CN107909607A - A kind of year regional vegetation coverage computational methods - Google Patents
A kind of year regional vegetation coverage computational methods Download PDFInfo
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Abstract
The invention discloses a kind of annual regional vegetation coverage computational methods, including：Accurate radiation calibration, atmospheric correction and geometrical registration are carried out to the same annual all remote sensing images in region, obtain remote sensing image result set of graphs；Cloud layer index, shadow index, background index and vegetation index based on structure, extract corresponding thematic information；Different phase remote sensing images are carried out with cloud algorithm respectively and goes shadow algorithm to handle；Space combination is carried out to Mono temporal vegetation index information；Multidate information in year is merged, computation year regional vegetation index information spatial distribution map；Finally, regional vegetation computation year coverage information space distribution map.This invention removes influence of the factors such as weather, landform, phase, vegetation pattern, environment to vegetation coverage, solve the problems, such as that annual regional vegetation coverage calculates, improve accuracy and the reliability of regional scale vegetation coverage calculating, operating process is simple, flexible, is easy to promote and apply in regional scale.
Description
Technical field
The present invention relates to a kind of annual regional vegetation coverage computational methods, belong to ecological construction, ecological environmental protection, calamity
The application fields such as evil monitoring, ecological functions assessment.
Background technology
Vegetation coverage is normally defined the planimetric area of vegetation (leaf, stem, branch) on ground and accounts for the Statistical Area gross area
Percentage, be one of the leading indicator for weighing surface vegetation situation, and the important indicator of local ecosystem environmental change,
In occupation of critical role in atmospheric thermodynamics, pedosphere, hydrosphere and biosphere.Vegetation coverage is considered as to carry out soil desert
Most effective index in the land deteriorations such as change, stony desertification, salination assessment；It reflects shadow of the ecosystem to the soil erosion
Ring, be the active factor for controlling the soil erosion, be the important indicator for carrying out soil erosion assessment and the comprehensive regulation, and hydrology life
A significant variable in states model, and then there is weight in local area ecological functional assessment and resource environment comprehensive bearing capacity assessment
Want meaning；Meanwhile vegetation coverage and vegetation evapotranspire it is close contact, and it is energy balance and water balance that vegetation, which is evapotranspired,
Important component, be soil-vegetation-atmosphere system water and heat transmission in a process, the monitoring needs root to evapotranspire
Different methods is used according to different vegetation coverages, vegetation coverage is the controlling elements important as one and exists；In addition,
Vegetation coverage is also a sensitive factor in global change research due, is many regions or even global climate model, carbon process
An essential variable in assessment models, can be variation of ecology and environment, regional vegetation produces force evaluating, land cover pattern change is supervised
Survey and useful information is provided.Therefore, it is each application neck to carry out the calculating of regional vegetation coverage and its raising of computational accuracy
Domain development is required, and ecological construction, environmental protection, the hydrology, agricultural, land resources utilization, disaster monitoring and the whole world are become
Change research etc. to be all of great significance.
At present, the computational methods of vegetation coverage can substantially be divided into two kinds：A kind of is traditional ground measurement method, another
Kind is remote sensing monitoring method.
Although traditional ground measurement method measurement accuracy is higher, this method is time-consuming, laborious, plus vegetation coverage in itself
With significant Spatio-temporal Distribution, this method is not suitable for regional scale research, and the research for being only applicable to smaller spatial dimension should
In.
The appearance of remote sensing technology is to monitor region vegetation coverage, or even global vegetation coverage change provides possibility.
The method of existing remote sensing monitoring vegetation coverage has very much, mainly includes：Empirical model method, Decomposition of Mixed Pixels method, physics mould
Type method, spectrum gradient method.Empirical model method is to be based on measured data, establishes corresponding statistical model or empirical regression model, into
And calculate vegetation coverage；Although this method is simple, is easy to calculate, substantial amounts of measured data is needed to establish correlation model,
Meanwhile need to establish different empirical models for different vegetation types, easily restricted by time, regional condition, vegetation pattern etc.,
There is larger uncertainty and limitation in big regional study, be not easy to promote and apply.Pixel Unmixing Models method is also referred to as
For Sub-pixel decompound method, i.e., under certain assumed condition, the remote sensing information in the pixel that multiple components are formed is decomposed, establishes pixel
Decomposition model, so as to obtain vegetation coverage.Pixel Unmixing Models mainly linear model, probabilistic model and fuzzy analysis
Model etc., wherein linear unmixed model is most widely used.Linear unmixed model assume reach sensor photon only with one
Component acts on, and different component is separate, is corresponding vegetation by ratio of the Numerical Methods Solve each component in pixel
Coverage, its precision is heavily dependent on the Rational choice of each end member, therefore it is not suitable for the remote sensing of low spatial resolution
Data application.Common linear unmixed model has pixel dichotomy, artificial neural network method, line spectrum analytic approach, more end members
Spectral mixing analysis etc., such method require the higher remotely-sensed data of spatial resolution.Physical model method is by studying light
Interaction with vegetation is to establish the model of vegetation spectral information and vegetation coverage physical relation, in theory with wide
Application, but this class model needs substantial amounts of associated parameter data, and existing satellite remote sensing date is ensureing the feelings of model accuracy
Condition, it is difficult to meet parameter request, and correlation model calculating is also more complicated, difficulty or ease are promoted in regional scale scope.Spectrum ladder
Degree method is proposed on the basis of analysis vegetation and soil reflection, and assumes that spectral reflectance is with wavelength line in the range of limited wavelength band
Property change, do not account for change of the vegetation soil area with wavelength, model error is larger.
With the progress of remote sensing technology, the spatial resolution of remote sensing image has no longer been the accurate limit for obtaining vegetation coverage
The factor processed, the revisiting period of remote sensing also greatly shorten.At the same time, because of weather, landform, sensor, factor, image are made an uproar in itself etc.
Sound (such as cloud layer, shade, background information) also increases therewith, how to be effectively reduced the influence of noise, while farthest
It has been that the technology for being badly in need of solving instantly is asked using the useful information in remote sensing image to improve the accuracy of vegetation coverage calculating
Topic.In addition, existing calculate vegetation coverage in research application, a relatively good phase of this area's vegetation growth is generally selected
Data represent the vegetation coverage of this area.And a regional vegetation cover type is often varied in reality,
Each type of vegetation its growth season is all different；Even same type of vegetation, because of edaphic condition, moisture condition, kind
Plant the difference that the differences such as time also result in its growth situation.Therefore, it is necessary to consider in regional extent different vegetation types and
The influence of growing environment, phase data when making full use of multiple, to improve the accuracy of regional vegetation coverage calculating, reliability.
Existing application needs not only to the vegetation coverage information of true representations this area, especially with the expansion in research range region
Greatly, the influence that the above problem accurately calculates vegetation coverage can be more and more significant.
The content of the invention
In view of the deficienciess of the prior art, the present invention proposes a kind of annual regional vegetation coverage computational methods, should
Method has taken into full account the interference of the noises such as cloud layer, shade, background, can excavate to greatest extent and using remote sensing image information,
The accuracy that existing vegetation coverage calculates is improved, operating process is simple, flexible, convenient to be promoted on larger regional scale
Using.
To solve the above problems, the technical solution used in the present invention is：
A kind of year regional vegetation coverage computational methods, comprise the following steps：
Same annual all remote sensing images of vegetation growing season inner region are carried out accurate radiation calibration and atmospheric radiation by S1
Correction, obtains the remote sensing image result figure that different phases represent atural object real reflectance；
S2, the remote sensing image result figure obtained by step S1, on the basis of any phase remote sensing image result figure, to other
Remote sensing image result figure carries out accurate geometrical registration, forms a remote sensing image result to match on geospatial coordinates
Set of graphs；
S3, for each phase remote sensing image result figure in remote sensing image result set of graphs obtained by step S2 carry out respectively with
Lower processing：
S3-1, calculates cloud layer index, vegetation index and shadow index for the phase remote sensing image result figure, obtains respectively
Corresponding index information spatial distribution map；
S3-2, background index is calculated for the phase remote sensing image result figure, and carries out binary conversion treatment, obtains a shadow
As background information spatial distribution map；
S3-3, the vegetation index information space distribution map obtained by step S3-1, carries out cloud algorithm process, the algorithm bag
Include following sub-step：
S3-3-1, carries out the phase remote sensing image result figure enhancing algorithm process of image, and by strengthening algorithm
The pixel sample in random acquisition cloud layer area is sample area in remote sensing image result figure after processing；
S3-3-2, space overlapping, system are carried out by cloud layer index information spatial distribution map obtained by the sample area and step S3-1
Meter calculates sample area and corresponds to the cumulative frequency of cloud layer index information numerical value from small to large；
S3-3-3, according to the cumulative frequency, sets a threshold range, and make two and cloud layer index information spatial distribution
The two-value hum pattern that figure matches；
S3-3-4, by vegetation index information space distribution map and step obtained by two two-value hum patterns of making and step S3-1
Image background information spatial distribution map obtained by rapid S3-2 carries out spatial operation, you can the vegetation index after the cloud layer noise that is eliminated
Information result figure；
S3-4, the vegetation index information space distribution map obtained by step S3-1, carries out shadow algorithm and handles, the algorithm
Including following sub-step：
S3-4-1, carries out the phase remote sensing image result figure enhancing algorithm process of image, and by strengthening algorithm
The pixel sample in random acquisition shadow region is sample area in remote sensing image result figure after processing；
S3-4-2, space overlapping, system are carried out by shadow index information space distribution map obtained by the sample area and step S3-1
Meter calculates sample area and corresponds to the cumulative frequency and ASSOCIATE STATISTICS variable of shadow index information value from small to large；
S3-4-3, according to the cumulative frequency and ASSOCIATE STATISTICS variable, sets a threshold range, and make two and refer to shade
The two-value hum pattern that number information space distribution map matches；
S3-4-4, by vegetation index information data space distribution map obtained by two two-value hum patterns of making and step S3-1
Spatial operation is carried out with step S3-2 gained image background information spatial distribution maps, you can the vegetation after the shade noise that is eliminated
Index information result figure；
S3-5, based on the image background information spatial distribution map made by step S3-2 and step S3-3 and step S3-4
Made two-value hum pattern, makes two new two-value hum patterns, and then utilizes new two-value hum pattern, to step S3-3 and
Vegetation index information result figure carries out the space combination of Mono temporal information obtained by step S3-4, and the final vegetation for generating the phase refers to
Number information result figure；
S4, based on the final vegetation index information result figure of each phase obtained by step S3, by year all phases in region
Final vegetation index information result figure merged, formed an image data for including multiple wave bands；
S5, on step S4 bases, the final vegetation index information result figure to covering same area, using maximum
Synthetic method, carries out maximum synthesis processing, obtains year regional vegetation index information spatial distribution map；
S6, carries out numerical operation processing to regional vegetation index information spatial distribution map obtained by step S5, eliminates cloud layer, the moon
The influence of shadow and background information to statistic analysis result；Accurately and reliably data source is provided to carry out statistical analysis in next step；
S7, based on step S6 acquired results, calculates vegetation coverage, up to the accurate vegetation coverage in the year region
Spatial distribution map.
Further, the cloud layer index CCI built in the step S3-1, its calculation formula are：CCI=(ρR+ρG+ρB)*
ρNIR, ρRFor the spectral reflectance values of red wave band, ρGFor the spectral reflectance values of green wave band, ρBFor the spectral reflectivity of blue wave band
Value, ρNIRFor remote sensing image near infrared band spectral reflectance values.
Further, vegetation index is normalized differential vegetation index NDVI in the step S3-1, which is：ρNIRFor remote sensing image near infrared band spectral reflectance values, ρRFor the spectral reflectivity of red wave band
Value.
Further, shadow index YYI calculation formula are described in the step S3-1：Its
Middle ρNIRFor remote sensing image near infrared band spectral reflectance values, ρSWIRFor the spectral reflectance values of short infrared wave band, NDVI is
Normalized differential vegetation index.
Further, the background index BGI built in the step S3-2, its calculation formula are：Wherein ρNIRFor remote sensing image near infrared band spectral reflectance values, ρRSpectrum for red wave band is anti-
Radiance rate value, ρGFor the spectral reflectance values of green wave band.
Further, binary conversion treatment refers to background information pixel assignment in background index BGI in the step S3-2
For -99, other pixels are assigned a value of 0, obtain an image background information spatial distribution map BGII.
Further, the enhancing algorithm process of image refers to that 2% linear stretch strengthens in the step S3-3-1；At random
Collecting sample pixel refers to the sampling window random acquisition cloud layer information pixel using 2 × 2, to avoid mistake from choosing non-cloud layer area picture
Member and cause error, it is desirable to sample pixel quantity be more than 200.
Further, the cloud layer when setting of threshold range refers to cumulative frequency reaching 2% in the step S3-3-3
Lower limit of the index information numerical value as threshold value, the upper threshold limit threshold value are infinity.
Further, two two-value hum patterns are CCIB1 in the step S3-3-3 and CCIB2, CCIB1 are to be directed to cloud layer
Index CCI, is assigned a value of 0, other values are assigned a value of 1, calculate and obtain by the numerical value in threshold range；CCIB2 is to be directed to cloud layer index
CCI, is assigned a value of -99, other values are assigned a value of 0, calculate and obtain by the numerical value in threshold range.
Further, the calculation formula of spatial operation is in the step S3-3-4：NDVICCI=NDVI*CCIB1+
CCIB2+BGII, wherein NDVICCITo eliminate the vegetation index information result figure after cloud layer noise, BGII is image background information
Spatial distribution map, CCIB1 and CCIB2 are that step S3-3-3 calculates gained two-value hum pattern.
Further, the enhancing algorithm process of image described in the step S3-4-1 refers to 2% linear stretch enhancing；
Random acquisition sample pixel refers to the sampling window random acquisition image shades information pixel using 2 × 2, non-to avoid mistake from choosing
Cloud layer area pixel and cause error, it is desirable to sample pixel quantity be more than 200.
When further, the setting of threshold range described in the step S3-4-3 refers to cumulative frequency reaching 95%
The upper limit of the shadow index information value as threshold value, minimum value statistical variable (sample area corresponds to shadow index numerical value) are used as threshold value
The lower limit of scope.
Further, two two-value hum patterns described in the step S3-4-3 are YYIB1 and YYIB2, YYIB1 are to be directed to
Shadow index YYI, is assigned a value of 0, other values are assigned a value of 1, calculate and obtain by the numerical value in threshold range；YYIB2 is to be directed to shade
Index YYI, is assigned a value of -99, other values are assigned a value of 0, calculate and obtain by the numerical value in threshold range.
Further, the calculation formula of spatial operation is in the step S3-4-4：NDVIYYI=NDVI*YYIB1+
YYIB2+BGII, wherein NDVIYYITo eliminate the vegetation index information result figure after shadow information, BGII is that step S3-2 is calculated
Gained image background information spatial distribution map, YYIB1 and YYIB2 are that step S3-4-3 calculates gained two-value hum pattern.
Further, two new two-value hum patterns are NDVI in the step S3-5B1And NDVIB2, its calculation formula point
It is not as follows：
NDVIB2=(NDVIB1+99)*(NDVIB1-1)
Wherein BGII is that step S3-2 calculates gained background information spatial distribution map, and CCIB1 is that step S3-3 calculates gained
Two-value hum pattern, YYIB1 are that step S3-4 calculates gained two-value hum pattern.
Further, space combination method, its calculation formula are as follows described in the step S3-5：
Wherein NDVICCIThe vegetation index information result figure after cloud layer noise, NDVI are eliminated obtained by step S3-3YYIFor step
The vegetation index information result figure after shadow information, NDVI are eliminated obtained by rapid S3-4B1And NDVIB2It is new for two in step S3-5
Two-value hum pattern.
Further, fusion method described in step S4 refers to synthesize multiple Mono temporal vegetation index information by wave band
Obtain an image spatially to match.
Further, maximum synthetic method refers to be directed to same pixel in the step S5, and the pixel is in different phases
Different numerical value is had, takes the maximum in these values, is maximum synthesis.
Further, the processing of numerical operation described in step S6 refers to regional vegetation index information space obtained by step S5
Numerical value is that -99 area assignment is NAN in distribution map, and carrying out statistical analysis for next step provides accurately and reliably data source.
Further, in the step S7 calculate vegetation coverage can using any type based on vegetation index information come
Estimate the method or model of vegetation coverage.
Further, the method for any type estimation vegetation coverage or model are included in Decomposition of Mixed Pixels method often
Pixel dichotomy, neural network model.
Further, the pixel dichotomy calculation formula is as follows：
Wherein VFC be pixel in vegetation coverage, NDVI be the pixel on normalized differential vegetation index, NDVIsoilFor
The NDVI values of no vegetative coverage pixel, NDVIvegFor completely by the pixel NDVI values of vegetative coverage；In the present invention, with 0.5%
The upper lower threshold value of confidence level interception area NDVI represents NDVI respectivelyvegAnd NDVIsoil, and then zoning vegetation coverage
VFC。
The beneficial effects of the present invention are：
1) it is an advantage of the invention that from analysis weather, landform, when the condition such as phase character regional vegetation coverage is calculated
Influence is started with, and is constructed the model algorithms such as cloud layer index, shadow index, background index, is farthest eliminated environmental background
Influence of the noise to vegetation coverage result of calculation, solves the problems, such as that annual regional vegetation coverage calculates, improves region
The accuracy and reliability that scale vegetation coverage calculates, operating process is simple, flexible, is easy to push away in Application in regional scale
Extensively.
2) method, the present invention take full advantage of the high time resolution of remotely-sensed data, multidate now compared with prior art
Feature, considers vegetation pattern in region, growing environment, the difference of Growing season, it is proposed that a kind of year vegetation coverage meter
Calculation method, improves vegetation coverage as measurement surface vegetation situation, the standard of local ecosystem environmental change leading indicator
Exactness, reliability and confidence level, and as ecology, the accuracy of the hydrology, climate model necessity input parameter, it has stronger
Universality and adaptability, easily promote and apply.
Brief description of the drawings
Fig. 1 is the flow diagram of the present invention；
Fig. 2 is the remote sensing image of four different phases；
Fig. 3 is cloud and goes the spatial operation principle schematic of shade；
Fig. 4 is the vegetation index information result figure of four phases through past cloud and after going Shadows Processing；
Fig. 5 is annual regional vegetation index information spatial distribution map；
Fig. 6 is the accurate vegetation coverage spatial distribution map in annual region.
Embodiment
In order to make the purpose , technical scheme and advantage of the present invention be clearer, with reference to the accompanying drawings and embodiments, it is right
The present invention is further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, and
It is not used in the restriction present invention.
The present embodiment by certain region with year, four different phases remote sensing image exemplified by illustrate.
With reference to Fig. 1, Fig. 1 is a kind of flow diagram of annual regional vegetation coverage computational methods of the present invention, including with
Lower step：
S1, collects all remote sensing images in the same annual Growing season in one's respective area, carries out accurate radiation calibration to it respectively
And Atmospheric radiation correction, obtain representing the remote sensing image result figure of atural object real reflectance.If Fig. 2 is the phase of one's respective area four
Remote sensing image, the first width figure (the first row left figure) is the remote sensing image in June in Fig. 2, the second width figure (first in Fig. 2
Row right figure) be July remote sensing image, the 3rd width figure (the second row left figure) is the remote sensing image of August part in Fig. 2, in Fig. 2
4th width figure (the second row right figure) is the remote sensing image of September part, in addition to seasonal difference, while is had different degrees of
The information such as cloud layer, clouds layer shadow, massif shade, ambient noise.Obtain the vegetation coverage of year one's respective area, any phase
Remotely-sensed data cannot represent the vegetation state in the region completely.
S2 is right on the basis of any phase remote sensing image result figure that can cover one's respective area based on step 1 acquired results
The remote sensing image of other three phases carries out accurate geometrical registration, makes the remote sensing image data of this four phases in geographical space
Match on coordinate, obtain the remote sensing image result set of graphs of a standard.
S3, for each phase remote sensing image result figure in remote sensing image result set of graphs obtained by step S2 carry out respectively with
Lower processing：
S3-1, calculates cloud layer index, vegetation index and shadow index for the phase remote sensing image result figure, obtains respectively
Corresponding index information spatial distribution map；
Due to satellite remote sensing sensor by atmospheric density, cloud layer change etc. influence, all there are cloud for many images of the same area
Layer occlusion issue.Cloud layer can make the remote sensing terrestrial object information of acquisition decay, or even loss.Cloud cover can reduce in the present embodiment
Or vegetation index information is covered, cause finally to calculate the accuracy of vegetation coverage, confidence level is greatly reduced.The present invention passes through
Go deep into response characteristic of the cloud layer information in satellite remote sensing sensor difference spectral band, and with vegetation, soil, water body, bare area,
Cities and towns etc. are in the response similarities and differences of identical band po sition, the common and shared spectral band of selection satellite remote sensing sensor, structure
One cloud layer index CCI.Cloud layer information can be efficiently extracted based on the cloud layer index, and then eliminate cloud layer information to vegetation
The influence that coverage calculates.The cloud layer index CCI calculation formula are：CCI=(ρR+ρG+ρB)*ρNIR, ρRFor the spectrum of red wave band
Reflectance value, ρGFor the spectral reflectance values of green wave band, ρBFor the spectral reflectance values of blue wave band, ρNIRFor remote sensing image near-infrared
Band spectrum reflectance value.
At present, normalized differential vegetation index NDVI is widely used in remote sensing monitoring vegetation growth status, while is also this
Embodiment calculates the basis of vegetation coverage.In addition, when to different object spectrum Analysis of response, normalized differential vegetation index
Being introduced into for NDVI can be effectively the atural object area of the low brightness values such as Water-Body Information in clouds layer shadow and massif shade and remote sensing image
Separate.Therefore, normalized differential vegetation index NDVI need to be calculated first, computational methods mainly utilize non-linear normalizing
Method relatively simple ratio vegetation index is handled, its calculation formula is：
ρNIRFor remote sensing image near infrared band spectral reflectance values, ρRFor the spectral reflectance values of red wave band.
Cloud layer except terrestrial object information is blocked and weakening effect in addition to, meanwhile, atural object of the cloud layer shade to shadow region itself
Also decrease to some degree acts on vegetation index information.If can be unblanketed by another phase by the terrestrial object information in shadow region
Remote sensing image information is substituted, and just can preferably improve the final accuracy for calculating vegetation coverage and confidence level.Except cloud layer
Shade presence is had, because of sun altitude, azimuth and orographic factor, massif shade is also a non-negligible part.
When analyzing the different spectral charactersiticss of targets, the reflectance value of clouds layer shadow and massif shade is very low, if by near infrared band spectrum
Reflectance value (ρNIR) with the spectral reflectance values (ρ of short infrared wave bandSWIR) sum up, shadow information can be widened well
It is but closer to the distance between Water-Body Information and shadow information with the distance between other terrestrial object informations, misjudgement is be easy to cause, both and not
Shade can preferably be separated with water body；Water body then can be significantly further widened by introducing normalized differential vegetation index (NDVI)
Distance between information and shadow information, and then a shadow index (YYI) is constructed, its calculation formula is：
Wherein, YYI is shadow index, ρNIRFor remote sensing image near infrared band spectral reflectance values, ρSWIRFor short-wave infrared
The spectral reflectance values of wave band, NDVI are normalized differential vegetation index.
S3-2, background index is calculated for the phase remote sensing image result figure, and carries out binary conversion treatment, obtains a shadow
As background information spatial distribution map；
In real work, there are many remote sensing images that whole distract can not be completely covered, unlapped region has the back of the body
Scape information exists (as shown in Figure 2), how to handle the efficiency of the practice that these information will directly affect whole technical method；In addition,
Background information after atmospheric correction by default value be 0, this can cause the vegetation index information value one with other atural objects
Sample, the accuracy that final vegetation coverage calculates will be influenced by dealing with improperly.The present invention is by analyzing different atural objects in each ripple
The spectral response characteristics and the order of magnitude of section, construct background index BGI, its calculation formula is：
Wherein ρNIRFor remote sensing image near infrared band spectral reflectance values, ρRFor the spectral reflectance values of red wave band, ρGFor
The spectral reflectance values of green wave band.The index effectively can only accurately distinguish background information and terrestrial object information area in image
Come, it is also necessary to further to be handled.The technical program, does it further binary conversion treatment, will be carried on the back in background index BGI
Scape information pixel is assigned a value of -99, other pixels are assigned a value of 0, an image background information spatial distribution map BGII is obtained, to make
Participate in calculating in next step for an information factor.
S3-3, the vegetation index information space distribution map obtained by step S3-1, carries out cloud algorithm process, the algorithm bag
Include following sub-step：
S3-3-1, before treatment, first carries out the phase remote sensing image result figure enhancing algorithm process of image, and
The pixel sample in random acquisition cloud layer area is sample area in the remote sensing image result figure after strengthening algorithm process, the shadow
The enhancing algorithm process of picture refers to 2% Linear Contrast Enhancement Algorithm processing, to increase different type of ground objects color contrasts, it is convenient into
The selection of row next step pixel sample, meanwhile, select 2% linear stretch algorithm also to reduce the influence of environmental background noise, protect
True degree is higher.To avoid mistake from selecting non-overcast area pixel, the random acquisition cloud on the striograph after strengthening algorithm process
The pixel in floor area uses 2 × 2 sampling window random acquisition cloud layer information pixel sample as sample area, to ensure data precision,
Sample pixel quantity is more than 200.
S3-3-2, space overlapping, system are carried out by cloud layer index information spatial distribution map obtained by the sample area and step S3-1
Meter calculates sample area and corresponds to the cumulative frequency of cloud layer index information numerical value from small to large；
S3-3-3, according to the cumulative frequency, sets a threshold range, and make two and cloud layer index information spatial distribution
The two-value hum pattern that figure matches；The lower limit of the threshold range is cloud layer index information numerical value when cumulative frequency reaches 2%,
The upper limit of the threshold range is infinity.Described two two-value hum patterns to match with cloud layer index information spatial distribution map
CCIB1 and CCIB2 are respectively designated as, CCIB1 is to be directed to cloud layer index CCI in step 3, by the numerical value assignment in threshold range
For 0, other values are assigned a value of 1, calculate and obtain, see in Fig. 3 2.；And CCIB2 is to be directed to cloud layer index CCI, by threshold range
Numerical value is assigned a value of -99, and other values are assigned a value of 0, calculates and obtains, sees in Fig. 3 3..
S3-3-4, by vegetation index information space distribution map and step obtained by two two-value hum patterns of making and step S3-1
Image background information spatial distribution map obtained by rapid S3-2 carries out spatial operation, you can the vegetation index after the cloud layer noise that is eliminated
Information result figure；The calculation formula of the spatial operation is：
NDVICCI=NDVI*CCIB1+CCIB2+BGII, wherein NDVICCITo eliminate the vegetation index letter after cloud layer noise
Result figure is ceased, BGII is image background information spatial distribution map, and CCIB1 and CCIB2 are that step S3-3-3 calculates gained two-value letter
Breath figure.More clearly to explain the principle of the spatial operation, contribute to the understanding to numerical procedure of the present invention, if Fig. 3 is the sky
Between calculating process principle schematic diagram.1. it is wherein NDVI, is 2. CCIB1, be 3. CCIB2, be 4. BGII, is 5. NDVICCI。
S3-4, the vegetation index information space distribution map obtained by step S3-1, carries out shadow algorithm and handles, the algorithm
Including following sub-step：
S3-4-1, carries out the phase remote sensing image result figure enhancing algorithm process of image, and by strengthening algorithm
The pixel sample in random acquisition shadow region is sample area in remote sensing image result figure after processing；
Before treatment, the enhancing algorithm process of image is carried out to the phase remote sensing image result figure first, and is being passed through
The pixel sample in random acquisition shadow region is sample area in remote sensing image result figure after enhancing algorithm process, the increasing of the image
Strong algorithms processing refers to 2% Linear Contrast Enhancement Algorithm processing, and to increase different type of ground objects color contrasts, convenient progress is next
Walk the selection of pixel sample, meanwhile, select 2% linear stretch algorithm also to reduce the influence of environmental background noise, fidelity compared with
It is high.To avoid mistake from selecting nonshaded area pixel, the pixel in random acquisition shadow region on the striograph after strengthening algorithm process
2 × 2 sampling window random acquisition shadow information pixel sample is used as sample area, to ensure data precision, sample pixel number
Amount is more than 200.
S3-4-2, space overlapping, system are carried out by shadow index information space distribution map obtained by the sample area and step S3-1
Meter calculates sample area and corresponds to the cumulative frequency and ASSOCIATE STATISTICS variable (sample area correspondence the moon of shadow index information value from small to large
Shadow exponential number)；
S3-4-3, according to the cumulative frequency and ASSOCIATE STATISTICS variable, sets a threshold range, cumulative frequency is reached 95%
When the upper limit of the shadow index information value as threshold value, lower limit of the minimum value statistical variable as threshold range, and make two
A two-value hum pattern to match with shadow index information space distribution map, is respectively designated as YYIB1 and YYIB2, YYIB1 is pin
To shadow index YYI, the numerical value in threshold range is assigned a value of 0, other values are assigned a value of 1, calculate and obtain；YYIB2 is for the moon
Shadow index YYI, is assigned a value of -99, other values are assigned a value of 0, calculate and obtain by the numerical value in threshold range.
S3-4-4, by vegetation index information data space distribution map obtained by two two-value hum patterns of making and step S3-1
Spatial operation is carried out with step S3-2 gained image background information spatial distribution maps, you can the vegetation after the shade noise that is eliminated
Index information result figure.The calculation formula of the spatial operation is：
NDVIYYI=NDVI*YYIB1+YYIB2+BGII, wherein NDVIYYITo eliminate the vegetation index letter after shadow information
Result figure is ceased, BGII is that step S3-2 calculates gained image background information spatial distribution map, and YYIB1 and YYIB2 are step S3-4-
3 calculate gained two-value hum pattern.The principle of the spatial operation is as in cloud removing algorithm, reference can be made to shown in Fig. 3.
S3-5, based on the image background information spatial distribution map made by step S3-2 and step S3-3 and step S3-4
Made two-value hum pattern, makes two new two-value hum patterns, is respectively designated as NDVIB1And NDVIB2, its calculation formula
It is as follows respectively：
NDVIB2=(NDVIB1+99)*(NDVIB1-1)
Wherein BGII is that step S3-2 calculates gained background information spatial distribution map, and CCIB1 is that step S3-3 calculates gained
Two-value hum pattern, YYIB1 are that step S3-4 calculates gained two-value hum pattern.
And then new two-value hum pattern is utilized, to the vegetation index information result after elimination cloud layer noise obtained by step S3-3
Vegetation index information result figure after figure and step S3-4 gained elimination shade noises carries out the space combination of Mono temporal information, raw
Into the final vegetation index information result figure of the phase.The space combination method calculation formula is as follows：
Wherein NDVICCIThe vegetation index information result figure after cloud layer noise, NDVI are eliminated obtained by step S3-3YYIFor step
The vegetation index information result figure after shadow information, NDVI are eliminated obtained by rapid S3-4B1And NDVIB2It is new for two in step S3-5
Two-value hum pattern.
If Fig. 4 is the final vegetation index information result figure of four phases through past cloud and after going Shadows Processing, wherein Fig. 4
In the first width figure (the first row left figure) be June final vegetation index information result figure, (the first row is right for the second width figure in Fig. 4
Figure) be July final vegetation index information result figure, the 3rd width figure (the second row left figure) is the final vegetation of August part in Fig. 4
Index information result figure, the 4th width figure (the second row right figure) is the final vegetation index information result figure of September part in Fig. 4.
S4, based on the final vegetation index information result figure of 4 phases obtained by step S3, by annual 4 phases in region
Final vegetation index information result figure merged, formed an image data for including multiple wave bands.In the present embodiment
It is to utilize the LayerStacking in ENVI that the final vegetation index information result figure synthesis of four phases obtained by step S3 exists
One image spatially to match.
S5, on step S4 bases, the final vegetation index information result figure of four phases to covering same area,
Using maximum synthetic method, maximum synthesis processing is carried out, obtains year regional vegetation index information spatial distribution map, such as figure
5 be maximum composite result figure.
S6, carries out numerical operation processing to regional vegetation index information spatial distribution map obtained by step S5, eliminates cloud layer, the moon
The influence of shadow and background information to statistic analysis result, carries out statistical analysis for next step and provides accurately and reliably data source.Tool
Body is that cloud layer information, shadow information and background information are all assigned -99 in above-mentioned calculating process, to avoid these pixels
Information participate in statistics calculate, avoid statistical error, needed before vegetation coverage is calculated be by the area assignment that numerical value is -99
NAN。
S7, based on step S6 acquired results, one's respective area vegetative coverage is calculated using the pixel dichotomy based on vegetation index
Degree, up to the accurate vegetation coverage spatial distribution map in the year region, as shown in Figure 6.The pixel dichotomy calculates public
Formula is as follows：
Wherein VFC be pixel in vegetation coverage, NDVI be the pixel on normalized differential vegetation index, NDVIsoilFor
The NDVI values of no vegetative coverage pixel, NDVIvegFor completely by the pixel NDVI values of vegetative coverage；In the present invention, with 0.5%
The upper lower threshold value of confidence level interception area NDVI represents NDVI respectivelyvegAnd NDVIsoil, and then zoning vegetation coverage
VFC。
Although reference be made herein to invention has been described for multiple explanatory embodiments of the invention, however, it is to be understood that
Those skilled in the art can be designed that a lot of other modifications and embodiment, these modifications and embodiment will fall in this Shen
Please be within disclosed spirit and spirit.More specifically, can in the range of disclosure, drawings and claims
A variety of variations and modifications are carried out with the building block to theme combination layout and/or layout.Except to building block and/or layout
Outside the modification and improvement of progress, to those skilled in the art, other purposes also will be apparent.
Claims (22)
1. a kind of year regional vegetation coverage computational methods, it is characterised in that comprise the following steps：
Same annual all remote sensing images of vegetation growing season inner region are carried out accurate radiation calibration and atmospheric radiation school by S1
Just, the remote sensing image result figure that different phases represent atural object real reflectance is obtained；
S2, the remote sensing image result figure obtained by step S1, on the basis of any phase remote sensing image result figure, to other remote sensing
Imaging results figure carries out accurate geometrical registration, forms a remote sensing image result atlas to match on geospatial coordinates
Close；
S3, following locate is carried out for each phase remote sensing image result figure in step S2 gained remote sensing image result set of graphs respectively
Reason：
S3-1, cloud layer index, vegetation index and shadow index are calculated for the phase remote sensing image result figure respectively, are obtained corresponding
Index information spatial distribution map；
S3-2, background index is calculated for the phase remote sensing image result figure, and carries out binary conversion treatment, obtains an image back of the body
Scape information space distribution map；
S3-3, for vegetation index information space distribution map obtained by step S3-1, carries out cloud algorithm process, the algorithm include with
Lower sub-step：
S3-3-1, carries out the phase remote sensing image result figure enhancing algorithm process of image, and by strengthening algorithm process
The pixel sample in random acquisition cloud layer area is sample area in remote sensing image result figure afterwards；
S3-3-2, space overlapping, statistics meter are carried out by cloud layer index information spatial distribution map obtained by the sample area and step S3-1
Calculate sample area and correspond to the cumulative frequency of cloud layer index information numerical value from small to large；
S3-3-3, according to the cumulative frequency, sets a threshold range, and make two and cloud layer index information spatial distribution map phase
Matched two-value hum pattern；
S3-3-4, by vegetation index information space distribution map and step obtained by two two-value hum patterns of making and step S3-1
Image background information spatial distribution map carries out spatial operation obtained by S3-2, you can the vegetation index letter after the cloud layer noise that is eliminated
Cease result figure；
S3-4, the vegetation index information space distribution map obtained by step S3-1, carries out shadow algorithm and handles, which includes
Following sub-step：
S3-4-1, carries out the phase remote sensing image result figure enhancing algorithm process of image, and by strengthening algorithm process
The pixel sample in random acquisition shadow region is sample area in remote sensing image result figure afterwards；
S3-4-2, space overlapping, statistics meter are carried out by shadow index information space distribution map obtained by the sample area and step S3-1
Calculate sample area and correspond to the cumulative frequency and ASSOCIATE STATISTICS variable of shadow index information value from small to large；
S3-4-3, according to the cumulative frequency and ASSOCIATE STATISTICS variable, sets a threshold range, and makes two and believe with shadow index
The two-value hum pattern that breath spatial distribution map matches；
S3-4-4, by vegetation index information data space distribution map and step obtained by two two-value hum patterns of making and step S3-1
Image background information spatial distribution map obtained by rapid S3-2 carries out spatial operation, the vegetation index information after the shade noise that is eliminated
Result figure；
S3-5, it is made based on the image background information spatial distribution map made by step S3-2 and step S3-3 and step S3-4
The two-value hum pattern of work, makes two new two-value hum patterns, and then utilizes new two-value hum pattern, to step S3-3 and step
Vegetation index information result figure carries out the space combination of Mono temporal information obtained by S3-4, generates the final vegetation index letter of the phase
Cease result figure；
S4, based on the final vegetation index information result figure of each phase obtained by step S3, by region year all phases most
Whole vegetation index information result figure is merged, and forms an image data for including multiple wave bands；
S5, on step S4 bases, the final vegetation index information result figure to covering same area, is synthesized using maximum
Method, carries out maximum synthesis processing, obtains year regional vegetation index information spatial distribution map；
S6, numerical operation processing is carried out to regional vegetation index information spatial distribution map obtained by step S5, eliminate cloud layer, shade and
Influence of the background information to statistic analysis result；
S7, based on step S6 acquired results, calculates vegetation coverage, up to the accurate vegetation coverage space in the year region
Distribution map.
A kind of 2. annual regional vegetation coverage computational methods as claimed in claim 1, it is characterised in that the step S3-1
The cloud layer index CCI of middle structure, its calculation formula are：CCI=(ρR+ρG+ρB)*ρNIR, ρRFor the spectral reflectance values of red wave band,
ρGFor the spectral reflectance values of green wave band, ρBFor the spectral reflectance values of blue wave band, ρNIRFor remote sensing image near infrared band spectrum
Reflectance value.
A kind of 3. annual regional vegetation coverage computational methods as claimed in claim 1, it is characterised in that the step S3-1
Middle vegetation index is normalized differential vegetation index NDVI, which is：For remote sensing
Image near infrared band spectral reflectance values, ρRFor the spectral reflectance values of red wave band.
A kind of 4. annual regional vegetation coverage computational methods as claimed in claim 1, it is characterised in that the step S3-1
Described in shadow index YYI calculation formula be：Wherein ρNIRFor remote sensing image near infrared band spectrum
Reflectance value, ρSWIRFor the spectral reflectance values of short infrared wave band, NDVI is normalized differential vegetation index.
A kind of 5. annual regional vegetation coverage computational methods as claimed in claim 1, it is characterised in that the step S3-2
The background index BGI of middle structure, its calculation formula are：Wherein ρNIRFor remote sensing image near-infrared
Band spectrum reflectance value, ρRFor the spectral reflectance values of red wave band, ρGFor the spectral reflectance values of green wave band.
A kind of 6. annual regional vegetation coverage computational methods as claimed in claim 1, it is characterised in that the step S3-2
Middle binary conversion treatment refers to background information pixel in background index BGI being assigned a value of -99, other pixels are assigned a value of 0, obtain one
Image background information spatial distribution map BGII.
A kind of 7. annual regional vegetation coverage computational methods as claimed in claim 1, it is characterised in that the step S3-
The enhancing algorithm process of image refers to 2% linear stretch enhancing in 3-1；Random acquisition sample pixel refers to adopt using 2 × 2
Sample window random acquisition cloud layer information pixel, sample pixel quantity are more than 200.
A kind of 8. annual regional vegetation coverage computational methods as claimed in claim 1, it is characterised in that the step S3-
Cloud layer index information numerical value when the setting of threshold range refers to cumulative frequency reaching 2% in 3-3, should as the lower limit of threshold value
Upper threshold limit threshold value is infinity.
A kind of 9. annual regional vegetation coverage computational methods as claimed in claim 1, it is characterised in that the step S3-
Two two-value hum patterns are CCIB1 in 3-3 and CCIB2, CCIB1 are to be directed to cloud layer index CCI, and the numerical value in threshold range is assigned
It is worth for 0, other values are assigned a value of 1, calculate and obtain；CCIB2 is to be directed to cloud layer index CCI, the numerical value in threshold range is assigned a value of-
99, other values are assigned a value of 0, calculate and obtain.
A kind of 10. annual regional vegetation coverage computational methods as claimed in claim 1, it is characterised in that the step S3-
The calculation formula of spatial operation is in 3-4：NDVICCI=NDVI*CCIB1+CCIB2+BGII, wherein NDVICCITo eliminate cloud layer
Vegetation index information result figure after noise, BGII are image background information spatial distribution maps, and CCIB1 and CCIB2 are step S3-
3-3 calculates gained two-value hum pattern.
A kind of 11. annual regional vegetation coverage computational methods as claimed in claim 1, it is characterised in that the step S3-
The enhancing algorithm process of image described in 4-1 refers to 2% linear stretch enhancing；Random acquisition sample pixel refers to use 2 × 2
Sampling window random acquisition image shades information pixel, sample pixel quantity be more than 200.
A kind of 12. annual regional vegetation coverage computational methods as claimed in claim 1, it is characterised in that the step S3-
Shadow index information value when the setting of threshold range described in 4-3 refers to cumulative frequency reaching 95% is as the upper of threshold value
Limit, lower limit of the minimum value statistical variable as threshold range.
A kind of 13. annual regional vegetation coverage computational methods as claimed in claim 1, it is characterised in that the step S3-
Two two-value hum patterns described in 4-3 are YYIB1 and YYIB2, YYIB1 are to be directed to shadow index YYI, by the number in threshold range
Value is assigned a value of 0, and other values are assigned a value of 1, calculates and obtains；YYIB2 is to be directed to shadow index YYI, and the numerical value in threshold range is assigned
It is worth for -99, other values are assigned a value of 0, calculate and obtain.
A kind of 14. annual regional vegetation coverage computational methods as claimed in claim 1, it is characterised in that the step S3-
The calculation formula of spatial operation is in 4-4：NDVIYYI=NDVI*YYIB1+YYIB2+BGII, wherein NDVIYYITo eliminate shade
Vegetation index information result figure after information, BGII are that step S3-2 calculates gained image background information spatial distribution map, YYIB1
It is that step S3-4-3 calculates gained two-value hum pattern with YYIB2.
A kind of 15. annual regional vegetation coverage computational methods as claimed in claim 1, it is characterised in that the step S3-
Two new two-value hum patterns are NDVI in 5B1And NDVIB2, its calculation formula is distinguished as follows：
<mrow>
<msub>
<mi>NDVI</mi>
<mrow>
<mi>B</mi>
<mn>1</mn>
</mrow>
</msub>
<mo>=</mo>
<mi>C</mi>
<mi>C</mi>
<mi>I</mi>
<mi>B</mi>
<mn>1</mn>
<mo>*</mo>
<mi>Y</mi>
<mi>Y</mi>
<mi>I</mi>
<mi>B</mi>
<mn>1</mn>
<mo>*</mo>
<mfrac>
<mrow>
<mo>(</mo>
<mi>B</mi>
<mi>G</mi>
<mi>I</mi>
<mi>I</mi>
<mo>+</mo>
<mn>99</mn>
<mo>)</mo>
</mrow>
<mn>99</mn>
</mfrac>
</mrow>
NDVIB2=(NDVIB1+99)*(NDVIB1-1)
Wherein BGII is that step S3-2 calculates gained background information spatial distribution map, and CCIB1 is that step S3-3 calculates gained two-value
Hum pattern, YYIB1 are that step S3-4 calculates gained two-value hum pattern.
A kind of 16. annual regional vegetation coverage computational methods as claimed in claim 1, it is characterised in that the step S3-
Space combination method described in 5, its calculation formula are as follows：
<mrow>
<msub>
<mi>NDVI</mi>
<mrow>
<mi>g</mi>
<mi>o</mi>
<mi>a</mi>
<mi>l</mi>
</mrow>
</msub>
<mo>=</mo>
<mfrac>
<mrow>
<mo>(</mo>
<mo>(</mo>
<mrow>
<msub>
<mi>NDVI</mi>
<mrow>
<mi>C</mi>
<mi>C</mi>
<mi>I</mi>
</mrow>
</msub>
<mo>+</mo>
<msub>
<mi>NDVI</mi>
<mrow>
<mi>Y</mi>
<mi>Y</mi>
<mi>I</mi>
</mrow>
</msub>
</mrow>
<mo>)</mo>
<mo>*</mo>
<msub>
<mi>NDVI</mi>
<mrow>
<mi>B</mi>
<mn>1</mn>
</mrow>
</msub>
<mo>)</mo>
</mrow>
<mn>2</mn>
</mfrac>
<mo>+</mo>
<msub>
<mi>NDVI</mi>
<mrow>
<mi>B</mi>
<mn>2</mn>
</mrow>
</msub>
</mrow>
Wherein NDVICCIThe vegetation index information result figure after cloud layer noise, NDVI are eliminated obtained by step S3-3YYIFor step
The vegetation index information result figure after shadow information, NDVI are eliminated obtained by S3-4B1And NDVIB2It is new for two in step S3-5
Two-value hum pattern.
A kind of 17. annual regional vegetation coverage computational methods as claimed in claim 1, it is characterised in that institute in step S4
Fusion method is stated to refer to synthesize multiple Mono temporal vegetation index information by wave band to obtain a shadow spatially to match
Picture.
A kind of 18. annual regional vegetation coverage computational methods as claimed in claim 1, it is characterised in that the step S5
Middle maximum synthetic method refers to be directed to same pixel, which has different numerical value in different phases, take in these values
Maximum, is maximum synthesis.
A kind of 19. annual regional vegetation coverage computational methods as claimed in claim 1, it is characterised in that institute in step S6
State numerical operation processing to refer to the area assignment for being -99 by numerical value in regional vegetation index information spatial distribution map obtained by step S5
For NAN.
A kind of 20. annual regional vegetation coverage computational methods as claimed in claim 1, it is characterised in that the step S7
Middle calculating vegetation coverage can estimate the method for vegetation coverage or mould using any type based on vegetation index information
Type..
21. a kind of annual regional vegetation coverage computational methods as claimed in claim 20, it is characterised in that described any one
The method or model of kind estimation vegetation coverage include common pixel dichotomy, neutral net mould in Decomposition of Mixed Pixels method
Type.
A kind of 22. annual regional vegetation coverage computational methods as claimed in claim 21, it is characterised in that the pixel two
Calculation of group dividing formula is as follows：
<mrow>
<mi>V</mi>
<mi>F</mi>
<mi>C</mi>
<mo>=</mo>
<mfrac>
<mrow>
<mo>(</mo>
<mi>N</mi>
<mi>D</mi>
<mi>V</mi>
<mi>I</mi>
<mo>-</mo>
<msub>
<mi>NDVI</mi>
<mrow>
<mi>s</mi>
<mi>o</mi>
<mi>i</mi>
<mi>l</mi>
</mrow>
</msub>
<mo>)</mo>
</mrow>
<mrow>
<mo>(</mo>
<msub>
<mi>NDVI</mi>
<mrow>
<mi>v</mi>
<mi>e</mi>
<mi>g</mi>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>NDVI</mi>
<mrow>
<mi>s</mi>
<mi>o</mi>
<mi>i</mi>
<mi>l</mi>
</mrow>
</msub>
<mo>)</mo>
</mrow>
</mfrac>
</mrow>
Wherein VFC be pixel in vegetation coverage, NDVI be the pixel on normalized differential vegetation index, NDVIsoilFor no vegetation
Cover the NDVI values of pixel, NDVIvegFor completely by the pixel NDVI values of vegetative coverage；In the present invention, with 0.5% confidence level
The upper lower threshold value of interception area NDVI represents NDVI respectivelyvegAnd NDVIsoil, and then zoning vegetation coverage VFC.
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