CN106503662A - A kind of appraisal procedure of Intertidal zone sargassum thunbergii algae bed stock number - Google Patents
A kind of appraisal procedure of Intertidal zone sargassum thunbergii algae bed stock number Download PDFInfo
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- CN106503662A CN106503662A CN201610938519.3A CN201610938519A CN106503662A CN 106503662 A CN106503662 A CN 106503662A CN 201610938519 A CN201610938519 A CN 201610938519A CN 106503662 A CN106503662 A CN 106503662A
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Abstract
The invention belongs to marine ecology scientific domain, is related to a kind of appraisal procedure of Intertidal zone sargassum thunbergii algae bed stock number, more particularly to a kind of 10 km2The Scientific evaluation method of the Intertidal zone sargassum thunbergii resource within area.There is larger systematic error during sargassum thunbergii stock assessment in prior art and be difficult to the technical deficiency of comprehensive assessment to overcome, the present invention adopts unmanned aerial vehicle remote sensing technology, remote sensing monitoring is carried out to Intertidal zone sargassum thunbergii algae bed in the withered damp phase, Inversion Calculation sargassum thunbergii kelp bed stock number, can complete the sargassum thunbergii kelp bed stock number assessment of large scale at short notice.The inventive method is efficient, convenient, accuracy is high, is suitable for the assessment of Intertidal Algae stock number.
Description
Technical field
The invention belongs to marine ecology scientific domain, is related to a kind of appraisal procedure of Intertidal zone sargassum thunbergii algae bed stock number,
More particularly to a kind of 10km2The Scientific evaluation method of the Intertidal zone sargassum thunbergii resource within area.
Background technology
Mainly there is two ways to the assessment mode of marine algae resource amount at present：
（1）Traditional approach：Some biological observation sample points are laid according to certain way to survey region, is respectively taken by statistical analysiss
The quantity of sampling point sample collecting, the stock number in evaluation studies region.But sargassum thunbergii kelp bed is mostly discontinuous, non-in Intertidal zone
It is uniformly distributed, when therefore estimating marine algae resource amount using traditional method, systematic error is larger.
（2）Satellite remote sensing mode：Can carry out ecological resources amount assessment to large scale scope vegetation resources, but remote sensing mode
Affected by sea in marine ecology research, part below sea cannot provide remote sensing data.Sargassum thunbergii is a kind of intertidal belt submarine
Algae, typically surfaces in the withered damp phase, and the time of satellite remote sensing has uncontrollability, and receives inside even from weather（Cloud layer, mist
Haze etc.）, therefore satellite remote sensing has certain technical limitation for the estimation of Intertidal Algae stock number.
This is based on, the present invention provides a kind of appraisal procedure of Intertidal zone sargassum thunbergii algae bed stock number.
Content of the invention
In order to overcome the above-mentioned deficiencies of the prior art, the present invention adopts unmanned aerial vehicle remote sensing technology, in the withered damp phase to Intertidal zone
Sargassum thunbergii algae bed carries out remote sensing monitoring, and Inversion Calculation sargassum thunbergii kelp bed stock number can complete large scale at short notice（10
km2Within）Sargassum thunbergii kelp bed stock number assessment.The inventive method is efficient, convenient, accuracy is high, is suitable for Intertidal Algae
The assessment of stock number.
The present invention is achieved through the following technical solutions above-mentioned technical purpose：
A kind of appraisal procedure of Intertidal zone sargassum thunbergii algae bed stock number, which comprises the steps：
1）Remote sensing images are gathered：Preset multiple biological photo control points, biological 1 × 1 m of photo control point sample area of control2, biological as control
Point chooses the representative sample prescription of different abundance, each abundance photo control point >=3, and complete biological photo control point GPS information and
Sargassum thunbergii quantity information is gathered；Intertidal zone sargassum thunbergii kelp bed is monitored using unmanned aerial vehicle remote sensing, obtains remote sensing images；
2）Remote sensing images are input into 10.0 softwares of ArcGIS, newly-built shapefile formatted files in Catalog, coordinate system are adopted
With WGS 1984, utm projection, image displaying ratio are set to 1:100, sargassum thunbergii distribution situation, editor is drawn according to remote sensing images
After the completion of preserve obtain remote sensing images file；Shapefile form text is set up in Catalog successively according to photo control point numbering
Part, according to photo control point numbering and sargassum thunbergii quantity information successively editing files, the biological photo control point GPS information according to record is drawn
Remote sensing images are input into ENVI4.7 softwares by sargassum thunbergii biology photo control point file, load biological photo control point in ENVI4.7 softwares
File, then exports the ROI fileinfos of ASCII fromat, in remote sensing images file in the ROI of the ASCII fromat that extracts
Corresponding color value information and image element information bar number is found in file；Described color value information includes R values, G-value, B values；With face
Colour is independent variable, and sargassum thunbergii quantity is dependent variable, and the color of image value and photo control point sargassum thunbergii quantity information extracted are entered
Line Multiple regression statistics are analyzed, and obtain regression analysis equation：
Y=（aR + bG + cB + d）×n
Wherein Y makes a living thing photo control point sargassum thunbergii quantity；R, G, B are respectively the R values in ROI image colouring information, G-value, B values；a、
B, c, d are respectively regression calculation computational constant coefficient；N values are that ROI image extracts image element information bar number；When regression analysis equation
Regression coefficient r2When >=0.8, Inversion Calculation model is more accurate, if r2＜ 0.8, then need to add photo control point or conversion returned
Statistical model.
3）Remote sensing images are input into ENVI4.7 softwares, loading sargassum thunbergii distribution area file in ENVI4.7 softwares, then
The ROI fileinfos of ASCII fromat are exported in remote sensing images file, are found in the ROI files of the ASCII fromat that extracts
Corresponding color value information and image element information bar number, described color value information R values, G-value, B values；By the sargassum thunbergii distribution that extracts
The color value information input model equation in area first calculates the corresponding Sargassum quantity of single pixel, then calculates rat-tail further according to formula
Algae algae bed total resources, wherein Y be sargassum thunbergii algae bed total resources, n be image element information bar number, YiSargassum number for single pixel
Amount.
The appraisal procedure of Intertidal zone as above sargassum thunbergii algae bed stock number, wherein unmanned plane configuration should be able to meet as follows
Condition：Fixed-wing formula unmanned plane, speed per hour >=50 km/h, cruising time >=1.5 h, observing and controlling radius >=10 km, wind resistance energy
Power >=4 grade, sensor valid pixel >=24,000,000, peak of flight >=2000 m.
The appraisal procedure of Intertidal zone as above sargassum thunbergii algae bed stock number, described unmanned plane is during aerial survey
Flight parameter：Flying height be 150-300 m, ship's control 80%, sidelapping degree 60%；Aerial survey selection of time spring tide is withered
Carry out in damp phase 0.5-2 h, intensity of illumination >=10000 lx during flight, select fine day wind-force≤4 grade.
The appraisal procedure of Intertidal zone as above sargassum thunbergii algae bed stock number, described remote sensing go out graph parameter and are：Coordinate
System adopts WGS 1984, elevation to select transverse Mercator projection using 1985 state height reference projection modes；Image band number
3-5, image pixel depth 8bit, pixel value 0-255, it is tiff that picture preserves form.
The appraisal procedure of Intertidal zone sargassum thunbergii algae bed stock number of the present invention, main compared with existing appraisal procedure
It is a technical advantage that：
1st, degree of accuracy is high：The resolution of satellite remote sensing images is 0.1-0.5m；And unmanned aerial vehicle remote sensing image resolution ratio is
0.05m.Therefore unmanned aerial vehicle remote sensing has higher accuracy compared to satellite remote sensing；Remote sensing images can accurately identify Intertidal zone Mus
The distributing position of tail algae algae bed further carries out quantity Inversion Calculation, overcomes the discontinuous, non-uniform Distribution of kelp bed distribution and causes
The systematic error of statistical analysiss, compares traditional approach and there is technical advance.
2nd, method is convenient：When larger to survey region, without the need for carrying out investigation and sampling to whole region, only need to be in a small range
Inversion Calculation is carried out to whole region by setting up Inversion Calculation model.
3rd, method efficiency high：Traditional method needs to lay some sample points in survey region, when survey region area increases
When, the quantity of sample point also increases therewith, therefore when survey region is larger, sampling work amount is higher and analytical cycle also therewith
Extend.This research method sets 10-20 biological photo control point to the region of different abundance before only needing remote sensing monitoring, you can to remote sensing
Monitoring image carries out biomass statistics, and when area increase is studied, without the need for the biological photo control point of substantial increase, you can realize extra large
The inverting estimation of algae total resources.
Description of the drawings
Fig. 1 kelp beds biology photo control point determines schematic diagram.
It is sargassum thunbergii actual distribution in the sargassum thunbergii scattergram that Fig. 2 is drawn according to remote sensing images, wherein atrouss region
Area.
The biological photo control point file that Fig. 3 is drawn according to GPS location, thing photo control point sampling area of making a living in its center area.
Specific embodiment
The present invention is further described below by way of specific embodiment, but and limits patent protection of the present invention never in any form
Scope, on the premise of thinking of the present invention is not changed, the conversion that is made or modification are intended to be included in those skilled in the art
Within protection scope of the present invention.
The foundation of 1 Intertidal zone sargassum thunbergii algae bed stock number Calculating model of the present invention of embodiment
1.1 unmanned aerial vehicle remote sensing preparation measures：
1.1.1 unmanned plane configuration is as follows：Fixed-wing formula unmanned plane, speed per hour >=50 km/h, cruising time >=1.5 h, observing and controlling
Radius >=10 km, wind loading rating >=4 grade, sensor valid pixel >=24,000,000, peak of flight >=2000 m.
1.1.2 unmanned plane during flying parameter：Need to obtain before unmanned plane during flying the spatial domain of relevant departments using license, flight
It is highly 150-300 m, ship's control 80%, sidelapping degree 60%.
1.1.3 default 10-20 biological photo control point, 1 × 1 m of photo control point sample area are needed before flying2（Such as Fig. 1 institutes
Show）, and photo control point GPS information and the collection of sargassum thunbergii quantity information is completed, it is representative that biological photo control point chooses different abundance
Sample prescription, each abundance photo control point >=3.
1.2 unmanned aerial vehicle remote sensing technical schemes：
1.2.1 the aerial survey time determines：The tide time according to survey region determines that aerial survey time, aerial survey select the spring tide withered damp phase
Carry out in 0.5-2 h, intensity of illumination >=10000 lx during flight, select fine day wind-force≤4 grade.
1.2.2 aerial survey parameter：Aerial survey scale is 1:500, course line number >=4,0.05 cm of ground resolution.
1.2.3 remote sensing goes out graph parameter：Coordinate system adopts WGS 1984, elevation to adopt 1985 state height reference projection sides
Formula selects transverse Mercator projection（Utm projection）.Image band number 3-5, image pixel depth 8bit, pixel value 0-255, figure
It is tiff that piece preserves form.
2nd, Inversion Calculation is carried out to sargassum thunbergii kelp bed using the inventive method with reference to ArcGIS, ENVI software.
2.1 determine sargassum thunbergii kelp bed areal area
2.1.1 sargassum thunbergii distribution area file is set up：Remote sensing images are input into 10.0 softwares of ArcGIS, newly-built in Catalog
Shapefile formatted files, are named as Distribution, and attribute is face file, and coordinate system is using WGS 1984, utm projection.
2.1.2 editor's sargassum thunbergii is distributed area file：Distribution files are edited in Editor instruments, and image shows
Ratio is set to 1:100, sargassum thunbergii distribution situation is drawn according to remote sensing images（See Fig. 2）, preserve after the completion of editor
Distribution files.
2.2 determine biological photo control point
2.2.1 sargassum thunbergii biology photo control point file is set up：Shapefile is set up in Catalog successively according to photo control point numbering
Formatted file, is named as XKD_ numberings, and attribute is face file, and coordinate system is using WGS 1984, utm projection.
2.2.2 sargassum thunbergii biology photo control point file is edited：According to photo control point numbering successively editing files, according to the life of record
Thing photo control point GPS information draws sargassum thunbergii biology photo control point file（See Fig. 3）, after the completion of editor, preserve file.
2.3 set up remote-sensing inversion computation model
2.3.1 ENVI softwares extract the image information of biological photo control point
Remote sensing images are input into ENVI4.7 softwares, and biological photo control point file is loaded in ENVI4.7 softwares, and by biological as controlling
Dot file saves as ROI（Concern area Region Of Interest）File.（Select in Available Vectors List
" File " is selected, " Export files to ROI ... "）
Then the ROI fileinfos of ASCII fromat are exported in remote sensing images file.（In remote sensing images " Tool ",
" Region Of Interest ", " Output ROI to ASCII ... ".）
Corresponding color value information is found in the ROI files of the ASCII fromat that extracts（Respectively R values, G-value, B values）, as
Metamessage bar number.
2.3.2 statistical analysiss set up Inversion Calculation model
With color value as independent variable, Sargassum quantity is dependent variable, to the color of image value that extracts（R values, G-value, B values）And as control
Point Sargassum quantity information carries out linear multiple regression statistical analysiss, obtains and looks back equation：
Y=（aR + bG + cB + d）×n
Y makes a living thing photo control point sargassum thunbergii quantity；
R, G, B are respectively the R values in ROI image colouring information, G-value, B values；
A, b, c, d are respectively regression calculation computational constant coefficient；
N values are that ROI image extracts image element information bar number.
Regression coefficient r when regression analysis equation2When >=0.8, Inversion Calculation model is more accurate, if r2＜ 0.8, then
Need to add photo control point or conversion returns statistical model.
2.4 calculate sargassum thunbergii kelp bed stock number using model
2.4.1 ENVI softwares extract sargassum thunbergii areal area image information
Remote sensing images are input into ENVI4.7 softwares, loading sargassum thunbergii distribution area file in ENVI4.7 softwares, and by biological picture
Control dot file saves as ROI（Concern area Region Of Interest）File.（In Available Vectors List
Select " File ", " Export files to ROI ... "）
Then the ROI fileinfos of ASCII fromat are exported in remote sensing images file.（In remote sensing images " Tool ",
" Region Of Interest ", " Output ROI to ASCII ... ".）
Corresponding color value information is found in the ROI files of the ASCII fromat that extracts（Respectively R values, G-value, B values）, as
Metamessage bar number.
2.4.2 using model assessment sargassum thunbergii stock number
Color value information input model equation by the sargassum thunbergii areal area that extracts（R values, G-value, B values）Single pixel pair is first calculated
The Sargassum quantity that answers, then calculates sargassum thunbergii algae bed total resources again.Wherein Y is sargassum thunbergii algae bed total resources, and n is that pixel is believed
Breath bar number, YiSargassum quantity for single pixel.
Embodiment 2 calculates Intertidal zone sargassum thunbergii algae bed stock number using appraisal procedure of the present invention
1st, unmanned aerial vehicle remote sensing：
Default 15 biological photo control points, 1 × 1 m of photo control point sample area is needed before flight2, and complete photo control point GPS information and
Sargassum thunbergii quantity information is gathered, and biological photo control point chooses the representative sample prescription of different abundance, each abundance photo control point >=3
Individual.
Using fixed-wing formula unmanned plane Shandong Rongcheng（E 122.5；N 37.2）Coastal 1.2 km2Region carry out remote sensing inspection
Survey, aerial survey selects to complete in spring tide withered 45 minutes damp phases, 25000-40000lx of intensity of illumination during aerial survey, 3 grades of wind-force.Aerial survey
Scale is 1:500, course line number 32,0.05 cm of ground resolution, flying height is 200 m, and ship's control 80% is other
To degree of overlapping 60%.
Coordinate system adopts WGS 1984, elevation to select transverse Mercator projection using 1985 state height reference projection modes
（Utm projection）.Image band number 3-5, image pixel depth 8bit, pixel value 0-255, it is tiff that picture preserves form.
2nd, Inversion Calculation is carried out to sargassum thunbergii kelp bed using the inventive method with reference to ArcGIS, ENVI software.
2.1 set up inverse model
Biological photo control point file is respectively created first with ArcGIS softwares, then biological photo control point text used in ENVI softwares
Part extracts color value（RGB）Carry out statistical analysiss.Such as following table:
R, G, B are respectively the R values in ROI image colouring information, G-value, B values；
Y makes a living thing photo control point sargassum thunbergii quantity；
N is pixel number.
Regression analysis equation：
Y/N = 0.072 R – 0.139 G + 0.059 B
I.e.： Y = （0.072 R – 0.139 G + 0.059 B）× N
Regression coefficient r2= 0.884 ≥0.8
2.2 Inversion Calculation
Area file is distributed using ArcGIS software creations sargassum thunbergii, then used in ENVI softwares, sargassum thunbergii distribution area file is carried
Take color value（RGB）, in the regression equation that pixel color value is substituted into inverse model, it is calculated YiValue, is then calculated this
Survey region sargassum thunbergii stock number is 61.2 ten thousand plants.
Claims (4)
1. a kind of appraisal procedure of Intertidal zone sargassum thunbergii algae bed stock number, which comprises the steps：
1）Remote sensing images are gathered：Preset multiple biological photo control points, biological 1 × 1 m of photo control point sample area of control2, biological photo control point
The representative sample prescription of different abundance, each abundance photo control point >=3 are chosen, and completes biological photo control point GPS information and Mus
Tail algae quantity information is gathered；Intertidal zone sargassum thunbergii kelp bed is monitored using unmanned aerial vehicle remote sensing, obtains remote sensing images；
2）Remote sensing images are input into 10.0 softwares of ArcGIS, newly-built shapefile formatted files in Catalog, coordinate system are adopted
With WGS 1984, utm projection, image displaying ratio are set to 1:100, sargassum thunbergii distribution situation, editor is drawn according to remote sensing images
After the completion of preserve obtain remote sensing images file；Shapefile form text is set up in Catalog successively according to photo control point numbering
Part, according to photo control point numbering and sargassum thunbergii quantity information successively editing files, the biological photo control point GPS information according to record is drawn
Remote sensing images are input into ENVI4.7 softwares by sargassum thunbergii biology photo control point file, load biological photo control point in ENVI4.7 softwares
File, then exports the ROI fileinfos of ASCII fromat, in remote sensing images file in the ROI of the ASCII fromat that extracts
Corresponding color value information and image element information bar number is found in file；Described color value information includes R values, G-value, B values；With face
Colour is independent variable, and sargassum thunbergii quantity is dependent variable, and the color of image value and photo control point sargassum thunbergii quantity information extracted are entered
Line Multiple regression statistics are analyzed, and obtain regression analysis equation：
Y=（aR + bG + cB + d）×n
Wherein Y makes a living thing photo control point sargassum thunbergii quantity；R, G, B are respectively the R values in ROI image colouring information, G-value, B values；a、
B, c, d are respectively regression calculation computational constant coefficient；N values are that ROI image extracts image element information bar number；When regression analysis equation
Regression coefficient r2When >=0.8, Inversion Calculation model is more accurate, if r2＜ 0.8, then need to add photo control point or conversion returned
Statistical model；
3）Remote sensing images are input into ENVI4.7 softwares, loading sargassum thunbergii distribution area file in ENVI4.7 softwares, then distant
The ROI fileinfos of ASCII fromat are exported in sense image file, are found corresponding in the ROI files of the ASCII fromat that extracts
Color value information and image element information bar number, described color value information R values, G-value, B values；By the sargassum thunbergii areal area that extracts
Color value information input model equation first calculates the corresponding Sargassum quantity of single pixel, then calculates sargassum thunbergii algae further according to formula
Bed total resources, wherein Y be sargassum thunbergii algae bed total resources, n be image element information bar number, YiSargassum quantity for single pixel.
2. the appraisal procedure of Intertidal zone sargassum thunbergii algae bed stock number as claimed in claim 1, it is characterised in that described nobody
Machine configuration should be able to meet following condition：Fixed-wing formula unmanned plane, speed per hour >=50 km/h, cruising time >=1.5 h, observing and controlling
Radius >=10 km, wind loading rating >=4 grade, sensor valid pixel >=24,000,000, peak of flight >=2000 m.
3. the appraisal procedure of Intertidal zone sargassum thunbergii algae bed stock number as claimed in claim 1, it is characterised in that described nobody
Flight parameter of the machine during aerial survey：Flying height be 150-300 m, ship's control 80%, sidelapping degree 60%；Boat
Surveying is carried out in the withered damp phase 0.5-2 h of selection of time spring tide, and intensity of illumination >=10000 lx during flight selects fine day wind-force≤4
Level.
4. the appraisal procedure of Intertidal zone sargassum thunbergii algae bed stock number as claimed in claim 1, it is characterised in that described remote sensing
Going out graph parameter is：Coordinate system adopts WGS 1984, elevation to select Transverse Mercator using 1985 state height reference projection modes
Projection；Image band number 3-5, image pixel depth 8bit, pixel value 0-255, it is tiff that picture preserves form.
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