production manager and peasant with the crop yield information feedback, rule and knowledge that the accumulation crop yield forms are to instruct crops tuning cultivation management etc.
Description of drawings
Fig. 1 is the crop yield degree of variation monitoring method process flow diagram of the embodiment of the invention;
Fig. 2 is the flow chart of steps of the acquisition Remote Sensing Yield Estimation data of the embodiment of the invention;
Fig. 3 is the remote sensing monitoring figure of Beijing's winter wheat growing area distribution in 2009 of the embodiment of the invention;
Fig. 4 is the final plot of winter wheat in 2009, Beijing vector data figure of the embodiment of the invention;
Fig. 5 is winter wheat yields remote sensing monitoring in 2009 figure of Beijing of the embodiment of the invention;
Fig. 6 is winter wheat yields degree of variation in 2009 the remote sensing monitoring figure of Beijing of the embodiment of the invention.
Embodiment
Below in conjunction with drawings and Examples, the specific embodiment of the present invention is described in further detail.Following examples are used to illustrate the present invention, but are not used for limiting the scope of the invention.
As shown in Figure 1, be the crop yield degree of variation monitoring method process flow diagram of the embodiment of the invention, the crop yield degree of variation monitoring method based on remote sensing technology and GIS technology that the present invention proposes comprises the following steps:
The step of step 1, acquisition Remote Sensing Yield Estimation data: the Remote Sensing Yield Estimation data that obtain different plot according to the remote sensing image data of the target crops that obtain.
Remote Sensing Yield Estimation is according to Biological Principles, and on the basis of the different spectral signatures of the various cereal crops of collection analysis, the face of land information that records of sensor is distinguished agrotype via satellite, the monitoring crop growing state, and before crop harvesting, the output of prediction crop.Remote Sensing Yield Estimation comprises two important contents: crop identification and sown area extract, growing way monitoring and yield forecast.The object of Remote Sensing Yield Estimation mostly is region class greatly, generally carries out the output estimation at difference province, city-level unit in the prior art; The present invention obtains crop yield data in the big regional extent by Remote Sensing Yield Estimation, on this basis, carries out the yield variation evaluation at different plot.As shown in Figure 2, the flow chart of steps for the acquisition Remote Sensing Yield Estimation data of the embodiment of the invention comprises:
It is the basis that accurately obtains crop growing state information that the remote sensing image that obtains is carried out pre-service, and present embodiment comprises radiation correcting to the pre-service of remote sensing image, and atmosphere is corrected and geometric correction; The purpose that radiation correcting and atmosphere are corrected is to eliminate the influence of the various factorss such as sensor self, atmosphere and sun altitude that are subjected to when remote sensing image obtains, obtain face of land real reflectance data, general using atmosphere radiation transportation simulator software such as 6S, MODTRAN etc. carry out, also can directly utilize atmosphere that the remote sensing professional software provides to correct the atmosphere radiation that module carries out image and correct, as the FLAASH module of the ENVI software of RSI company;
Geometric correction is to utilize ground control point to correct the geometry deformation of the remote sensing image that various factors causes, remote sensing image is carried out the geographic coordinate location, integrate for how much of realization and standard picture, standard map and standard facet vector data, geometric correction can be realized by the geometric correction function of ENVI software.
The extraction of target crops is the extraction basis of crop yield remote-sensing inversion and ground block message.Present embodiment is at the crop growthing development feature of being studied, obtain study area multi-temporal remote sensing image data, remote sensing image is carried out after the pre-service, by remote sensing software pretreated data are realized computer automatic sorting, extract the space distribution situation of different target crops, the extraction result of target crops is generally the space distribution grid map of certain crops, can obtain the space distribution raster data of target crops by figure, according to the difference of image resolution, crops distribution yardstick can be from the district level to provincial so that bigger regional rank.Different with agricultural output assessment of the prior art is: when the crop yield degree of variation monitoring of carrying out the plot yardstick, not only to carry out the crop yield remote sensing monitoring, because goal in research is different natural plot, therefore, also need to obtain the up-to-date information of block boundary naturally simultaneously.
Yield remote sensing estimating techniques research as one of agricultural output assessment core content, from simple spectrum of initial stage or index statistical regression model, develop into the region growing simulation Remote Sensing Model stage of today based on crop physiology and ecology mechanism, no matter the research slave rational faculty, popularity, still from comprehensive, application aspect, all obtained progress in various degree.
Present embodiment utilizes available research achievements, in crop growth period, obtain multi-source multidate satellite remote sensing date, simultaneously, at the crop key developmental stages, obtain field crop growing way parameter, ultimate capacity and correlation parameter, comprehensive various crop yield remote sensing appraising model carries out the output estimation, to obtain large tracts of land crop yield data.
Remote Sensing Yield Estimation of the prior art can be the yield data that unit obtains crop in the regional extent with the pixel, but variation for inside, plot output, because the shortage of plot data boundary, then seldom relate to, the present invention is directed to this problem, obtain big area crops yield data by the Remote Sensing Yield Estimation technology, with the proportion of crop planting plot is research object, the monitoring method based on the crop yield degree of variation in plot of integrated remote sensing and GIS technology is proposed, in the district, the county, economize, cities etc. are than on the large scale, and realization is quick at inside, plot, accurately, the monitoring and the evaluation of real-time crop yield degree of variation.
The step that step 2, plot boundary information obtain: obtain the crop yield data on the pixel yardstick and obtain the border facet vector data in different plot according to raster data.
The present invention is directed to the plot and carry out the crop yield variation detection, therefore, need obtain the plot data boundary in real time.The extraction of plot boundary information is carried out at the sorted raster data of crop, and the present invention adopts following two kinds of methods to extract the data boundary in plot:
(1) utilize the remote sensing image processing software ENVI of RSI company that sorted raster data is converted into facet vector data, with existing land use data stack, the polar plot layer Intersect algorithm that ArcView 3.3 softwares by ESRI company provide, to two-layer facet vector data stack cutting, extract the ground block boundary, for the ground blocks of data that obtains, again by carrying out visual contrast with up-to-date remotely-sensed data, block boundary information is revised over the ground in ArcView software, as final natural plot facet vector data.
(2) be guiding with existing land use data, classification results at the study area crops, utilize easy health software (ECognition) that raster data is carried out OO cutting apart again, partitioning algorithm can be used for reference the method that ECognition software provides, and object element is divided into some plot subobject and generates the plot facet vector data.
This process of present embodiment is utilized VB to call the GIS secondary development tool bag Mabobjects2.2 computing of programming and is finished.At first, with the plot facet vector data is computing unit, the crop yield information that different plot and its are comprised is corresponding, calculate the feature of all yield datas that each plot comprises one by one, calculate content and comprise the output minimum value that each plot comprises, maximal value, average, the standard deviation and the coefficient of variation; Secondly, in calculating process, the facet vector data editting function of utilizing MO2.2 to provide, with result of calculation as attribute information, add in the facet vector data of plot, make up new crop yield database, except that the output relevant information based on different plot, this database also identifies different block area sizes, information such as girth and ground class title.
The step that step 4, thematic map are made: the yield variation degree of the target crops in each plot is made thematic map according to the crop yield database.
After setting up, can be carried out to figure to the yield variation degree of the target crops in all plot in the whole regional extent, to realize macroscopical monitoring and evaluation of large-scale crop yield degree of variation based on the crop yield database in plot.
Embodiment 1
Present embodiment is example with the Beijing area, by the enforcement of this method, finally obtains the monitoring result of Beijing area winter wheat yields degree of variation in 2009.Use crop yield degree of variation monitoring method of the present invention, present embodiment may further comprise the steps:
1, the step of obtaining and handling of remote sensing image:
2009 years, obtain study area Landsat TM remote sensing image 4 scapes season altogether in winter wheat growth, to obtain the date to be respectively March 30, April 15, May 17 and June 2, corresponding respectively winter wheat is stood up phase, jointing stage, boot stage and pustulation period.All Landsat images adopt the dark goal method under the 6S model supports to carry out the atmosphere correction, have 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 step of target crops extraction:
Utilize 2009 March 30 year, April 15, May 17 and June 2 days Landsat5TM winter wheat growth season remote sensing image adopts the decision tree classification method, and Beijing area winter wheat growing area is extracted.Extract the result as shown in Figure 3, be the remote sensing monitoring figure that Beijing of embodiment of the invention winter wheat growing area in 2009 distributes, obtain the raster data of winter wheat.
3, the step obtained of block message:
Utilize easy health software (ECognition) that the raster data that previous step obtains is carried out OO cutting apart again, cut apart for the winter wheat growing area that obtains among Fig. 3, obtain the plot boundary information, in ARCVIEW3.3 software, dividing the type polar plot by the farmland, Beijing area in 2006 that obtains with the high resolution ratio satellite remote-sensing image visual interpretation compares, winter wheat remote sensing image in 2009 information extraction simultaneously superposes, by visual interpretation, determine area, Tongzhou, Beijing in 2009 winter wheat planting site block boundary.Winter wheat ground blocks of data as shown in Figure 4, is the final plot of winter wheat in 2009, Beijing vector data figure of the embodiment of the invention through after revising.
4, the step of crop yield remote sensing estimation:
2009 annual winter wheat are chosen more than 30 sampling point in major grain producing areas such as Tongzhou, Beijing, Shunyi in growth period, cooperate satellite to pass by the time, have obtained winter wheat key developmental stages colony growing way parameter, in harvest time all are tested sampling point, obtain its yield data; According to the winter wheat colony growing way data and the yield data that obtain, set up the winter wheat yields remote sensing monitoring with multidate satellite remote-sensing image information, carry out inverting by this model, obtain yield of wheat Monitoring Data on a large scale, as shown in Figure 5, be winter wheat yields remote sensing monitoring in 2009 figure of Beijing of the embodiment of the invention.
5, at the foundation of the crop yield database in plot:
For each plot, utilize VB to extract the yield values of plot institute all pixel correspondences of correspondence in conjunction with GIS secondary development control MO programming, calculate this plot not phase NDVI minimum value, maximal value, average, standard deviation and the coefficient of variation simultaneously, and this information added to new field in the record of plot vector file correspondence, generate the crop yield database.
6, the step of thematic map making:
Utilize GIS software to generate study area winter wheat yields degree of variation evaluation map, realize that the result is winter wheat yields degree of variation in 2009 the remote sensing monitoring figure of Beijing of the embodiment of the invention as shown in Figure 6 to the monitoring of the inner winter wheat yields variation in different plot.
As can be seen from the above embodiments, the technical scheme of the embodiment of the invention has made full use of remote sensing image data can be repeatedly, instantaneous, the harmless characteristics of obtaining on a large scale " planar " object spectrum information, carry out the evaluation of crop yield degree of variation at natural plot, having overcome the investigation of crop yield degree of variation in the past wastes time and energy, inefficient shortcoming, increasing work efficiency, when alleviating working strength, effectively raise crop yield degree of variation 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 the technology of the present invention principle; can also make some improvement and modification, these improve and modification also should be considered as protection scope of the present invention.
Claims (6)
1. crop yield degree of variation monitoring method is characterized in that, may further comprise the steps:
Step 1, the remote sensing image data of the target crops that obtain handled obtain raster data, calculate the Remote Sensing Yield Estimation data in different plot according to described raster data;
Step 2, obtain the crop yield data on the pixel yardstick and obtain the facet vector data in different plot according to described raster data;
Step 3, set up the crop yield database in different plot;
Step 4, the yield variation degree of the target crops in each plot is made thematic map according to the crop yield database.
2. crop yield degree of variation monitoring method as claimed in claim 1 is characterized in that described step 1 specifically comprises:
Step 11, obtain the multi-temporal remote sensing image of target crop growth phase, described remote sensing image is carried out pre-service;
Step 12, to the extraction of classifying of pretreated remote sensing image, obtain the raster data of target crops in the target plot; Crop yield degree of variation to different plot is monitored;
Step 13, according to described raster data, carry out the output estimation in conjunction with the growing way parameters of described target crops and ultimate capacity, obtain the Remote Sensing Yield Estimation data in different plot.
3. crop yield degree of variation monitoring method as claimed in claim 1 is characterized in that described step 3 specifically comprises:
The described facet vector data and the Remote Sensing Yield Estimation data in different plot are superposeed, obtain crop yield database based on the plot object element.
4. crop yield degree of variation monitoring method as claimed in claim 3 is characterized in that described step 3 also comprises: obtain the polar plot based on the plot object element.
5. crop yield degree of variation monitoring method as claimed in claim 1 is characterized in that described step 3 specifically comprises:
Facet vector data with the plot is a computing unit, and the crop yield information that different plot and its comprise is corresponding, calculates the feature of the yield data that each plot comprises one by one;
Result of calculation as attribute information, is added in the described facet vector data, make up crop yield database based on different plot.
6. crop yield degree of variation monitoring method as claimed in claim 5 is characterized in that described yield data comprises: output maximal value, minimum value, average, standard deviation and the coefficient of variation.
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN103000077A (en) * | 2012-11-27 | 2013-03-27 | 中国科学院东北地理与农业生态研究所 | Method for carrying out mangrove forest map making on intermediate resolution remote sensing image by utilizing object-oriented classification method |
| CN103294905A (en) * | 2013-05-20 | 2013-09-11 | 北京农业信息技术研究中心 | Object-oriented crop seedtime monitoring method |
| CN107085662A (en) * | 2017-05-12 | 2017-08-22 | 首都师范大学 | A kind of method that earthquake intensity is extracted based on nighttime light data |
| CN109960781A (en) * | 2019-03-21 | 2019-07-02 | 中国农业科学院农业资源与农业区划研究所 | A method of updating world crops total output raster data |
| CN112364302A (en) * | 2020-11-11 | 2021-02-12 | 中国科学院东北地理与农业生态研究所 | Ecological system attribute component composition structure description method fusing attribute grading information |
| CN114331753A (en) * | 2022-03-04 | 2022-04-12 | 阿里巴巴达摩院(杭州)科技有限公司 | Intelligent farm work method and device and control equipment |
| CN115713700A (en) * | 2022-11-23 | 2023-02-24 | 广东省国土资源测绘院 | Method for collecting typical crop planting samples in cooperation with open space |
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| CN1731216A (en) * | 2005-08-19 | 2006-02-08 | 广州地理研究所 | A remote sensing detection and evaluation method for the area and production of large-area crop raising |
| CN101699315A (en) * | 2009-10-23 | 2010-04-28 | 北京农业信息技术研究中心 | Monitoring device and method for crop growth uniformity |
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Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN103000077A (en) * | 2012-11-27 | 2013-03-27 | 中国科学院东北地理与农业生态研究所 | Method for carrying out mangrove forest map making on intermediate resolution remote sensing image by utilizing object-oriented classification method |
| CN103294905A (en) * | 2013-05-20 | 2013-09-11 | 北京农业信息技术研究中心 | Object-oriented crop seedtime monitoring method |
| CN103294905B (en) * | 2013-05-20 | 2016-02-10 | 北京农业信息技术研究中心 | OO crops date of seeding monitoring method |
| CN107085662A (en) * | 2017-05-12 | 2017-08-22 | 首都师范大学 | A kind of method that earthquake intensity is extracted based on nighttime light data |
| CN107085662B (en) * | 2017-05-12 | 2020-08-25 | 首都师范大学 | Method for extracting seismic intensity based on night light data |
| CN109960781A (en) * | 2019-03-21 | 2019-07-02 | 中国农业科学院农业资源与农业区划研究所 | A method of updating world crops total output raster data |
| CN109960781B (en) * | 2019-03-21 | 2020-10-30 | 中国农业科学院农业资源与农业区划研究所 | Method for updating global crop total yield grid data |
| CN112364302A (en) * | 2020-11-11 | 2021-02-12 | 中国科学院东北地理与农业生态研究所 | Ecological system attribute component composition structure description method fusing attribute grading information |
| CN114331753A (en) * | 2022-03-04 | 2022-04-12 | 阿里巴巴达摩院(杭州)科技有限公司 | Intelligent farm work method and device and control equipment |
| CN115713700A (en) * | 2022-11-23 | 2023-02-24 | 广东省国土资源测绘院 | Method for collecting typical crop planting samples in cooperation with open space |
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Application publication date: 20110413 |