CN110110595B - Farmland image and medicine hypertrophy data analysis method based on satellite remote sensing image - Google Patents
Farmland image and medicine hypertrophy data analysis method based on satellite remote sensing image Download PDFInfo
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Abstract
The invention relates to the technical fields of remote sensing technology and geographic information systems, in particular to a farmland image and medicine hypertrophy data analysis method based on satellite remote sensing images, which comprises the following steps: 1) Acquiring satellite image remote sensing data; 2) Preprocessing remote sensing data; 3) Extracting the crop planting type; 4) Extracting the land block vector information of the satellite image; 5) Entering attribute field information for the land parcel vector; 6) Investigation is conducted on the entered land block attribute fields; 7) And carrying out big data operation and screening analysis on the collected land parcel information. The farmland image and medicine hypertrophy data analysis method based on the satellite remote sensing image has the advantages of high farmland data precision, high definition degree and strong data persuasion.
Description
Technical Field
The invention relates to the technical fields of remote sensing technology and geographic information systems, in particular to a farmland image and medicine hypertrophy data analysis method based on satellite remote sensing images.
Background
With the development of the times and science, the traditional farming mode is basically disappeared, and instead, the novel farming mode with the modern characteristics of industrialization and science and technology is adopted. China is a large agricultural country, and along with the rising of technologies and the pushing of technologies, emerging technologies including big data, remote sensing technologies and geographic information systems are widely applied to agriculture, and the technology has a yield increasing means for crops; more and more chemical drugs are scattered into farmlands, such as fertilizers, herbicides, etc.; their advent has greatly increased crop yields, while the disadvantage to most farmers is that the variety and brand of chemicals are not known how to choose to increase crop yields to a greater extent; how to provide farmers with fine management of land parcels and how to scientifically select chemical fertilizers and pesticides for farmlands to achieve the purpose of increasing yield is a difficult problem to be solved.
Disclosure of Invention
The invention provides a farmland image and medicine hypertrophy data analysis method based on satellite remote sensing images, which aims to solve the problem of low farmland refinement management in the prior art and has data persuasion.
The technical scheme adopted by the invention is as follows:
a farmland image and medicine hypertrophy data analysis method based on satellite remote sensing images comprises the following steps:
1) Acquiring satellite image remote sensing data: adopting a sub-meter-level domestic high-resolution satellite image with resolution of 0.8 meter-a high-resolution second-number image;
2) Preprocessing remote sensing data: preprocessing the acquired satellite images by using ENVI software, wherein the preprocessing process comprises radiation calibration, atmospheric correction, orthographic correction, image registration and image enhancement;
3) Extracting crop planting types: classifying and extracting the types of cultivated crops in the cultivated land by using a remote sensing image classification algorithm or a deep learning method;
4) Extracting the land parcel vector information of the satellite image: extracting land parcel vectors according to satellite images, wherein the land parcel vectors comprise the operations of extracting, drawing, modifying, integrating and sleeving the land parcel boundaries of farmers, and finally obtaining the space position information of the land parcel;
5) Establishing a complete attribute table for a land parcel vector image layer output by a land parcel vector extraction module by using a geographic information system Arcmap software, wherein the attribute table is recorded in land type, seed type, sowing method, seed play amount, sowing time, irrigation times in a crop growth period, irrigation water amount per unit area, fertilizer type, fertilizer brand, fertilization time, fertilization method, fertilization amount per unit area, herbicide type, herbicide brand, herbicide application method, application amount per unit area, harvesting time of crops and yield per unit area;
6) Investigation of the entered parcel attribute field: investigation is conducted on the recorded attribute fields of the land parcels by combining the space position information in the step 4), and the investigation result is recorded into the attribute fields of the corresponding land parcels;
7) Big data operation and screening analysis are carried out on the collected land parcel information: and carrying out operation analysis operation on the attribute information of the land by using a big data operation analysis module to finally obtain a required land evaluation report and analysis report.
Further, the atmospheric correction in step 2) adopts a flash atmospheric correction model, the orthographic correction is processed by using rpb files based on GF2 satellite, and the image enhancement is assisted in identifying crops by using 2% stretching and HSV color space transformation.
Further, the crop planting type extraction method in the step 3) includes building a four-layer decision tree for two classification and adopting the latest convolutional neural network method for extraction.
Further, the block vector extraction in the step 4) comprises the steps of extracting, drawing, modifying, integrating, outputting and matching of the block boundary of the farmer; the method comprises the following specific steps:
41 Land plots with land areas of farmers larger than 5 mu are extracted by using a rule-based object-oriented information extraction tool in ENVI, and land plots with areas smaller than 5 mu are extracted by using a method of manually drawing by using an Arcmap software of a geographic information system;
42 After the farmer land parcels are extracted, carrying out boundary modification by using Arcmap software, integrating all the extracted land parcels, and outputting a land parcel vector map layer.
Further, the investigation of the land parcel attribute field in step 6) includes the following steps:
61 Performing refined processing again on the soil types by using remote sensing images on the basis of the nationwide soil types, and then assigning the attributes into the vectorized plots through an attribute extraction tool of arcgis, so as to obtain the soil types of each plot;
62 Inversion is carried out on the ground temperature through thermal infrared data of Landsat8 data, the ground surface temperature is detected in real time, the growth state of crops and the water shortage condition of the ground surface are monitored, the condition of each land block is accurately detected according to different types and temperatures of soil, inversion is carried out by adopting MODIS data aiming at large-scale temperature investigation, in addition, the Kriging interpolation method is carried out by using a daily weather site under the frame of the MODIS data, so that the time precision is ensured, and the space precision is ensured;
63 Simultaneously, inverting the N element content and the growth condition of the crops by using a remote sensing satellite, detecting the growth condition of the crops by using NDVI time sequence data, and timely remedying and specially managing the areas with poor growth condition;
64 Predicting the maturity of crops in different areas according to the historical accumulated temperature and the accumulated rainfall, ensuring that the crops are harvested at the most proper time and ensuring the maximization of the yield;
65 And (3) counting the yield of the land parcels, optimizing the crop yield model according to a large amount of yield statistic data, and continuously correcting the model.
The invention has the beneficial effects that:
1. the satellite remote sensing image is applied to the farmland medicine hypertrophy data analysis method, so that visual analysis can be carried out on land plots, and in addition, detailed information of different farmlands can be compared, so that fine farmland management can be realized;
2. the detailed attribute information of the farmland is investigated and uploaded to the cloud end through the Internet, so that intelligent management analysis can be systematically carried out on the farmland, the broken chain between the position and the attribute information is solved, the attribute information of the land is investigated in an auxiliary mode by using a remote sensing big data analysis method, and macroscopic high efficiency is realized;
3. providing screening and inquiring of farmland attribute information, and acquiring an online report of the investigated land attribute information in real time;
4. the data is visually compared and analyzed by using the big data algorithm model, so that an analysis conclusion is more persuasive, the result is more scientific and reliable, a scientific basis can be provided for government agricultural departments to make decisions, and the method can be popularized and applied to similar big data analysis in forestry growth vigor.
In a word, the farmland image and medicine hypertrophy data analysis method based on the satellite remote sensing image has the advantages of being high in farmland data precision, high in definition degree and strong in data persuasion.
Drawings
Fig. 1 is a flowchart of a farmland image and medicine hypertrophy data analysis method based on satellite remote sensing images.
Detailed Description
The core of the invention is to provide a farmland image and medicine hypertrophy data analysis method based on satellite remote sensing images.
The invention is further described below with reference to the accompanying drawings:
the scheme provides a farmland image and medicine hypertrophy data analysis method based on satellite remote sensing images, which comprises the following steps of;
1) Satellite remote sensing image acquisition: and cutting out the high-resolution second-order image of the domestic satellite with the resolution of 0.8 m in the investigation region. The larger the investigation area is, the more investigation targets are, the more plots with basically the same growth environment are obtained after big data operation analysis, and the more accurate and reliable the report output by the big data operation analysis module is.
2) The satellite images in the range of the research area are preprocessed: the preprocessing process is completed by remote sensing software ENVI, and the processing steps mainly comprise radiation calibration, atmospheric correction, orthographic correction, image registration and image enhancement; the atmospheric correction adopts a flash atmospheric correction model, orthographic correction is processed by using rpb files based on GF2 satellite, and image enhancement is assisted in identifying crops by using 2% stretching and HSV color space transformation.
3) Sorting crops: the method comprises the steps of extracting features of crops by using samples collected by field, then establishing a 4-layer decision tree for two-classification, wherein the precision is 90%, extracting by adopting the latest convolutional neural network method in the large-area extraction process, and under the condition of better image quality, the precision is 93%;
4) Carrying out land parcel vector extraction on a result image preprocessed by the satellite remote sensing image: the land parcel vector extraction mainly comprises the steps of extracting, drawing, modifying, integrating, outputting and sleeving the land parcel boundary of a peasant household. In order to improve efficiency and meet the precision requirement on land area, the method is carried out according to the following standard that land areas with areas larger than 5 mu are extracted by using a rule-based object-oriented information extraction tool in ENVI and land areas with areas smaller than 5 mu are extracted by using a method of manually drawing by using an Arcmap software of a geographic information system. After the land is extracted, the ArcMAP software is used for modifying and integrating all the extracted land by the boundary, and a land vector layer is output. The output block vector is sleeved with the image to obtain the space position information of the block.
5) Land parcel vector attribute field entry: the geographic information system Arcmap software is used for establishing a complete attribute table for a land parcel vector map layer, and attribute fields are used for recording soil types, seed types, sowing methods, seed sowing amounts, sowing time, irrigation times in a crop growth period, irrigation water amount per unit area, fertilizer types, fertilizer brands, fertilization time, fertilization methods, fertilization amount per unit area, herbicide types, herbicide brands, herbicide application methods, application amount per unit area, crop harvesting time, yield per unit area and the like.
6) Investigation of the entered parcel attribute field: according to the space position information of the land parcel obtained in the step 4), the position of the land parcel can be accurately found out. Investigation of land mass information of the land mass, such as soil property type, seed type, sowing method, seed sowing amount, sowing time, irrigation times in a crop growth period, irrigation water amount per unit area, fertilizer type, fertilizer brand, fertilization time, fertilization method, fertilizer amount per unit area, herbicide type, herbicide brand, herbicide application method, application amount per unit area, crop harvesting time and yield per unit area recorded in the step 4) according to the position of the land mass;
the algorithm flow adopted by the attribute information investigation is supplemented:
(1) Performing secondary refinement treatment on the soil types of the whole country by using the remote sensing image, and then assigning the attribute into the vectorized land by using an attribute extraction tool of arcgis, thereby obtaining the soil type of each land;
(2) Inversion is carried out on the temperature of the ground through thermal infrared data of Landsat8 data, the temperature of the ground surface is detected in real time, the growth state of crops and the water shortage condition of the ground surface are monitored, the condition of each land block is accurately detected according to different types and temperatures of soil, inversion is carried out by adopting MODIS data aiming at large-scale temperature investigation, in addition, the Kriging interpolation method is carried out by using a daily meteorological site under the framework of the MODIS data, so that the time precision is ensured, and the space precision is ensured;
(3) Meanwhile, the N element content and the growth state analysis of crops are inverted by using a remote sensing satellite, the growth state of the crops is detected by using NDVI (Normalized Difference Vegetation Index, normalized differential vegetation index, standard differential vegetation index) time sequence data, and timely remediation and special management are carried out on the areas with poor growth state;
(4) According to the historical accumulated temperature and the accumulated rainfall, the maturity of crops in different areas is predicted, the crops are harvested at the most proper time, and the maximization of the yield is ensured;
(5) Counting the yield of the land parcels, optimizing a crop yield model according to a large amount of yield statistic data, and continuously correcting the model;
(6) And (3) inputting farmland attribute information, namely inputting the result of farmland attribute information inquiry into a corresponding land parcel attribute field in a land parcel vector attribute field input module.
7) Big data operation and screening analysis are carried out on the collected land parcel information: and inquiring and screening the attribute table of the farmland attribute information by utilizing big data operation, filtering out unnecessary data, displaying only important information, and outputting a land parcel evaluation report and an analysis report. For example, when the difference of the annual crop yield caused by using different chemical fertilizer brands under the basically same growth environment is obtained, the soil types, seed types, sowing methods, seed sowing amounts, sowing time, irrigation times in the growth period of crops, irrigation water amount per unit area, fertilizer types, fertilization time, fertilization methods, fertilization amount per unit area, herbicide types, herbicide brands, herbicide application methods, application amounts per unit area and harvesting time information of crops are extracted from all data by using big data operation, and analysis reports are output after screening of the same growth environment of crops is completed. The report can only distinguish fertilizer brands and unit area yields under the same growing environment. The higher the yield per unit area of the same fertilizer brand, the better the effect of the fertilizer brand, and the worse the effect.
According to the specific embodiment, the farmland image and medicine hypertrophy data analysis method based on the satellite remote sensing image has the advantages of high farmland data precision, high definition degree and strong data persuasion.
Claims (5)
1. A farmland image and medicine hypertrophy data analysis method based on satellite remote sensing images is characterized in that: comprising the following steps:
1) Acquiring satellite image remote sensing data: adopting a sub-meter-level domestic high-resolution satellite image with resolution of 0.8 meter-a high-resolution second-number image;
2) Preprocessing remote sensing data: preprocessing the acquired satellite images by using ENVI software, wherein the preprocessing process comprises radiation calibration, atmospheric correction, orthographic correction, image registration and image enhancement;
3) Extracting crop planting types: classifying and extracting the types of cultivated crops in the cultivated land by using a remote sensing image classification algorithm or a deep learning method;
4) Extracting the land parcel vector information of the satellite image: extracting land parcel vectors according to satellite images, wherein the land parcel vectors comprise the operations of extracting, drawing, modifying, integrating and sleeving the land parcel boundaries of farmers, and finally obtaining the space position information of the land parcel;
5) Establishing a complete attribute table for a land parcel vector image layer output by a land parcel vector extraction module by using a geographic information system Arcmap software, wherein the attribute table is recorded in land type, seed type, sowing method, seed play amount, sowing time, irrigation times in a crop growth period, irrigation water amount per unit area, fertilizer type, fertilizer brand, fertilization time, fertilization method, fertilization amount per unit area, herbicide type, herbicide brand, herbicide application method, application amount per unit area, harvesting time of crops and yield per unit area;
6) Investigation of the entered parcel attribute field: investigation is conducted on the recorded attribute fields of the land parcels by combining the space position information in the step 4), and the investigation result is recorded into the attribute fields of the corresponding land parcels;
7) Big data operation and screening analysis are carried out on the collected land parcel information: and carrying out operation analysis operation on the attribute information of the land by using a big data operation analysis module to finally obtain a required land evaluation report and analysis report.
2. The method for analyzing farmland image and medicine hypertrophy data based on satellite remote sensing images as set forth in claim 1, wherein the method comprises the following steps: the atmospheric correction in step 2) adopts a flash atmospheric correction model, the orthographic correction is processed by using rpb files based on GF2 satellite self-contained, and the image enhancement is assisted in identifying crops by using 2% stretching and HSV color space transformation.
3. The method for analyzing farmland image and medicine hypertrophy data based on satellite remote sensing images as set forth in claim 1, wherein the method comprises the following steps: the crop planting type extraction method in the step 3) comprises the steps of establishing a four-layer decision tree for two classification and adopting the latest convolutional neural network method for extraction.
4. The method for analyzing farmland image and drug hypertrophy data based on satellite remote sensing images as claimed in claim 1, wherein the method comprises the following steps: the land parcel vector extraction in the step 4) comprises the steps of extracting, drawing, modifying, integrating, outputting and matching the land parcel boundary of a peasant household; the method comprises the following specific steps:
41 Land plots with land areas of farmers larger than 5 mu are extracted by using a rule-based object-oriented information extraction tool in ENVI, and land plots with areas smaller than 5 mu are extracted by using a method of manually drawing by using an Arcmap software of a geographic information system;
42 After the farmer land parcels are extracted, carrying out boundary modification by using Arcmap software, integrating all the extracted land parcels, and outputting a land parcel vector map layer.
5. The method for analyzing farmland image and medicine hypertrophy data based on satellite remote sensing images as set forth in claim 1, wherein the method comprises the following steps: the investigation of the land parcel attribute field in the step 6) comprises the following steps:
61 Performing refined processing again on the soil types by using remote sensing images on the basis of the nationwide soil types, and then assigning the attributes into the vectorized plots through an attribute extraction tool of arcgis, so as to obtain the soil types of each plot;
62 Inversion is carried out on the ground temperature through thermal infrared data of Landsat8 data, the ground surface temperature is detected in real time, the growth state of crops and the water shortage condition of the ground surface are monitored, the condition of each land block is accurately detected according to different types and temperatures of soil, inversion is carried out by adopting MODIS data aiming at large-scale temperature investigation, in addition, the Kriging interpolation method is carried out by using a daily weather site under the frame of the MODIS data, so that the time precision is ensured, and the space precision is ensured;
63 Simultaneously, inverting the N element content and the growth condition of the crops by using a remote sensing satellite, detecting the growth condition of the crops by using NDVI time sequence data, and timely remedying and specially managing the areas with poor growth condition;
64 Predicting the maturity of crops in different areas according to the historical accumulated temperature and the accumulated rainfall, ensuring that the crops are harvested at the most proper time and ensuring the maximization of the yield;
65 And (3) counting the yield of the land parcels, optimizing the crop yield model according to a large amount of yield statistic data, and continuously correcting the model.
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| CN112988003B (en) * | 2019-12-13 | 2023-07-21 | 广州极飞科技股份有限公司 | Agronomic information display method, device, storage medium and processor |
| WO2021226976A1 (en) * | 2020-05-15 | 2021-11-18 | 安徽中科智能感知产业技术研究院有限责任公司 | Soil available nutrient inversion method based on deep neural network |
| CN112215717A (en) * | 2020-10-13 | 2021-01-12 | 江西省农业科学院农业经济与信息研究所 | Agricultural information management system based on electronic map and aerial photography information |
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| CN113487439A (en) * | 2021-07-08 | 2021-10-08 | 哈尔滨航天恒星数据系统科技有限公司 | Multi-parameter-based parcel risk assessment method |
| CN113762788A (en) * | 2021-09-13 | 2021-12-07 | 中化现代农业有限公司 | Method and system for building farmland data model and portrait |
| CN114067158B (en) * | 2021-11-17 | 2022-09-23 | 江苏天汇空间信息研究院有限公司 | Farmland use state monitoring system and method applying multi-source remote sensing data |
| CN114467663A (en) * | 2022-03-02 | 2022-05-13 | 黑龙江省农业科学院耕作栽培研究所 | Production method of high-quality rice of rice in cold region |
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