CN106033052B - A kind of vegetation information extraction method based on high-spectral data sensitive band - Google Patents
A kind of vegetation information extraction method based on high-spectral data sensitive band Download PDFInfo
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- CN106033052B CN106033052B CN201510110106.1A CN201510110106A CN106033052B CN 106033052 B CN106033052 B CN 106033052B CN 201510110106 A CN201510110106 A CN 201510110106A CN 106033052 B CN106033052 B CN 106033052B
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Abstract
The invention belongs to environmental remote sensing fields, and in particular to a kind of vegetation information extraction method based on high-spectral data sensitive band.The present invention is the following steps are included: Step 1: vegetation sample data acquires；Step 2: improved vegetation index formula is wanted in selection；The relative coefficient of chlorophyll test value Step 3: the vegetation index value and chlorophyll test value or canopy under calculating different-waveband are averaged；Step 4: drawing relative coefficient with wavelength change curve graph；Step 5: determining sensitive band；Step 6: improving vegetation index using sensitive band；Step 7: vegetation information extraction.The invention can be improved vegetation information inversion accuracy, target mistake is reduced to mention or leak and mentions probability, environmental pollution Quantitative Monitoring, vegetation Growing state survey, forest mapping, biomass estimation, Crop Estimation, pest and disease monitoring, in terms of with important application value.
Description
Technical field
The invention belongs to environmental remote sensing fields, and in particular to a kind of vegetation information based on high-spectral data sensitive band mentions
Take method.
Background technique
Vegetation information on remote sensing images mainly pass through green plants leaf and Vegetation canopy spectral characteristic and its difference,
Variation reflection.Different elements or certain significant condition have various differences to different spectrum channel vegetation informations obtained from vegetation
Correlation.But for complicated remote sensing of vegetation, only planted with individual wave bands or the analysis comparison of multiple unicast segment datas to extract
Obvious limitation is had by information, thus often select Multi-spectral Remote Sensing Data through analytic operation (add, subtract, multiplication and division etc. is linear or
Nonlinear combination mode), generate the numerical value with certain indicative significance such as certain pairs of vegetation growing ways, biomass, i.e., so-called plant
By index.It uses a kind of simple and effective form --- and spectral signal is only used, other auxiliary informations are not required to, to realize to plant
The expression of status information, qualitatively and quantitatively to evaluate vegetative coverage, growth vigor and biomass etc..
Since the 1960s, scientist extracts and simulates various biophysics using remotely-sensed data and becomes
Amount.The chlorophyll content, biomass and health status etc. that numerous studies all use vegetation index to reflect green vegetation.By
Currently, the vegetation index for adapting to some special-purpose in some area is relatively more, effect is also relatively good, but it is suitable for mostly
Area, multiduty vegetation index are fewer.
High-spectrum remote-sensing has spectral resolution high (nanoscale), wave band continuity strong (in visible light near infrared band
Number is up to up to a hundred), the features such as spectral information amount is big.Therefore, the application of high-spectral data keeps information extraction stronger.It is general next
It says, when index construction, wave band used was all the sensitive band of reflected parameter.But the high-spectral data large number of for wave band,
How accurate selection sensitive band therein becomes a great problem.In addition, in spectral index refutation process, for difference
Test block, different vegetation sensitive bands also there is certain otherness, then further increase the difficulty of parametric inversion.Most
Eventually, cause inversion accuracy not high, inversion result inaccuracy.Therefore, how calibrated in the large number of high-spectral data of wave band
The suitable sensitive band of true selection is crucial.Therefore it needs a kind of invention and proposes a kind of EO-1 hyperion sensitive band selection and plant
By index improved method, to improve inversion accuracy.
Summary of the invention
The technical problem to be solved in the present invention is to provide a kind of vegetation information extractions based on high-spectral data sensitive band
Method determines vegetation sensitive band, proposes a kind of improvement by analyzing the correlation of vegetation hyper spectral reflectance and chlorophyll test value
The method of vegetation index inverting vegetation information.
In order to solve the above technical problems, a kind of vegetation information extraction side based on high-spectral data sensitive band of the present invention
Method, the present invention the following steps are included:
Step 1: vegetation sample data acquires, test block is chosen, determines and chooses plant, the sample leaf of plant is chosen in acquisition
Piece, or the canopy spectra data of the single research plant of measurement；
Step 2: improved vegetation index formula is wanted in selection, measure respectively the reflected spectrum of each leaf samples with
Chlorophyll test value perhaps measures the single canopy for choosing plant and is averaged chlorophyll test value for the reflected spectrum or canopy of leaf samples
Spectroscopic data brings vegetation index formula into and calculates vegetation index value, and the spectral reflectivity under the same band and chlorophyll test value or
Person's canopy be averaged chlorophyll test value carry out linear fit, carry out correlation analysis, obtain relative coefficient；
The related of chlorophyll test value Step 3: the spectral reflectivity and the chlorophyll test value or canopy that calculate under different-waveband are averaged
Property coefficient；
Step 4: drawing relative coefficient with wavelength change curve graph using the correlation coefficient value calculated in step 3；
Step 5: determining sensitive band, wave band corresponding to several biggish extreme values of relative coefficient absolute value is
The sensitive band of the test block；
Step 6: improving vegetation index using sensitive band, referred to using vegetation in determining sensitive band replacement step two
In number formula with correspond to wave band similar in sensitive band, the wave band of no close sensitive band continues to participation operation and is not replaced；
Step 7: vegetation information extraction is extracted different using improved vegetation index formula direct inversion vegetation information
Vegetation parameter.
The 30%~50% of research plant sum is to choose plant, required research plant needed for choosing in the step one
When sum is greater than 500,80~120 are chosen to choose plant.
Leaf samples are to choose plant in the blade of the same growth site in same growth period in the step one.
The beneficial technical effect of the present invention lies in: the invention can be improved vegetation information inversion accuracy, reduces target mistake and mentions
Or leakage mentions probability, in environmental pollution Quantitative Monitoring, vegetation Growing state survey, forest mapping, biomass estimation, Crop Estimation, disease
Insect pest monitoring, early-warning and predicting etc. have important application value.
Detailed description of the invention
Fig. 1 is a kind of process of the vegetation information extraction method based on high-spectral data sensitive band provided by the present invention
Figure；
Fig. 2 is related coefficient with wavelength change curve.
Specific embodiment
Invention is further described in detail with reference to the accompanying drawings and examples.
Specific embodiment one
As shown in Figure 1, a kind of vegetation information extraction method based on high-spectral data sensitive band of the present invention includes following
Step:
Step 1: choosing test block, determine and choose plant, a leaf samples are chosen on each selection plant, or
The single canopy spectra data for choosing plant of measurement；
Step 2: improved vegetation index formula is wanted in selection, measure respectively the reflected spectrum of each leaf samples with
Chlorophyll test value, perhaps the single canopy for choosing plant of measurement is averaged chlorophyll test value for the reflected spectrum or hat of leaf samples
Layer spectroscopic data brings vegetation index formula in the prior art into and calculates vegetation index value, and the spectral reflectance under the same band
Rate and chlorophyll test value or the canopy chlorophyll test value that be averaged carry out linear fit, and progress correlation analysis obtains relative coefficient；
The related of chlorophyll test value Step 3: the spectral reflectivity and the chlorophyll test value or canopy that calculate under different-waveband are averaged
Property coefficient；
Step 4: as shown in Fig. 2, drafting relative coefficient utilizes the phase calculated in step 3 with wavelength change curve graph
Property coefficient value is closed, draws curve graph, i.e., relative coefficient is with wavelength change curve graph；
Step 5: determine sensitive band, the big several poles of the relative coefficient absolute value in Fig. 2, such as including point a, b
The corresponding wave band of value is the sensitive band of the test block；
Step 6: improving vegetation index using sensitive band, prior art original vegetation is replaced using determining sensitive band
In exponential formula with correspond to wave band similar in sensitive band, the wave band of no close sensitive band continues to participate in operation not replaced
It changes；
Step 7: vegetation information extraction is extracted different using improved vegetation index formula direct inversion vegetation information
Vegetation parameter.
Specific embodiment two
This specific embodiment and the difference of embodiment one be, in step 1 by the 30% of required research plant sum~
50% is determined as choosing plant, when required research plant sum is greater than 500, chooses 80~120 to choose plant.
Specific embodiment three
This specific embodiment and the difference of embodiment one, two are that leaf samples are same plant same in step 1
The blade of the same growth site in one growth period, to ensure the science and comparativity of experimental data.
The present invention is explained in detail above in conjunction with drawings and examples, but the present invention is not limited to above-mentioned implementations
Example, within the knowledge of a person skilled in the art, can also make without departing from the purpose of the present invention
Various change out.The content being not described in detail in the present invention can use the prior art.
Claims (3)
1. a kind of vegetation information extraction method based on high-spectral data sensitive band, it is characterised in that the following steps are included:
Step 1: vegetation sample data acquires, test block to be chosen, determines and chooses plant, the leaf samples of plant are chosen in acquisition, or
The canopy spectra data of the single research plant of person's measurement；
Step 2: improved vegetation index formula is wanted in selection, reflected spectrum and the leaf for measuring each leaf samples respectively are green
Perhaps the single canopy for choosing plant of measurement is averaged chlorophyll test value for the reflected spectrum or canopy spectra of leaf samples to element value
Data bring vegetation index formula into and calculate vegetation index value, and the spectral reflectivity under the same band and chlorophyll test value or hat
The average chlorophyll test value of layer carries out linear fit, carries out correlation analysis, obtains relative coefficient；
It is averaged the correlation system of chlorophyll test value Step 3: calculating spectral reflectivity and the chlorophyll test value or canopy under different-waveband
Number；
Step 4: drawing relative coefficient with wavelength change curve graph using the correlation coefficient value calculated in step 3；
Step 5: determining sensitive band, wave band corresponding to several biggish extreme values of relative coefficient absolute value is the reality
Test the sensitive band in area；
Step 6: vegetation index is improved using sensitive band, it is public using vegetation index in determining sensitive band replacement step two
In formula with correspond to wave band similar in sensitive band, the wave band of no close sensitive band continues to participate in operation to be not replaced；
Step 7: improved vegetation index formula direct inversion chlorophyll is extracted different vegetation ginsengs by vegetation information extraction
Number.
2. a kind of vegetation information extraction method based on high-spectral data sensitive band according to claim 1, feature
Be: for the 30%~50% of research plant sum needed for choosing in the step one to choose plant, required research plant is total
When number is greater than 500,80~120 are chosen to choose plant.
3. a kind of vegetation information extraction method based on high-spectral data sensitive band according to claim 2, feature
Be: leaf samples are to choose plant in the blade of the same growth site in same growth period in the step one.
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| CN107255621B (en) * | 2017-05-18 | 2020-11-20 | 成都理工大学 | High vegetation coverage area remote sensing prospecting method based on plant chlorophyll value characteristic change |
| CN107688003B (en) * | 2017-09-04 | 2020-06-30 | 南京大学 | Blade reflectivity satellite remote sensing extraction method for eliminating vegetation canopy structure and earth surface background influence |
| CN109142236A (en) * | 2018-09-13 | 2019-01-04 | 航天信德智图（北京）科技有限公司 | The withered masson pine identifying system of infection pine nematode based on high score satellite image |
| CN109521437B (en) * | 2018-12-05 | 2023-07-21 | 武汉大学 | Multispectral laser radar wavelength selection method for vegetation biochemical parameter detection |
| CN110160967A (en) * | 2019-04-16 | 2019-08-23 | 安徽大学 | A kind of total nitrogen content evaluation method of crop canopies blade |
| CN110487793A (en) * | 2019-08-29 | 2019-11-22 | 北京麦飞科技有限公司 | Pest and disease damage time DYNAMIC DISTRIBUTION monitoring method and system |
| CN111999255B (en) * | 2020-08-13 | 2024-01-19 | 中冶建筑研究总院（深圳）有限公司 | Marine environment steel structure coating detection method, device, server and storage medium |
| CN113536937A (en) * | 2021-06-17 | 2021-10-22 | 海南省林业科学研究院(海南省红树林研究院) | Mangrove forest ecological quantitative inversion method based on remote sensing technology |
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| CN102426153B (en) * | 2011-11-21 | 2015-09-16 | 南京农业大学 | A kind of Wheat plant moisture monitoring method based on canopy high spectral index |
| CN102853822B (en) * | 2012-09-13 | 2015-02-04 | 中国科学院对地观测与数字地球科学中心 | Method and device for measuring vegetation spatial distribution state |
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