CN116518935A - Rice planting distribution and planting intensity recognition method, device and equipment - Google Patents
Rice planting distribution and planting intensity recognition method, device and equipment Download PDFInfo
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Abstract
The invention provides a method, a device and equipment for identifying rice planting distribution and planting intensity, which comprise the steps of obtaining remote sensing images and numerical elevation data of target years of a target area to be subjected to rice planting distribution and planting intensity extraction, wherein the target area comprises a plurality of areas, removing low-quality pixels, radiometric calibration, atmosphere correction and image cutting from the remote sensing images, obtaining normalized vegetation indexes, land surface water indexes and surface temperatures, removing non-cultivated land masks, obtaining rice transplanting signals, meeting the conditions that the rice transplanting signals, certain surface temperatures and certain land surface water indexes are the primarily screened rice planting distribution areas, obtaining rice signal frequencies, obtaining accurate rice planting distribution areas according to the rice signal frequencies, judging single-season rice and double-season rice, and obtaining rice planting intensity.
Description
Technical Field
The invention relates to the technical field of agricultural remote sensing, in particular to a method, a device and equipment for identifying rice planting distribution and planting intensity.
Background
Rice is an important grain crop. It is reported that rice provides approximately 19% of the energy to humans and that more than 50% of the population contains rice in dietary structure. In addition, rice affects aspects of human life, such as: water resource consumption, greenhouse gas emissions, changes in local climate, etc.
In recent years, due to the increase of the labor cost of planting, some paddy fields are transformed into dry lands, and double-cropping rice planting in partial areas is also changed into single-cropping rice planting, or the abandoned cultivation of partial paddy fields. The traditional information collection through field investigation generally has serious time lag, so that the related information of the water resource scheduling management department cannot be updated in time, and the actual water resource management and configuration are influenced.
There are some methods available for extraction of rice planting distribution, but there are some limitations. Wherein collection by field investigation or data statistics is time consuming and laborious, and time delay is extremely strong, and real-time water management is difficult to provide and timely effective information in actual production. The identification of rice by remote sensing is a mainstream method at present, and has the obvious advantages of low cost and high timeliness, but the existing method also has some problems. For example: (1) The existing method is mostly limited to be used in a small range, such as a irrigated area level, a municipal administration area or a provincial administration area, and the like, and is not used or evaluated on a large area scale (national range); (2) The method has no time portability, part of parameters or recognition standards in the existing method are determined according to specific data of specific years, and the recognition effect may not be ideal under the background of planting habit change or climate change; (3) The data dependency is obvious, most of parameters are obtained according to the field investigation data, a large amount of labor cost is needed, and the quality of the identification effect depends on the quality and the number of samples; (4) The method for identifying the planting strength of the rice is few, and the existing method is basically oriented to all crops, so that the precision of the method can not be ensured for the rice; the method (5) has higher complexity and weak operability; although the existing methods can achieve better effects, the existing methods have higher complexity, have too high learning cost for first-line production personnel or management departments, and have higher popularization difficulty.
Disclosure of Invention
According to the defects of the prior art, the invention aims to provide a method, a device and equipment for identifying rice planting distribution and planting intensity, which can rapidly and accurately acquire large-scale rice planting distribution and planting intensity.
In order to solve the technical problems, the invention adopts the following technical scheme:
a rice planting distribution and planting intensity identification method comprises the following steps:
step 1, acquiring remote sensing images and numerical elevation data of target years of a target area to be subjected to rice planting distribution and planting intensity extraction, wherein the target area comprises a plurality of areas;
step 2, data preprocessing, namely removing low-quality pixels from the remote sensing image, and performing radiometric calibration, atmospheric correction and image cutting;
step 3, taking the normalized vegetation index NDVI as a rice canopy greenness identification index, and simultaneously obtaining land surface water body index LSWI and surface temperature LST;
step 4, removing the non-cultivated land mask;
step 5, obtaining a rice transplanting signal S according to the normalized vegetation index NDVI and the land surface water body index LSWI t At the same time satisfy the rice transplanting signal S t The points of a certain surface temperature LST and a certain land surface water body index LST are initially screened rice planting distribution areas;
and 6, obtaining a rice signal frequency F, obtaining an accurate rice planting distribution area according to the rice signal frequency F, judging single-cropping rice and double-cropping rice, and obtaining the rice planting intensity.
Further, in the step 3, the calculation formula of the normalized vegetation index NDVI is:
wherein B is rNIR 、B rred 、B rSWIR The near infrared band reflectivity, the red light band reflectivity and the short wave infrared band reflectivity of each pixel obtained after radiometric calibration and atmospheric correction are respectively.
Further, in the step 3, inversion is performed on the surface temperature through the thermal infrared band, and a calculation formula of the surface temperature LST is as follows:
wherein ε NB Emissivity of the thermal infrared band is dimensionless; r is R C Is the corrected thermal radiation from the surface in W/m 2 /sr/μm；K 1 Is constant and has the unit of W/m 2 /sr/μm；K 2 Is constant and has the unit of K.
Further, in step 4, the water mask, sparse vegetation, evergreen vegetation and terrain mask are removed;
if the average LSWI is greater than 0.35 and the average NDVI is less than 0.20, judging that the mask is a water mask;
judging sparse vegetation if the maximum NDVI is less than 0.50;
manufacturing a evergreen vegetation mask by taking the average NDVI of more than 0.7 as a standard;
when the altitude is higher than 2000m, or the gradient is higher than 0.7, it is determined as a terrain mask.
Further, in step 5, at the early stage of rice growth and development, the rice planting distribution area is covered by a water layer, at this time, the land surface water index LSWI is larger than the normalized vegetation index NDVI, and a rice transplanting signal S is calculated t For each pixel, for exampleThe following calculation:
where α is the difference between LSWI and NDVI;
the discrete rice planting time window is used for independently judging the surface temperature of each remote sensing image, and the calculation formula of the surface temperature judgment index is as follows:
when LSWI and NDVI are small, LSWI > NDVI can be met, a limiting condition beta is set, and corresponding non-paddy field points are removed:
by rice signal S tf Preliminary screening of rice planting distribution areas:
S tf ＝S t ×T×W (7)
when S is tf =1, indicating that a region may be a rice planting distribution area; when S is tf =0, indicating that a region cannot be a rice planting distribution area.
Further, the frequency F of the rice signal is obtained:
F＝∑S tf /∑N (8)
wherein N represents a monitor pixel, and N is 1 if it is a non-defective pixel, and 0 if it is a defective pixel; sigma S tf The number of times of signal occurrence of transplanting in the same pixel in one year; sigma N is the total observation times of the same pixel after defective pixels are removed within one year, and is equal to the number of remote sensing images after unsatisfactory images are removed;
when the F is 5 percent and 35 percent, the corresponding pixels in a certain area are judged to be the rice planting distribution areas.
Further, according to the data of historical statistics annual-differentiation, the planting proportion of the single-cropping rice and the double-cropping rice in a certain region in the target year is determined, according to the planting proportion, the identification result of the rice planting distribution area and the accumulated value of the frequency F are combined, the dividing frequency F ' of the single-cropping rice and the double-cropping rice in the region is determined, and then 5% -F ' is the single-cropping rice, and F ' to 35% is the single-cropping rice.
A rice planting distribution and planting intensity recognition device, comprising:
the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring remote sensing images and numerical elevation data of target years of a target area to be subjected to rice planting distribution and planting intensity extraction, and the target area comprises a plurality of areas;
the data preprocessing module is used for removing low-quality pixels from the remote sensing image and performing radiometric calibration, atmospheric correction and image cutting;
the basic parameter acquisition module is used for taking the normalized vegetation index NDVI as a rice canopy greenness identification index and simultaneously acquiring land surface water body index LSWI and surface temperature LST;
the non-cultivated land mask removing module is used for removing the non-cultivated land mask;
the primary selection module of the rice planting distribution area is used for acquiring a rice transplanting signal S according to the normalized vegetation index NDVI and the land surface water body index LSWI t At the same time satisfy the rice transplanting signal S t The points of a certain surface temperature LST and a certain land surface water body index LST are initially screened rice planting distribution areas;
the rice planting distribution and planting intensity recognition module is used for acquiring a rice signal frequency F, acquiring an accurate rice planting distribution area according to the rice signal frequency F, judging single-cropping rice and double-cropping rice, and acquiring the rice planting intensity.
The rice planting distribution and planting intensity identification device comprises a processor and a memory for storing a computer program capable of running on the processor, wherein the processor is used for executing the steps of the rice planting distribution and planting intensity identification method according to any one of the above steps when running the computer program.
A computer storage medium having a computer program stored therein, wherein the computer program, when executed by a processor, implements the steps of the rice planting distribution and planting intensity recognition method according to any one of the above.
Compared with the prior art, the invention has the following advantages and beneficial effects:
according to the method, the device and the equipment for identifying the rice planting distribution and the planting intensity, provided by the invention, remote sensing images and numerical elevation data of the target year of the target area are used as initial data, the data dependence is avoided, the method can be popularized and used in a large area scale, ground investigation data is not needed, the labor cost is reduced, the previous identification method is simplified, the large-range rice planting distribution and planting intensity identification can be realized, the identification effect is good, and the quick identification can be realized.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application. The exemplary embodiments of the present invention and the descriptions thereof are for explaining the present invention and do not constitute an undue limitation of the present invention. In the drawings:
FIG. 1 is a flowchart of a rice planting distribution and planting intensity recognition method according to the present invention.
Fig. 2 is a verification chart of rice planting area results extracted based on rice signals in China 2014-2019 according to an embodiment of the present invention.
Fig. 3 is a drawing of single double cropping rice extraction verification in the examples of the invention in guangxi, guangdong, hunan and Jiangxi 2014-2019.
FIG. 4 is a schematic diagram of the partitioning method of single and double cropping rice according to the present invention.
FIG. 5 is a diagram showing a rice planting distribution and planting intensity recognition device according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.
The invention provides a rice planting distribution and planting intensity recognition method, which is shown in fig. 1 and comprises the following steps:
step 1, acquiring remote sensing images and numerical elevation data of target years of a target area to be subjected to rice planting distribution and planting intensity extraction, wherein the target area comprises a plurality of areas;
step 2, data preprocessing, namely removing low-quality pixels from the remote sensing image, and performing radiometric calibration, atmospheric correction and image cutting;
step 3, taking the normalized vegetation index NDVI as a rice canopy greenness identification index, and simultaneously obtaining land surface water body index LSWI and surface temperature LST;
step 4, removing the non-cultivated land mask;
step 5, obtaining a rice transplanting signal S according to the normalized vegetation index NDVI and the land surface water body index LSWI t At the same time satisfy the rice transplanting signal S t The points of a certain surface temperature LST and a certain land surface water body index LST are initially screened rice planting distribution areas;
and 6, obtaining a rice signal frequency F, obtaining an accurate rice planting distribution area according to the rice signal frequency F, judging single-cropping rice and double-cropping rice, and obtaining the rice planting intensity.
According to the rice planting distribution and planting intensity recognition method provided by the invention, remote sensing images and numerical elevation data of the target year of the target area are used as initial data, the data dependence is avoided, the method can be popularized and used in a large area scale, ground investigation data are not needed, labor cost is reduced, the previous recognition method is simplified, large-scale rice planting distribution and planting intensity recognition can be realized, the recognition effect is good, and the rapid recognition can be realized.
The related art is few in methods for identifying the planting intensity of rice, and the existing methods are basically oriented to all crops, so that the precision of the rice can not be ensured.
In the invention, in the step 1, remote sensing images and numerical elevation data of the year in which the rice planting distribution and the planting intensity extraction are to be performed are obtained through an American geological survey agency network (http:// earthocore. Usgs. Gov) or other approaches.
In the present invention, in the step 2, the low quality pixel is a bad pixel point caused by cloud cover, mountain shadow, sensor fault, etc., and cannot reflect actual ground information, so that it is necessary to reject. In the process of removing the low-quality pixels, judging through the quality QA wave band of the remote sensing image, and if the QA wave band shows cloud cover or mountain shadow and the like, assigning the pixels as null values (null).
In the present invention, in the step 2, in the radiometric calibration process, the original remote sensing image is recorded in a binary manner, which is called DN (Digital Number) value. Radiometric calibration is the conversion of DN values into the external surface reflectivity of the atmosphere, i.e. apparent reflectivity, with the aim of eliminating errors that the sensor itself generates when taking images. If processed by local software, the original image may be radiorated by calling Radiometric Calibriation module in ENVI 5.3.
In the step 2, in the process of performing atmospheric correction, factors such as oxygen, water vapor, methane, ozone, carbon dioxide and the like in the atmosphere have influence on the reflection of the ground object, so that the image needs to be subjected to atmospheric correction to eliminate the influence of atmospheric molecules and aerosol scattering, and the actual reflectivity of the ground object is inverted. If the processing is performed by local software, the FLAASH module in ENVI may be invoked to perform atmospheric correction on the radioscaled image.
In the step 2, in the image clipping process, since the remote sensing image coverage area is larger than the target area, the area outside the target area needs to be clipped and removed, so as to improve the data processing efficiency. And carrying out irregular cutting according to the target area vector range file, and converting the target area vector range file and the remote sensing image data into the same coordinate system before cutting.
In the step 3, normalized vegetation index (NDVI) is calculated. NDVI has good performance in vegetation identification, so that the NDVI can be used as a rice canopy green degree identification index, and the value is [ -1,1]. Land Surface Water Index (LSWI) calculation. LSWI is the normalized index of near infrared band and short infrared band, and because of adopting the short infrared band sensitive to moisture, the LSWI can effectively extract ground objects such as water body, and the value is [ -1,1].
The calculation formula of the normalized vegetation index NDVI is:
wherein B is rNIR 、B rred 、B rSWIR The near infrared band reflectivity, the red light band reflectivity and the short wave infrared band reflectivity of each pixel obtained after radiometric calibration and atmospheric correction are respectively.
In the step 3, inversion is performed on the surface temperature through a thermal infrared band, and a calculation formula of the surface temperature LST is as follows:
wherein ε NB Emissivity of the thermal infrared band is dimensionless; r is R C Is the corrected thermal radiation from the surface in W/m 2 /sr/μm；K 1 Is constant and has the unit of W/m 2 /sr/μm；K 2 Is constant and has the unit of K.
In step 4, removing the water mask, sparse vegetation, evergreen vegetation and terrain mask;
and removing the water mask. The water mask covers the wetland, pond, lake, river and the like. Since these underlying surfaces are covered by the water layer for a long time, and vegetation is relatively small. Thus there will be a higher LSWI and a lower NDVI. In the whole year, if the average LSWI is greater than 0.35 and the average NDVI is less than 0.20, determining that the water body is the water body, and making indexes of the following masks by taking the year as a time unit;
sparse vegetation. Sparse masks include urban construction land, saline-alkali soil and other vegetation that is very sparsely planted. Throughout the year, a maximum NDVI of less than 0.50 is determined as sparse vegetation, and a maximum NDVI of less than 0.50 is determined as sparse vegetation;
evergreen vegetation. The evergreen mask includes evergreen vegetation such as evergreen broadleaf forest, evergreen conifer forest, and the like. It can maintain a substantially higher degree of greenness throughout the entire fertility stage. Therefore, making the evergreen vegetation mask by taking the average NDVI of more than 0.7 as a standard and making the evergreen vegetation mask by taking the average NDVI of more than 0.7 as a standard;
and (5) topographic masking. Due to the irrigation requirements of rice, rice is generally cultivated in low-altitude plains. Thus, the determination is made at altitude and gradient. When the altitude is higher than 2000m, or the gradient is higher than 0.7, the terrain mask is judged.
And superposing the water mask, the sparse vegetation, the evergreen vegetation and the terrain mask to obtain the non-cultivated land mask.
In the invention, in the step 5, in the early stage of rice growth and development, the rice planting distribution area is covered by a water layer, at the moment, the land surface water index LSWI is larger than the normalized vegetation index NDVI, and a rice transplanting signal S is calculated t The following calculation is performed for each pixel:
where α is the difference between LSWI and NDVI to improve the regional applicability and portability of the method.
According to the invention, due to the thermophilic characteristic of rice, the surface temperature is used for judging the planting time window, the planting time window is discretized, namely, the time judgment is carried out without using a time interval, the temperature of each remote sensing image is independently judged, and the error caused by temperature fluctuation is reduced.
Specifically, the discretization rice planting time window is used for independently judging the surface temperature of each remote sensing image, and the calculation formula of the surface temperature judgment index is as follows:
when the surface temperature of a certain area is higher than 10 ℃, the area is possibly a rice planting distribution area, and when the surface temperature of the certain area is lower than 10 ℃, the area is not suitable for rice planting, the area is not possibly a rice planting distribution area.
In the present invention, the land moisture index W is determined. Beta is a judging condition of the surface water index, when LSWI and NDVI are small, the underlying surface spectrum index also meets the condition that LSWI > NDVI, so that the corresponding non-paddy field points are eliminated by taking beta as a limiting condition:
by rice signal S tf Preliminary screening of rice planting distribution areas:
S tf ＝S t ×T×W (7)
when S is tf =1, indicating that a region may be a rice planting distribution area; when S is tf =0, indicating that a region cannot be a rice planting distribution area.
In the invention, corresponding recommended values are formulated according to the identification effect and the rice planting habit, wherein alpha is 0.05 in Yunnan, hubei, sichuan and Jiangsu, and alpha is 0.15 in other areas; in Jiangxi, chongqing, anhui and Hunan, beta is 0, and beta in other areas is 0.15; in areas lacking ground truth, it is recommended that α be 0.05 and β be 0.15.
Obtaining the frequency F of rice signals:
F＝∑S tf /∑N (8)
wherein N represents a monitor pixel, and N is 1 if it is a non-defective pixel, and 0 if it is a defective pixel; sigma S tf The number of times of signal occurrence of transplanting in the same pixel in one year; sigma N is the total observation times of the same pixel after defective pixels are removed within one year, and is equal to the number of remote sensing images after unsatisfactory images are removed;
when the F is 5 percent and 35 percent, the corresponding pixels in a certain area are judged to be the rice planting distribution areas.
According to the data of historical statistics annual-differentiation, determining the planting proportion of single-season rice and double-season rice in a certain region in a target year, determining the dividing frequency F ' of the single-season rice and double-season rice in the region according to the planting proportion and the identification result of a rice planting distribution area and the accumulated value of the frequency F, wherein 5% -F ' is the single-season rice, F ' to 35% is the single-season rice, and applying the standard to identification of other years.
As shown in fig. 4, for example, the ratio of single-double cropping rice in a certain province is 1:1 (calculated according to the statistical annual-differentiation), calculating to obtain the F threshold cumulative frequency of the single-double-cropping rice as 50% (1/2); and determining a corresponding dividing frequency F 'according to the accumulated frequency, wherein the dividing frequency F' (26%) is the dividing threshold value of the provincial single-double-cropping rice. A single-season rice is used for F less than 26%, and a double-season rice is used for F equal to or more than 26%.
According to the invention, the discretization time (or LST) window is adopted, and the same time window is not used for judging any more, so that errors caused by temperature fluctuation are avoided; the erroneous judgment of some abnormal pixels is eliminated by the limitation of the surface water index; the method for judging the planting intensity threshold value through historical statistics annual-image (easy-to-acquire data) is provided, and paddy fields of double-cropping rice and single-cropping rice can be obtained rapidly.
In one embodiment of the invention, in the Chinese range, the rice planting distribution and planting intensity recognition method provided by the invention is used for recognizing the rice planting distribution and planting intensity in 2014-2019, the specific effects are shown in fig. 2 and 3, wherein the analysis is performed by taking Guangxi, guangdong, hunan and Jiangxi as examples of main planting provinces of single-double-cropping rice, and the rice planting distribution and planting intensity recognition method provided by the invention has better effects.
According to the method for identifying the rice planting distribution and the planting intensity, china is taken as an example, and the rice planting distribution and the planting intensity in 2014-2019 are studied. Landsat7 and Landsat8 are taken as example remote sensing images.
Secondary data of Landsat7/8 from the united states geological survey, which has been pre-processed, is acquired directly on Google Earth Engine without the need for radiocalibration, atmospheric correction and surface temperature calculations. And then eliminating abnormal pixel points through bit operation, namely, eliminating pixels with binary codes of 1 in the third bit or the fifth bit of the QA wave band. And cutting the Chinese boundary vector diagram by using the Chinese boundary vector diagram.
NDVI and LSWI are calculated using equations (1) and (2).
And (3) determining the non-cultivated land mask according to the specific parameters of the step three. Comprising the following steps: a waterbody mask, a sparse vegetation mask, an evergreen vegetation mask and a terrain mask. And then superposing the masks to obtain the non-cultivated land mask. And superposing the obtained non-cultivated land mask and the processed remote sensing image to obtain the remote sensing image corresponding to the preliminary cultivated land.
Calculating a rice signal, and extracting a rice planting area and planting intensity. Firstly, according to the formula (4-8) and corresponding parameters, the planting condition of the rice is obtained. And then calculating the F value according to 2014 statistics of the data of Guangxi, guangdong, hunan and Jiangxi yearbooks. As shown in FIG. 2, the verification graph of the rice planting area result extracted based on the rice signal in China 2014-2019 is shown in FIG. 3, and the verification graph of the single-double-cropping rice extraction in Guangxi, guangdong, hunan and Jiangxi 2014-2019 is shown in FIG. 3.
The invention also provides a device for identifying the rice planting distribution and the planting intensity, as shown in fig. 5, comprising:
the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring remote sensing images and numerical elevation data of target years of a target area to be subjected to rice planting distribution and planting intensity extraction, and the target area comprises a plurality of areas;
the data preprocessing module is used for removing low-quality pixels from the remote sensing image and performing radiometric calibration, atmospheric correction and image cutting;
the basic parameter acquisition module is used for taking the normalized vegetation index NDVI as a rice canopy greenness identification index and simultaneously acquiring land surface water body index LSWI and surface temperature LST;
the non-cultivated land mask removing module is used for removing the non-cultivated land mask;
the primary selection module of the rice planting distribution area is used for acquiring a rice transplanting signal S according to the normalized vegetation index NDVI and the land surface water body index LSWI t At the same time satisfy the rice transplanting signal S t The points of a certain surface temperature LST and a certain land surface water body index LST are initially screened rice planting distribution areas;
the rice planting distribution and planting intensity recognition module is used for acquiring a rice signal frequency F, acquiring an accurate rice planting distribution area according to the rice signal frequency F, judging single-cropping rice and double-cropping rice, and acquiring the rice planting intensity.
The invention also provides a rice planting distribution and planting intensity identification device, which comprises a processor and a memory for storing a computer program capable of running on the processor, wherein the processor is used for executing the steps of the rice planting distribution and planting intensity identification method according to any one of the above steps when running the computer program.
The memory in the embodiment of the invention is used for storing various types of data so as to support the operation of the rice planting distribution and planting intensity identification equipment. Examples of such data include: any computer program for operating on a rice planting distribution and planting intensity identification device.
The rice planting distribution and planting intensity recognition method disclosed by the embodiment of the invention can be applied to a processor or realized by the processor. The processor may be an integrated circuit chip having signal processing capabilities. In the implementation process, each step of the rice planting distribution and planting intensity identification method can be completed through an integrated logic circuit of hardware in a processor or instructions in a software form. The processor may be a general purpose processor, a digital signal processor (DSP, digital SignalProcessor), or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like. The processor may implement or perform the methods, steps, and logic blocks disclosed in embodiments of the present invention. The general purpose processor may be a microprocessor or any conventional processor or the like. The steps of the method disclosed in the embodiment of the invention can be directly embodied in the hardware of the decoding processor or can be implemented by combining hardware and software modules in the decoding processor. The software module may be located in a storage medium, the storage medium is located in a memory, and the processor reads information in the memory, and combines with hardware to complete the steps of the rice planting distribution and planting intensity identification method provided by the embodiment of the invention.
In an exemplary embodiment, the rice planting distribution and planting intensity identification device may be implemented by one or more application specific integrated circuits (ASIC, application Specific Integrated Circuit), DSPs, programmable logic devices (PLD, programmable Logic Device), complex programmable logic devices (CPLD, complex Programmable LogicDevice), FPGAs, general purpose processors, controllers, microcontrollers (MCU, micro Controller Unit), microprocessors (microprocessers), or other electronic elements for performing the aforementioned methods.
It will be appreciated that the memory can be either volatile memory or nonvolatile memory, and can include both volatile and nonvolatile memory. Wherein the nonvolatile Memory may be Read Only Memory (ROM), programmable Read Only Memory (PROM, programmable Read-Only Memory), erasable programmable Read Only Memory (EPROM, erasable Programmable Read-Only Memory), electrically erasable programmable Read Only Memory (EEPROM, electrically Erasable Programmable Read-Only Memory), magnetic random access Memory (FRAM, ferromagnetic random access Memory), flash Memory (Flash Memory), magnetic surface Memory, optical disk, or compact disk Read Only Memory (CD-ROM, compact Disc Read-Only Memory); the magnetic surface memory may be a disk memory or a tape memory. The volatile memory may be random access memory (RAM, random AccessMemory), which acts as external cache memory. By way of example, and not limitation, many forms of RAM are available, such as static random access memory (SRAM, static Random Access Memory), synchronous static random access memory (SSRAM, synchronous Static Random Access Memory), dynamic random access memory (DRAM, dynamic Random Access Memory), synchronous dynamic random access memory (SDRAM, synchronousDynamic Random Access Memory), double data rate synchronous dynamic random access memory (ddr sdram, double Data Rate Synchronous Dynamic Random Access Memory), enhanced synchronous dynamic random access memory (ESDRAM, enhanced Synchronous Dynamic Random Access Memory), synchronous link dynamic random access memory (SLDRAM, syncLink Dynamic Random Access Memory), direct memory bus random access memory (DRRAM, direct Rambus Random Access Memory). The memory described by embodiments of the present invention is intended to comprise, without being limited to, these and any other suitable types of memory.
The invention also provides a computer storage medium, wherein the computer storage medium stores a computer program, and the method is characterized in that when the computer program is executed by a processor, the steps of the rice planting distribution and planting intensity identification method are realized.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (10)
1. The rice planting distribution and planting intensity identification method is characterized by comprising the following steps of:
step 1, acquiring remote sensing images and numerical elevation data of target years of a target area to be subjected to rice planting distribution and planting intensity extraction, wherein the target area comprises a plurality of areas;
step 2, data preprocessing, namely removing low-quality pixels from the remote sensing image, and performing radiometric calibration, atmospheric correction and image cutting;
step 3, taking the normalized vegetation index NDVI as a rice canopy greenness identification index, and simultaneously obtaining land surface water body index LSWI and surface temperature LST;
step 4, removing the non-cultivated land mask;
step 5, according toObtaining a rice transplanting signal S by normalizing vegetation index NDVI and land surface water body index LSWI t At the same time satisfy the rice transplanting signal S t The points of a certain surface temperature LST and a certain land surface water body index LST are initially screened rice planting distribution areas;
and 6, obtaining a rice signal frequency F, obtaining an accurate rice planting distribution area according to the rice signal frequency F, judging single-cropping rice and double-cropping rice, and obtaining the rice planting intensity.
2. The rice planting distribution and planting intensity identification method according to claim 1, wherein:
in the step 3, the calculation formula of the normalized vegetation index NDVI is:
wherein B is rNIR 、B rred 、B rSWIR The near infrared band reflectivity, the red light band reflectivity and the short wave infrared band reflectivity of each pixel obtained after radiometric calibration and atmospheric correction are respectively.
3. The rice planting distribution and planting intensity identification method according to claim 1, wherein:
in the step 3, inversion is performed on the surface temperature through a thermal infrared band, and a calculation formula of the surface temperature LST is as follows:
wherein ε NB Emissivity of the thermal infrared band is dimensionless; r is R C Is the corrected thermal radiation from the surface in W/m 2 /sr/μm；K 1 Is constant and has the unit of W/m 2 /sr/μm；K 2 Is constant and has the unit of K.
4. The rice planting distribution and planting intensity identification method according to claim 1, wherein:
in step 4, removing the water mask, sparse vegetation, evergreen vegetation and terrain mask;
if the average LSWI is greater than 0.35 and the average NDVI is less than 0.20, judging that the mask is a water mask;
judging sparse vegetation if the maximum NDVI is less than 0.50;
manufacturing a evergreen vegetation mask by taking the average NDVI of more than 0.7 as a standard;
when the altitude is higher than 2000m, or the gradient is higher than 0.7, it is determined as a terrain mask.
5. The rice planting distribution and planting intensity identification method according to claim 1, wherein:
in step 5, at early stage of rice growth and development, the rice planting distribution area is covered by a water layer, at this time, the land surface water index LSWI is larger than the normalized vegetation index NDVI, and a rice transplanting signal S is calculated t The following calculation is performed for each pixel:
where α is the difference between LSWI and NDVI;
the discrete rice planting time window is used for independently judging the surface temperature of each remote sensing image, and the calculation formula of the surface temperature judgment index is as follows:
when LSWI and NDVI are small, LSWI > NDVI can be met, a limiting condition beta is set, and corresponding non-paddy field points are removed:
by rice signal S tf Preliminary screening of rice planting distribution areas:
S tf ＝S t ×T×W (7)
when S is tf =1, indicating that a region may be a rice planting distribution area; when S is tf =0, indicating that a region cannot be a rice planting distribution area.
6. The method for identifying rice planting distribution and planting intensity according to claim 5, wherein:
obtaining the frequency F of rice signals:
F＝∑S tf /∑N (8)
wherein N represents a monitor pixel, and N is 1 if it is a non-defective pixel, and 0 if it is a defective pixel; sigma S tf The number of times of signal occurrence of transplanting in the same pixel in one year; sigma N is the total observation times of the same pixel after defective pixels are removed within one year, and is equal to the number of remote sensing images after unsatisfactory images are removed;
when the F is 5 percent and 35 percent, the corresponding pixels in a certain area are judged to be the rice planting distribution areas.
7. The rice planting distribution and planting intensity recognition method according to claim 6, wherein:
according to the data of historical statistics annual-differentiation, determining the planting proportion of single-cropping rice and double-cropping rice in a certain region in a target year, and according to the planting proportion, combining the identification result of a rice planting distribution area and the accumulated value of frequency F, determining the dividing frequency F ' of the single-cropping rice and the double-cropping rice in the region, wherein 5% -F ' is the single-cropping rice, and F ' to 35% is the single-cropping rice.
8. The utility model provides a rice planting distribution and planting intensity recognition device which characterized in that includes:
the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring remote sensing images and numerical elevation data of target years of a target area to be subjected to rice planting distribution and planting intensity extraction, and the target area comprises a plurality of areas;
the data preprocessing module is used for removing low-quality pixels from the remote sensing image and performing radiometric calibration, atmospheric correction and image cutting;
the basic parameter acquisition module is used for taking the normalized vegetation index NDVI as a rice canopy greenness identification index and simultaneously acquiring land surface water body index LSWI and surface temperature LST;
the non-cultivated land mask removing module is used for removing the non-cultivated land mask;
the primary selection module of the rice planting distribution area is used for acquiring a rice transplanting signal S according to the normalized vegetation index NDVI and the land surface water body index LSWI t At the same time satisfy the rice transplanting signal S t The points of a certain surface temperature LST and a certain land surface water body index LST are initially screened rice planting distribution areas;
the rice planting distribution and planting intensity recognition module is used for acquiring a rice signal frequency F, acquiring an accurate rice planting distribution area according to the rice signal frequency F, judging single-cropping rice and double-cropping rice, and acquiring the rice planting intensity.
9. A rice planting distribution and planting intensity recognition device is characterized in that: a memory comprising a processor and a computer program for storing a computer program capable of running on the processor, the processor being adapted to perform the steps of the rice planting distribution and planting intensity identification method according to any of the preceding claims 1-7 when the computer program is run.
10. A computer storage medium having a computer program stored therein, wherein the computer program, when executed by a processor, implements the steps of the rice planting distribution and planting intensity identification method according to any one of claims 1-7.
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| CN117576572A (en) * | 2024-01-16 | 2024-02-20 | 杭州稻道农业科技有限公司 | Comprehensive planting and raising paddy rice planting coverage extraction method, device and medium |
| CN117690017A (en) * | 2023-11-16 | 2024-03-12 | 宁波大学 | Single-season and double-season rice extraction method considering physical time sequence characteristics |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117690017A (en) * | 2023-11-16 | 2024-03-12 | 宁波大学 | Single-season and double-season rice extraction method considering physical time sequence characteristics |
| CN117576572A (en) * | 2024-01-16 | 2024-02-20 | 杭州稻道农业科技有限公司 | Comprehensive planting and raising paddy rice planting coverage extraction method, device and medium |
| CN117576572B (en) * | 2024-01-16 | 2024-06-14 | 杭州稻道农业科技有限公司 | Comprehensive planting and raising paddy rice planting coverage extraction method, device and medium |
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