CN110398465A - A kind of cultivation of porphyra biomass estimation method based on spectral remote sensing image - Google Patents
A kind of cultivation of porphyra biomass estimation method based on spectral remote sensing image Download PDFInfo
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Abstract
The present invention provides a kind of cultivation of porphyra biomass estimation method based on spectral remote sensing image, it is reflectance spectrum and its biomass correlativity based on cultivating seaweed, selected characteristic spectrum wave band and characteristic parameter, construct the multispectral remote sensing monitoring model of cultivation of porphyra.The real non-destructive monitoring of large area seaweed biomass is obtained by the inverting to multi-spectral remote sensing image according to model.The present invention gives a kind of biomass estimation method of new culturing economic seaweed, this method has many advantages, such as real non-destructive, high throughput and effectively reduces a large amount of field investigation workloads.This method can be applied in the growth monitoring of culturing economic seaweed, and can digitize for sea farming and provide theoretical and experiment basis.The method of the present invention compares traditionally surface sample measuring method, time saving and energy saving, can reach the lossless large area measurement to target area, while higher than satellite remote sensing measurement accuracy, obtain in time, the timeliness of measurement is good.
Description
Technical field
The invention belongs to biomass remote sensing monitoring technical fields, and in particular to a kind of based on near-earth multi-spectrum remote sensing image
Cultivation of porphyra biomass estimation method.
Background technique
Laver culture concentrates on intertidal zone and immediate offshore area, and ground or sea operation are easily complicated and changeable by marine environment
It influences, it is grown so far and the monitoring of growing way, still shortage high-precision, high throughput and digitlization drastically influence aquaculture industry
Sustainable health development.
Traditional sampling determination method is mostly used to the biomass estimation of cultivating seaweed at present, needs a wide range of collecting sample,
The a large amount of artificial and times are expended, are influenced to be difficult to by marine environment limitation timely and representative, and are sampled as lossy
, directly affect the growth and yield of economical alga.
Increasingly developed near-earth remote sensing technology has quick, accurate, lossless and can be carried out long-term, dynamic and continuous macroscopic view
Monitoring advantage is used widely in crops, meadow and forest conservation by land, and in coastal seawater cultivation field
Using now focusing mostly on both ways:
First is that the identification and division of culture zone, the contrast characteristic of application and form are obvious and smaller with change in time and space；
Second is that the measurement of water body or substrate chlorophyll, moisture and the content of organic matter or salinity, measure object are spatially divided
Cloth is relatively uniform, amplitude of fluctuation is relatively small.
And coastal waters economical alga cultivates in water body mostly, in terms of Remote Sensing Information Extraction and marine sample acquisition two
It will be unfavorable for the building of model by the interference of seawater and ocean current.
Summary of the invention
The purpose of the present invention is to provide a kind of quick, accurate, lossless and high-throughput cultivation of porphyra biomass estimation sides
Method is allowed to macroscopical field monitoring suitable for long-term dynamics, promotes the foundation and development of sea-farming industry new model.
The biomass estimation method of cultivation of porphyra of the invention, comprises the following steps that
1) target area high-resolution multi-spectral near-earth remote sensing image is obtained:
In target area to be determined, in it is sunny or it is partly cloudy be completely exposed the water surface to cultivating seaweed around noon, benefit
With UAV flight's high-resolution multi-spectral instrument and moving camera, height is clapped back and forth at the height 40m of target area
It takes the photograph, course line spacing is 2m, obtains multispectral image；
The high-resolution multi-spectral instrument is high-resolution multi-spectral instrument RedEdge-M sensor；Moving camera is
Firefly 8s moving camera；
Spectral reflectance calibration data obtains: spectral reflectance calibration plate being placed in ground, is using UAV flight's pixel
1280*960 pixels, the high-resolution multi-spectral instrument and moving camera at 47.2 ° of visual angle depart 2-5 meters of hoverings, multispectral
Camera shoots 5 channel multispectral data of 2-3 group to calibration plate, obtains reflection calibration plate image to be used for multispectral reflectivity school
It is quasi-；
Wherein Five-channel is respectively blue light (B, central wavelength 475nm, 20nm wave is wide), green light (G, central wavelength 560nm,
20nm wave is wide), feux rouges (R, central wavelength 668nm, 10nm wave are wide), red side (RE, central wavelength 717nm, 10nm wave are wide) and close
Infrared (NIR, central wavelength 840nm, 40nm wave are wide)；
2) remote sensing image data pre-processes:
The high-resolution multi-spectral image that will acquire utilizes the automatic triangulation function of Pix4D software, carries out certainly
Dynamic aerial triangulation, load need to splice and just penetrate the data of correction, and by wave band load step 1) the reflection calibration that obtains
Panel image, input reflection rate automatically process the multispectral image of acquisition.
3) by treated, multispectral image executes band spectrum operation small routine in ENVI5.1 software, chooses close red
Wave section ρNIRWith red spectral band ρRFor the relevant characteristic wave bands of biomass, vegetation therein is extracted using band math small routine
Index；Wherein ratio vegetation index RVI=ρNIR/ρR；Difference vegetation index DVI=ρNIR-ρR；Normalized differential vegetation index NDVI=
(ρNIR-ρR)/(ρNIR+ρR)；
4) spectral vegetation indexes substitute into model, estimate biomass, wherein regression model formula is as follows: Biomass (g/m2)
=235.30DVI+12.91RVI+8.90NDVI+8.52 calculates the biomass of target area cultivating seaweed.
The method of the present invention compares traditionally surface sample measuring method, time saving and energy saving, can reach to the lossless big of target area
Area estimation, at the same it is higher than satellite remote sensing measurement accuracy, it obtains in time, the timeliness of measurement is good.
Specific embodiment
The present invention constructs cultivating seaweed biomass spectroscopic data model according to the spectral signature of seaweed, further to expand
Near-earth remote sensing in sea-farming industry application, establish algae culturing environmental health evaluation system, provide new technology support.
The present invention is described in detail below with reference to embodiment.
Embodiment 1: the building process of regression model
1) acquisition of target area high-resolution multi-spectral image:
Target area is the laver culture sea area near the fishing port Fu Xin of Shandong Province, Lanshan District, Rizhao City, and longitude and latitude is specifically
119 ° 26 ' 1 ' of east longitude surveys that fly the time be in sunny to 119 ° 26 ' 6 ' of east longitude and north latitude 35-degree 15 ' 23 ' to north latitude 35-degree 15 ' 25 '
After morning.
Before flight, spectral reflectance calibration plate is placed in ground, is using EcoDrone UAS-8 UAV flight's pixel
1280*960 pixels, the high-resolution multi-spectral instrument RedEdge-M sensor and Firefly 8s at 47.2 ° of visual angle move phase
Machine departs 2-5 meters of hoverings, shoots 5 channel multispectral datas of 2-3 group spectral reflectance calibration plate, be used for it is multispectral not
It is calibrated with channel reflection rate.
Wherein, Five-channel is respectively blue light (B, central wavelength 475nm, 20nm wave is wide), green light (G, central wavelength 560nm,
20nm wave is wide), feux rouges (R, central wavelength 668nm, 10nm wave are wide), red side (RE, central wavelength 717nm, 10nm wave are wide) and close
Infrared (NIR, central wavelength 840nm, 40nm wave are wide).
Utilize EcoDrone UAS-8 UAV flight's high-resolution multi-spectral instrument RedEdge-M sensor and Firefly
8s moving camera is completely exposed in water surface 1h in cultivation of porphyra, and height is shot back and forth at the height 40m of target area,
Course line spacing is 2m, obtains high-resolution multi-spectral image.
2) remote sensing image data pre-processes:
The high-resolution multi-spectral image for the target area that will acquire is handled using Pix4D software, certainly using software
Dynamic aerial triangulation function, load need to splice and just penetrate the data of correction, and by wave band load step 1) reflection that obtains
Alignment surface project picture, input reflection rate automatically process the multispectral image of acquisition.
3) by treated, multispectral image executes band spectrum operation small routine in ENVI5.1 software, according to seaweed
Reflective spectral property chooses the special reflectance spectrum wave band of seaweed, includes near infrared band ρNIRWith red spectral band ρRFor biomass
Relevant characteristic wave bands extract vegetation index therein using band math small routine；Wherein ratio vegetation index RVI=ρNIR/
ρR；Difference vegetation index DVI=ρNIR-ρR；Normalized differential vegetation index NDVI=(ρNIR-ρR)/(ρNIR+ρR)。
4) sea area biomass data acquisition is surveyed:
It is winged synchronous with the imaging survey of unmanned plane spectral remote sensing, it is total that cultivating seaweed lace curtaining is randomly selected in target aquaculture sea area region
27, every lace curtaining takes 3 sample prescriptions, every sample prescription 0.60m*0.60m.All sample prescriptions are positioned by high-precision GPS.Every sample prescription biomass is
It dries to constant weight after all seaweed in sample prescription are removed through 80 DEG C, is weighed and obtained using the balance that precision is 0.01g.
5) estimation of biomass model construction:
It is returned according to the biomass data of 81 sample prescriptions and the combination of one or more vegetation index that sample on the spot
Return fitting, establishes the linearly or nonlinearly regression model of one or more vegetation index.Such as:
Biomass=280.58DVI+21.92
Biomass=65.48RVI+104.28NDVI-45.11
Biomass=268.43DVI+17.14NDVI+21.18
Biomass=420.75NDVI2+178.50NDVI+17.03
Biomass=235.30DVI+12.91RVI+8.90NDVI+8.52
6) model accuracy is evaluated:
The accuracy of each model carries out root mean square (RMSE) by estimation biomass and ground actual measurement biomass, precision
(Ac) and degree of fitting (R2) etc. index assessments model accuracy and valuation and measured value similarity.
WhereinWherein X and Y difference
For actual measurement biomass and estimation biomass, i is corresponding sample prescription, and n is gross sample number formulary,For being averaged for all sample prescription biomass
Number.
It is final to determine that optimal models are Biomass (g/m2)=235.30DVI+12.91RVI+8.90NDVI+8.52, R2For
0.93, RMSE 5.67, Ac 82.36%.
The application for the model that embodiment 2 is established
The measurement of Shandong Province, Lanshan District, Rizhao City laver culture area seaweed biomass
1) acquisition of target area high-resolution multi-spectral near-earth remote sensing image
Target area is 119 ° 25 ' 12.5 ' to 119 ° 25 ' of east longitude of east longitude in the laver culture area of Shandong Province, Lanshan District, Rizhao City
17.5 ' the regions surrounded with north latitude 35-degree 15 ' 12 ' to north latitude 35-degree 15 ' 15.5 '.
In it is sunny around noon, EcoDrone UAS-8 UAV flight's pixel be 1280*960 pixels, visual angle
47.2 ° of high-resolution multi-spectral instrument RedEdge-M sensor and Firefly 8s moving camera, liftoff 2-5 meters of hovering, shooting
5 channel multispectral datas of 2-3 group spectral reflectance calibration plate；It is completely exposed in water surface 1h in cultivating seaweed, above target area
Height is shot back and forth at height 40m, and course line spacing is 2m, obtains multispectral image.
2) remote sensing image data is handled
The high-resolution multi-spectral image that will acquire runs automatic triangulation function, load using Pix4D software
The data for needing to splice and just penetrate correction load reflection alignment surface project picture by wave band, and input reflection rate automatically processes acquisition
Multispectral image.
Different-waveband spectrum operation small routine is run using ENVI5.1 software, extracts vegetation index therein.Wherein ratio
Vegetation index RVI=ρNIR/ρR；Difference vegetation index DVI=ρNIR-ρR；Normalized differential vegetation index NDVI=(ρNIR-ρR)/(ρNIR+
ρR)。
3) sea area biomass data acquisition is surveyed:
Surveyed with unmanned plane light spectrum image-forming fly it is synchronous, in randomly selecting on cultivating seaweed lace curtaining totally 28 samples in target area
Side, every sample prescription 0.60m*0.60m, high-precision GPS positioning.Every sample prescription biomass removed by all seaweed in sample prescription after through 80 DEG C
It dries to constant weight, is weighed and obtained using the balance that precision is 0.01g.
4) compared with measured value estimates biomass with spectral model:
In the multispectral image spliced, according to GPS positioning point coordinate in sampling on the spot, obtain corresponding to 28 sample prescriptions
Different vegetation indexs, pass through above-mentioned optimal estimation of biomass Model B iomass (g/m2)=235.30DVI+12.91RVI+
8.90NDVI+8.52, estimation biomass be respectively every square metre: 27.16g, 72.87g, 66.82g, 159.63g, 96.70g,
51.57g、212.28g、7.39g、81.01g、128.96g、60.48g、3.35g、26.80g、207.22g、76.03g、
51.57g、50.09g、129.96g、107.51g、80.20g、68.22g、55.32g、89.69g、39.11g、24.612g、
17.492g, 23.90g and 141.814g；
Measured value be respectively as follows: 34.31g, 72.06g, 63.28g, 141.94g, 94.94g, 60.33g, 219.64g,
6.53g、77.611g、129.89g、51.44g、8.472g、14.22g、205.75g、78.81g、60.33g、41.19g、
130.89g, 102.78g, 69.89g, 66.11g, 45.89g, 94.17g, 31.00g, 11.97g, 12.03g, 26.92g and
151.44g。
The root mean square of model estimated value and measured value is 7.46, precision 90.07%, illustrates the mould that the method for the present invention is established
Type has practicability, can be used for the Accurate Determining of target area cultivation of porphyra biomass.
Claims (6)
1. a kind of cultivation of porphyra biomass estimation method based on spectral remote sensing image, which is characterized in that the method includes
Following step:
1) target area high-resolution multi-spectral near-earth remote sensing images are obtained:
In target area to be determined, in it is sunny or it is partly cloudy around noon, be completely exposed the water surface to cultivating seaweed, utilize nothing
Man-machine carrying high-resolution multi-spectral instrument and the moving camera height at the height 40m of target area are shot back and forth, are navigated
Line spacing is 2m, obtains multispectral image；
Spectral reflectance calibration data obtains: spectral reflectance calibration plate being placed in ground, is 1280* using UAV flight's pixel
960pixels, the high-resolution multi-spectral instrument and moving camera at 47.2 ° of visual angle depart 2-5 meters of hoverings, multispectral camera pair
Calibration plate shoots 5 channel multispectral data of 2-3 group, obtains reflection calibration plate image to calibrate for multispectral reflectivity；
2) remote sensing image data pre-processes:
The high-resolution multi-spectral image that will acquire utilizes the automatic triangulation function of Pix4D software, carries out automatic empty
Intermediate cam measurement, load need to splice and just penetrate the data of correction, and by wave band load step 1) reflection that obtains calibrates panel
Image data, input reflection rate automatically process the multispectral image of acquisition；
3) by treated, multispectral image executes band spectrum operation small routine in ENVI5.1 software, chooses near-infrared wave
Section ρNIR, red side wave section ρREWith red spectral band ρRFor the relevant characteristic wave bands of biomass, extracted wherein using band math small routine
Vegetation index；Wherein ratio vegetation index RVI=ρNIR/ρR；Difference vegetation index DVI=ρNIR-ρR；Normalized differential vegetation index
NDVI=(ρNIR-ρR)/(ρNIR+ρR)；
4) spectral vegetation indexes substitute into model, estimate biomass, wherein regression model formula is as follows: Biomass (g/m2)=
235.30DVI+12.91RVI+8.90NDVI+8.52 calculates the biomass of target area cultivating seaweed.
2. the method as described in claim 1, which is characterized in that described 1) in high-resolution multi-spectral instrument be high-resolution
Multispectral instrument RedEdge-M sensor.
3. the method as described in claim 1, which is characterized in that described 1) in moving camera be Firefly 8s move phase
Machine.
4. the method as described in claim 1, which is characterized in that described 1) in Five-channel multispectral data be respectively indigo plant
Light, central wavelength 475nm, 20nm wave are wide；Green light, central wavelength 560nm, 20nm wave are wide；Feux rouges, central wavelength 668nm, 10nm
Wave is wide；Red side, central wavelength 717nm, 10nm wave is wide and near-infrared, central wavelength 840nm, 40nm wave are wide.
5. a kind of for calculating the model of cultivation of porphyra biomass, which is characterized in that the model is described in claim 1
Method establish.
6. model as claimed in claim 5, which is characterized in that the formula of the model is as follows: Biomass (g/m2)=
235.30DVI+12.91RVI+8.90NDVI+8.52；
Wherein ratio vegetation index RVI=ρNIR/ρR；Difference vegetation index DVI=ρNIR-ρR；Normalized differential vegetation index NDVI=
(ρNIR-ρR)/(ρNIR+ρR)；
Infrared band ρNIR, red side wave section ρREWith red spectral band ρRFor the relevant characteristic wave bands of biomass.
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