CN110647873A - Peanut planting area remote sensing identification method and system - Google Patents
Peanut planting area remote sensing identification method and system Download PDFInfo
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Abstract
The invention provides a remote sensing identification method and a remote sensing identification system for peanut planting area, which take high-resolution remote sensing data as a main data source and combine with a peanut planting system to establish a remote sensing extraction method for peanut planting area, mainly solve the problems of interference of crops in the same period and breakage of peanut plots, realize extraction of small plots and non-scale planting plots, eliminate the interference of various crops in the same period, establish a mode of combining computer interpretation and manual interpretation aiming at the conditions of inconsistent peanut sowing time, inconsistent cultivation modes and the like, and respectively aim at the characteristics of universality of a large area and uniqueness of the small area of peanut planting, and have high interpretation precision.
Description
Technical Field
The invention relates to the technical field of ground feature recognition, in particular to a peanut planting area remote sensing recognition method and system.
Background
Peanuts are one of the most important oil crops in the world and have a great importance in the production of oil in the world. With the increasing of the yield in recent years, the method stably lives the first eight oil crops in China. The acquisition of the peanut planting area has important significance for accurately estimating the peanut yield, making agricultural policies and ensuring the national grain and oil safety.
The method for acquiring the planting area of the bulk crops comprises two methods of manual step-by-step filling statistics and remote sensing monitoring. The manual statistical method has low efficiency, large subjective factors and lack of spatial attributes. The remote sensing monitoring can realize large-area synchronous observation, has high efficiency, objectivity and accuracy and comprehensive comparability of data, and is an important technology for monitoring the crop planting area. ZL201510241648.2 remote sensing monitoring method of winter wheat based on vegetation index increment in growth period utilizes vegetation index EVI increment in early growth period and later growth period established based on MODIS data of full growth period and 500m spatial resolution each day to extract the planting area of the winter wheat.
ZL201310656333.5 a winter wheat remote sensing identification method for detecting soft and hard changes, which utilizes 30m environment No. 1 satellite data to identify the planting area of winter wheat based on the characteristic that DN values of jointing stage and sowing stage are greatly increased. In the existing research, the remote sensing identification of the planting area of winter wheat is basically carried out, and the report of a remote sensing identification method of the planting area of peanuts is not seen.
The existing crop planting area extraction is directed at crops which are obviously extracted, regularly planted in plain areas and plots in large scale, and the extraction method is not suitable for small crops which are planted in hilly and mountainous areas, broken plots and flower arrangement.
Disclosure of Invention
The invention aims to provide a peanut planting area remote sensing identification method and a peanut planting area remote sensing identification system, which aim to solve the problem that the existing crop planting area extraction method in the prior art is not suitable for hilly and mountainous areas, land parcel crushing and flower arrangement planting, realize extraction of small land parcels and non-scale planting land parcels, eliminate interference of various crops in the same period and improve interpretation precision.
In order to achieve the technical purpose, the invention provides a peanut planting area remote sensing identification method, which comprises the following steps:
s1, measuring the ground sample of the peanuts and the same-period easily-mixed ground objects by using a sub-meter GPS, and marking sample points;
s2, selecting monitoring time according to a planting system, and obtaining high-score No. 6 PMS multispectral data of each monitoring time;
s3, extracting DN values of data in different stages of the land feature to establish a vegetation index, establishing a planting area extraction decision tree based on the analysis of the land feature spectrum and the vegetation index, eliminating the soil feature which is easy to mix in the same stage based on the decision tree, and correcting the interpretation result by combining artificial visual interpretation;
s4, outputting the peanut planting area to extract a thematic map.
Preferably, the spatial resolution of the high-resolution No. 6 PMS multispectral data is 9.92697m, and the high-resolution No. 6 PMS multispectral data comprises 4 bands of blue, green, red and near infrared.
Preferably, the method further comprises:
based on remote sensing image processing software and geographic information system software, projection conversion, geometric fine correction and farmland cutting preprocessing work are carried out on satellite data in each period.
Preferably, the method further comprises:
and before outputting the peanut planting area extraction thematic map, performing precision evaluation on the ground sampling points, comparing the extraction result of the sampling points with the sampling point ground object types, calculating interpretation precision, and if the interpretation precision is more than 90%, checking, otherwise, readjusting the decision tree.
The invention also provides a peanut planting area remote sensing identification system, which comprises:
the sampling point selection module is used for measuring the ground sampling directions of the peanuts and the synchronous easily-mixed ground objects and marking the sampling points by utilizing the sub-meter GPS;
the spectrum data acquisition module is used for selecting monitoring time according to a planting system and acquiring high-resolution No. 6 PMS multispectral data of each monitoring time;
the decision tree establishing module is used for extracting DN values of data in different periods of different surface features to establish a vegetation index, establishing a planting area extraction decision tree based on the analysis of the surface feature spectrum and the vegetation index, eliminating the miscible surface features in the same period based on the decision tree, and correcting an interpretation result by combining artificial visual interpretation;
and the thematic map output module is used for outputting the peanut planting area and extracting the thematic map.
Preferably, the spatial resolution of the high-resolution No. 6 PMS multispectral data is 9.92697m, and the high-resolution No. 6 PMS multispectral data comprises 4 bands of blue, green, red and near infrared.
Preferably, the system further comprises:
and the preprocessing module is used for carrying out projection conversion, geometric fine correction and farmland cutting preprocessing on satellite data in each period based on remote sensing image processing software and geographic information system software.
Preferably, the system further comprises:
and the precision evaluation module is used for evaluating the precision of the ground sampling points before outputting the peanut planting area extraction thematic map, comparing the extraction results of the sampling points with the types of the sampling points and the ground objects, calculating the interpretation precision, and passing the test when the interpretation precision is more than 90 percent, or readjusting the decision tree.
The effect provided in the summary of the invention is only the effect of the embodiment, not all the effects of the invention, and one of the above technical solutions has the following advantages or beneficial effects:
compared with the prior art, the remote sensing extraction method for the peanut planting area is established by taking high-resolution remote sensing data as a main data source and combining a peanut planting system, the problems of interference of crops in the same period and breakage of peanut plots are mainly solved, extraction of small plots and non-scale planting plots is realized, interference of various crops in the same period is eliminated, a mode of combining computer interpretation and manual interpretation is established according to the conditions of inconsistent peanut sowing time, inconsistent cultivation modes and the like, the characteristics of universality of a large area and uniqueness of a small area of peanut planting are respectively aimed, and interpretation precision is high.
Drawings
FIG. 1 is a flow chart of a peanut planting area remote sensing identification method provided in the embodiment of the invention;
FIG. 2 is a graph showing the ndvi difference between day 11/6 and day 1/5 in the example of the present invention;
FIG. 3 is a graph of the average brightness of 3 crops in 8 months and 28 days according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a spring peanut planting area extraction decision tree structure provided in an embodiment of the present invention;
FIG. 5 is a thematic map of peanut planting area extraction provided in the embodiment of the present invention;
fig. 6 is a block diagram of a peanut planting area remote sensing identification system provided in the embodiment of the invention.
Detailed Description
In order to clearly explain the technical features of the present invention, the following detailed description of the present invention is provided with reference to the accompanying drawings. The following disclosure provides many different embodiments, or examples, for implementing different features of the invention. To simplify the disclosure of the present invention, the components and arrangements of specific examples are described below. Furthermore, the present invention may repeat reference numerals and/or letters in the various examples. This repetition is for the purpose of simplicity and clarity and does not in itself dictate a relationship between the various embodiments and/or configurations discussed. It should be noted that the components illustrated in the figures are not necessarily drawn to scale. Descriptions of well-known components and processing techniques and procedures are omitted so as to not unnecessarily limit the invention.
The peanut planting area remote sensing identification method and system provided by the embodiment of the invention are explained in detail below with reference to the accompanying drawings.
As shown in figure 1, the embodiment of the invention discloses a peanut planting area remote sensing identification method, which comprises the following steps:
s1, measuring the ground sample of the peanuts and the same-period easily-mixed ground objects by using a sub-meter GPS, and marking sample points;
s2, selecting monitoring time according to a planting system, and obtaining high-score No. 6 PMS multispectral data of each monitoring time;
s3, extracting DN values of data in different stages of the land feature to establish a vegetation index, establishing a planting area extraction decision tree based on the analysis of the land feature spectrum and the vegetation index, eliminating the soil feature which is easy to mix in the same stage based on the decision tree, and correcting the interpretation result by combining artificial visual interpretation;
s4, outputting the peanut planting area to extract a thematic map.
The embodiment of the invention establishes a remote sensing extraction method of peanut planting area by taking high-resolution remote sensing data as a main data source and combining a peanut planting system, and mainly solves the problems of crop interference and peanut plot breakage in the same period.
The method comprises the steps of measuring ground samples of peanuts and the same-period easily-mixed ground objects and marking sample points by using a sub-meter GPS, wherein in order to ensure that pure pixels are formed, a land parcel is not less than 30m x 30m, the samples are used for analyzing crop characteristics, and the sample points are used for precision inspection of interpretation results. The peanuts belong to crops with tolerance to barren and drought, have long growth period, generally are planted once a year, are not waterlogging tolerant, are suitable for growing in sandy loam with good air permeability, and are planted in hilly areas with good barren and water drainage for a long time. The method is characterized in that the land blocks are broken in the hilly areas of China, are distributed in an arc terrace shape according to land reclamation, and are subjected to remote sensing land feature identification, and the number of mixed pixels is large. The larger the spatial resolution of satellite data is, the higher the crop extraction precision is, the high-resolution No. 6 PMS multispectral data with the spatial resolution of 9.92697m is selected, the coverage period is 41 days, 4 wave bands of blue (0.45-0.52um), green (0.52-0.59um), red (0.63-0.69um) and near infrared (0.77-0.89um) are covered, the data distortion is weak, the overlapping performance of multi-phase images is good, and the identification of broken land blocks is facilitated.
Based on the sub-meter GPS, 33 groups of ground samples are drawn by using a geographic information software platform, and 207 sample points (verification points) are obtained.
And selecting monitoring time according to a planting system. As the peanuts in China are mainly distributed in the central plain, the homoeopathic miscible crops comprise 9 kinds of spring corn, summer corn, spring sweet potato, summer peanut, tobacco leaves, facility vegetables, strawberries, taros and the like. The spring peanuts are sown in the middle and last ten days of 4 months, and the biomass is larger than that of the spring corns and taros sown in the same period and the spring pachyrhizus transplanted in the middle and last ten days of 5 months when the spring peanuts are sown in the last ten days of 6 months; summer peanuts and summer sweet potatoes are planted after 6 months of winter wheat is harvested, after greenhouse vegetables and greenhouse strawberries are planted for 6 months and pulled out, the peritoneum is sterilized at high temperature, the overall biomass of the land blocks planted by the 4 kinds of land species is in a reduction trend, and the method is obviously different from spring peanuts; and (3) after 8 months, the transplanted tobacco leaves begin to turn yellow in the middle ten days of 4 months, the obvious difference is obtained from the peanut, and satellite data are selected for monitoring in the first 5 th month, the first 6 th month and the second 3 rd month after the peanut is sowed.
Based on the remote sensing data processing professional software and the geographic information system professional platform software, basic data primary processing work such as projection conversion, geometric fine correction, farmland cutting and the like is carried out on the three-phase satellite data.
And extracting three-phase satellite data spectrum information of 33 groups of ground sample parties, extracting typical ground object satellite spectrum data according to 10 ground object analysis spectrum features, and establishing a classification decision tree. Establishing an interested area of 10 land feature samples through remote sensing image software, and calculating the average value of the brightness values of four wave bands and a normalized difference vegetation index, such as the land feature spectrum and the vegetation index average value of day 5 and 1 in table 1 and the land feature spectrum and the vegetation index average value of day 11 and 6 in table 2:
TABLE 1
TABLE 2
According to the planting system of the area, the growth characteristics of land features are analyzed, according to the ndvi difference value between 11 days in 6 months and 1 day in 5 months, as shown in figure 2, summer corn, summer sweet potato, summer peanuts, facility vegetables, strawberries and taros can be obviously removed, and the three crops of the remaining spring peanuts, spring corns and tobacco leaves are relatively close. Comparing the average values of the band brightness of 3 crops in 28 days in 8 months, as shown in fig. 3, the brightness value of the band 4 of the peanut in spring is obviously higher than that of the corn and tobacco leaves in spring. Based on the analysis of the spectra of the 10 land features and the vegetation index, a spring peanut planting area extraction decision tree is established, and the structure of the decision tree is shown in fig. 4.
Variables of the spring peanut planting area extraction decision tree are defined as table 3, and the meaning of each node is as table 4:
TABLE 3
TABLE 4
And carrying out computer decision tree classification by establishing a decision tree, and correcting the preliminary classification result by combining Google earth and near-term 0.5 m high spatial resolution remote sensing data of a heaven and earth map by an auxiliary manual identification method to obtain an interpretation result. And (3) performing precision evaluation by using 207 ground sampling points, comparing the extraction result of the sampling points with the ground object types of the sampling points, calculating interpretation precision, and checking when the interpretation precision is more than 90%, or readjusting the decision tree to perfect visual interpretation.
Adding administrative division boundaries, legends, compass needles and scales, and drawing a peanut planting area extraction thematic map as shown in fig. 5.
The embodiment of the invention takes high-resolution remote sensing data as a main data source, combines a peanut planting system, establishes a remote sensing extraction method of peanut planting area, mainly solves the problems of interference of crops in the same period and breakage of peanut plots, realizes extraction of small plots and non-scale planting plots, eliminates the interference of various crops in the same period, establishes a mode of combining computer interpretation and manual interpretation aiming at the conditions of inconsistent peanut sowing time, inconsistent cultivation modes and the like, and has high interpretation precision aiming at the characteristics of universality of large areas and uniqueness of small areas of peanut planting.
As shown in fig. 6, the embodiment of the invention also discloses a peanut planting area remote sensing identification system, which comprises:
the sampling point selection module is used for measuring the ground sampling directions of the peanuts and the synchronous easily-mixed ground objects and marking the sampling points by utilizing the sub-meter GPS;
the spectrum data acquisition module is used for selecting monitoring time according to a planting system and acquiring high-resolution No. 6 PMS multispectral data of each monitoring time;
the decision tree establishing module is used for extracting DN values of data in different periods of different surface features to establish a vegetation index, establishing a planting area extraction decision tree based on the analysis of the surface feature spectrum and the vegetation index, eliminating the miscible surface features in the same period based on the decision tree, and correcting an interpretation result by combining artificial visual interpretation;
and the thematic map output module is used for outputting the peanut planting area and extracting the thematic map.
The method comprises the steps of measuring ground sample space of peanuts and the same-period easily-mixed ground objects and marking sample points through a sub-meter GPS, wherein in order to ensure that pure pixels are formed, a land parcel is not smaller than 30m x 30m, the sample space is used for analyzing crop characteristics, and the sample points are used for precision inspection of interpretation results. The peanuts belong to crops with tolerance to barren and drought, have long growth period, generally are planted once a year, are not waterlogging tolerant, are suitable for growing in sandy loam with good air permeability, and are planted in hilly areas with good barren and water drainage for a long time. The method is characterized in that the land blocks are broken in the hilly areas of China, are distributed in an arc terrace shape according to land reclamation, and are subjected to remote sensing land feature identification, and the number of mixed pixels is large. The larger the spatial resolution of satellite data is, the higher the crop extraction precision is, the high-resolution No. 6 PMS multispectral data with the spatial resolution of 9.92697m is selected, the coverage period is 41 days, 4 wave bands of blue (0.45-0.52um), green (0.52-0.59um), red (0.63-0.69um) and near infrared (0.77-0.89um) are covered, the data distortion is weak, the overlapping performance of multi-phase images is good, and the identification of broken land blocks is facilitated.
Based on the sub-meter GPS, 33 groups of ground samples are drawn by using a geographic information software platform, and 207 sample points (verification points) are obtained.
And selecting monitoring time according to a planting system. As the peanuts in China are mainly distributed in the central plain, the homoeopathic miscible crops comprise 9 kinds of spring corn, summer corn, spring sweet potato, summer peanut, tobacco leaves, facility vegetables, strawberries, taros and the like. The spring peanuts are sown in the middle and last ten days of 4 months, and the biomass is larger than that of the spring corns and taros sown in the same period and the spring pachyrhizus transplanted in the middle and last ten days of 5 months when the spring peanuts are sown in the last ten days of 6 months; summer peanuts and summer sweet potatoes are planted after 6 months of winter wheat is harvested, after greenhouse vegetables and greenhouse strawberries are planted for 6 months and pulled out, the peritoneum is sterilized at high temperature, the overall biomass of the land blocks planted by the 4 kinds of land species is in a reduction trend, and the method is obviously different from spring peanuts; and (3) after 8 months, the transplanted tobacco leaves begin to turn yellow in the middle ten days of 4 months, the obvious difference is obtained from the peanut, and satellite data are selected for monitoring in the first 5 th month, the first 6 th month and the second 3 rd month after the peanut is sowed.
The system also comprises a preprocessing module which is used for carrying out preliminary processing work on basic data such as projection conversion, geometric fine correction, farmland cutting and the like on the third-stage satellite data based on the remote sensing data processing professional software and the geographic information system professional platform software.
And extracting three-phase satellite data spectrum information of 33 groups of ground sample parties, extracting typical ground object satellite spectrum data according to 10 ground object analysis spectrum features, and establishing a classification decision tree. And establishing the interested areas of 10 ground feature samples through remote sensing image software, and calculating the average value of the brightness values of the four wave bands and the normalized difference vegetation index.
And analyzing the growth characteristics of the land features according to the planting system of the area.
And carrying out computer decision tree classification by establishing a decision tree, and correcting the preliminary classification result by combining Google earth and near-term 0.5 m high spatial resolution remote sensing data of a heaven and earth map by an auxiliary manual identification method to obtain an interpretation result.
The system also comprises an accuracy evaluation module which is used for carrying out accuracy evaluation by using 207 ground sampling points, comparing the extraction result of the sampling points with the ground object types of the sampling points, calculating interpretation accuracy, passing the inspection when the interpretation accuracy is more than 90 percent, and readjusting the decision tree to perfect visual interpretation otherwise.
Adding administrative division boundaries, legends, compass and scales, and drawing a peanut planting area extraction thematic map.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (8)
1. A peanut planting area remote sensing identification method is characterized by comprising the following steps:
s1, measuring the ground sample of the peanuts and the same-period easily-mixed ground objects by using a sub-meter GPS, and marking sample points;
s2, selecting monitoring time according to a planting system, and obtaining high-score No. 6 PMS multispectral data of each monitoring time;
s3, extracting DN values of data in different stages of the land feature to establish a vegetation index, establishing a planting area extraction decision tree based on the analysis of the land feature spectrum and the vegetation index, eliminating the soil feature which is easy to mix in the same stage based on the decision tree, and correcting the interpretation result by combining artificial visual interpretation;
s4, outputting the peanut planting area to extract a thematic map.
2. The remote sensing identification method for peanut planting area according to claim 1, wherein the spatial resolution of the high-score No. 6 PMS multispectral data is 9.92697m, and the high-score No. 6 PMS multispectral data comprises 4 bands of blue, green, red and near infrared.
3. The remote sensing identification method for peanut planting area according to claim 1 or 2, characterized in that the method further comprises:
based on remote sensing image processing software and geographic information system software, projection conversion, geometric fine correction and farmland cutting preprocessing work are carried out on satellite data in each period.
4. The remote sensing identification method for peanut planting area according to claim 1 or 2, characterized in that the method further comprises:
and before outputting the peanut planting area extraction thematic map, performing precision evaluation on the ground sampling points, comparing the extraction result of the sampling points with the sampling point ground object types, calculating interpretation precision, and if the interpretation precision is more than 90%, checking, otherwise, readjusting the decision tree.
5. The peanut planting area remote sensing identification system is characterized by comprising:
the sampling point selection module is used for measuring the ground sampling directions of the peanuts and the synchronous easily-mixed ground objects and marking the sampling points by utilizing the sub-meter GPS;
the spectrum data acquisition module is used for selecting monitoring time according to a planting system and acquiring high-resolution No. 6 PMS multispectral data of each monitoring time;
the decision tree establishing module is used for extracting DN values of data in different periods of different surface features to establish a vegetation index, establishing a planting area extraction decision tree based on the analysis of the surface feature spectrum and the vegetation index, eliminating the miscible surface features in the same period based on the decision tree, and correcting an interpretation result by combining artificial visual interpretation;
and the thematic map output module is used for outputting the peanut planting area and extracting the thematic map.
6. The remote sensing peanut planting area identification system of claim 5, wherein said high-resolution No. 6 PMS multispectral data spatial resolution is 9.92697m, comprising 4 bands of blue, green, red, and near infrared.
7. The remote sensing peanut planting area identification system of claim 5 or 6, further comprising:
and the preprocessing module is used for carrying out projection conversion, geometric fine correction and farmland cutting preprocessing on satellite data in each period based on remote sensing image processing software and geographic information system software.
8. The remote sensing peanut planting area identification system of claim 5 or 6, further comprising:
and the precision evaluation module is used for evaluating the precision of the ground sampling points before outputting the peanut planting area extraction thematic map, comparing the extraction results of the sampling points with the types of the sampling points and the ground objects, calculating the interpretation precision, and passing the test when the interpretation precision is more than 90 percent, or readjusting the decision tree.
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