CN115204479A - Crop survival area prediction method and system based on multi-source remote sensing data - Google Patents
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Abstract
The invention discloses a crop survival area prediction method and system based on multi-source remote sensing data, wherein the system comprises a data acquisition layer, a data acquisition layer and a data acquisition layer, wherein the data acquisition layer is used for acquiring digital elevation data and environmental data of a crop planting area; the equipment service layer is used for storing the digital elevation data and the environmental data collected by the data acquisition layer and transmitting the digital elevation data and the environmental data to the core calculation layer; and the core calculation layer is used for training a prediction model of the crop survival area by adopting a machine learning algorithm according to the received digital elevation data and the environment data so as to complete the prediction of the crop survival area.
Description
Technical Field
The invention relates to a crop survival area prediction method based on machine learning and multi-source remote sensing data, and belongs to the field of crop ecological suitability evaluation.
Background
Global changing background brings potential climate risk to crop planting, and more attention is paid to limiting factor identification, introduction, annual scene assessment and the like in crop climate suitability research. Xie Chun et al achieved crop introduction evaluation. Cross-uka et al achieved the identification of inner mongolia corn restriction factors. The hierarchical analysis method can construct a comprehensive index system, realize modeling on agricultural production conditions, and identify the influence of various indexes such as weather, climate and agricultural production conditions on agriculture. The research on the influence of more climate change on agricultural production mainly analyzes the change of basic meteorological elements.
Data such as satellite remote sensing, soil, reanalysis and terrain are used abroad to develop multi-level agricultural evaluation analysis and research. Summarizing the current research, the current research mainly aims at the influence of climate change in a wider range on agriculture, and lacks of climate change evaluation aiming at area interior refinement; more researches are based on site data for modeling, and a climate suitability evaluation method for comprehensively utilizing data is lacked. Qualitatively, quantitatively and positionally evaluating the ecological suitability of the crops in a certain area, and providing basis for further reasonable layout, opening up more high-quality tobacco bases and realizing the sustainable development of tobacco.
The meteorological elements are elements indicating the physical state and physical phenomena of the atmosphere, and mainly comprise air temperature, precipitation, wind, air pressure, humidity, cloud cover, radiation, sunshine, atmospheric visibility, weather phenomena and the like.
The meteorological element distributed simulation technology mainly aims at the requirements of high spatial resolution and high temporal resolution besides meeting certain simulation precision. The meteorological element distributed simulation mainly comprises horizontal plane simulation and simulation under the undulating terrain, wherein the influence of the topographic factors is not considered in the horizontal plane simulation, and the meteorological element distribution under the influence of the topographic relief is considered under the undulating terrain. The rapid development of meteorological element distributed simulation technology is promoted by the development of technologies such as remote sensing and geographic information. Common meteorological element distributed simulation methods are generally classified into spatial interpolation methods, model methods, and the like. For the interpretability problem of the machine learning algorithm, a model independent method and the like are utilized to carry out error calculation and statistics of the machine learning model, and a quality estimation product of the model is output. And carrying out comprehensive comparison evaluation on the physical model and the machine learning model, and knowing the applicability characteristics of different models in time, space and regions.
Disclosure of Invention
In order to solve the problems, the invention provides a system and a method for predicting a crop survival area based on multi-source remote sensing data.
The invention adopts the following technical scheme for solving the technical problems:
crop survival area prediction system based on multisource remote sensing data includes:
the data acquisition layer is used for collecting digital elevation data and environmental data of the crop planting area;
the equipment service layer is used for storing the digital elevation data and the environmental data collected by the data acquisition layer and transmitting the digital elevation data and the environmental data to the core calculation layer;
and the core calculation layer is used for training a prediction model of the crop survival area by adopting a machine learning algorithm according to the received digital elevation data and the environment data so as to complete the prediction of the crop survival area.
Further, according to the digital elevation data, calculating the area gradient, the slope direction, the undulation degree, the plane curvature, the vertical curvature, the terrain humidity index, the terrain roughness index, the multi-resolution valley bottom flatness, the convergence index and the form protection index of the crop planting area.
Further, the environmental data includes vegetation index, geographic data, and meteorological data.
Further, the vegetation index is fused by multi-source remote sensing data: firstly, performing quality control on various satellite remote sensing data, and screening out high-quality pixels meeting set requirements; secondly, performing spatial resampling on the remote sensing data with the resolution higher than the set resolution, and aggregating the average value of the remote sensing data on each month scale; thirdly, filtering the remote sensing data; thirdly, performing data fusion through geographical weighted regression; and finally, carrying out consistent correction on the fusion data by adopting a linear correction method.
Further, the prediction model of the crop survival area is trained by using X-Gboost.
Further, the device service layer and the core computation layer are implemented by an intelligent edge gateway, and the data acquisition layer transmits data to the intelligent edge gateway through a Zigbee protocol.
The crop survival area prediction method based on the multi-source remote sensing data is used for predicting the crop survival area based on the system.
A computer-readable storage medium comprising a stored program, wherein the program when executed performs the prediction method described above.
An electronic device comprising a memory having a computer program stored therein and a processor arranged to perform the above-mentioned prediction method by means of the computer program.
Compared with the prior art, the invention adopting the technical scheme has the following technical effects: a crop survival area prediction model is established by integrating a small amount of observation data, utilizing large data of weather, geography, economy and the like, combining crop growth process mechanisms and influence mechanisms, based on weather geography large data and machine learning technology and based on an X-Gboost algorithm, and the prediction of the crop survival area combining agricultural data and the large data is realized.
Detailed Description
The technical solution of the present invention is further illustrated by the following specific examples.
The invention designs a crop survival area prediction system based on multi-source remote sensing data, which comprises the following steps:
the data acquisition layer is used for collecting digital elevation data and environmental data of the crop planting area;
the equipment service layer is used for storing the digital elevation data and the environmental data collected by the data acquisition layer and transmitting the digital elevation data and the environmental data to the core calculation layer;
and the core computing layer is used for training a prediction model of the crop survival area by adopting a machine learning algorithm according to the received digital elevation data and the environment data so as to complete the prediction of the crop survival area.
Further, from the digital elevation data, a zone Slope (Slope), an Aspect (Aspect), a waviness (Undulation), a plane Curvature (Plan Curvature), a Vertical Curvature (Vertical Curvature), a terrain humidity Index (Topographic humidity Index), a terrain roughness Index (Ruggedness Index), a multi-resolution valley flatness (MRVBF), a Convergence Index (Convergence Index), a morphology Protection Index (Morphometric Protection Index), and the like are calculated.
Further, the environmental data includes vegetation index, geographic data, and meteorological data, as shown in table 1:
TABLE 1
Further, the vegetation index is fused by multi-source remote sensing data: firstly, performing quality control on various satellite remote sensing data, and screening out high-quality pixels meeting set requirements; secondly, carrying out spatial resampling on the remote sensing data with the resolution higher than the set resolution, and aggregating the average value of the remote sensing data on each month scale; thirdly, filtering the remote sensing data; thirdly, performing data fusion through geographical weighted regression; and finally, performing consistency correction on the fusion data by adopting a linear correction method.
In one embodiment, the development of a fusion algorithm for multi-source data includes:
(1) Remote sensing data preprocessing: and (3) performing quality control on data such as sentinels 2, LANDSAT, MODIS, AVHRR and the like, and screening high-quality pixels.
(2) Remote sensing data space-time resampling: for high resolution LANDSAT images, spatial resampling to 1km is performed. And aggregating the average value of the remote sensing values on each monthly scale.
(3) Filtering the remote sensing data: due to the influence of cloud and fog, the remote sensing image has a vacancy, and the function is used for fitting the annual temperature change trend so as to fill the space. Common filtering methods include SG filtering, linear regression, and the like, and five-point linear smoothing is used in the research for filtering.
(4) And (3) geographic weighted regression data fusion: and (3) carrying out size reduction on the AVHRR-5KM ground temperature data by using the aggregated LANDSAT-1KM data and MODIS-1KM data, constructing a geographical weighted regression equation, and calculating to obtain an AVHRR-1KM ground temperature data set with the time range of 1982-2020 and the time resolution of one month.
(5) Data consistency correction: since the auxiliary data is changed into MODIS since 2002, the pixels in the same place and month need to be corrected, and the high-precision data which are meteorological data and grid data are obtained by using a linear correction method.
In one embodiment, remote sensing data fusion, such as NDVI classification, includes:
(1) data resampling to 240m and 960m resolution;
(2) calculating the NDVI;
(3) studies were conducted to identify the pixels most likely to be contaminated with snow using daily air temperatures (less than 0 ℃ for 5 consecutive days) and to replace those pixels with the values of NDVI closest to uncontaminated winter;
(4) fitting a smoothing curve from the time series of NDVI data using a Savitzky Golay filter;
(5) and (4) retrieving vegetation phenological parameters by adopting a dynamic threshold value method. Defining an NDVI ratio of 20% as a dynamic threshold value for determining SOS, and in order to eliminate the influence of sparse vegetation and bare soil on the NDVI, researching and extracting pixels with the annual average NDVI greater than 0.1, and determining EOS by adopting a threshold value of 60%. The extraction of vegetation phenological parameters is mainly done in Google Earth Engine.
Core code example:
the process comprises the following steps:
a) Loading corresponding data set according to ID of data set
b) B4 and B5 wave bands for specifying date data are screened out through dates
c) Splitting the image containing B4 and B5 wave bands into single-wave-band images
d) Each element of the set is subjected to NDVI index calculation by utilizing circulation to obtain an NDVI vegetation index set of the whole time period
e) Obtaining lunar synthetic image data by maximum image fusion algorithm
f) And loading the obtained NDVI index image to an interactive map to realize map display.
Further, a crop survival area prediction model is trained by using the X-Gboost.
Further, the device service layer and the core computation layer are implemented by an intelligent edge gateway, and the data acquisition layer transmits data to the intelligent edge gateway through a Zigbee protocol.
A computer-readable storage medium comprising a stored program, wherein the program when executed performs the prediction method described above.
An electronic device comprising a memory having a computer program stored therein and a processor arranged to perform the above-mentioned prediction method by means of the computer program.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), rambus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
It should be noted that the above description of the embodiments is only for the purpose of assisting understanding of the method of the present application and the core idea thereof, and that those skilled in the art can make several improvements and modifications to the present application without departing from the principle of the present application, and these improvements and modifications are also within the protection scope of the claims of the present application.
Claims (9)
1. Crop survival area prediction system based on multisource remote sensing data, its characterized in that includes:
the data acquisition layer is used for collecting digital elevation data and environmental data of the crop planting area;
the equipment service layer is used for storing the digital elevation data and the environmental data collected by the data acquisition layer and transmitting the digital elevation data and the environmental data to the core calculation layer;
and the core computing layer is used for training a prediction model of the crop survival area by adopting a machine learning algorithm according to the received digital elevation data and the environment data so as to complete the prediction of the crop survival area.
2. The crop habitat prediction system based on multi-source remote sensing data of claim 1, wherein an area slope, a slope direction, a waviness, a plane curvature, a vertical curvature, a terrain humidity index, a terrain roughness index, a multi-resolution valley bottom flatness, a convergence index, a morphology protection index of a crop planting area are calculated from the digital elevation data.
3. The crop habitat prediction system based on multi-source remote sensing data of claim 1, wherein the environmental data comprises vegetation index, geographic data, meteorological data.
4. The crop habitat prediction system based on multi-source remote sensing data of claim 3, wherein the vegetation index is fused by multi-source remote sensing data: firstly, performing quality control on various satellite remote sensing data, and screening out high-quality pixels meeting set requirements; secondly, performing spatial resampling on the remote sensing data with the resolution higher than the set resolution, and aggregating the average value of the remote sensing data on each month scale; thirdly, filtering the remote sensing data; thirdly, performing data fusion through geographical weighted regression; and finally, carrying out consistent correction on the fusion data by adopting a linear correction method.
5. The crop habitat prediction system based on multi-source remote sensing data of claim 1, wherein a crop habitat prediction model is trained using X-Gboost.
6. The crop survival area prediction system based on multi-source remote sensing data according to claim 1, wherein the device service layer and the core computing layer are implemented by an intelligent edge gateway, and the data acquisition layer transmits data to the intelligent edge gateway through a Zigbee protocol.
7. The method for predicting the crop survival area based on the multi-source remote sensing data is characterized in that the crop survival area is predicted based on the system as claimed in any one of claims 1 to 6.
8. A computer-readable storage medium, comprising a stored program, wherein the program when executed performs the prediction method of claim 7.
9. An electronic device comprising a memory and a processor, wherein the memory has stored therein a computer program, and the processor is configured to execute the prediction method of claim 7 by the computer program.
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