CN110930048A - Crop drought risk assessment system and method based on disaster mechanism process - Google Patents
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Abstract
The invention belongs to the technical field of crop drought risk assessment, and discloses a crop drought risk assessment system and method based on a disaster-forming mechanism process, wherein the crop drought risk assessment system comprises: the drought monitoring system comprises a meteorological data acquisition module, a temperature acquisition module, a soil humidity acquisition module, a main control module, a data comparison module, a drought level determination module, a drought index calculation module, a remote sensing monitoring module, an evaluation module, a drought threshold prediction module, a drought early warning module, a power supply module, a data storage module, a terminal module and a display module. The drought threshold value is predicted in a big data mode adopted by the drought threshold value prediction module, so that the possibility of future drought and the drought level are predicted in advance; meanwhile, whether the hydrological drought occurs and the strength of the occurrence can be accurately predicted and early-warned in advance through the drought early-warning module, so that time is won for a drought-fighting department, the risk of the hydrological drought disaster is reduced, and the agricultural economic loss is reduced.
Description
Technical Field
The invention belongs to the technical field of crop drought risk assessment, and particularly relates to a crop drought risk assessment system and method based on a disaster mechanism process.
Background
Although sufficient water supply is needed for crop growth and development, the requirement of crop growth on water can be effectively solved through irrigation, drought and water shortage are often difficult to avoid for regional agriculture, particularly for the crop planting industry, and timely and accurate grasp of crop drought situation spatial-temporal dynamic information is an urgent need for agricultural drought resistance, disaster reduction and high yield and income guarantee (2003 Zhihao, etc. 2005) for remote sensing, the spatial-temporal information of all elements on the ground surface in a regional range can be timely and accurately acquired, and therefore the method has an important application value in regional crop drought situation monitoring and becomes a main technical means (invar 2015) for acquiring regional crop drought situation spatial-temporal dynamic information, and further developing the remote sensing monitoring technical method research of regional crop drought situation is an important development direction for agricultural remote sensing application.
China is a large population and simultaneously is the sustainable and stable development of agriculture in the large country, is the first major affair related to the county of China and is the basic guarantee for promoting the stable development of agriculture and the continuous increase of farmers (Luliangqi et al 1996 Tanghuajun et al 2003) once the agricultural drought occurs, the crop yield can be reduced, the plant with difficulty in drinking water by people and livestock can stop producing rivers, and the industrial and agricultural production is seriously influenced (Xuelaborate et al 2002) Rural and peasant) problem development and agricultural drought monitoring and early warning to master the time-space information in the occurrence and development processes of agricultural drought, and the method has very important practical significance for improving the agricultural water efficiency and saving the agricultural water for reasonable agricultural irrigation.
The most common observation of the traditional drought monitoring method based on national station networks is meteorological data or soil moisture content measured by stations to evaluate and judge the occurrence and development of drought (shaoxing, etc. 2003), and the essence of the traditional monitoring method for auxiliary judgment of drought degree by using some hydrological, social and economic data is that the degree and range of drought are inferred by point observation, which is a method that is difficult to provide time-space information for obtaining accurate and timely agricultural drought occurrence and development and often expends a large amount of manpower and financial resources to infer the time-space dynamic change of the soil moisture content of farmland in a large area range by fixed point observation, and is also difficult to accurately judge the degree and range of agricultural drought for the soil moisture content monitoring method of farmland in a relatively small area range by point estimation, so that the traditional drought monitoring method is difficult to make relevant departments accurately grasp and understand the time-space dynamic state of the soil moisture in a large range and the time-space dynamic state of agricultural drought and the extent in time-space dynamic state in the agricultural drought in a Degree of occurrence and development.
In recent 20 years, with the rapid development of satellite remote sensing technology, agricultural drought monitoring in a large range by means of remote sensing has been carried out at home and abroad, and more agricultural drought monitoring research and practical application are developed; the remote sensing is to obtain the electromagnetic wave energy reflected or radiated by the earth surface through a sensor and analyze and research the comprehensive characteristics of the earth surface according to the received electromagnetic wave energy intensity, can fully utilize the spectrum, time, space and direction (multi-angle) information of the surface of a ground object, can monitor the growth vigor and the soil moisture condition of farmland crops due to obvious difference of the electromagnetic wave characteristics of different earth surface substances by utilizing the remote sensing information so as to judge the time-space dynamic change of the occurrence and development of regional agricultural drought (2010-Lei-Li, etc. 2007) agricultural drought remote sensing monitoring method has the advantages that the method can quickly carry out comprehensive synchronous observation on the whole region, can obtain continuous planar information comprehensively analyzing the planar information related to the earth surface characteristics of the farmland through frequent and durable comprehensive observation, and establishes a corresponding remote sensing inversion model so as to further understand the farmland soil moisture time-space dynamic state of the large regional range and combine the long farmland crops The application of the remote sensing technology to the drought monitoring based on the traditional limited ground observation point is a revolutionary improvement of the agriculture drought monitoring, and the agriculture drought remote sensing monitoring has the characteristics of macroscopic timeliness and great manpower and financial resource saving, can quickly perform monitoring analysis on the whole large area range and is beneficial to performing statistical analysis on the drought degree and range, so that the remote sensing technology has great application in the agriculture drought monitoring instead of the traditional ground point observation in recent years.
Since the satellite system can provide continuous data in time and space and acquire information of ground drought change at any time, the shortcoming of drought monitoring by using data such as meteorological data is overcome, and the accuracy and timeliness of agricultural drought monitoring are improved by combining the traditional ground observation and remote sensing monitoring method in actual work because the agricultural drought remote sensing monitoring method is difficult to realize the accuracy of agricultural drought remote sensing monitoring under the condition of cloud, especially the accuracy of monitoring result cannot meet the requirement of national agricultural governing department due to the fact that the accuracy and timeliness of agricultural drought monitoring are improved by combining the traditional ground observation and remote sensing monitoring method in the advanced research field of agricultural drought monitoring and early warning by combining the traditional ground observation and remote sensing monitoring method in the actual work is the urgent need of the prior national agricultural drought monitoring and early warning reduction macroscopical decision of China in recent years The remote sensing monitoring method for agricultural drought has the advantages that the method can quickly and periodically carry out multi-band and multi-angle observation on a large area to obtain continuous planar information about the surface characteristics of the farmland, comprehensively analyze the spatial information, establish a corresponding remote sensing inversion model, and further know the occurrence and development degree and range of the agricultural drought by combining the farmland crop growth situation and the time-space dynamic state of the farmland soil moisture in a large area range; however, in the existing crop drought risk assessment, the drought threshold value cannot be predicted; the early warning of meteorological hydrology and drought cannot be accurately carried out.
Meanwhile, remote sensing data obtained by an aviation or aerospace sensor used for drought identification, such as land satellite Landsat data, medium-resolution imaging spectrometer (MODIS) data, National Oceanic Atmosphere Administration (NOAA) satellite data and the like, have achieved important achievements in regional or global drought crop monitoring and evaluation, however, due to the limitation of data acquisition by a single sensor, high-time, high-space and high-spectrum remote sensing data cannot be effectively obtained, and therefore monitoring accuracy and timeliness are obviously insufficient. In view of this, how to obtain remote sensing data of high time, high space and high spectrum, and identify crop drought in the remote sensing data and evaluate risks becomes a technical problem to be solved at present.
In summary, the problems of the prior art are as follows:
(1) when the existing crop drought risk assessment is carried out, the drought threshold value cannot be predicted; meanwhile, early warning on meteorological hydrodroughts cannot be accurately carried out.
(2) The existing single sensor has limitation in obtaining data, and can not effectively obtain remote sensing data of high time, high space and high spectrum, so that the monitoring precision and timeliness have obvious defects.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a crop drought risk assessment system and method based on a disaster mechanism process.
The invention is realized in this way, a crop drought risk assessment system based on a disaster-forming mechanism process, the crop drought risk assessment system comprising:
the drought monitoring system comprises a meteorological data acquisition module, a temperature acquisition module, a soil humidity acquisition module, a main control module, a data comparison module, a drought level determination module, a drought index calculation module, a remote sensing monitoring module, an evaluation module, a drought threshold prediction module, a drought early warning module, a power supply module, a data storage module, a terminal module and a display module;
the meteorological data acquisition module is connected with the main control module and is used for acquiring meteorological data of the crop area through a meteorological monitoring instrument;
the temperature acquisition module is connected with the main control module and used for acquiring crop environment temperature data through a temperature instrument;
the soil humidity acquisition module is connected with the main control module and is used for acquiring soil humidity data of crops through the humidity sensor;
the main control module is connected with the meteorological data acquisition module, the temperature acquisition module, the soil humidity acquisition module, the data comparison module, the drought level determination module, the drought index calculation module, the remote sensing monitoring module, the evaluation module, the drought threshold prediction module, the drought early warning module, the power supply module, the data storage module, the terminal module and the display module and is used for controlling each module to normally work through the single chip microcomputer;
the data comparison module is connected with the main control module and used for comparing the collected data of the crop area with the normal indexes through a comparison program;
the drought level determining module is connected with the main control module and used for determining the drought level according to the comparison result;
the drought index calculation module is connected with the main control module and used for calculating the crop drought index through a calculation program;
the remote sensing monitoring module is connected with the main control module and is used for monitoring the region of the crop through a remote sensing monitoring device carried by the single chip microcomputer;
the evaluation module is connected with the main control module and is used for evaluating the drought risk of the crops by combining an evaluation program with remote sensing monitoring data;
the drought threshold prediction module is connected with the main control module and used for predicting the crop environment drought threshold through a prediction program;
the drought early warning module is connected with the main control module and used for predicting and warning the drought condition of crops through the warning device;
the power supply module is connected with the main control module and used for providing electric energy for the system through the solar cell panel;
the data storage module is connected with the main control module and used for storing the collected crop area rainfall, environment temperature and soil humidity data and the real-time data of the comparison result, the drought level, the evaluation result, the prediction result and the early warning information through the cloud server;
the terminal module is connected with the main control module and used for transmitting the collected data of the rainfall amount of the crop area, the environmental temperature and the soil humidity to the mobile terminal through the cloud server, and the real-time data of the comparison result, the drought level, the evaluation result, the prediction result and the early warning information are transmitted to the mobile terminal;
and the display module is connected with the main control module and used for displaying the collected crop area precipitation, the environment temperature and the soil humidity data and the comparison result, the drought level, the evaluation result, the prediction result and the early warning information through the display.
The invention also aims to provide a crop drought risk assessment method based on a disaster-forming mechanism process, which comprises the following steps:
acquiring meteorological data of an agricultural region through a meteorological monitoring instrument; collecting crop environment temperature data through a temperature instrument; and collecting crop soil humidity data through a humidity sensor.
Step two, controlling each module to work normally through a single chip microcomputer; comparing the collected data of the crop area with the normal indexes through a comparison program; and determining the drought level according to the comparison result.
Calculating the crop drought index through a calculation program; monitoring the region of the crop by a remote sensing monitoring device carried by a singlechip; and evaluating the drought risk of the crops by an evaluation program.
Step four, predicting the drought threshold of the crop environment through a prediction program; predicting and alarming the crop drought condition through an alarming device; and the solar panel provides electric energy for the system.
And fifthly, storing the collected crop area precipitation, environment temperature and soil humidity data and the real-time data of the comparison result, the drought level, the evaluation result, the prediction result and the early warning information through a cloud server.
And step six, transmitting the collected crop area precipitation, the environment temperature, the soil humidity data, the comparison result, the drought level, the evaluation result, the prediction result and the real-time data of the early warning information to the mobile terminal through the cloud server.
And seventhly, displaying the collected crop area precipitation, the environment temperature and the soil humidity data, the comparison result, the drought level, the evaluation result, the prediction result and the early warning information by using a display through a display module.
Further, in the third step, the crop drought risk assessment method comprises the following steps:
the method comprises the following steps of (I) respectively obtaining remote sensing data with high spatial resolution of a target area to be measured, remote sensing data with high time resolution of the target area to be measured and remote sensing data with high spectral resolution of the target area to be measured by using a remote sensing monitoring device.
And (II) performing space-time fusion on the remote sensing data with the high spatial resolution and the remote sensing data with the high temporal resolution.
And (III) performing space-spectrum fusion on the data subjected to space-time fusion and the remote sensing data with the high spectral resolution to obtain the remote sensing data with high time, high space and high spectral resolution of the target area to be measured.
(IV) acquiring crop planting area information according to the remote sensing data with high time, high space and high spectral resolution.
And (V) identifying the crop drought by using a preset drought identification model according to the crop planting area information, and identifying the drought in different historical periods.
And (VI) performing risk assessment on the crop drought according to the identification result of the crop drought.
Further, in step (i), after the obtaining of the remote sensing data with high spatial resolution of the target area to be measured, the remote sensing data with high temporal resolution of the target area to be measured, and the remote sensing data with high spectral resolution of the target area to be measured, respectively, before the performing space-time fusion on the remote sensing data with high spatial resolution and the remote sensing data with high temporal resolution, the method further includes:
1) filtering the remote sensing data with the high time resolution by using an S-G filtering algorithm, and performing space-time fusion on the remote sensing data with the high spatial resolution and the remote sensing data with the high time resolution, wherein the specific steps are as follows:
and performing space-time fusion on the remote sensing data with the high spatial resolution and the filtered remote sensing data with the high temporal resolution.
2) The space-spectrum fusion is carried out on the data after the space-time fusion and the remote sensing data with the high spectral resolution to obtain the remote sensing data with high time, high space and high spectral resolution of the target area to be measured, and the method specifically comprises the following steps:
and performing space-spectrum fusion on the data subjected to space-time fusion and the filtered remote sensing data with the high spectral resolution to obtain the remote sensing data with high time, high space and high spectral resolution of the target area to be detected.
Further, in step (ii), the performing space-time fusion on the remote sensing data with high spatial resolution and the remote sensing data with high temporal resolution includes:
performing space-time fusion on the remote sensing data with the high spatial resolution and the remote sensing data with the high temporal resolution by utilizing an improved self-adaptive remote sensing image space-time fusion model ESTRAFM algorithm; or
The space-spectrum fusion of the data after the space-time fusion and the remote sensing data with the high spectral resolution comprises the following steps:
and performing space-spectrum fusion on the data subjected to space-time fusion and the remote sensing data with the high spectral resolution by using a multi-spectral image Spectral Resolution Enhancement Method (SREM) fusion algorithm.
Further, in step (iv), the obtaining of the crop planting area information according to the remote sensing data with high time, high space and high spectral resolution includes:
①, obtaining EVI time sequence data of the target area to be measured according to the remote sensing data with high time, high space and high spectral resolution.
②, generating an EVI time sequence curve of normal crops without drought stress and an EVI time sequence curve of crops to be subjected to drought identification according to the EVI time sequence data, and extracting vegetation in the remote sensing data with high time, space and spectral resolution through the slope change of the generated EVI time sequence curve and the comparison of the phenological period of the crops without drought stress, wherein the vegetation comprises partial natural vegetation and all farmland crops.
③ field crops are extracted from the extracted vegetation using a 1/3 height width algorithm.
④ according to the digital elevation model and the slope map, the slope land and the flat land are distinguished from the extracted farmland crops.
Further, in step (v), identifying the crop drought by using a preset drought identification model according to the crop planting area information includes:
identifying crop drought of the extracted sloping fields by using a preset drought identification model according to the crop planting area information; and identifying the crop drought of the extracted flat cultivated land by using a preset drought identification model according to the crop planting area information.
Further, in the fourth step, the drought threshold prediction method comprises the following steps:
(1) performing large data range validation: the large data range includes year, region range, drought level.
(2) Performing large data range analysis: and analyzing data in a large data range to obtain a drought threshold value.
(3) Carrying out drought event identification: and comparing and judging the drought event aiming at the drought threshold value.
(4) And (3) predicting the drought threshold: and (4) predicting a future drought threshold value according to the steps (1) to (3).
Further, the age limit includes a natural year since a record; the region range takes the drought threshold prediction region as the circle center and is within 100 km; the drought levels include small, medium, large and extra large.
Further, in the fourth step, the method for predicting and alarming the drought condition of the crops comprises the following steps:
1) calculation of SSI index and SPI index on a 1-24 month time scale.
2) Determination of a suitable SPI index time scale.
3) And (4) counting different drought characteristics of meteorological drought and hydrologic drought events.
4) And (4) constructing an evolution model of the transmission from meteorological drought to hydrological drought.
5) And (4) determining meteorological drought threshold conditions required by the occurrence of different drought levels of hydrological drought.
6) And early warning and reporting are carried out on weather drought and hydrologic drought events.
Further, in the step 1), the calculation of the SSI index and the SPI index requires flow and rainfall data, respectively.
In the step 2), the Pearson correlation between the weather and drought SPI indexes with different time scales and the hydrologic and drought SSI index is calculated, and the time scale corresponding to the SPI index with the most obvious correlation to the SSI index is the optimal scale of the watershed weather and drought and is marked as SPIn to be used as the basis for the calculation and analysis of the subsequent weather and drought.
In the step 3), the drought characteristics comprise drought occurrence time, drought ending time, drought duration time, average drought strength and drought intensity.
In the step 4), fitting the correlation between the meteorological drought and the hydrological drought in the duration, the average drought strength and the drought intensity by adopting a linear or nonlinear model; the linear or nonlinear fitting model comprises a simple unary linear function, an exponential function, a logarithmic function, a polynomial function and a power function.
In the step 5), according to the different drought characteristic response models constructed in the step 4), determining the meteorological drought conditions required by the occurrence of the hydrological drought with different drought levels comprises the following steps:
a. and (4) given the drought grade of a hydrological drought, and reversely deducing the drought strength of the corresponding meteorological drought.
b. And (5) assigning the weather drought duration by taking a month as a step length, and calculating the hydrologic drought duration.
c. Respectively assigning the product of the average meteorological drought strength and the drought duration and the product of the average hydrographic drought strength and the drought duration to a response function of the drought intensity, and if the equation is established, determining the corresponding meteorological drought average drought strength and the corresponding drought duration as the meteorological condition for generating the extra-drought hydrographic drought; if the equation is not satisfied, on the basis of the step b, the step size is +1, and trial calculation is continued until the equation is satisfied.
The invention has the advantages and positive effects that: the drought monitoring indexes of the occurrence degree and the grade standard of the range of the drought can be objectively described through the drought threshold prediction module; predicting the drought threshold value by adopting a big data mode to forecast the possibility of future drought and the drought level in advance; meanwhile, on the basis of determining the optimal time scale of the correlation between the weather drought index SPI index and the hydrological drought, an evolution model of the transmission from the weather drought to the hydrological drought is respectively constructed on the drought duration, the drought strength and the drought intensity through a drought early warning module, and a reference scheme of the weather drought threshold range required by different levels of the hydrological drought in the humid and semi-humid area is provided; the method can accurately predict and early warn whether the hydrographic drought occurs and the strength of the hydrographic drought, wins time for drought-resistant departments, reduces the risk of the hydrographic drought disaster and reduces the agricultural economic loss.
According to the remote sensing monitoring module and the evaluation module, the crop drought recognition and risk are evaluated through a remote sensing space-time spectrum fusion technology, and the crop drought in the fused remote sensing data with high time resolution, high time resolution and high spectral resolution can be recognized and risk evaluated through the space-time spectrum fusion of the remote sensing data with high space resolution, the remote sensing data with high time resolution and the remote sensing data with high spectral resolution of a target area to be measured, so that the result is more accurate, and the timeliness is higher.
Drawings
Fig. 1 is a flow chart of a crop drought risk assessment method based on a disaster-forming mechanism process according to an embodiment of the present invention.
FIG. 2 is a block diagram of a crop drought risk assessment system based on a disaster mechanism process according to an embodiment of the present invention;
in the figure: 1. a meteorological data acquisition module; 2. a temperature acquisition module; 3. a soil humidity acquisition module; 4. a main control module; 5. a data comparison module; 6. a drought level determination module; 7. a drought index calculation module; 8. a remote sensing monitoring module; 9. an evaluation module; 10. a drought threshold prediction module; 11. a drought early warning module; 12. a power supply module; 13. a data storage module; 14. a terminal module; 15. and a display module.
Detailed Description
In order to further understand the contents, features and effects of the present invention, the following embodiments are illustrated and described in detail with reference to the accompanying drawings.
The structure of the present invention will be described in detail below with reference to the accompanying drawings.
As shown in fig. 2, the crop drought risk assessment system based on the disaster mechanism process provided by the embodiment of the present invention includes: the drought control system comprises a meteorological data acquisition module 1, a temperature acquisition module 2, a soil humidity acquisition module 3, a main control module 4, a data comparison module 5, a drought level determination module 6, a drought index calculation module 7, a remote sensing monitoring module 8, an evaluation module 9, a drought threshold prediction module 10, a drought early warning module 11, a power supply module 12, a data storage module 13, a terminal module 14 and a display module 15.
The meteorological data acquisition module 1 is connected with the main control module 4 and is used for acquiring meteorological data of an agricultural area through a meteorological monitoring instrument;
the temperature acquisition module 2 is connected with the main control module 4 and is used for acquiring crop environment temperature data through a temperature meter;
the soil humidity acquisition module 3 is connected with the main control module 4 and is used for acquiring soil humidity data of crops through a humidity sensor;
the main control module 4 is connected with the meteorological data acquisition module 1, the temperature acquisition module 2, the soil humidity acquisition module 3, the data comparison module 5, the drought level determination module 6, the drought index calculation module 7, the remote sensing monitoring module 8, the evaluation module 9, the drought threshold prediction module 10, the drought early warning module 11, the power supply module 12, the data storage module 13, the terminal module 14 and the display module 15, and is used for controlling the modules to normally work through a single chip microcomputer;
the data comparison module 5 is connected with the main control module 4 and is used for comparing the acquired data with the normal indexes through a comparison program;
the drought level determining module 6 is connected with the main control module 4 and used for determining the drought level according to the comparison result;
the drought index calculation module 7 is connected with the main control module 4 and used for calculating the crop drought index through a calculation program;
the remote sensing monitoring module 8 is connected with the main control module 4 and is used for monitoring the region of the crop through a remote sensing monitoring device carried by the singlechip;
the evaluation module 9 is connected with the main control module 4 and is used for evaluating the drought risk of the crops through an evaluation program;
the drought threshold prediction module 10 is connected with the main control module 4 and used for predicting the crop environment drought threshold through a prediction program;
the drought early warning module 11 is connected with the main control module 4 and used for carrying out prediction alarm on crops;
the power supply module 12 is connected with the main control module 4 and used for providing electric energy for the system through a solar panel;
the data storage module 13 is connected with the main control module 4 and used for storing the collected crop area precipitation, environment temperature and soil humidity data and the real-time data of the comparison result, the drought level, the evaluation result, the prediction result and the early warning information through the cloud server;
the terminal module 14 is connected with the main control module 4 and used for transmitting the collected real-time data of the rainfall amount of the crop area, the environmental temperature and the soil humidity data, the comparison result, the drought level, the evaluation result, the prediction result and the early warning information to the mobile terminal through the cloud server;
and the display module 15 is connected with the main control module 4 and used for displaying the collected crop area precipitation, the environment temperature and the soil humidity data, the comparison result, the drought level, the evaluation result, the prediction result and the early warning information through a display.
As shown in fig. 1, the method for evaluating drought risk of crops based on a disaster-forming mechanism process provided by the embodiment of the present invention includes the following steps:
s101: acquiring meteorological data of an agricultural region through a meteorological monitoring instrument; collecting crop environment temperature data through a temperature instrument; and collecting crop soil humidity data through a humidity sensor.
S102: each module is controlled to work normally through a single chip microcomputer; comparing the collected data of the crop area with the normal indexes through a comparison program; and determining the drought level according to the comparison result.
S103: calculating the crop drought index through a calculation program; monitoring the region of the crop by a remote sensing monitoring device carried by a singlechip; and evaluating the drought risk of the crops by an evaluation program.
S104: predicting the drought threshold of the crop environment through a prediction program; predicting and alarming the crop drought condition through an alarming device; and the solar panel provides electric energy for the system.
S105: and storing the collected crop area precipitation, environment temperature and soil humidity data and the real-time data of the comparison result, the drought level, the evaluation result, the prediction result and the early warning information through the cloud server.
S106: and transmitting the collected real-time data of crop area precipitation, environment temperature, soil humidity data, comparison results, drought levels, evaluation results, prediction results and early warning information to the mobile terminal through the cloud server.
S107: and the display module is used for displaying the collected crop area precipitation, the environment temperature, the soil humidity data, the comparison result, the drought level, the evaluation result, the prediction result and the early warning information by using the display.
The crop drought risk assessment method in S103 provided by the embodiment of the invention comprises the following steps:
the method comprises the following steps of (I) respectively obtaining remote sensing data with high spatial resolution of a target area to be measured, remote sensing data with high time resolution of the target area to be measured and remote sensing data with high spectral resolution of the target area to be measured by using a remote sensing monitoring device.
And (II) performing space-time fusion on the remote sensing data with the high spatial resolution and the remote sensing data with the high temporal resolution.
And (III) performing space-spectrum fusion on the data subjected to space-time fusion and the remote sensing data with the high spectral resolution to obtain the remote sensing data with high time, high space and high spectral resolution of the target area to be measured.
(IV) acquiring crop planting area information according to the remote sensing data with high time, high space and high spectral resolution.
And (V) identifying the crop drought by using a preset drought identification model according to the crop planting area information, and identifying the drought in different historical periods.
And (VI) performing risk assessment on the crop drought according to the identification result of the crop drought.
After the step (i) provided in the embodiment of the present invention respectively obtains the remote sensing data with high spatial resolution of the target area to be measured, the remote sensing data with high temporal resolution of the target area to be measured, and the remote sensing data with high spectral resolution of the target area to be measured, before performing space-time fusion on the remote sensing data with high spatial resolution and the remote sensing data with high temporal resolution, the method further includes:
1) filtering the remote sensing data with the high time resolution by using an S-G filtering algorithm, and performing space-time fusion on the remote sensing data with the high spatial resolution and the remote sensing data with the high time resolution, wherein the specific steps are as follows:
and performing space-time fusion on the remote sensing data with the high spatial resolution and the filtered remote sensing data with the high temporal resolution.
2) The space-spectrum fusion is carried out on the data after the space-time fusion and the remote sensing data with the high spectral resolution to obtain the remote sensing data with high time, high space and high spectral resolution of the target area to be measured, and the method specifically comprises the following steps:
and performing space-spectrum fusion on the data subjected to space-time fusion and the filtered remote sensing data with the high spectral resolution to obtain the remote sensing data with high time, high space and high spectral resolution of the target area to be detected.
The space-time fusion of the remote sensing data with high spatial resolution and the remote sensing data with high temporal resolution in the step (II) provided by the embodiment of the invention comprises the following steps:
performing space-time fusion on the remote sensing data with the high spatial resolution and the remote sensing data with the high temporal resolution by utilizing an improved self-adaptive remote sensing image space-time fusion model ESTRAFM algorithm; or
The space-spectrum fusion of the data after the space-time fusion and the remote sensing data with the high spectral resolution comprises the following steps:
and performing space-spectrum fusion on the data subjected to space-time fusion and the remote sensing data with the high spectral resolution by using a multi-spectral image Spectral Resolution Enhancement Method (SREM) fusion algorithm.
In step (iv) provided by the embodiment of the present invention, obtaining crop planting area information according to the remote sensing data with high time, high space, and high spectral resolution includes:
①, obtaining EVI time sequence data of the target area to be measured according to the remote sensing data with high time, high space and high spectral resolution.
②, generating an EVI time sequence curve of normal crops without drought stress and an EVI time sequence curve of crops to be subjected to drought identification according to the EVI time sequence data, and extracting vegetation in the remote sensing data with high time, space and spectral resolution through the slope change of the generated EVI time sequence curve and the comparison of the phenological period of the crops without drought stress, wherein the vegetation comprises partial natural vegetation and all farmland crops.
③ field crops are extracted from the extracted vegetation using a 1/3 height width algorithm.
④ according to the digital elevation model and the slope map, the slope land and the flat land are distinguished from the extracted farmland crops.
The method for identifying the crop drought by using the preset drought identification model according to the crop planting area information in the step (V) comprises the following steps:
identifying crop drought of the extracted sloping fields by using a preset drought identification model according to the crop planting area information; and identifying the crop drought of the extracted flat cultivated land by using a preset drought identification model according to the crop planting area information.
The drought threshold prediction method in S104 provided by the embodiment of the present invention is as follows:
(1) performing large data range validation: the large data range includes year, region range, drought level.
(2) Performing large data range analysis: and analyzing data in a large data range to obtain a drought threshold value.
(3) Carrying out drought event identification: and comparing and judging the drought event aiming at the drought threshold value.
(4) And (3) predicting the drought threshold: and (4) predicting a future drought threshold value according to the steps (1) to (3).
The years provided by the embodiment of the invention comprise natural years since the record; the region range takes the drought threshold prediction region as the circle center and is within 100 km; the drought levels include small, medium, large and extra large.
The method for predicting and alarming the crop drought condition in S104 provided by the embodiment of the invention comprises the following steps:
1) calculation of SSI index and SPI index on a 1-24 month time scale.
2) Determination of a suitable SPI index time scale.
3) And (4) counting different drought characteristics of meteorological drought and hydrologic drought events.
4) And (4) constructing an evolution model of the transmission from meteorological drought to hydrological drought.
5) And (4) determining meteorological drought threshold conditions required by the occurrence of different drought levels of hydrological drought.
6) And early warning and reporting are carried out on weather drought and hydrologic drought events.
The calculation of the SSI index and the SPI index in step 1) provided by the embodiment of the present invention requires traffic and rainfall data, respectively.
In the step 2) provided by the embodiment of the invention, the Pearson correlation between the weather drought SPI indexes with different time scales and the hydrologic drought SSI index is calculated, and the time scale corresponding to the SPI index with the most obvious correlation to the SSI index is the optimal scale of the watershed weather drought and is marked as SPIn to be used as the basis for the subsequent weather drought calculation and analysis.
In the step 3) provided by the embodiment of the invention, the drought characteristics comprise drought occurrence time, drought ending time, drought duration time, average drought strength and drought intensity.
In the step 4) provided by the embodiment of the invention, a linear or nonlinear model is adopted to fit the correlation between the meteorological drought and the hydrological drought on the drought duration, the average drought intensity and the drought intensity; the linear or nonlinear fitting model comprises a simple unary linear function, an exponential function, a logarithmic function, a polynomial function and a power function.
In the step 5) provided by the embodiment of the invention, according to the different drought characteristic response models constructed in the step 4), the determination steps of the meteorological drought conditions required for the occurrence of the hydrological drought with different drought levels are as follows:
a. and (4) given the drought grade of a hydrological drought, and reversely deducing the drought strength of the corresponding meteorological drought.
b. And (5) assigning the weather drought duration by taking a month as a step length, and calculating the hydrologic drought duration.
c. Respectively assigning the product of the average meteorological drought strength and the drought duration and the product of the average hydrographic drought strength and the drought duration to a response function of the drought intensity, and if the equation is established, determining the corresponding meteorological drought average drought strength and the corresponding drought duration as the meteorological condition for generating the extra-drought hydrographic drought; if the equation is not satisfied, on the basis of the step b, the step size is +1, and trial calculation is continued until the equation is satisfied.
The above description is only for the preferred embodiment of the present invention, and is not intended to limit the present invention in any way, and all simple modifications, equivalent changes and modifications made to the above embodiment according to the technical spirit of the present invention are within the scope of the technical solution of the present invention.
Claims (10)
1. A crop drought risk assessment system based on a disaster mechanism process is characterized by comprising the following components:
the meteorological data acquisition module is connected with the main control module and is used for acquiring meteorological data of the crop area through a meteorological monitoring instrument;
the temperature acquisition module is connected with the main control module and used for acquiring crop environment temperature data through a temperature instrument;
the soil humidity acquisition module is connected with the main control module and is used for acquiring soil humidity data of crops through the humidity sensor;
the main control module is connected with the meteorological data acquisition module, the temperature acquisition module, the soil humidity acquisition module, the data comparison module, the drought level determination module, the drought index calculation module, the remote sensing monitoring module, the evaluation module, the drought threshold prediction module, the drought early warning module, the power supply module, the data storage module, the terminal module and the display module and is used for controlling each module to normally work through the single chip microcomputer;
the data comparison module is connected with the main control module and used for comparing the collected data of the crop area with the normal indexes through a comparison program;
the drought level determining module is connected with the main control module and used for determining the drought level according to the comparison result;
the drought index calculation module is connected with the main control module and used for calculating the crop drought index through a calculation program;
the remote sensing monitoring module is connected with the main control module and is used for monitoring the region of the crop through a remote sensing monitoring device carried by the single chip microcomputer;
the evaluation module is connected with the main control module and is used for evaluating the drought risk of the crops by combining an evaluation program with remote sensing monitoring data;
the drought threshold prediction module is connected with the main control module and used for predicting the crop environment drought threshold through a prediction program;
the drought early warning module is connected with the main control module and used for predicting and warning the drought condition of crops through the warning device;
the power supply module is connected with the main control module and used for providing electric energy for the system through the solar cell panel;
the data storage module is connected with the main control module and used for storing the collected crop area rainfall, environment temperature and soil humidity data and the real-time data of the comparison result, the drought level, the evaluation result, the prediction result and the early warning information through the cloud server;
the terminal module is connected with the main control module and used for transmitting the collected data of the rainfall amount of the crop area, the environmental temperature and the soil humidity to the mobile terminal through the cloud server, and the real-time data of the comparison result, the drought level, the evaluation result, the prediction result and the early warning information are transmitted to the mobile terminal;
and the display module is connected with the main control module and used for displaying the collected crop area precipitation, the environment temperature and the soil humidity data and the comparison result, the drought level, the evaluation result, the prediction result and the early warning information through the display.
2. A crop drought risk assessment method based on a disaster-forming mechanism process, applying the crop drought risk assessment system based on a disaster-forming mechanism process according to claim 1, wherein the crop drought risk assessment method based on a disaster-forming mechanism process comprises the following steps:
acquiring meteorological data of an agricultural region through a meteorological monitoring instrument; collecting crop environment temperature data through a temperature instrument; collecting crop soil humidity data through a humidity sensor;
step two, controlling each module to work normally through a single chip microcomputer; comparing the collected data of the crop area with the normal indexes through a comparison program; determining the drought level according to the comparison result;
calculating the crop drought index through a calculation program; monitoring the region of the crop by a remote sensing monitoring device carried by a singlechip; evaluating the drought risk of the crops through an evaluation program;
step four, predicting the drought threshold of the crop environment through a prediction program; predicting and alarming the crop drought condition through an alarming device; providing electric energy for the system through a solar panel;
storing the collected crop area precipitation, the environment temperature and the soil humidity data and the real-time data of the comparison result, the drought level, the evaluation result, the prediction result and the early warning information through a cloud server;
step six, transmitting the collected crop area precipitation, the environment temperature, the soil humidity data and the comparison result, the drought level, the evaluation result, the prediction result and the real-time data of the early warning information to the mobile terminal through the cloud server;
and seventhly, displaying the collected crop area precipitation, the environment temperature and the soil humidity data, the comparison result, the drought level, the evaluation result, the prediction result and the early warning information by using a display through a display module.
3. The crop drought risk assessment method based on disaster-forming mechanism process as claimed in claim 2, wherein in step three, the crop drought risk assessment method comprises the following steps:
respectively acquiring remote sensing data with high spatial resolution of a target area to be detected, remote sensing data with high time resolution of the target area to be detected and remote sensing data with high spectral resolution of the target area to be detected by using a remote sensing monitoring device;
(II) performing space-time fusion on the remote sensing data with the high spatial resolution and the remote sensing data with the high temporal resolution;
(III) performing space-spectrum fusion on the data subjected to space-time fusion and the remote sensing data with the high spectral resolution to obtain the remote sensing data with high time, high space and high spectral resolution of the target area to be measured;
(IV) acquiring crop planting area information according to the remote sensing data with high time, high space and high spectral resolution;
(V) identifying crop drought by using a preset drought identification model according to the crop planting area information, and identifying the drought in different historical periods;
and (VI) performing risk assessment on the crop drought according to the identification result of the crop drought.
4. The method for evaluating the drought risk of crops based on the disaster-forming mechanism process as claimed in claim 3, wherein in step (I), after the obtaining of the remote sensing data with high spatial resolution of the target area to be measured, the remote sensing data with high temporal resolution of the target area to be measured and the remote sensing data with high spectral resolution of the target area to be measured, respectively, and before the performing the spatio-temporal fusion of the remote sensing data with high spatial resolution and the remote sensing data with high temporal resolution, the method further comprises:
1) filtering the remote sensing data with the high time resolution by using an S-G filtering algorithm, and performing space-time fusion on the remote sensing data with the high spatial resolution and the remote sensing data with the high time resolution, wherein the specific steps are as follows:
performing space-time fusion on the remote sensing data with the high spatial resolution and the filtered remote sensing data with the high temporal resolution;
2) the space-spectrum fusion is carried out on the data after the space-time fusion and the remote sensing data with the high spectral resolution to obtain the remote sensing data with high time, high space and high spectral resolution of the target area to be measured, and the method specifically comprises the following steps:
and performing space-spectrum fusion on the data subjected to space-time fusion and the filtered remote sensing data with the high spectral resolution to obtain the remote sensing data with high time, high space and high spectral resolution of the target area to be detected.
5. The method for evaluating drought risk of crop based on disaster-forming mechanism process as claimed in claim 3, wherein in step (II), the spatio-temporal fusion of the high-spatial-resolution remote sensing data and the high-temporal-resolution remote sensing data comprises:
performing space-time fusion on the remote sensing data with the high spatial resolution and the remote sensing data with the high temporal resolution by utilizing an improved self-adaptive remote sensing image space-time fusion model ESTRAFM algorithm; or the space-spectrum fusion is carried out on the data after the space-time fusion and the remote sensing data with the high spectral resolution, and the method comprises the following steps:
and performing space-spectrum fusion on the data subjected to space-time fusion and the remote sensing data with the high spectral resolution by using a multi-spectral image Spectral Resolution Enhancement Method (SREM) fusion algorithm.
6. The method for evaluating the drought risk of crops based on the disaster-forming mechanism process as claimed in claim 3, wherein in the step (IV), the obtaining of the crop planting area information according to the remote sensing data with high time, high space and high spectral resolution comprises:
①, acquiring EVI time sequence data of a target area to be detected according to the remote sensing data with high time, space and spectral resolution;
②, generating an EVI time sequence curve of normal crops without drought stress and an EVI time sequence curve of crops to be subjected to drought identification according to the EVI time sequence data, and extracting vegetation in the remote sensing data with high time, high space and high spectral resolution through the slope change of the generated EVI time sequence curve and the comparison of the phenological period of the crops without drought stress, wherein the vegetation comprises part of natural vegetation and all farmland crops;
③ extracting field crops from the extracted vegetation using a 1/3 height width algorithm;
④ distinguishing slope farmland and flat farmland from the extracted farmland crops according to the digital elevation model and the slope map;
in step (v), identifying the crop drought by using a preset drought identification model according to the crop planting area information includes:
identifying crop drought of the extracted sloping fields by using a preset drought identification model according to the crop planting area information; and identifying the crop drought of the extracted flat cultivated land by using a preset drought identification model according to the crop planting area information.
7. The crop drought risk assessment system method based on disaster-forming mechanism process as claimed in claim 2, wherein in the fourth step, the drought threshold prediction method is as follows:
(1) performing large data range validation: the large data range comprises the age limit, the region range and the drought level;
(2) performing large data range analysis: carrying out data analysis aiming at the large data range to obtain a drought threshold value;
(3) carrying out drought event identification: comparing and judging the drought event aiming at the drought threshold value;
(4) and (3) predicting the drought threshold: and (4) predicting a future drought threshold value according to the steps (1) to (3).
8. The method according to claim 7, wherein the years include recorded natural years; the region range takes the drought threshold prediction region as the circle center and is within 100 km; the drought levels include small, medium, large and extra large.
9. The method for evaluating the drought risk of crops based on the disaster mechanism process as claimed in claim 2, wherein in the fourth step, the method for predicting and alarming the drought condition of crops is as follows:
1) calculating the SSI index and the SPI index of 1-24 month time scale;
2) determining a suitable SPI index time scale;
3) counting different drought characteristics of meteorological drought and hydrological drought events;
4) constructing an evolution model of the transmission of meteorological drought to hydrological drought;
5) determining meteorological drought threshold conditions required by hydrological drought with different drought levels;
6) and early warning and reporting are carried out on weather drought and hydrologic drought events.
10. The method for evaluating the drought risk of crops based on the disaster-forming mechanism process as claimed in claim 9, wherein in step 1), the calculation of the SSI index and the SPI index requires traffic and rainfall data, respectively;
in the step 2), calculating the Pearson correlation between the weather and drought SPI indexes with different time scales and the hydrologic and drought SSI index, wherein the time scale corresponding to the SPI index with the most obvious correlation with the SSI index is the optimal scale of the watershed weather and drought, and is marked as SPIn to be used as the basis for the calculation and analysis of the subsequent weather and drought;
in the step 3), the drought characteristics comprise drought occurrence time, drought ending time, drought duration, average drought strength and drought intensity;
in the step 4), fitting the correlation between the meteorological drought and the hydrological drought in the duration, the average drought strength and the drought intensity by adopting a linear or nonlinear model; the linear or nonlinear fitting model comprises a simple unary linear function, an exponential function, a logarithmic function, a polynomial function and a power function;
in the step 5), according to the different drought characteristic response models constructed in the step 4), determining the meteorological drought conditions required by the occurrence of the hydrological drought with different drought levels comprises the following steps:
a. the drought level of a hydrological drought is given, and the corresponding meteorological drought strength is reversely deduced;
b. assigning the weather drought duration with the month as a step length, and calculating the hydrological drought duration;
c. respectively assigning the product of the average meteorological drought strength and the drought duration and the product of the average hydrographic drought strength and the drought duration to a response function of the drought intensity, and if the equation is established, determining the corresponding meteorological drought average drought strength and the corresponding drought duration as the meteorological condition for generating the extra-drought hydrographic drought; if the equation is not satisfied, on the basis of the step b, the step size is +1, and trial calculation is continued until the equation is satisfied.
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