CN105784604A - Plant declination level detecting method - Google Patents
Plant declination level detecting method Download PDFInfo
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Abstract
The invention provides a plant declination level detecting method.The method comprises the steps that hyperspectral data information of plants is obtained; the obtained hyperspectral data information is processed to obtain first-order differential spectrum parameters, continuum removal spectrum parameters, vegetation indexes and spectrum feature wave bands; the first-order differential spectrum parameters, the continuum removal spectrum parameters, the vegetation indexes and the spectrum feature wave bands are utilized for setting up a prediction model based on the plant declination level of a partial least square regression method, and a discrimination model based on the plant declination level of Fisher linearity identification and analysis; the prediction model and the discrimination model are utilized for carrying out comparison, validation and analysis to obtain the declination level of plants.The plant declination level detecting method can detect the declination level of the plants fast and accurately without loss.
Description
Technical field
The present invention relates to plnat monitoring field, particularly to a kind of method of Non-Destructive Testing plant recession grade based on hyperspectral technique.
Background technology
In recent years; desertification trend arid, semiarid zone is more and more obvious; prevent and treat desertification of land; maintain species diversity, protect natural vegetation, especially Endangered species; significant; also improve the ecological environment simultaneously, maintain ecological balance, improve ecosystem service function in the urgent need to.Caulis et Folium Ammopiptanthi Mongolici is ancient desert deleted species; being listed in Chinese Second Class Key Protected Plant, sand-fixation soil-retention performance is good, saves special adversity gene; it is the mankind's valuable gene banks of carrying out genetic research engineering, the origin of ermophyte fauna in the middle part of Study In Asian is had important scientific research value.But in recent decades, owing to dust storm is big, rainfall is few, and temperature raises year by year, evaporation is strong to be waited harsh climate factor and herds, reclaim wasteland, the artificial destruction such as mining, Caulis et Folium Ammopiptanthi Mongolici growing environment runs down, add the single of its modes of reproduction, Caulis et Folium Ammopiptanthi Mongolici distribution area is more and more less, and population quantity declines increasingly, is in Critical Condition.Therefore, utilize certain technological means to diagnose endangered plants decline grade in time and its protection work is had important references value.
In nature, any atural object all has the Electromagnetic radiation laws of himself, different atural objects are due to the difference of its chemical constituent and physical arrangement, there is unique and stable spectral reflectance and Absorption Characteristics, hyperspectral technique is based on this principle, by remote sensor while principle object, obtain the spectral signature information of atural object.Hyperspectral technique replaces traditional method gradually due to its quick, lossless feature, becomes the new method of vegetation growth state diagnosis and is widely used.
Summary of the invention
For problem above, patent purpose of the present invention is in that to devise the detection method of a kind of plant recession grade, the decline grade of detection plant that can be quick, accurate, lossless.Technical solution of the present invention is as follows:
A kind of detection method of plant recession grade, including:
Obtain the high-spectral data information of plant；
The high-spectral data information obtained is processed, respectively obtains the first derivative spectra parameter, continuum removal spectrum parameter, vegetation index and spectral signature wave band；
Utilize described the first derivative spectra parameter, continuum to remove spectrum parameter, vegetation index and spectral signature wave band and set up the forecast model of the plant recession grade based on partial least-squares regression method and the discrimination model of the plant recession grade based on Fisher linear discriminant analysis；
Utilize described forecast model and discrimination model to carry out contrast verification analysis, draw plant recession grade.
Further, the high-spectral data information to obtaining of the present invention carries out process and farther includes:
The high-spectral data information obtained is carried out first differential process, obtains blue limit, yellow limit and the position on red limit, area and maximum reflectivity.
Further, the high-spectral data information to obtaining of the present invention carries out process and farther includes:
The high-spectral data information obtained is carried out continuum removal process, obtains the wave band degree of depth in 550 750nm and wave band area.
nullFurther，Vegetation index of the present invention includes greenness index GI，Index RVSI is coerced on the red limit of vegetation，Normalized differential vegetation index NDVI，The red limit NDRE of normalization difference，Canopy chlorophyll content index CCCI，Triangle vegetation index TVI，Photochemistry reflection index PRI，Chlorophyll absorption rate index CARI，The Chlorophyll absorption rate index M CARI revised，The Chlorophyll absorption rate changed and reflectance index TCARI，Normalization chlorophyll Ratio index NPCI，Anthocyanidin reflection index ARI，The anthocyanidin index SIPI of absolute construction，Water content index W I，Normalized water content index NDWI，Damage Sensitivity Index DSSI2，The Normalized difference vegetation index NBNDVI of narrow-band，Nitrogen content index NRI，Physiological reflex indices P hRI and plant senescence indices P SRI.
Further, foundation of the present invention farther includes based on the forecast model of the plant recession grade of partial least-squares regression method:
To described the first derivative spectra parameter, continuum removes spectrum parameter and spectral signature wave band is analyzed, extract the spectrum parameter the highest with vegetation decline degree of correlation, carry out wave band overlap simultaneously, extract the spectral signature wave band that degree of correlation that vegetation is failed is the highest, utilize SPSS software to set up the plant recession grade forecast model based on Partial Least-Squares Regression Model.
Further, foundation of the present invention farther includes based on the discrimination model of the plant recession grade of Fisher linear discriminant analysis:
To described the first derivative spectra parameter, continuum removes spectrum parameter and spectral signature wave band carries out independent T inspection, traveling wave of going forward side by side section is overlapping, extract the highest spectral signature wave band of degree of correlation that vegetation is failed and spectrum parameter, utilize SPSS software to set up the discrimination model based on Fisher linear discriminant analysis.
Further, the forecast model based on the plant recession grade of partial least-squares regression method of the present invention is more than 0.1 with correlation coefficient, and significance degree is foundation less than 0.05, described spectral signature wave band and spectrum parameter is carried out screening and draws.
Further, the discrimination model based on the plant recession grade of Fisher linear discriminant analysis of the present invention be with significant difference degree less than 0.05 for foundation, described spectral signature wave band and spectrum parameter are carried out screening and draw.
Accompanying drawing explanation
Referring to accompanying drawing, embodiments of the present invention is further illustrated, wherein:
Fig. 1 is the flow chart of the detection method of a kind of plant recession grade of the present invention；
Fig. 2 is the wave band overlapping results figure of the embodiment of the present invention one；
Fig. 3 is the accuracy test result figure of the forecast model that the embodiment of the present invention one is set up.
Detailed description of the invention
Below in conjunction with the drawings and specific embodiments, the present invention is described in further detail.
The present invention proposes the detection method of a kind of plant recession grade, refers to Fig. 1, comprises the steps:
Obtain the high-spectral data information of plant；
The high-spectral data information obtained is processed, respectively obtains the first derivative spectra parameter, continuum removal spectrum parameter, vegetation index and spectral signature wave band；
Utilize described the first derivative spectra parameter, continuum to remove spectrum parameter, vegetation index and spectral signature wave band and set up the forecast model of the plant recession grade based on partial least-squares regression method and the discrimination model of the plant recession grade based on Fisher linear discriminant analysis；
Utilize described forecast model and discrimination model to carry out contrast verification analysis, draw plant recession grade.
The described high-spectral data information to obtaining carries out process and farther includes: the high-spectral data information obtained is carried out first differential process, obtains blue limit, yellow limit and the position on red limit, area and maximum reflectivity, thus obtaining the first derivative spectra parameter.
The described high-spectral data information to obtaining carries out process and farther includes: the high-spectral data information obtained is carried out continuum removal process, obtains the wave band degree of depth in 550 750nm and wave band area, thus obtaining continuum to remove spectrum parameter.
Embodiment one:
In order to make the purpose of the present invention, technical scheme and advantage clearly understand, the present invention crosses over whole growth and the Caulis et Folium Ammopiptanthi Mongolici of decline stage by choosing five strains in the wild, and diagnoses its decline grade by the detection method of the present invention.
(1) field trial: choose five strains in West Dongting Lake Region Nature Reserve and cross over whole growth and the Caulis et Folium Ammopiptanthi Mongolici of decline stage; select the weather of ceiling unlimited; ASD EO-1 hyperion instrument is utilized to gather its Canop hyperspectrum data in distance 0.5 meter of eminence of canopy, about 1500 high-spectral data results of final acquisition.
(2) data process: original high-spectral data carries out first differential and continuum removal processes, and utilize formula in table 1 to calculate the value obtaining each spectrum parameter.
Table 1. calculates the computing formula of spectrum parameter
Described vegetation index includes GI, RVSI, NDVI, NDRE, CCCI, TVI, PRI, CARI, MCARI, TCARI, NPCI, ARI, SIPI, WI, NDWI, DSSI2, NBNDVI, NRI, PhRI and PSRI.
Spectral band and spectrum parameter and vegetation decline grade are carried out correlation analysis, analyze result and consult table 2:
Table 2. spectrum parameter degree of correlation result to vegetation decline grade
| SFs | R2 | SFs | R2 |
| Db | 0.086* | TVI | 0.553* 4 --> |
| λb | 0.057* | PRI | 0.004 |
| SDb | 0.203* | CARI | 0.001 |
| Dy | 0.291* | MCARI | 0.274* |
| λy | 0.029 | TCARI | 0.270* |
| Sdy | 0.393* | NPCI | 0.063* |
| Dr | 0.000 | ARI | 0.122* |
| λr | 0.100* | SIPI | 0.362* |
| SDr | 0.001 | WI | 0.375* |
| MBD | 0.434* | NDWI | 0.561* |
| AUC | 0.434* | DSSI2 | 0.012 |
| GI | 0.399* | NBNDVI | 0.405* |
| RVSI | 0.363* | NRI | 0.505* |
| NDVI | 0.572* | PhRI | 0.103* |
| NDRE | 0.420* | PSRI | 0.327* |
| CCCI | 0.474* |
Note: * represents significant difference degree p < 0.05, n=36.
Described spectrum parameter and spectral signature wave band being analyzed, extracts the spectrum parameter the highest with vegetation decline degree of correlation, carry out wave band overlap simultaneously, wave band overlapping results consults Fig. 2；Extracting the spectral signature wave band that degree of correlation that vegetation is failed is the highest, utilize SPSS software to set up the plant recession grade forecast model based on Partial Least-Squares Regression Model, in model, the coefficient of each variable consults table 3.The described forecast model based on the plant recession grade of partial least-squares regression method is more than 0.1 with correlation coefficient, and significance degree is foundation less than 0.05, to described spectrum
Characteristic wave bands and spectrum parameter carry out screening and draw.
The accuracy test result of the forecast model that table 3. is set up
| Coefficient | R2 | RMSE | |
| MBD | –8.745* | 0.918 | 0.553 |
| NDVI | –69.444* | - | - |
| NDRE | –43.711* | - | - |
| TVI | –0.996* | - | - |
| TCARI | –101.327* | - | - |
| SIPI | 83.416* | - | - |
| WI | –47.772* | - | - |
| NDWI | 129.764* | - | - |
| NBNDVI | 31.930* | - | - |
| NRI | 23.784* | - | - |
| PhRI | –288.225* | - | - |
| Constant | 51.512* | - | - |
Note:*Represent significance p < 0.05, n=36.
Utilizing remaining 40% to carry out proof-tested in model precision, assay consults Fig. 3.
Described spectrum parameter and spectral signature wave band are carried out independent T inspection, traveling wave of going forward side by side section is overlapping, result consults Fig. 2 and table 4, extract the highest spectral signature wave band of degree of correlation that vegetation is failed and spectrum parameter, SPSS software is utilized to set up the discrimination model based on Fisher linear discriminant analysis, the coefficient of each variable consults table 5, calculates this model differentiation accuracy to experimental data and testing data, and result consults table 6.The described discrimination model based on the plant recession grade of Fisher linear discriminant analysis be with significant difference degree less than 0.05 for foundation, described spectral signature wave band and spectrum parameter are carried out screening and draw.
Spectrum parameter and the dependency of different decline grades that table 4. calculates and to different decline grades
Plant diversity sensitivity
Note: p-r and p-t represents spectrum parameter and the dependency of different decline grades and respectively to different decline grade plant diversity sensitivitys；"+", represents sensitivity p < 0.05.
The coefficient of each variable of FLDA model that table 5. is set up
The table 6.FLDA model accuracy to decline grade discrimination
Note: OA (%) represents overall accuracy；P. ' sa. and U. ' sa. represents implementer and user accuracy respectively.
Utilize SPSS software to the correlation analysis (result consults table 2) between all spectral indexes and plant recession grade, filter out and the grade significant correlation that fails, and the coefficient of determination (R2) maximum vegetation index.By comparing the degree of correlation (R between this vegetation index and the set up forecast model of the present invention and decline grade2), the precision of checking the set up forecast model of the present invention.
Utilize SPSS software that the plant of all spectral indexes and different decline grade is carried out discriminant analysis, result consults table 7, filter out the vegetation index that decline grade discrimination accuracy is the highest, compare with the discrimination model set up herein, the precision of checking the set up discrimination model of the present invention.
The OA (%) of all spectrum that table 7. calculates and kappa coefficient
| SFs | OA (%) | Kappa | SFs | OA (%) | Kappa |
| Db | 32 | -1.086 | TVI | 62 | 0.392 |
| λb | 27 | -1.704 | PRI | 46 | -0.179 |
| SDb | 41 | -0.469 | CARI | 46 | -0.179 |
| Dy | 49 | -0.058 | MCARI | 46 | -0.179 |
| λy | 27 | -1.704 | TCARI | 46 | -0.179 |
| Sdy | 46 | -0.179 | NPCI | 35 | -0.849 |
| Dr | 32 | -1.086 | ARI | 41 | -0.469 8 --> |
| λr | 27 | -1.704 | SIPI | 62 | 0.392 |
| SDr | 38 | -0.646 | WI | 51 | 0.054 |
| MBD | 68 | 0.521 | NDWI | 62 | 0.392 |
| AUC | 68 | 0.521 | DSSI2 | 30 | -1.367 |
| GI | 65 | 0.459 | NBNDVI | 76 | 0.679 |
| RVSI | 49 | -0.058 | NRI | 60 | 0.319 |
| NDVI | 68 | 0.521 | PhRI | 41 | -0.469 |
| NDRE | 65 | 0.459 | PSRI | 68 | 0.521 |
| CCCI | 51 | 0.054 |
Note: OA (%) represents total accuracy.
(3) in all spectrum parameters, plant recession grade forecast precision is the highest
Be NDVI, the NDVI coefficient of determination (R2) in experimental data and accuracy test data it is 0.572 and 0.480 respectively.The plant recession grade forecast model based on PLSR model that the present invention the sets up coefficient of determination in experimental data and accuracy test data is 0.918 and 0.711 respectively.Compared with NDVI, the precision of prediction of the set up model of the present invention has been respectively increased 60.49% and 48.13% in experimental data and accuracy test data.In all spectrum parameters, to plant recession grade discrimination accuracy the highest be NBNDVI, NBNDVI accuracy (OA) in experimental data and accuracy test data is 76% and 60% respectively.The plant recession grade discrimination model based on FLDA model that the present invention sets up accuracy (OA) in experimental data and accuracy test data is 100% and 90% respectively.Compared with NBNDVI, the differentiation accuracy of the set up model of the present invention has been respectively increased 31.8% and 50% in experimental data and accuracy test data.The Nondestructive method of experiment show plant recession of the present invention grade is effective.
The detailed description of the invention of present invention described above, is not intended that limiting the scope of the present invention.Any technology according to the present invention is conceived made various other and is changed accordingly and deformation, should be included in the protection domain of the claims in the present invention.
Claims (8)
1. the detection method of a plant recession grade, it is characterised in that including:
Obtain the high-spectral data information of plant；
The high-spectral data information obtained is processed, respectively obtains the first derivative spectra parameter, continuum removal spectrum parameter, vegetation index and spectral signature wave band；
Utilize described the first derivative spectra parameter, continuum to remove spectrum parameter, vegetation index and spectral signature wave band and set up the forecast model of the plant recession grade based on partial least-squares regression method and the discrimination model of the plant recession grade based on Fisher linear discriminant analysis；
Utilize described forecast model and discrimination model to carry out contrast verification analysis, draw plant recession grade.
2. the detection method of a kind of plant recession grade according to claim 1, it is characterised in that the described high-spectral data information to obtaining carries out process and farther includes:
The high-spectral data information obtained is carried out first differential process, obtains blue limit, yellow limit and the position on red limit, area and maximum reflectivity.
3. the detection method of a kind of plant recession grade according to claim 1, it is characterised in that the described high-spectral data information to obtaining carries out process and farther includes:
The high-spectral data information obtained is carried out continuum removal process, obtains wave band degree of depth 550-750 nanometer interior and wave band area.
null4. the detection method of a kind of plant recession grade according to claim 1，It is characterized in that，Described vegetation index includes greenness index GI，Index RVSI is coerced on the red limit of vegetation，Normalized differential vegetation index NDVI，The red limit NDRE of normalization difference，Canopy chlorophyll content index CCCI，Triangle vegetation index TVI，Photochemistry reflection index PRI，Chlorophyll absorption rate index CARI，The Chlorophyll absorption rate index M CARI revised，The Chlorophyll absorption rate changed and reflectance index TCARI，Normalization chlorophyll Ratio index NPCI，Anthocyanidin reflection index ARI，The anthocyanidin index SIPI of absolute construction，Water content index W I，Normalized water content index NDWI，Damage Sensitivity Index DSSI2，The Normalized difference vegetation index NBNDVI of narrow-band，Nitrogen content index NRI，Physiological reflex indices P hRI and plant senescence indices P SRI.
5. the detection method of a kind of plant recession grade according to claim 1, it is characterised in that described foundation farther includes based on the forecast model of the plant recession grade of partial least-squares regression method:
To described the first derivative spectra parameter, continuum removes spectrum parameter and spectral signature wave band is analyzed, extract the spectrum parameter the highest with vegetation decline degree of correlation, carry out wave band overlap simultaneously, extract the spectral signature wave band that degree of correlation that vegetation is failed is the highest, utilize SPSS software to set up the plant recession grade forecast model based on Partial Least-Squares Regression Model.
6. the detection method of a kind of plant recession grade according to claim 1, it is characterised in that described foundation farther includes based on the discrimination model of the plant recession grade of Fisher linear discriminant analysis:
To described the first derivative spectra parameter, continuum removes spectrum parameter and spectral signature wave band carries out independent T inspection, traveling wave of going forward side by side section is overlapping, extract the highest spectral signature wave band of degree of correlation that vegetation is failed and spectrum parameter, utilize SPSS software to set up the discrimination model based on Fisher linear discriminant analysis.
7. the detection method of a kind of plant recession grade according to claim 1 or 5, it is characterized in that, the described forecast model based on the plant recession grade of partial least-squares regression method is more than 0.1 with correlation coefficient, significance degree is foundation less than 0.05, described spectral signature wave band and spectrum parameter is carried out screening and draws.
8. the detection method of a kind of plant recession grade according to claim 1 or 6, it is characterized in that, the described discrimination model based on the plant recession grade of Fisher linear discriminant analysis be with significant difference degree less than 0.05 for foundation, described spectral signature wave band and spectrum parameter are carried out screening and draw.
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