CN106682756B - RS/GIS-based cordyceps sinensis yield prediction model - Google Patents

RS/GIS-based cordyceps sinensis yield prediction model Download PDF

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CN106682756B
CN106682756B CN201510764432.4A CN201510764432A CN106682756B CN 106682756 B CN106682756 B CN 106682756B CN 201510764432 A CN201510764432 A CN 201510764432A CN 106682756 B CN106682756 B CN 106682756B
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cordyceps sinensis
yield
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snow
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黄璐琦
朱寿东
郭兰萍
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Institute of Materia Medica of CAMS
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Abstract

A Cordyceps yield prediction model based on RS (Remote Sensing)/GIS (Geographic Information System) technology solves the problem of how to scientifically predict the annual yield of Cordyceps sinensis, a wild Chinese medicinal material. The model is based on RS (Remote Sensing)/GIS (Geographic Information System) technology, utilizes the relationship between environmental factors such as snow line elevation, average temperature in the harvesting period (4 months and 5 months), precipitation, sunshine duration and the like and the yield of the cordyceps sinensis, adopts a weighted geometric average method, predicts the yield of the cordyceps sinensis in the current year in the last 6 months of each year, and has the accuracy of over 82.16 percent through inspection. The research can provide basic data and information for the health and sustainable development of the cordyceps industry.

Description

RS/GIS-based cordyceps sinensis yield prediction model
Technical Field
The invention belongs to the field of wild medicinal plant resource quantity prediction
Background
At present, more than 500 generalized cordyceps species are known in the world, wherein the cordyceps is a unique and precious traditional Chinese medicine resource in China and is a thallus consisting of a stroma and a sclerotium, which is developed after cordyceps (Ophiococcus dynamics) infects lepidoptera hepialus larvae. The cordyceps sinensis generally grows in shrubs and meadows of mountain yin slopes and semi-yin slopes with the altitude of more than 3000m, is mainly distributed in Qinghai-Tibet plateaus and marginal areas in China, is sporadically distributed in Nipol and Heimalaya mountain high-cold meadows out of China, and the mining amount of the cordyceps sinensis in China accounts for more than 98% of the total mining amount in the world.
Because the natural cordyceps has strict parasitism and requires a special ecological geographical environment, the wild resources are very limited. On the other hand, the cordyceps sinensis cannot be artificially or semi-artificially cultured and only can grow naturally, so that the storage capacity of the cordyceps sinensis is rapidly reduced under the condition of increasing prices. According to statistics, the national yield reaches more than 100 tons before the 50 th of the 20 th century, 50-80 tons in the early 60 th of the century, and only 5-15 tons in the 90 th of the century. Since the 70 s, the natural reserves are reduced rapidly due to global climate change, ecological environment destruction, and plunder-type mining and digging, and the slow growth of the natural reserves and low natural resource updating capability. According to the general survey result of Chinese herbal medicine resources in the third country in the middle of the 80 s, the wild storage amount is only about 400 tons, and the whole trend is also declined year by year. At present, natural cordyceps sinensis resources are endangered to extinction and are listed as the second-level key protection wild plants of China.
For various reasons, from the third general survey of traditional Chinese medicine resources to the present, the cordyceps sinensis is not comprehensively investigated, particularly, the specific distribution and the storage amount of the cordyceps sinensis in Tibet, and related departments can only obtain some basic conditions from the mining range of people in various counties, and the basic conditions are empirical data. Therefore, accurate grasp of the yield information of cordyceps sinensis is the basis for sustainable development of cordyceps sinensis resources, and is a problem to be solved urgently.
Disclosure of Invention
The invention selects environmental factors such as snow line elevation, average air temperature of harvesting periods (4 months and 5 months), precipitation, sunshine duration and the like based on the quantitative relation between the distribution of cordyceps sinensis and the change of climate environment, and adopts a weighted geometric averaging method to predict the yield of cordyceps sinensis in the current year in the first 6 months of each year. The model comprises the following steps:
the method comprises the following steps: and constructing a climate environment factor based on the RS/GIS. The climate environmental factors include: the snow line elevation of 5 months in the area where the cordyceps sinensis is located, the precipitation of 4 months, 5 months and 6 months, the average air temperature of 4 months, 5 months and 6 months and the sunshine duration of 4 months, 5 months and 6 months are 10 factors.
Step two: and establishing a relation between the climate environmental factors and the yield of the cordyceps sinensis. And respectively determining the correlation coefficient between each factor and the yield of the cordyceps sinensis by using a correlation analysis tool. And according to experience, determining factors of the selected model according to the magnitude of the correlation coefficient, wherein the correlation coefficient of each factor and the yield of the cordyceps sinensis is the weight of the factor.
Step three: and constructing a cordyceps yield prediction model by using the weighted geometric mean model. And model accuracy test is carried out by using historical data.
Step four: and predicting the yield of the cordyceps sinensis in the current year. Inputting the climate environmental factor data of the current year into the model in the last 6 th month of the current year to obtain the predicted value of the yield of the cordyceps sinensis of the current year.
In the first step, the specific process comprises the following steps:
the method comprises the following steps: the method for constructing the snow line elevation of the area where the cordyceps sinensis is located comprises the following steps: the snow lines were extracted using NDSI index (Normalized difference snow index) [15 ]. NDSI is an index for observing the quantitative quantity of snow and ice, and snow has strong visible light reflection and strong short-wave infrared absorption characteristics. The accumulated snow has a high reflectance in the vicinity of 0.5m and a low reflectance in the vicinity of 1.6m and 2.1 m. The calculation formula is:
NDSI＝(Ref0.555um-Ref1.640um)/(Ref0.555um+Ref1.640um) (1)
ref in equation (1)0.555um、Ref1.640umThe reflectivities at 0.555um and 1.64um, respectively, and the area where NDSI ═ 0.4 is the ice and snow covered area. And taking the 'average value-2 standard deviation' of the extracted snow line elevation as the lowest elevation of the snow line.
The first step is: the construction method of the relevant factors of the precipitation, the average temperature and the sunshine duration comprises the following steps: and finding weather stations in the region according to the latitude and longitude of the region where the cordyceps sinensis is located, and using the average value of the weather stations as the value of the environmental factor.
In the step one, the specific process of calculating the elevation of the snow line comprises the following steps:
step a: converting DN-valued image into reflectivity image
Depending on the source of the remote sensing image, there are two cases, one for the Landsat TM5/Landsat TM7 image and the other for the Landsat TM8 image.
For Landsat TM5 and Landsat TM7 images:
the 0.555um and 1.64um bands correspond to the 2 nd and 5 th bands of the image, respectively. The calculation process is as follows:
the first step is as follows: respectively calculating the radiance L value of each pixel of each wave band:
Figure BSA0000123140320000021
in formula (2), QCAL is a DN value (Digital Number, pixel brightness value) of a certain pixel, i.e., QCAL ═ DN. QCALmaxFor maximum value that the picture element can take, QCALminIs the minimum value that the picture element can take. L ismaxMaximum radiance of this band, LminThe information of the above parameters can be obtained from the header file of the remote sensing image as the minimum radiance of the wave band.
The second step is that: calculating the reflectivity (albedo, reflectivity) p of each wave band
Figure BSA0000123140320000022
In the formula (3), D is the distance between the day and the ground, and the parameter information can be obtained from the header file of the image. ESUN (Solar exothermal Spectral radiances, or ESUNI) is the atmospheric top-layer Solar irradiance that can be obtained from information regularly measured and published by a remote sensing authority, as generally shown in Table 1:
TABLE 1 atmospheric top solar irradiance illumination (ESUNI)
Table 1 Solar Exoatmospheric Spectral Irradiances(ESUNI)(w/cm2·ster·μm)
Wave band TM1 TM2 TM3 TM4 TM5 TM7
L5TM 1957 1829 1557 1047 219.3 74.52
L7ETM+ 1969 1840 1551 1044 225.7 82.07
Theta is the zenith angle of the sun, theta is 90-beta, beta is the solar altitude, and the solar altitude information can be obtained from the head file of the remote sensing image.
② for Landsat TM8 images:
the 0.555um and 1.64um bands correspond to the 3 rd and 6 th bands of the image, respectively. The calculation process is as follows:
the first step is as follows: respectively calculating reflectivity rho lambda 'of each wave band'
ρλ′＝Mρ*Qcal+Aρ (4)
In equation (4), ρ λ' is the reflectivity without correction for the solar altitude, MρMultipliers of each band, AρIs the number of corrections for each band, ρ is the number of bands, QcalIs the pel value for that band.
The second step is that: corrected rho lambda 'by sun altitude'
Figure BSA0000123140320000031
In the formula (5), ρ λ is the corrected reflectance, θSZIs the zenith angle of the sun. ThetaSEIs the solar altitude angle and can be obtained from the head file of the remote sensing image.
Step b: calculating NDSI on the generated reflectance image
For Landsat TM5/7, NDSI ═ B2-B5)/(B2+ B5; for Landsat TM8, NDSI ═ B3-B6)/(B3+ B6. In the formula, B2, B5, B3 and B7 represent reflectance images of each band, respectively.
Step c: extracting a region of 0.4 or more on the NDSI
According to the MODIS product specification, areas with NDSI greater than or equal to 0.4 are considered to be covered by snow. The snow covered area is extracted using the band math tool in the ENVI software.
Step d: extracting the boundary of the snow area
Because the area covered by the extracted snow is a grid pattern, the area is converted into a vector by utilizing ENVI software and then is a polygon, and then the polygon is converted into a line type vector pattern by utilizing an Arctols tool, so that the snow line is extracted.
Step e: extracting minimum elevation at snow line by using spatial analysis
By means of Arcmap 10 software, DEM (Digital Elevation Model) data are loaded, spatial analysis is conducted on the DEM data and snow line distribution data, Elevation information at a snow line is extracted, and finally abnormal values of image edges are removed.
In the second step, the specific process comprises the following steps:
step two, firstly: and respectively calculating the correlation coefficient R between each factor and the yield of the cordyceps sinensis by utilizing a linear correlation analysis tool.
Step two: and sorting according to the sequence of the correlation coefficients from large to small, and selecting the first 4 factors as input factors of the model. The correlation coefficient of each factor and the yield of the cordyceps sinensis is the weight of the factor.
In the third step, the specific process comprises the following steps:
step three, firstly: and establishing a single-factor linear regression model of the cordyceps sinensis yield and each factor based on the cordyceps sinensis yield data and each factor data of the time sequence.
Step three: using the weighted geometric mean model to construct a cordyceps yield prediction model if the linear function of each factor and the cordyceps yield is fi，i＝1，2，...And N are the number of input factors. Each factor has a weight ratio of wiThe minimum value is 1. The yield Y of the cordyceps sinensis is shown as a formula (6):
Figure BSA0000123140320000041
step three: and (4) bringing environmental factor data of the past years into the obtained model to obtain a predicted value and an actual value. Particularly, if environmental factor data of a certain year cannot be acquired due to objective reasons (for example, the remote sensing image has large cloud content, so that snow line elevation cannot be extracted, and for example, meteorological factor data of a time relation is not published yet), the prediction model is corrected, the factor is removed, and modeling and prediction of the current year are performed by using the remaining factors only. The mean and maximum deviations of the predictions for each year are calculated.
In the fourth step, the specific process comprises the following steps:
step four, firstly: in the first 6 th of the year, 4 kinds of climate environmental factor data are collected, manufactured and collated.
Step four and step two: inputting the sorted climate environmental factor data into the model to obtain the predicted value of the yield of the cordyceps sinensis in the current year. If some climate environment factor data cannot be obtained, the factor is deleted from the model, and the model can be ensured to have higher precision only by utilizing the residual factors for prediction.

Claims (3)

1. A method for predicting the yield of cordyceps sinensis based on an RS/GIS technology is characterized in that the yield of cordyceps sinensis in the current year is predicted in the first 6 th month of each year by using the relation among snow line elevation, average temperature in a harvesting period, precipitation, sunshine duration and the yield of cordyceps sinensis based on the RS/GIS technology and adopting a weighted geometric averaging method, and the method comprises the following steps:
the method comprises the following steps: and (3) constructing a climate environment factor based on RS/GIS: the climate environmental factors include: the snow line elevation of 5 months in the area where the cordyceps sinensis is located, the precipitation of 4 months, 5 months and 6 months, the average temperature of 4 months, 5 months and 6 months and the sunshine duration of 4 months, 5 months and 6 months are 10 factors;
the method for constructing the snow line elevation of the area where the cordyceps sinensis is located comprises the following steps: the NDSI index is used for extracting the snow line, the NDSI is an index for observing the quantitative quantity of the snow, the snow has strong visible light reflection and strong short-wave infrared absorption characteristics, the snow has high reflectivity near 0.5m and lower reflectivity near 1.6m and 2.1m, and the calculation formula is as follows:
NDSI＝(Ref0.555um-Ref1.640um)/(Ref0.555um+Ref1.640um) (1)
ref in equation (1)0.555um、Ref1.640umReflectivities at 0.555um and 1.64um, respectively, NDSI>The area equal to 0.4 is the ice and snow covered area; taking the 'average value-2 standard deviation' of the extracted snow line elevation as the lowest elevation of the snow line;
wherein the precipitation, the average temperature and the sunshine duration are as follows: finding weather stations in the region according to the longitude and latitude of the region where the cordyceps sinensis is located, and using the average value of the weather stations as the value of the environmental factor;
step two: establishing a relation between climate environmental factors and the yield of the cordyceps sinensis: determining the correlation coefficient between each factor and the yield of the cordyceps sinensis by using a correlation analysis tool, and determining the factors of the selected model according to the magnitude sorting of the correlation coefficients, wherein the correlation coefficient between each factor and the yield of the cordyceps sinensis is the weight of the factor;
step three: constructing a cordyceps yield prediction model by using a weighted geometric mean model, and performing model precision inspection by using historical data;
if the linear function of the prediction factor and the yield of the cordyceps sinensis is fiI is 1,2, …, N, N is the factor number of the final selected model, and the weight ratio of each factor is wiAnd the yield Y of the cordyceps sinensis is the formula (2):
Figure FDA0003234396460000011
step four: predicting the yield of the cordyceps sinensis in the current year: inputting the ecological factor data of the current year into the model in the last 6 th month of the current year to obtain the predicted value of the yield of the cordyceps sinensis of the current year.
2. The method for predicting cordyceps sinensis yield based on the RS/GIS technology according to claim 1, wherein in the second step, the weight of each factor is obtained according to the ratio of each factor to the correlation coefficient of cordyceps sinensis yield, and the minimum value is 1.
3. The method for predicting yield of cordyceps sinensis based on the RS/GIS technology according to claim 1, wherein in the fourth step, if some relevant factor data cannot be obtained, the factor is deleted from the model, and the model can be predicted with high accuracy only by using the residual factors.
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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101480143A (en) * 2009-01-21 2009-07-15 华中科技大学 Method for predicating single yield of crops in irrigated area
JP2010166851A (en) * 2009-01-22 2010-08-05 Chiharu Hongo Method and device for predicting crop yield
CN104732299A (en) * 2015-04-03 2015-06-24 中国农业科学院农业信息研究所 Maize yield combined prediction system and method
CN104851048A (en) * 2015-05-28 2015-08-19 四川农业大学 Method for determining spatial distribution of suitable transplanting period of flue-cured tobacco in complicated hilly area

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101480143A (en) * 2009-01-21 2009-07-15 华中科技大学 Method for predicating single yield of crops in irrigated area
JP2010166851A (en) * 2009-01-22 2010-08-05 Chiharu Hongo Method and device for predicting crop yield
CN104732299A (en) * 2015-04-03 2015-06-24 中国农业科学院农业信息研究所 Maize yield combined prediction system and method
CN104851048A (en) * 2015-05-28 2015-08-19 四川农业大学 Method for determining spatial distribution of suitable transplanting period of flue-cured tobacco in complicated hilly area

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
中国冬虫夏草分布格局与环境变化对其分布的影响;杨大荣等;《中国草地学报》;20100731;第22-27页 *

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