CN109816270B - Method for determining remote sensing optimal diagnosis time period of diamond-back moth damage - Google Patents
Method for determining remote sensing optimal diagnosis time period of diamond-back moth damage Download PDFInfo
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Abstract
The invention relates to a method for determining a remote sensing optimal diagnosis time period of diamond-back moth damage. (1) Determining a diagnosis threshold value of the diamond-back moth damage based on the actually measured data and a mutation theory; (2) respectively obtaining the remote sensing response characteristics of the just bamboo moth hazard detection based on the obtained multi-time point remote sensing images, including the leaf area index LAI. The method comprises the steps of establishing time sequence data of indexes according to a characteristic spectrum index CSI, a normalized difference mountain vegetation index NDMVI, a global vegetation humidity index GVCI and the like, and extracting the damage grade of the bamboo moth; (3) and counting insect pest risk evaluation index values, drawing a curve graph, and respectively determining the optimal monitoring and early warning time period of the insect pest. The invention can provide important basis for remote sensing detection and accurate prevention and control of the bamboo moth pests.
Description
Technical Field
The invention relates to the fields of forestry, ecology and geography, in particular to a method for determining the optimal remote sensing diagnosis time period of diamond-back moth damage.
Background
The Phyllostachys Pubescens (Pantanaphylyssachao) belongs to the genus Bombycis of the family Lepidoptera, the family Bombycis Pubescens, and is mainly distributed in provinces such as Fujian, Jiangxi, Zhejiang, Hunan, Sichuan, Guizhou, Jiangsu, Guangdong, Guangxi and the like in China, is one of the most main leaf-eating pests of bamboos, and has become an important factor which threatens the health of bamboo forests, especially bamboo forests and restricts the development of the bamboo industry. The insect pest has the characteristics of wide occurrence area, strong harm degree, high spreading speed and the like, and is called as a fire without smoking. For a long time, people are dedicated to developing research work of remote sensing accurate monitoring of forest diseases and insect pests, some achievements are obtained, but the remote sensing accurate monitoring method is difficult to apply to actual work of forest disease and pest control, and the overall effect is poor. One of the reasons is that in the aspect of remote sensing application research, the damage mechanism of the insect pest to the host is very complex, information reflected on a remote sensing image needs to be deeply mined, people pay more attention to how the remote sensing technology is used for monitoring the insect pest, and the research and development of the monitoring or evaluation technology in or after the disaster are emphasized. Compared with the method, the early insect source point is effectively discovered, the optimal window time is determined according to the life history and the hazard rule of the insect pests so as to guide accurate prevention and control, and the method has more important significance for controlling the outbreak and large-area spread of the insect pests and effectively reducing loss. In the previous research work, a method for detecting the damage of the bamboo moth coupled with the remote sensing response characteristics is established, and the method is the basis of the research and development of the technology.
The bamboo moth takes many generations in one year and has various morphological characteristics such as imago, egg, larva, pupa, cocoon and the like, and the influence of pests on hosts can be possibly detected by remote sensing only when a certain threshold value is reached; in the growth process of the healthy bamboo forest, the external form and the internal physiology naturally change, so that the abnormity of each remote sensing response characteristic can be timely and accurately found, and the method plays an important role in monitoring and early warning of insect pest remote sensing. Therefore, the invention carries out mutation detection on the time sequence data based on the remote sensing response characteristics to find a mutation point, and constructs a method for determining the optimal remote sensing diagnosis time period of the bamboo moth damage.
Disclosure of Invention
The invention aims to provide a method for determining the optimal remote sensing diagnosis time period of the bamboo moth damage, which can provide important basis for remote sensing detection and accurate prevention and control of the bamboo moth pests.
In order to realize the purpose, the technical scheme of the invention is as follows: a method for determining the optimal remote sensing diagnosis time period of the bamboo moth hazard comprises the following steps:
step S1, determining a bamboo moth hazard diagnosis threshold: setting state variables reflecting the damage of the plecoglosoma into the insect pest level of the damage of the plecoglosoma, and taking the greenness, the humidity, the characteristic spectral index and the LAI4 control variables as 4 remote sensing response characteristics of the damage of the plecoglosoma, so as to select a butterfly mutation model corresponding to the dimensions of the state variables and the control variables; then, determining a diamond-back moth hazard diagnosis threshold value by using the actually measured data; wherein the content of the first and second substances,
the butterfly factor a represents the greenness, namely the normalized difference mountain vegetation index NDMVI;
the partial aberration factor b represents humidity, namely global vegetation humidity index GVCI;
the subdivision factor c represents a characteristic spectral index, namely CSI;
CSI＝[NIR+(NIR-R)]×(R-G)
the regularization factor d represents the LAI;
LAI＝0.1645NDMVI+99.6455B+7.8617G+68.9153R－17.8800NIR－39.5195)
in the above formulas, G, R, NIR and SWIR represent the reflectivity of green light, red light, near infrared and short wave infrared bands respectively; subscript min represents the minimum value of the corresponding band;
step S2, establishing time series of each index and extracting insect pest grades: collecting remote sensing image data, and according to the distribution position of the measured sample points and the interference range of each image cloud layer, eliminating the sample points which are positioned outside the image frame range and covered by the cloud layer, and removing the cloud layer in each period of remote sensing image; extracting remote sensing response characteristics NDMVI, GVCI, CSI and LAI of corresponding pixel positions based on the remaining effective sampling points, establishing time sequence data of each index, and further extracting insect pest grades of the diamond-back moth damage;
step S3, determining an optimal monitoring and early warning period: according to the inherent contradictory unity relationship of the mutation model, the influence of each control variable on the state variable is divided into primary and secondary positions, and the primary and secondary positions of each control variable of the butterfly mutation model are a, b, c and d in sequence; according to the importance ranking given in random forests, the butterfly mutation model obtained is:
V(s)＝s6+as4+bs3+cs2+ds
in the formula: s represents the insect pest grade of the damage of the bamboo moths;
based on a mutation theory, calculating insect pest risk evaluation index values at each time point, counting and drawing a curve graph, judging the condition of the just bamboo moth damage risk evaluation index values, and determining the optimal early warning period and the optimal monitoring period of just bamboo moth damage according to the determined just bamboo moth damage early warning threshold value and in combination with the life history of just bamboo moth.
In an embodiment of the present invention, in step S1, the actual measurement data is used to determine the diagnosis threshold for the diamond-back moth damage, and different control variables need to be normalized to calculate the mutation levels corresponding to the control variables, where each formula is as follows:
wherein x isa、xb、xc、xdAre the step values corresponding to the control variables a, b, c, d.
Compared with the prior art, the invention has the following beneficial effects: the method can provide important basis for remote sensing detection and accurate prevention and control of the bamboo moth pests.
Drawings
FIG. 1 is a schematic flow chart of the method of the present invention.
Fig. 2 is a remote sensing image time series data according to an embodiment of the invention.
Fig. 3 is NDMVI time series data.
Fig. 4 is GVMI time-series data.
Fig. 5 is CSI time series data.
Fig. 6 is LAI time series data.
Fig. 7 is a trend of the evaluation value of the risk of the bamboo moth damage.
Detailed Description
The technical scheme of the invention is specifically explained below with reference to the accompanying drawings.
The invention provides a method for determining a remote sensing optimal diagnosis time period of a bamboo moth hazard, which comprises the following steps:
step S1, determining a bamboo moth hazard diagnosis threshold: setting state variables reflecting the damage of the bamboo moths as insect pest grades, and taking 4 control variables of greenness, humidity, LAI and characteristic spectral index as 4 remote sensing response characteristics of the damage of the bamboo moths, thereby selecting a butterfly mutation model corresponding to the dimensions of the state variables and the control variables; then, determining a diamond-back moth hazard diagnosis threshold value by using the actually measured data; wherein the content of the first and second substances,
the butterfly factor a represents the greenness, namely the normalized difference mountain vegetation index NDMVI;
the partial aberration factor b represents humidity, namely global vegetation humidity index GVCI;
the subdivision factor c represents a characteristic spectral index, namely CSI;
CSI＝[NIR+(NIR-R)]×(R-G)
the regularization factor d represents the LAI;
LAI＝0.1645NDMVI+99.6455B+7.8617G+68.9153R－17.8800NIR－39.5195)
and determining the early warning threshold value of the just-bamboo moth damage by using the actually measured data of 28 days in 2017, 2 months. Because of the possible differences in the units of measure for the different control variables, they were normalized to dimensionless values in the range of (0, 1) (table 2); respectively calculating the mutation series (s value) corresponding to each control variable by using a normalization formula; the research shows that all the characteristics of the host bamboo forest show corresponding variation trend along with the increase of the damage level of the bamboo moth, and the characteristics can be seen to have interaction relation with each other, so that the potential function value V is obtained by following the 'complementary' principle, and the larger the value is, the smaller the damage degree of the bamboo forest by the bamboo moth is, and vice versa.
With slight hazard of VMean valueAnd VMean value-VStandard of meritThe difference is used as the upper limit and the lower limit of the threshold range for diagnosing the damage of the bamboo moth, namely (0.8296, 0.8517).
Step S2, establishing time series of each index and extracting insect pest grades: collecting remote sensing image data, and according to the distribution position of the measured sample points and the interference range of each image cloud layer, eliminating the sample points which are positioned outside the image frame range and covered by the cloud layer, and removing the cloud layer in each period of remote sensing image; extracting remote sensing response characteristics NDMVI, GVCI, CSI and LAI of corresponding pixel positions based on the remaining effective sampling points, establishing time sequence data of each index, and further extracting insect pest grades of the diamond-back moth damage;
step S3, determining an optimal monitoring and early warning period: according to the inherent contradiction unity relationship of the mutation model, the influence of each control variable on the state variable is divided into primary and secondary parts, and the primary and secondary parts of each control variable of the butterfly model are a (butterfly factor), b (distortion factor), c (subdivision factor) and d (regular factor) in sequence. According to the importance ranking given in the random forest, the resulting model is:
V(s)＝s6+as4+bs3+cs2+ds
in the formula: s represents the insect pest grade of the bamboo moths; a. b, c and d represent the green degree, humidity, characteristic spectral index and LAI respectively.
When comprehensive evaluation is performed by using a mutation theory, different criteria are needed to discuss according to different interrelations among control variables. When the control variables (a, b, c and d) have no obvious mutual correlation, selecting the minimum value from the mutation level values corresponding to the control variables as a system value(s) according to the principle of selecting small from large to medium; when obvious mutual correlation exists among the control variables, taking the average value of the corresponding mutation level values of each control variable as a system value according to the 'complementation' principle.
Based on a mutation theory, calculating insect pest risk evaluation index values at each time point, counting and drawing a curve graph, judging the condition of the bamboo moth hazard risk evaluation index values, and determining the optimal early warning period and the optimal monitoring period of the bamboo moth hazard according to the determined early warning threshold value of the bamboo moth hazard in combination with the life history of the bamboo moth.
In step S1, the actual measurement data is used to determine the diagnosis threshold for the diamond-back moth damage, and different control variables need to be normalized to calculate the mutation levels corresponding to the control variables, where the specific common model and the normalization formula are shown in table 1.
TABLE 1 common mutation model M, S, B and normalization formula
Wherein x isa、xb、xc、xdAre the step values corresponding to the control variables a, b, c, d.
And setting state variables reflecting the damage of the bamboo moths as insect pest grades, determining 4 remote sensing response characteristics of the damage of the bamboo moths by using the greenness, the humidity, the LAI and the characteristic spectrum indexes, and selecting a butterfly mutation model corresponding to the state variables and the control variable dimension based on the state variables and the control variable dimension for research.
The following is a specific embodiment of the present invention.
The invention discloses a method for determining the optimal remote sensing diagnosis time period of diamond-back moth damage, which is realized as follows:
(1) determination of diagnosis threshold value of diamond-back moth damage
According to the method, the state variable reflecting the damage of the bamboo moths is set as the insect pest level, the greenness, the humidity, the LAI and the characteristic spectrum index are 4 remote sensing response characteristics for determining the damage of the bamboo moths, and based on the characteristics, a butterfly mutation model corresponding to the state variable and the control variable dimension is selected. The butterfly factor a represents the greenness, namely the normalized difference mountain vegetation index NDMVI; the partial aberration factor b represents humidity, namely global vegetation humidity index GVCI; the subdivision factor c represents a characteristic spectral index, namely CSI; the regularization factor d represents the LAI. Insect pest data are actually measured in the Yanping district of Fujian province in 2017, 2, 28 and the early warning threshold value of the damage of the Phyllostachys bambusoides is determined by using the data. Since there may be differences in the measurement units of different control variables, the control variables are normalized to dimensionless values (table 2) in the range of (0, 1), and the mutation numbers (s-values) corresponding to the control variables are calculated by using the normalization formula. The characteristics of the bamboo forest show corresponding change trends along with the increase of the damage grade of the bamboo moths, and the mutual interaction relationship of the characteristics can be seen, so that the risk evaluation index V of the bamboo moths is obtained according to the 'complementary' principle.
Table 2 control variable normalization data (part)
(2) Establishing time sequence of each index and extracting insect pest grade
Taking the Landsat 8OLI image as an example, the images in the 2016 month 2 to 2017 month 1 of the flattened area are collected, and the more the number of scenes of the images, the more dense the time is, the better the time is. However, due to the influence of the satellite transit period, the terrain, the weather and other factors, high-quality data cannot be collected frequently, such as cloud coverage and the like, but the image quality corresponding to a plurality of field sampling points needs to be ensured as much as possible, otherwise, the result is influenced to a certain extent. Fig. 2 is a preprocessed Landsat 8OLI remote sensing image collected in this example.
According to the distribution position of the measured sample points and the interference range of each image cloud layer, eliminating sample points which are positioned outside the image frame range and covered by the cloud layer, and removing the cloud layer in each period of remote sensing image; and extracting remote sensing response characteristics NDMVI, GVCI, CSI and LAI of corresponding pixel positions based on the residual effective sampling points, and establishing time sequence data of each index (figures 3-6). A method for detecting the damage of the bamboo moths based on a coupled remote sensing response characteristic extracts the damage grade of the bamboo moths.
(3) Determining optimal monitoring and early warning periods
Based on the mutation theory, insect pest risk evaluation index values at each time point are calculated (table 3), a scatter diagram is counted and drawn, and a curve is used for approximation (fig. 4). And selecting harmless points for mutation detection. The results show that the evaluation index value of the just-bamboo moth hazard risk drops from 2016 (2 month and 9), rises back from 2016 (2 month and 25), falls back again after reaching the peak value at 2016 (5 month and 15), and rises back gradually after dropping to the valley bottom at 2016 (9 month and 4). The results are analyzed by comparing the life history of the bamboo moth, the bamboo moth overwinter with eggs or 1-2 instar larvae, and the larvae begin to take food when the air temperature reaches above 8 ℃, so the evaluation index value of the damage risk of the bamboo moth falls from the first 2 months in 2016. And in the early stage of 3 months at the end of 2 months, the larvae begin to eat a large amount of bamboo leaves, the just bamboo moth hazard risk evaluation index value falls to the bottom of the grains during the period, then the larvae begin to pupate and cocoate, the overwintering generation feeding is finished, and the just bamboo moth hazard risk evaluation index value begins to rise again. The just bamboo moth pupates to become an adult in 5 months, the adult eclosion spawns in the period from the late 5 months to the early 6 months, then the eggs hatch to become larvae, which are the first generation of larvae and are the most serious generation, and the just bamboo moth hazard risk evaluation index value begins to decline from the early 5 months. In late 7 th month, the larvae begin to pupate and cocoate, and the first generation of larvae finish feeding gradually; in the first 8 th to middle 8 th months, the adults break cocoons and begin to lay eggs, then the second generation larvae appear and begin to take food, and the evaluation index value of the just bamboo moth hazard risk falls to the bottom of the valley in the early 9 th month. And (4) pupa and cocoons are formed immediately after the larvae finish taking food, and the evaluation index value of the just bamboo moth hazard risk immediately rises again after the second generation of food is finished.
TABLE 3 time series data of evaluation index for danger of bamboo moth
And according to the determined early warning threshold value of the damage of the bamboo moth, setting 2016, 2 and 9 days and 2016, 5 and 15 days as mutation nodes. And determining the last 2 th and middle 5 th of each year as the optimal early warning period of the just bamboo moth damage in the Yangtze region by combining the life history of the just bamboo moth. The evaluation index value curve of the just bamboo moth damage risk shows valley values at 25 days 2 month and 4 days 9 month in 2016, corresponding time periods are respectively positioned before and after the overwintering generation and the second generation larva finishes eating, so that the last ten days 2 month to the last ten days 2 month and the middle ten days 5 month to the last ten days 9 month in each year are determined as the optimal monitoring time period of just bamboo moth damage in the Yanping area.
The above are preferred embodiments of the present invention, and all changes made according to the technical scheme of the present invention that produce functional effects do not exceed the scope of the technical scheme of the present invention belong to the protection scope of the present invention.
Claims (2)
1. A method for determining the optimal remote sensing diagnosis time period of the just-bamboo moth hazard is characterized by comprising the following steps:
step S1, determining a bamboo moth hazard diagnosis threshold: setting state variables reflecting the damage of the plecoglosoma into the insect pest grade of the damage of the plecoglosoma, taking the greenness, the humidity, the characteristic spectral index and LAI4 control variables as 4 remote sensing response characteristics of the damage of the plecoglosoma, selecting a butterfly mutation model corresponding to the dimension of the state variables and the control variables, and determining a plecoglosoma diagnosis threshold value by utilizing the measured data; wherein the content of the first and second substances,
the butterfly factor a represents the greenness, namely the normalized difference mountain vegetation index NDMVI;
the partial aberration factor b represents humidity, namely global vegetation humidity index GVCI;
the subdivision factor c represents a characteristic spectral index, namely CSI;
CSI＝[NIR+(NIR-R)]×(R-G)
the regularization factor d represents the LAI;
LAI＝0.1645NDMVI+99.6455B+7.8617G+68.9153R－17.8800NIR－39.5195)
in the above formulas, G, R, NIR and SWIR represent the reflectivity of green light, red light, near infrared and short wave infrared bands in sequence; subscript min represents the minimum value of the corresponding band;
step S2, establishing time series of each index and extracting insect pest grades: collecting remote sensing image data, and according to the distribution position of the measured sample points and the interference range of each image cloud layer, eliminating the sample points which are positioned outside the image frame range and covered by the cloud layer, and removing the cloud layer in each period of remote sensing image; extracting remote sensing response characteristics NDMVI, GVCI, CSI and LAI of corresponding pixel positions based on the remaining effective sampling points, establishing time sequence data of each index, and further extracting insect pest grades of the diamond-back moth damage;
step S3, determining an optimal monitoring and early warning period: the primary and secondary positions of each control variable of the butterfly mutation model are a, b, c and d in sequence, and the butterfly mutation model is obtained according to the importance sequence given in the random forest:
V(s)＝s6+as4+bs3+cs2+ds
in the formula: s represents the insect pest grade of the just bamboo moth damage;
based on a mutation theory, calculating insect pest risk evaluation index values at each time point, counting and drawing a curve graph, judging the condition of the just bamboo moth damage risk evaluation index values, and determining the optimal early warning period and the optimal monitoring period of just bamboo moth damage according to the determined just bamboo moth damage early warning threshold value and in combination with the life history of just bamboo moth.
2. The method for determining the remote sensing optimal diagnosis period of the bamboo moth damage, as claimed in claim 1, wherein in step S1, the measured data is used to determine the diagnosis threshold of the bamboo moth damage, and different control variables are respectively calculated by using a normalization formula to obtain the mutation levels corresponding to the control variables, wherein each formula is as follows:
wherein x isa、xb、xc、xdThe mutation series corresponding to the control variables a, b, c and d.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102507504A (en) * | 2011-10-25 | 2012-06-20 | 南京林业大学 | Hyperspectral monitoring method for pine wilt disease of masson pines |
| CN102507453A (en) * | 2011-10-25 | 2012-06-20 | 南京林业大学 | Hyperspectral monitoring method of pine wood nematode disease of black pine |
| CN108333140A (en) * | 2018-02-01 | 2018-07-27 | 福州大学 | Characteristic spectrum index and detection method for rigid bamboo poison moth hazard detection |
| CN108596254A (en) * | 2018-04-25 | 2018-09-28 | 福州大学 | The rigid bamboo poison moth hazard detection method of coupled vane multilist sign |
| CN108828147A (en) * | 2018-06-14 | 2018-11-16 | 福州大学 | A kind of rigid bamboo poison moth hazard detection method coupling remote sensing response characteristic |
Family Cites Families (1)
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Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102507504A (en) * | 2011-10-25 | 2012-06-20 | 南京林业大学 | Hyperspectral monitoring method for pine wilt disease of masson pines |
| CN102507453A (en) * | 2011-10-25 | 2012-06-20 | 南京林业大学 | Hyperspectral monitoring method of pine wood nematode disease of black pine |
| CN108333140A (en) * | 2018-02-01 | 2018-07-27 | 福州大学 | Characteristic spectrum index and detection method for rigid bamboo poison moth hazard detection |
| CN108596254A (en) * | 2018-04-25 | 2018-09-28 | 福州大学 | The rigid bamboo poison moth hazard detection method of coupled vane multilist sign |
| CN108828147A (en) * | 2018-06-14 | 2018-11-16 | 福州大学 | A kind of rigid bamboo poison moth hazard detection method coupling remote sensing response characteristic |
Non-Patent Citations (2)
| Title |
|---|
| 基于Fisher判别分析与随机森林的马尾松毛虫害检测;许章华等;《光谱学与光谱分析》;20180915(第09期);全文 * |
| 马尾松毛虫危害程度的高光谱监测方法;彭隆赞等;《湖北林业科技》;20170628(第03期);全文 * |
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