CN109684929A - Terrestrial plant ECOLOGICAL ENVIRONMENTAL MONITORING method based on multi-sources RS data fusion - Google Patents
Terrestrial plant ECOLOGICAL ENVIRONMENTAL MONITORING method based on multi-sources RS data fusion Download PDFInfo
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Abstract
The present invention relates to ECOLOGICAL ENVIRONMENTAL MONITORING fields, disclose a kind of terrestrial plant ECOLOGICAL ENVIRONMENTAL MONITORING method based on multi-sources RS data fusion, more efficiently to realize identification, classification and the dynamic monitoring of target area vegetation pattern.The present invention vertically shoots target area with unmanned plane, and generates unmanned aerial vehicle remote sensing three-dimensional modeling；The spectral remote sensing image for obtaining target area by satellite remote sensing again, satellite remote-sensing image is merged with the model that unmanned aerial vehicle remote sensing three-dimensional modeling generates；Fused image is calculated and divided again；The characteristic parameter index for participating in object oriented classification is determined again, and the vegetation pattern of monitoring is classified and extracted；Confusion matrix is constructed again, nicety of grading is evaluated using confusion matrix, extracts the size and distribution situation of all kinds of vegetation later；Comparative analysis finally is overlapped to the classification results of two phases, calculates the dynamic range and area of different vegetation types.The present invention is suitable for terrestrial plant ECOLOGICAL ENVIRONMENTAL MONITORING.
Description
Technical field
The present invention relates to ECOLOGICAL ENVIRONMENTAL MONITORING fields, in particular to the terrestrial plant ecology based on multi-sources RS data fusion
Method of environmental monitoring.
Background technique
Currently, China's ecological and environmental monitoring network existence range and element cover incomplete, construction plan, standard criterion and letter
Breath publication disunity, the level of IT application and degree of share be not high, and monitoring is combined not close with supervising, and monitoring data quality needs to be mentioned
High outstanding problem, it is difficult to meet Ecological Civilization Construction needs, affects the science, authority and government authority of monitoring,
It must accelerate to promote ecological and environmental monitoring network construction.
Simultaneously in recent years, UAV system to civil nature, miniaturization, modular development, make it the operation is more convenient,
Higher, the customizable degree of stability is higher, and most of type has had reached the standard of industrial application.Meanwhile unmanned plane low latitude
Remote sensing possesses higher spatial resolution compared with satellite remote sensing, and the more flexible activity duration, richer product achievement is more efficient
Flow chart of data processing, is affected by atmospheric effects smaller, has a good application prospect.On the other hand, computer hardware technique is winged
Speed development, under UAV system development and the drive of the market demand, powerful figure and data processing software layer go out not
Thoroughly, there is significant progress in terms of aerial images data processing, so that large-scale image data is quickly handled, extracts and answered
With being possibly realized.
The present invention is based on the data fusions of unmanned plane low altitude remote sensing image and satellite image, with object-oriented classification method
Research area vegetation is identified and classified, sufficiently excavates unmanned plane high resolution image and satellite multispectral image to vegetation
The potentiality of identification, classification further expand application of the unmanned plane Technology of low altitude remote sensing in terms of terrestrial plant ecological monitoring.
Summary of the invention
The technical problem to be solved by the present invention is providing a kind of terrestrial plant ecology ring based on multi-sources RS data fusion
Border monitoring method, more efficiently to realize identification, classification and the dynamic monitoring of target area vegetation pattern.
To solve the above problems, the technical solution adopted by the present invention is that: the terrestrial plant based on multi-sources RS data fusion
ECOLOGICAL ENVIRONMENTAL MONITORING method, includes the following steps:
Step 1 vertically shoots target area terrestrial plant with UAV flight's Visible Light Camera, the image of shooting
There must be certain degree of overlapping；
Step 2 matches the different angle of monitoring objective region acquisition, several raw video photos progress coordinate system of elevation
Quasi-, region entirety adjustment and multi-angle of view image dense Stereo Matching；
Step 3 carries out the extraction of image point cloud data to the image after matching；
Step 4 generates the TIN triangulation network according to image point cloud data, then carries out texture mapping, to realize area to be tested
Reconstructing three-dimensional model, generate the DSM digital surface model of area to be tested；
Step 5, the spectral remote sensing image that the corresponding target area with the unmanned plane time is obtained by satellite remote sensing；
Step 6 pre-processes the spectral remote sensing image of the target area of acquisition；
Step 7, the DSM digital surface mould for generating pretreated satellite remote-sensing image and unmanned aerial vehicle remote sensing three-dimensional modeling
Type carries out wave band and visual fusion, while retaining image spectral band and information, improves the spatial resolution of image, increases
The high level data wave band of image；
Step 8 is calculated and is divided automatically to fused image, and optimum segmentation scale is exported；
Step 9, according to the requirement and principle of practical terrestrial plant ECOLOGICAL ENVIRONMENTAL MONITORING, integrated spectral information, image texture,
Graphic structure and high level data are finely adjusted and correct to the optimum segmentation scale of output, obtain suitable target area terrestrial plant
The segmentation scale of ECOLOGICAL ENVIRONMENTAL MONITORING actual optimum；
Step 10, according to terrestrial plant ECOLOGICAL ENVIRONMENTAL MONITORING vegetation classification type, in conjunction with spectral information, image texture,
Shape and structure and high level data information determine the characteristic parameter index for participating in object oriented classification；
Step 11, the characteristic parameter index selected using step 10, to target area terrestrial plant ECOLOGICAL ENVIRONMENTAL MONITORING
Vegetation pattern is classified according to the type and requirement of regulation；
Step 12 extracts the vegetation classification classification of the target area after supervised classification；
Step 13, using the Accuracy Assessment based on location information, according to the number of samples of confusion matrix accuracy assessment
Calculation formula calculates the checkpoint number guaranteed under evaluation precision；
Step 14, the difficulty and clarity chosen according to actual samples point, are ensuring to meet evaluation precision minimum quantity
On the basis of it is required that, actual check point measurement number is determined, and test a little on the spot according to number and corresponding requirement
Measurement and statistics；
Step 15, building confusion matrix, calculate total nicety of grading and Kappa coefficient, comment the nicety of grading of model
Valence, it is ensured that whether the precision of classification meets practical terrestrial plant ECOLOGICAL ENVIRONMENTAL MONITORING and investigation result；
Step 16, on the fusion evaluation for meet nicety of grading according to classification type extract all kinds of vegetation size and
Distribution situation；
Step 17, the second phase image that same target area is calculated according to the step of appeal 1-16, obtain each of the second phase
The size and distribution situation of vegetation pattern；
The classification results of two phases are overlapped comparative analysis by step 18, calculate the dynamic change of different vegetation types
Range and area provide technical support to the data quantitativeization work of terrestrial plant ECOLOGICAL ENVIRONMENTAL MONITORING.
Further, step 1 when shooting, longitudinal overlap degree >=60%, sidelapping degree >=30%.
Further, step 2 may also include that
Step 201, system are handled several initial image photos using oblique photograph modeling software；
Step 202 carries out co-registration of coordinate systems used using the dominating pair of vertices image that RTK has been surveyed；
Step 203, oblique photograph software carry out region entirety adjustment and multi-angle of view image dense Stereo Matching to image.
Further, in step 4, TIN is further produced to the point off density cloud extracted in image using oblique photograph software
Then the triangulation network carries out texture mapping, produce DSM digital surface model, then exports DOM number positive photograph picture and DEM number height
Journey model.
Further, the spectral remote sensing image for the target area that step 6 pair obtains pre-processes can include: radiation school
Just, geometric correction, image mosaic and image are cut.When pretreatment:
Radiant correction (radiation calibration, atmospheric correction, landform spoke need to be carried out for more (height) spectrum images of original satellite remote sensing
Penetrate correction) and geometric correction, guarantee the accuracy of image spectrum and geological information；
According to unmanned aerial vehicle remote sensing take photo by plane area carry out more (height) spectrum images of satellite remote sensing splicing and cutting, guarantee target
The range in region is consistent.
Further, in step 7, first the spectral band of satellite remote sensing spectrum image data can be melted with panchromatic wave-band
It closes, improves the spatial resolution of spectral band；Again by spectral band and the fused satellite remote sensing spectrum image of panchromatic wave-band with
The digital orthoimage DOM of unmanned aerial vehicle remote sensing building is merged, and while retaining image spectral band and information, is further mentioned
The spatial resolution of high image, while increasing the high level data wave band of image.
Further, in step 8, fused image can be carried out according to ESP multi-scale division appraisal tool automatic
It calculates and divides.
Further, step 10 can be used nearest neighbor algorithm and exercise supervision classification.
Further, in step 10, spectral information may include NDVI index, LAI index, chlorophyll content in leaf blades, blade
Moisture content, the Brightness brightness value of multiple spectral bands, Std object wave band standard deviation and associated reflections attribute；Image
Texture may include the real-texture details of image textures；Shape and structure may include Border index boundary index, Density close
Degree and Length/Width length-width ratio.
Further, it in step 15, is calculated, is obtained using the image error matrix that object oriented classification software produces
Producer's precision, user's precision, total nicety of grading and Kappa coefficient out.
The beneficial effects of the present invention are: in the present invention, unmanned plane Technology of low altitude remote sensing has that timeliness is strong, precision is high, model
Enclose the advantages such as wide and noncontacting measurement.The threedimensional model of building can accurately measure the indexs such as volume, area and length, can export
The data type of the general formats such as DEM digital elevation model, DOM digital orthoimage and DSM digital surface model, having can grasp
The property made is strong, and equipment cost is lower, and sampling rate is fast, precision and the features such as high resolution；Satellite remote-sensing image is merged simultaneously, is made point
The characteristic index of class image is more comprehensively and perfect, includes more (height) spectral informations, textured pattern, shape and structure and high-layer
According to etc..
The method of object oriented classification can be different from traditional Remote Image Classification, be no longer limited to single spectrum
Identification and differential counting joined the characteristic indexs such as textured pattern, shape and structure and high level data, and utilize ESP optimum segmentation
Dimensional analysis tool is established and is formulated the optimum segmentation scale under different target area image, it is raw to utilize it as terrestrial plant
State environmental monitoring sorting technique has apparent advantage in ECOLOGICAL ENVIRONMENTAL MONITORING.
The present invention is based on the technologies such as multi-sources RS data fusion and object oriented classification, substantially increase terrestrial plant ecology
Ability that is accurate in environmental monitoring work, efficiently, quantitatively obtaining, analyze, calculate and handle different vegetation monitoring index data, together
When also the revolutionary bandwagon effect for changing Monitoring Result and mode.On the whole, distant based on unmanned plane low-altitude remote sensing, satellite
The application of sense data fusion and the terrestrial plant ecological monitoring technology of object oriented classification can fill up ECOLOGICAL ENVIRONMENTAL MONITORING work
In technological gap to vegetation Quantitative Monitoring content without conventional monitoring method and means, and field operation monitoring is greatly improved
The degree of automation.
Detailed description of the invention
Fig. 1 is the flow chart of embodiment.
Specific embodiment
The present invention is directed to the inverting of satellite remote sensing data mapping (the multispectral, EO-1 hyperion, laser thunder proposed in the prior art
Reach and synthetic aperture radar) and several monitoring methods such as field investigation all have that applicability is poor, and accuracy is lower asks
Topic provides a kind of terrestrial plant ecology prison based on unmanned plane low-altitude remote sensing, satellite remote sensing date fusion and object oriented classification
Survey method, first, system is with fixed-wing UAV flight Visible Light Camera to target area according to design air strips, height and sortie
It is vertically shot, while acquisition is uniformly distributed the control point with different elevations.Second, system obtains monitoring objective region
Different angle, several raw video photos progress co-registration of coordinate systems used of elevation, region entirety adjustment and multi-angle of view image intensive
Match, generate point off density cloud, further generates the TIN triangulation network by texture mapping and generate the true three-dimension mould of area to be tested
Type DSM.Third, system obtain with the fixed-wing unmanned plane time corresponding target area image quality by satellite remote sensing
Preferable more (height) spectral remote sensing images, and carry out radiant correction (radiation calibration, atmospheric correction, terrain radiant correction), geometry
The pretreatments such as correction, image mosaic and image cutting.4th, system is by pretreated satellite remote-sensing image and unmanned aerial vehicle remote sensing
The digital surface model DSM that three-dimensional modeling generates carries out wave band and visual fusion, is retaining more (height) spectral bands of image and letter
While breath, the spatial resolution of image is improved, increases the high level data wave band of image.5th, system is according to ESP
(Estimation of Scale Parameter) multi-scale division appraisal tool to fused image carry out it is automatic calculate and
Segmentation, the optimum segmentation scale that output ESP tool analysis obtains.And comprehensive more (height) spectral informations, image texture, graphic structure
The optimum segmentation scale that ESP is exported is finely adjusted and is corrected with high level data, obtains suitable target area terrestrial plant ecology
The segmentation scale of environmental monitoring actual optimum.6th, type (packet of the system according to terrestrial plant ECOLOGICAL ENVIRONMENTAL MONITORING vegetation classification
Include draft, shrub, arbor, wherein arbor further divides into several typical tree species), in conjunction with spectral information, image texture,
The information such as shape and structure and high level data determine the characteristic parameter index for participating in object oriented classification；It is carried out using nearest neighbor algorithm
Supervised classification is classified and is extracted according to the type and requirement of regulation to the vegetation pattern of terrestrial plant ECOLOGICAL ENVIRONMENTAL MONITORING.
7th, system uses Accuracy Assessment and confusion matrix based on location information, calculates total nicety of grading and Kappa coefficient pair
The nicety of grading of model is evaluated.8th, system is extracted on the fusion evaluation for meet nicety of grading according to classification type each
The size and distribution situation of class vegetation.9th, the second phase image of same target area is handled after the same method, is obtained
The size and distribution situation of each vegetation pattern of the second phase out.Finally, being overlapped the classification results of two phases to score
Analysis, calculates the dynamic range and area of different vegetation types, to the data quantitative of terrestrial plant ECOLOGICAL ENVIRONMENTAL MONITORING
Work provides technical support.Unmanned plane low-altitude remote sensing and object oriented classification technology have strong operability, and equipment cost is lower,
The advantages such as sampling rate is fast, precision and the higher, noncontacting measurement of resolution ratio, while satellite remote-sensing image is merged, make shadow of classifying
The characteristic index of picture is more comprehensively and perfect, includes more (height) spectral informations, textured pattern, shape and structure and high level data
Deng.Utilize it as terrestrial plant ECOLOGICAL ENVIRONMENTAL MONITORING sorting technique has apparent advantage in ECOLOGICAL ENVIRONMENTAL MONITORING.
On the whole, the terrestrial plant ecological monitoring based on unmanned plane low-altitude remote sensing, satellite remote sensing date fusion and object oriented classification
The application of technology can be filled up in ECOLOGICAL ENVIRONMENTAL MONITORING work to vegetation Quantitative Monitoring content without conventional monitoring method and means
Technological gap, and the degree of automation of field operation monitoring is greatly improved.
Embodiment:
The technical scheme of the present invention will be further described with reference to the accompanying drawings and embodiments, and embodiment is just for the sake of side
It helps reader to more fully understand technical solution of the present invention, is not intended to limit the invention scope of protection of the claims.
It is provided by the invention to be based on the technologies such as multi-sources RS data fusion and object oriented classification, substantially increase terrestrial plant
It is accurate in the work of object ECOLOGICAL ENVIRONMENTAL MONITORING, efficiently, quantitatively obtain, analyze, calculate and handle different vegetation monitoring index data
Ability, while the also revolutionary bandwagon effect for changing Monitoring Result and mode.It fills up in ECOLOGICAL ENVIRONMENTAL MONITORING work to plant
Technological gap by Quantitative Monitoring content without conventional monitoring method and means, and the automatic of field operation monitoring is greatly improved
Change degree establishes solid foundation for the informationization of terrestrial plant ECOLOGICAL ENVIRONMENTAL MONITORING.
As shown in Figure 1, the first step understands the feelings such as geographical terrain, vegetative coverage, the water system in monitoring objective region in this example
Air strips, sortie and the elevation of aerial survey have been planned in condition, the requirements such as binding model scale bar, precision in advance.Using fixed-wing unmanned plane
Visible Light Camera is carried vertically to shoot target area.While it being controlled according to being uniformly distributed with the requirement of different elevations
Point measurement guarantees to obtain the precision after image modeling.Image Acquisition tool can be airborne single-lens reflex camera or digital camera, as long as can
The image capture device for meeting mechanism and image processing requirements all can be used as Image Acquisition tool use.
In this example, it is used as to the ecological sensitive areas of a 3000m*10000m selected first monitoring objective region, does 60
Size be 2m*2m control point sign board, be uniformly placed in monitoring region and different elevations (position of big rise and fall will place,
The progressive position of height step will place), and measurement and note by RTK to the central point progress absolute coordinate at 60 control points
Record.Aerial survey is carried out to monitoring region with unmanned plane low-latitude flying, obtains initial image photo.The specific method is as follows:
Secondly, be flying height 800m to the aerial survey parameter in monitoring region, flight air strips 4, ground resolution 1m, than
Example ruler 1:1000,90 ° of vertical shooting angle, longitudinal air strips degree of overlapping 70% are other to 35%.
Then, the initial image photo obtained is handled using oblique photograph modeling software, by the ground of measurement
Control point carries out co-registration of coordinate systems used, then region entirety adjustment and multi-angle of view image dense Stereo Matching, generates point off density cloud, then into one
Step generates the TIN triangulation network and generates the true three-dimension model of area to be tested by texture mapping.
Second step is obtained with more (height) spectroscopic datas of unmanned plane time consistent satellite, and the image cloud amount that is averaged is small
In 15%, and image quality is preferable, wherein more (height) spectral band spatial resolution≤30m, panchromatic wave-band spatial resolution≤
15m, spectral band number >=8, and pass through radiant correction (radiation calibration, atmospheric correction, terrain radiant correction), geometric correction, shadow
As inlaying and the pretreatments such as image cutting.
Third step, the digital surface that system generates pretreated satellite remote-sensing image and unmanned aerial vehicle remote sensing three-dimensional modeling
Model DOM carries out wave band and visual fusion, while retaining more (height) spectral bands of image and information, improves the space of image
Resolution ratio increases the high level data wave band of image.
Fused image is imported object oriented classification software, according to ESP (Estimation of Scale by the 4th step
Parameter) multi-scale division appraisal tool is calculated and is divided, the optimum segmentation scale that output ESP tool analysis obtains
It is 123,225 and 514 3 layers.According still further to the requirement and principle of terrestrial plant ECOLOGICAL ENVIRONMENTAL MONITORING, comprehensive more (height) spectral informations,
Image texture, graphic structure and high level data are finely adjusted and correct to the optimum segmentation scale that ESP is exported, and obtain suitable target
The segmentation scale of region terrestrial plant ECOLOGICAL ENVIRONMENTAL MONITORING actual optimum is 123,225 and 514.
5th step, type of the system according to terrestrial plant ECOLOGICAL ENVIRONMENTAL MONITORING vegetation classification, believes in conjunction with multiwave spectrum
Breath, image surface real-texture and Border index boundary index, Density density, the shapes such as Length/Width length-width ratio
Shape structural information determines that the characteristic parameter index for participating in object oriented classification is NDVI index, LAI index, leaf chlorophyll contain
Measure (Cab) and leaf water content (Cw), Brightness brightness value, Std object wave band standard deviation, the boundary Border index
Index, Density density, Length/Width length-width ratio and altitude data etc. are exercised supervision classification using nearest neighbor algorithm, to land
The vegetation pattern of plant ECOLOGICAL ENVIRONMENTAL MONITORING is classified and is extracted according to the type and requirement of regulation.
6th step uses Accuracy Assessment and confusion matrix based on location information, meter to the classification image after extraction
Total nicety of grading and Kappa coefficient are calculated, the nicety of grading of model is evaluated.Wherein total nicety of grading is 77.78%,
Kappa coefficient is 0.752, meets required precision.
Finally, handling the second phase image of same target area after the same method, each vegetation class of the second phase is obtained
The size and distribution situation of type, and comparative analysis is overlapped to the classification results of two phases, calculate different vegetation types
Dynamic range and area.Total nicety of grading of second phase image is that 80.14%, Kappa coefficient is 0.773, meets essence
Degree requires.Specific two issue is shown in Table 1 according to result of variations:
Target area terrestrial plant two phases dynamic monitoring changing value
1 hm of table2
Unmanned plane low-altitude remote sensing and object oriented classification technology are a kind of strong operabilities, at low cost, the higher synthesis of precision
Computer image processing technology.The point cloud data that target area can be extracted from the photo to material object shooting, to generate
The data such as the TIN triangulation network, DEM/DOM realize quick reconstructing three-dimensional model.Satellite remote-sensing image is merged simultaneously, makes shadow of classifying
The characteristic index of picture is more comprehensively and perfect, includes more (height) spectral informations, textured pattern, shape and structure and high level data
Deng.Divided using the optimal scale that target image may be implemented in object oriented classification technology and the image of multi-source index divides automatically
Class, while guaranteeing higher nicety of grading, thus completely, accurately obtain different times monitoring objective region difference vegetation
The situation of change of type.New monitoring technology has the advantages such as fast sampling rate, precision and the higher, noncontacting measurement of resolution ratio.By
This, it is believed that the terrestrial plant ecology prison based on unmanned plane low-altitude remote sensing, satellite remote sensing date fusion and object oriented classification
Survey method possesses preferable application prospect in ECOLOGICAL ENVIRONMENTAL MONITORING, and quantifying for terrestrial plant dynamic monitoring not only may be implemented
Change, real time implementation and digitized processing, analysis and displaying, and substantially increase the automatic of terrestrial plant ECOLOGICAL ENVIRONMENTAL MONITORING work
Change, information-based ability, provides more full and accurate data for terrestrial plant investigation, the design of terrestrial plant environmental protective measure etc..
Claims (10)
1. the terrestrial plant ECOLOGICAL ENVIRONMENTAL MONITORING method based on multi-sources RS data fusion, which comprises the steps of:
Step 1 vertically shoots target area terrestrial plant with UAV flight's Visible Light Camera, and the image of shooting must have
There is certain degree of overlapping；
Step 2, to monitoring objective region obtain different angle, elevation several raw video photos carry out co-registration of coordinate systems used,
Region entirety adjustment and multi-angle of view image dense Stereo Matching；
Step 3 carries out the extraction of image point cloud data to the image after matching；
Step 4 generates the TIN triangulation network according to image point cloud data, then carries out texture mapping, to realize the three of area to be tested
Dimension module is rebuild, and the digital surface model of area to be tested is generated；
Step 5, the spectral remote sensing image that the corresponding target area with the unmanned plane time is obtained by satellite remote sensing；
Step 6 pre-processes the spectral remote sensing image of the target area of acquisition；
Step 7 carries out the digital surface model that pretreated satellite remote-sensing image and unmanned aerial vehicle remote sensing three-dimensional modeling generate
Wave band and visual fusion improve the spatial resolution of image, increase image while retaining image spectral band and information
High level data wave band；
Step 8 is calculated and is divided automatically to fused image, and optimum segmentation scale is exported；
Step 9, according to the requirement and principle of practical terrestrial plant ECOLOGICAL ENVIRONMENTAL MONITORING, integrated spectral information, image texture, figure
Structure and high level data are finely adjusted and correct to the optimum segmentation scale of output, obtain suitable target area terrestrial plant ecology
The segmentation scale of environmental monitoring actual optimum；
Step 10, the type according to terrestrial plant ECOLOGICAL ENVIRONMENTAL MONITORING vegetation classification, in conjunction with spectral information, image texture, shape
Structure and high level data information determine the characteristic parameter index for participating in object oriented classification；
Step 11, the characteristic parameter index selected using step 10, to the vegetation of target area terrestrial plant ECOLOGICAL ENVIRONMENTAL MONITORING
Type is classified according to the type and requirement of regulation；
Step 12 extracts the vegetation classification classification of the target area after supervised classification；
Step 13, using the Accuracy Assessment based on location information, calculated according to the number of samples of confusion matrix accuracy assessment
Formula calculates the checkpoint number guaranteed under evaluation precision；
Step 14, the difficulty and clarity chosen according to actual samples point, are ensuring to meet evaluation precision minimum quantity requirement
On the basis of, determine actual check point measurement number, and require according to number and accordingly the field survey tested a little
And statistics；
Step 15, building confusion matrix, calculate total nicety of grading and Kappa coefficient, evaluate the nicety of grading of model, really
Whether the precision for protecting classification meets practical terrestrial plant ECOLOGICAL ENVIRONMENTAL MONITORING and investigation result；
Step 16, size and the distribution for extracting all kinds of vegetation according to classification type on the fusion evaluation for meet nicety of grading
Situation；
Step 17, the second phase image that same target area is calculated according to the step of appeal 1-16, obtain each vegetation of the second phase
The size and distribution situation of type；
The classification results of two phases are overlapped comparative analysis by step 18, calculate the dynamic range of different vegetation types
And area.
2. the terrestrial plant ECOLOGICAL ENVIRONMENTAL MONITORING method based on multi-sources RS data fusion as described in claim 1, feature
Be, step 1 when shooting, longitudinal overlap degree >=60%, sidelapping degree >=30%.
3. the terrestrial plant ECOLOGICAL ENVIRONMENTAL MONITORING method based on multi-sources RS data fusion as described in claim 1, feature
It is, step 2 further include:
Step 201, system are handled several initial image photos using oblique photograph modeling software；
Step 202 carries out co-registration of coordinate systems used using the dominating pair of vertices image that RTK has been surveyed；
Step 203, oblique photograph software carry out region entirety adjustment and multi-angle of view image dense Stereo Matching to image.
4. the terrestrial plant ECOLOGICAL ENVIRONMENTAL MONITORING method based on multi-sources RS data fusion as described in claim 1, feature
Be, in step 4, the TIN triangulation network further produced to the point off density cloud extracted in image using oblique photograph software, then into
Row texture mapping produces DSM digital surface model, then exports DOM number positive photograph picture and DEM digital elevation model.
5. the terrestrial plant ECOLOGICAL ENVIRONMENTAL MONITORING method based on multi-sources RS data fusion as described in claim 1, feature
It is, it includes: radiant correction, geometric correction, image that the spectral remote sensing image for the target area that step 6 pair obtains, which carries out pretreatment,
It inlays and is cut with image.
6. the terrestrial plant ECOLOGICAL ENVIRONMENTAL MONITORING method based on multi-sources RS data fusion as described in claim 1, feature
It is, in step 7, first merges the spectral band of satellite remote sensing spectrum image data with panchromatic wave-band, improves spectrum wave
The spatial resolution of section；Spectral band and the fused satellite remote sensing spectrum image of panchromatic wave-band and unmanned aerial vehicle remote sensing are constructed again
Digital orthoimage DOM merged, retain image spectral band and while information, further increase the space point of image
Resolution, while increasing the high level data wave band of image.
7. the terrestrial plant ECOLOGICAL ENVIRONMENTAL MONITORING method based on multi-sources RS data fusion as described in claim 1, feature
It is, in step 8, fused image is calculated and divided automatically according to ESP multi-scale division appraisal tool.
8. the terrestrial plant ECOLOGICAL ENVIRONMENTAL MONITORING method based on multi-sources RS data fusion as described in claim 1, feature
It is, step 10 is exercised supervision classification using nearest neighbor algorithm.
9. the terrestrial plant ECOLOGICAL ENVIRONMENTAL MONITORING method based on multi-sources RS data fusion as described in claim 1, feature
It is, in step 10, spectral information includes NDVI index, LAI index, chlorophyll content in leaf blades, leaf water content, Duo Geguang
Compose Brightness brightness value, Std object wave band standard deviation and the associated reflections attribute of wave band；Image texture includes image textures
Real-texture details；Shape and structure includes Border index boundary index, Density density and Length/Width long
Wide ratio.
10. the terrestrial plant ECOLOGICAL ENVIRONMENTAL MONITORING method based on multi-sources RS data fusion as described in claim 1, feature
Be, in step 15, using object oriented classification software produce image error matrix calculated, obtain producer's precision,
User's precision, total nicety of grading and Kappa coefficient.
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