CN115909063A - Medium-resolution rice extraction method and system - Google Patents
Medium-resolution rice extraction method and system Download PDFInfo
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Abstract
The application discloses a medium-resolution rice extraction method and system, wherein the method comprises the following steps: selecting a planting vector patch of a sample rice in a ploughing area; fitting a curve equation according to the planting vector patches; determining a rice extraction self-adaptive NDVI change threshold value based on a fitting equation; and (4) according to the adaptive NDVI change threshold of the rice extraction, completing the extraction of the rice planting area. The vegetation index NDVI is used as a single parameter, inversion is easy, and remote sensing extraction parameter conditions are concise; aiming at a research area, the NDVI value change curve of the rice growth season is constructed by using medium-resolution remote sensing image data such as sentinel-2, landsat TM and the like, so that the problem of insufficient fineness of the constructed NDVI change curve caused by mixed pixels of low-resolution remote sensing images such as MODIS and the like is solved; the scattered sample rice planting vector patches are used, and the influence of the missing of the available remote sensing images in a single area due to weather is reduced.
Description
Technical Field
The application relates to the field of rice remote sensing extraction, in particular to a medium-resolution rice extraction method and system.
Background
Rice is an important grain crop in China, and is called three staple grains in China together with wheat and corn. Accurately and efficiently identifying the rice planting area is an important means for knowing the distribution condition of rice planting. The remote sensing technology has the characteristics of wide range, strong dynamic property and good timeliness, and compared with the traditional field statistical method, the method can greatly improve the working efficiency of man-machine interaction rice interpretation and also creates possibility for large-range automatic rice identification.
At present, the most common rice extraction method based on remote sensing images is a threshold classification method, namely, the remote sensing images reflecting different phenological periods of rice in a rice growing season are selected, and parameter change thresholds of different phenological periods are set through repeated adjustment and test according to parameter differences of the rice and other vegetation through specific remote sensing inversion parameters such as vegetation indexes and moisture indexes, so that decision tree classification extraction is carried out. However, changes in the dates of the acquired remote sensing images may result in differences in the relevant parameters and change thresholds. For example, the growing season of rice in southern regions often coincides with the rainy season, resulting in poor quality of remote-sensing images and difficulty in obtaining remote-sensing images of ideal phenological dates or stages. The method adopts the medium-resolution remote sensing image as a data source, combines the phenological characteristics of the rice different from other crops and the change condition of the vegetation index NDVI, and realizes the self-adaptive judgment of the rice NDVI change threshold value in the rice growing season by constructing a time-dependent change equation of the rice NDVI. The application aims to provide reference for the work of large-scale and automatic identification of rice planting areas.
Disclosure of Invention
The method extracts the rice according to the NDVI index change conditions of the rice in the sowing period, the mature period and the harvesting period. And (3) defining time nodes or intervals of sowing, maturing and harvesting, and constructing a curve equation of the NDVI of the rice in the research area along with the change of time.
In order to achieve the purpose, the application discloses a medium-resolution rice extraction method, which comprises the following steps:
selecting a planting vector patch of a sample rice in a ploughing area;
fitting a curve equation according to the planting vector patch;
determining a rice extraction self-adaptive NDVI change threshold value based on the fitting equation;
and finishing the extraction of the rice planting area according to the self-adaptive NDVI change threshold of the rice extraction.
Preferably, the method for selecting the planting vector patch comprises the following steps:
acquiring high-grade remote sensing data and land utilization classification data covering a research area; and extracting a plurality of planting vector patches from the high-score remote sensing data and the land utilization classification data.
Preferably, the method of fitting the curve equation comprises: importing the planting vector patch into a computing platform to perform NDVI inversion computation; determining the rice planting date according to the local rice plant calendar, and drawing a time-dependent change curve of the NDVI of the rice in a growing season by taking the calendar days after sowing as a time axis; and fitting the curve equation according to the change curve.
Preferably, the method for extracting the adaptive NDVI variation threshold includes: respectively obtaining medium-resolution remote sensing images of the seeding date, the mature period and the harvesting period of the paddy rice in a research area, determining the calendar days after seeding corresponding to each scene image according to the difference value between the date of the medium-resolution remote sensing images and the seeding date of the paddy rice, substituting the calendar days into the curve equation, and calculating the difference value of expected NDVI values of the mature period, the seeding period and the harvesting period, the mature period as the self-adaptive NDVI index change threshold value.
Preferably, the method for extracting the rice planting area comprises the following steps: inverting the medium-resolution remote sensing images at each moment of the seeding period, the mature period and the harvesting period to obtain an NDVI index condition distribution map; and obtaining the rice planting area in the NDVI index condition distribution map according to the adaptive NDVI index change threshold.
The application also provides a medium resolution rice extraction system, which comprises: the device comprises an acquisition module, a fitting module, an extraction module and a calibration module;
the collection module is used for selecting planting vector patches of sample rice in a cultivated land area;
the fitting module is used for fitting a curve equation according to the planting vector patch;
the extraction module is used for determining a rice extraction self-adaptive NDVI change threshold value based on the fitting equation;
the calibration module is used for completing the extraction of the rice planting area according to the self-adaptive NDVI change threshold value of the rice extraction.
Preferably, the working range of the acquisition module comprises: acquiring high-grade remote sensing data and land utilization classification data covering a research area; and extracting a plurality of planting vector patches from the high-score remote sensing data and the land utilization classification data.
Preferably, the workflow of the fitting module includes: importing the planting vector patch into the fitting module, and performing NDVI inversion calculation; determining the rice planting date according to the local rice plant calendar, and drawing a time-dependent change curve of the NDVI of the rice in a growing season by taking the calendar days after sowing as a time axis; and fitting the curve equation according to the change curve.
Preferably, the workflow of the extraction module includes: respectively obtaining medium-resolution remote sensing images of the seeding time, the mature period and the harvesting time of the paddy rice in a research area, determining the calendar days after seeding corresponding to each scene image according to the difference value between the date of the medium-resolution remote sensing images and the seeding date of the paddy rice, substituting the calendar days into the curve equation, and calculating the expected NDVI value difference value of the mature period-seeding time and the harvesting period-mature period as the self-adaptive NDVI index change threshold value.
Preferably, the work flow of the calibration module includes: inverting the medium-resolution remote sensing images at each moment of the seeding period, the mature period and the harvesting period to obtain an NDVI index condition distribution map; and obtaining the rice planting area according to the adaptive NDVI index change threshold in the NDVI index condition distribution map.
Compared with the prior art, the beneficial effects of this application are as follows:
the vegetation index NDVI is used as a single parameter, inversion is easy, and remote sensing extraction parameter conditions are concise; aiming at a research area, the NDVI value change curve of the rice growth season is constructed by using medium-resolution remote sensing image data such as sentinel-2, landsat and the like, so that the problem of insufficient fineness of the constructed NDVI change curve caused by mixed pixels of low-resolution remote sensing images such as MODIS and the like is solved; the method has the advantages that the scattered sample rice planting vector patches are used, so that the influence of the missing of the available remote sensing images in a single area due to weather is reduced; the method realizes the self-adaptation of the NDVI index change threshold value for extracting the rice region, and can determine the expected NDVI index value of the rice of the remote sensing image at any time point in a growing season and the phenological change extraction threshold value; according to the method and the device, the rice extraction is only carried out on the farmland range rather than the whole region of the research area, the condition that the farmland is divided into the rice by mistake is eliminated, and the rice extraction precision is improved.
Drawings
In order to more clearly illustrate the technical solution of the present application, the drawings needed to be used in the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for a person skilled in the art to obtain other drawings without any inventive exercise.
FIG. 1 is a schematic flow chart of a method according to a first embodiment of the present application;
FIG. 2 is a detailed flow chart of a second embodiment of the present application;
FIG. 3 is a distribution diagram of the cultivated land in tea county according to the second embodiment of the present application;
FIG. 4 is a sample rice vector patch in a portion of the farmland according to the second embodiment of the present application;
FIG. 5 is a graph showing the NDVI values of mid-season rice in the first season as a function of the number of days after sowing in a sample of the tea field area according to example two of the present application;
FIG. 6 shows a region identified by the planting of the rice in tea county as example II of the present application;
fig. 7 is a schematic structural diagram of a system according to a third embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be described clearly and completely with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only some embodiments of the present application, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, the present application is described in further detail with reference to the accompanying drawings and the detailed description.
Example one
As shown in fig. 1, a schematic flow chart of a method according to a first embodiment of the present application includes the steps of: selecting a planting vector patch of a sample rice in a ploughing area; fitting a curve equation according to the planting vector patch; determining a rice extraction self-adaptive NDVI change threshold value based on a fitting equation; and (4) according to the adaptive NDVI change threshold value of the rice extraction, completing the extraction of the rice planting area.
Firstly, selecting a rice planting vector patch of a sample of a farmland area. High-grade remote sensing data and land utilization classification data covering a research area are obtained, and part of rice planting vector patches in the area are selected for researching the change condition of the rice planting index NDVI in the growing season. In the first embodiment, the number of the selected rice patches is not less than 10 and is distributed in different arable land ranges to be representative.
And then, drawing a time-varying NDVI (normalized difference of viscosity) index curve of the rice growth season and fitting a curve equation. And (3) importing the rice sample vector patches selected in the above steps into a computing platform, wherein in the first embodiment, the computing platform adopts a GEE platform. And (3) carrying out NDVI inversion calculation on the medium-resolution image data set (including sentinel-2, landsat-8 and the like) in the rice growth season within one year in the rice vector patch by utilizing the advantages of large data volume and strong calculation capacity of the GEE platform. Determining the rice planting date according to the local rice plant calendar, drawing a time-varying curve of the rice NDVI index in a growing season by taking the calendar days after sowing as a time axis, and fitting an expression equation.
In addition, a rice extraction adaptive NDVI change threshold needs to be determined. With continuous maturation of rice after sowing, the NDVI index continuously rises after sowing until maturation and reduction after harvesting. And (3) extracting the rice according to the NDVI difference of the rice in the mature period-sowing period and the harvest period-mature period. Respectively obtaining medium-resolution remote sensing images of the seeding time, the maturity time and the harvesting time of paddy rice in a research area, determining the calendar days after seeding corresponding to each scene of image according to the difference value between the date of the remote sensing image and the date of the paddy rice seeding, substituting into the fitted equation in the steps to calculate the expected NDVI value of the corresponding image date, and calculating the expected NDVI value difference value of the maturity time, the seeding time and the harvesting time and the maturity time as an NDVI index change threshold value for extracting a paddy rice area.
And finally, extracting a rice planting area. And (4) carrying out inversion on the medium-resolution remote sensing images at each moment of the seeding period, the maturing period and the harvesting period to obtain an NDVI index condition distribution diagram, and extracting a rice planting region in a farmland range according to the self-adaptive maturity period-seeding period and harvesting period-maturity period change obtained in the step (3).
Example two
In the following, the specific implementation process of the present application will be described by taking the arable land area in tealing county, shoal, han nan as an example. The detailed flow is shown in fig. 2.
(1) And (3) acquiring the classification data of the land utilization in the tea mausoleum county and extracting the farmland distribution range, wherein the result is shown in a figure 3.
(2) High-resolution remote sensing data of 2021 year rice growth season in tea country are obtained, parts are selected and determined to be sample areas for rice planting based on a high-resolution remote sensing image base map, and the result is shown in figure 4.
(3) And (3) introducing the sample rice area into the GEE platform, and performing inversion by combining remote sensing images of sentinels-2, landsat and the like in the GEE platform to obtain an NDVI time sequence curve of the sample rice area.
(4) And fitting the NDVI time sequence curve obtained in the last step to obtain an equation of the change of the NDVI index along with the calendar days after sowing, wherein the result is shown in figure 5.
(5) According to the rice farming calendar in the tea-mausoleum area, sentinel-2 optical remote sensing images of rice in the sowing period, the maturation period and the harvesting period, which are good in quality and low in cloud amount, are selected, and the NDVI distribution condition of crops in the research area in each period is inverted.
TABLE 1
(6) And (3) according to the equation of substituting the post-sowing date corresponding to the remote sensing image into the equation (4), respectively calculating expected NDVI value difference values of the maturity stage-sowing period and the harvest stage-maturity stage, and taking the difference values as NDVI change threshold values for extracting the rice area, wherein the results are shown in a table 2.
TABLE 2
(7) Extracting the crops in the region of the farmland in the tea county which meet the threshold range according to the rice extraction threshold obtained in the step (6) (maturity-seeding time delta NDVI =0.225, harvest time-maturity time delta NDVI = -0.163) to obtain the rice, wherein the results are shown in fig. 6.
(8) And (3) combining a high-resolution remote sensing image base map, randomly generating 100 sample points in the farmland range, and distributing the sample point proportion of the rice region and the non-rice region according to the proportion of the region area extracted as rice in the step (7) and the non-rice farmland area so as to ensure that the selection of the sample points is representative. Forming a confusion matrix according to the classification type and the actual ground object type, and evaluating the precision of the classification result, wherein the result is shown in a table 3.
TABLE 3
EXAMPLE III
As shown in fig. 7, a schematic structural diagram of a system according to a third embodiment of the present application includes: the device comprises an acquisition module, a fitting module, an extraction module and a calibration module; the collection module is used for selecting planting vector patches of sample rice in a cultivated land area; the fitting module is used for fitting a curve equation according to the planting vector patch; the extraction module is used for determining a rice extraction self-adaptive NDVI change threshold value based on a fitting equation; and the calibration module is used for completing the extraction of the rice planting area according to the self-adaptive NDVI change threshold value of the rice extraction.
Wherein, the working range of collection module includes: acquiring high-grade remote sensing data and land utilization classification data covering a research area; and (3) extracting planting vector patches from the high-resolution remote sensing data and the land utilization classification data, wherein the number of the planting vector patches is not less than 10 and the planting vector patches are dispersed in different arable land ranges.
Further, the workflow of the fitting module includes: importing the planting vector patch into a fitting module, and performing NDVI inversion calculation; determining the rice planting date according to the local rice plant calendar, and drawing a time-dependent change curve of the NDVI index of the rice in a growing season by taking the calendar days after sowing as a time axis; and fitting a curve equation according to the change curve.
And the work flow of the extraction module comprises the following steps: respectively obtaining medium-resolution remote sensing images of the seeding time, the mature period and the harvest period of the paddy rice in the research area, determining the calendar days after seeding corresponding to each scene image according to the difference value between the date of the medium-resolution remote sensing images and the seeding date of the paddy rice, substituting the calendar days into a curve equation, and calculating the expected NDVI value difference value of the mature period-seeding time and the harvest period-mature period as a self-adaptive NDVI index change threshold value.
Finally, the work flow of the calibration module comprises: inverting the medium-resolution remote sensing images at each moment of the seeding period, the mature period and the harvesting period to obtain an NDVI index condition distribution map; and obtaining the rice planting area according to the adaptive NDVI index change threshold in the NDVI index situation distribution diagram.
The above-described embodiments are merely illustrative of the preferred embodiments of the present application, and do not limit the scope of the present application, and various modifications and improvements made to the technical solutions of the present application by those skilled in the art without departing from the spirit of the present application should fall within the protection scope defined by the claims of the present application.
Claims (10)
1. A medium-resolution rice extraction method is characterized by comprising the following steps:
selecting a planting vector patch of a sample rice in a ploughing area;
fitting a curve equation according to the planting vector patch;
determining a rice extraction self-adaptive NDVI change threshold value based on the fitting equation;
and finishing the extraction of the rice planting area according to the self-adaptive NDVI change threshold of the rice extraction.
2. The method for extracting medium resolution rice as claimed in claim 1, wherein the method for selecting the planting vector patch comprises: acquiring high-grade remote sensing data and land utilization classification data covering a research area; and extracting a plurality of planting vector patches from the high-grade remote sensing data and the land utilization classification data.
3. The method of claim 1, wherein the step of fitting the curve equation comprises: importing the planting vector patch into a computing platform to perform NDVI inversion computation; determining the rice planting date according to the local rice plant calendar, and drawing a time-dependent change curve of the NDVI index of the rice in a growing season by taking the calendar days after sowing as a time axis; and fitting the curve equation according to the change curve.
4. The method of claim 1, wherein the method of extracting the adaptive NDVI variation threshold comprises: respectively obtaining medium-resolution remote sensing images of the seeding date, the mature period and the harvesting period of the paddy rice in a research area, determining the calendar days after seeding corresponding to each scene image according to the difference value between the date of the medium-resolution remote sensing images and the seeding date of the paddy rice, substituting the calendar days into the curve equation, and calculating the difference value of expected NDVI values of the mature period, the seeding period and the harvesting period, the mature period as the self-adaptive NDVI index change threshold value.
5. The method for extracting medium resolution rice as claimed in claim 1, wherein the method for extracting the rice growing area comprises: inverting the medium-resolution remote sensing images at each moment of the seeding period, the mature period and the harvesting period to obtain an NDVI index condition distribution map; and obtaining the rice planting area according to the adaptive NDVI index change threshold in the NDVI index condition distribution map.
6. A medium resolution rice extraction system, comprising: the device comprises an acquisition module, a fitting module, an extraction module and a calibration module;
the collection module is used for selecting planting vector patches of sample rice in a cultivated land area;
the fitting module is used for fitting a curve equation according to the planting vector patch;
the extraction module is used for determining a rice extraction self-adaptive NDVI change threshold value based on the fitting equation;
the calibration module is used for completing the extraction of the rice planting area according to the rice extraction self-adaptive NDVI change threshold.
7. The medium resolution rice extraction system of claim 6, wherein the operating range of the collection module comprises: acquiring high-grade remote sensing data and land utilization classification data covering a research area; and extracting a plurality of planting vector patches from the high-grade remote sensing data and the land utilization classification data.
8. The system of claim 6, wherein the workflow of the fitting module comprises: importing the planting vector patch into the fitting module to perform NDVI inversion calculation; determining the rice planting date according to the local rice plant calendar, and drawing a time-dependent change curve of the NDVI of the rice in a growing season by taking the calendar days after sowing as a time axis; and fitting the curve equation according to the change curve.
9. The medium resolution rice extraction system of claim 6, wherein the workflow of the extraction module comprises: respectively obtaining medium-resolution remote sensing images of the seeding date, the mature period and the harvesting period of the paddy rice in a research area, determining the calendar days after seeding corresponding to each scene image according to the difference value between the date of the medium-resolution remote sensing images and the seeding date of the paddy rice, substituting the calendar days into the curve equation, and calculating the difference value of expected NDVI values of the mature period, the seeding period and the harvesting period, the mature period as the self-adaptive NDVI index change threshold value.
10. The system for middle-resolution rice extraction according to claim 6, wherein the workflow of the calibration module includes: inverting the medium-resolution remote sensing images at each moment of the sowing period, the mature period and the harvesting period to obtain an NDVI index condition distribution map; and obtaining the rice planting area according to the adaptive NDVI index change threshold in the NDVI index condition distribution map.
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