CN109615166A - A kind of steppe vegetation degeneration remote-sensing monitoring method and device - Google Patents
A kind of steppe vegetation degeneration remote-sensing monitoring method and device Download PDFInfo
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Abstract
The embodiment of the present invention provides a kind of steppe vegetation degeneration remote-sensing monitoring method and device, this method comprises: based on remotely-sensed data obtain grassland vegetation coverage and naked husky area ratio, the biomass based on remotely-sensed data and ground sample data acquisition grassland；Based on vegetation coverage, naked husky area ratio and the biomass weight coefficient shared when reflecting steppe vegetation situation, steppe vegetation composite index GVSI is obtained.The Multiple factors attributes, more fully reflection steppe vegetation degraded condition such as the vegetation coverage, biomass and naked husky area ratio on grassland are comprehensively considered；It avoids monofactor and carries out the problem that grass-land deterioration remote sensing monitoring evaluation precision is low, stability is poor；The degradation level of calculating is small to ratio error with on-site inspection degradation level, combines many factors, and evaluation is more efficient, has more reliability, helps to promote and apply.
Description
Technical field
The present embodiments relate to ecological monitoring technical fields, supervise more particularly, to a kind of steppe vegetation degeneration remote sensing
Survey method and apparatus.
Background technique
Different degrees of degeneration has occurred using natural grasslands for the whole nation 90%, and wherein the medium above degree occurs in half
Degeneration, grassland ecology problem is severe.If grassland in Xinjiang distributed areas are wide, it is not only the main support of pasture animal husbandry and enriches
Biological gene library, also safeguard ecological safety, protection oasis and in terms of have important Ecology Action.Grassland
Vegetation is various plants or the general term of phytobiocoenose for being covered in grassland region and being suitable for environmental condition, and ecological ragime is that ground border is planted
By the similarity degree with the ground border climax community, steppe vegetation ecological ragime is the overall state of steppe vegetation, and reflection grassland is planted
The health degree or degree of degeneration of quilt, are usually indicated with steppe vegetation coverage, biomass etc..
Conventional method by sample field investigation obtains the information such as vegetation coverage, biomass, to steppe vegetation ecology shape
Condition is evaluated.Steppe vegetation coverage, biomass etc. are the single index for reflecting steppe vegetation situation, can be never ipsilateral
Steppe vegetation feature is reflected in face, but the stability of each single index is easy to be influenced by vegetation and environmental change.
Carrying out grass-land deterioration evaluation using remote sensing technology has the apparent advantages such as quick, large scale, but due to grassland class
Type is complicated, environmental modification is big, and the single remote sensing of the vegetation key parameters such as steppe vegetation coverage, biomass is calculated with remotely-sensed data
Model, influence of the stability of index vulnerable to vegetation and environmental change, it is difficult to accurate evaluation large scale, complicated grassland characteristic area
Steppe vegetation degraded condition.
Summary of the invention
The embodiment of the present invention provides a kind of a kind of grassland for overcoming the above problem or at least being partially solved the above problem
Vegetation degeneration remote-sensing monitoring method and device.
In a first aspect, the embodiment of the present invention provides a kind of steppe vegetation degeneration remote-sensing monitoring method, comprising:
Vegetation coverage and naked husky area ratio based on remotely-sensed data acquisition grassland, are based on remotely-sensed data and ground sample number
According to the biomass for obtaining grassland；
Based on vegetation coverage, naked husky area ratio and the biomass weight coefficient shared when reflecting steppe vegetation situation,
Obtain steppe vegetation composite index GVSI.
Second aspect, the embodiment of the present invention provide a kind of steppe vegetation remote sensing monitoring device, comprising:
Parameter monitoring module, for obtained based on remotely-sensed data grassland vegetation coverage and naked husky area ratio, based on distant
Feel the biomass of data and ground sample data acquisition grassland；
Index analysis module, for reflecting steppe vegetation situation based on vegetation coverage, naked husky area ratio and biomass
The weight coefficient of Shi Suozhan obtains steppe vegetation composite index GVSI.
The third aspect, the embodiment of the present invention provides a kind of electronic equipment, including memory, processor and is stored in memory
Computer program that is upper and can running on a processor, is realized when the processor executes described program as first aspect provides
Method the step of.
Fourth aspect, the embodiment of the present invention provide a kind of non-transient computer readable storage medium, are stored thereon with calculating
Machine program is realized as provided by first aspect when the computer program is executed by processor the step of method.
The embodiment of the present invention proposes a kind of steppe vegetation degeneration remote-sensing monitoring method and device, has comprehensively considered grassland
The Multiple factors attributes, more fully reflection steppe vegetation degraded condition such as vegetation coverage, biomass and naked husky area ratio；It avoids
Monofactor carries out the problem that grass-land deterioration remote sensing monitoring evaluation precision is low, stability is poor；The degradation level of calculating with adjust on the spot
It is small to ratio error to look into degradation level, combines many factors, evaluation is more efficient, has more reliability, helps to promote and apply.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is this hair
Bright some embodiments for those of ordinary skill in the art without creative efforts, can be with root
Other attached drawings are obtained according to these attached drawings.
Fig. 1 is the steppe vegetation degeneration remote-sensing monitoring method schematic diagram according to the embodiment of the present invention；
Fig. 2 is the steppe vegetation degeneration remote-sensing monitoring method idiographic flow schematic diagram according to the embodiment of the present invention；
Fig. 3 is the steppe vegetation degeneration remote sensing monitoring schematic device according to the embodiment of the present invention；
Fig. 4 is the entity structure schematic diagram according to the electronic equipment of the embodiment of the present invention.
Specific embodiment
In order to make the object, technical scheme and advantages of the embodiment of the invention clearer, below in conjunction with the embodiment of the present invention
In attached drawing, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that described embodiment is
A part of the embodiment of the present invention, instead of all the embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art
Every other embodiment obtained without creative efforts, shall fall within the protection scope of the present invention.
Remote sensing technology provides effective way and technology branch for a wide range of, quick, accurate measurements steppe vegetation ecological ragime
Support, it has also become the important means of steppe vegetation condition monitoring.Grass-land deterioration remote sensing monitoring evaluation key technology first is that establish
Steppe vegetation integrated status remote sensing monitoring and method, by remotely-sensed data inverting steppe vegetation key parameter, by each vegetation
Key parameter carries out organic coupling, constructs steppe vegetation integrated status index model, realizes steppe vegetation degeneration remote sensing monitoring
Quick and precisely evaluate.
Carrying out grass-land deterioration evaluation using remote sensing technology has the apparent advantages such as quick, large scale, but due to grassland class
Type is complicated, environmental modification is big, and the single remote sensing of the vegetation key parameters such as steppe vegetation coverage, biomass is calculated with remotely-sensed data
Model, influence of the stability of index vulnerable to vegetation and environmental change, it is difficult to accurate evaluation large scale, complicated grassland characteristic area
Steppe vegetation degraded condition.
Due to influence of the stability vulnerable to vegetation and environmental change of single Remote Sensing Model index, the present invention is respectively implemented
Example provides solution in the progress steppe vegetation condition monitoring evaluation of multiple indexs, has specially comprehensively considered steppe vegetation
The Multiple factors attributes, more fully reflection steppe vegetation degraded condition such as coverage, grassland biomass and naked husky area ratio.Below will
Expansion explanation and introduction are carried out by multiple embodiments.
Fig. 1 is a kind of steppe vegetation degeneration remote-sensing monitoring method flow chart provided in an embodiment of the present invention, comprising:
S1, the vegetation coverage that grassland is obtained based on remotely-sensed data and naked husky area ratio, are based on remotely-sensed data and ground sample
The biomass on square data acquisition grassland；
S2, based on vegetation coverage, naked husky area ratio and the biomass weight system shared when reflecting steppe vegetation situation
Number, obtains steppe vegetation composite index GVSI.
In the present embodiment, comprehensively considered the Multiple factors attributes such as steppe vegetation coverage, biomass and naked husky area ratio,
More fully reflect steppe vegetation degraded condition, overcomes the single-factors remote sensing monitoring mould such as steppe vegetation biomass, vegetation coverage
The unstability of type avoids monofactor and carries out the problem that grass-land deterioration remote sensing monitoring evaluation precision is low, stability is poor.
Specifically, in the present embodiment, it can be by establishing high-precision steppe vegetation coverage, grassland biology respectively
The single-factor computation model of amount, naked husky area ratio, carries out dimensionless standardization to single-factor and assigns weight, construct grassland
Vegetation Synthesized Index Model is monitored evaluation to steppe vegetation degraded condition.
In the present embodiment, as shown in Figure 2, further includes:
S0, the remotely-sensed data and ground sample data for obtaining grassland to be monitored；
On the basis of the various embodiments described above, the vegetation coverage on grassland is obtained based on remotely-sensed data, is specifically included:
S111, the pure exposed soil normalized differential vegetation index that grassland is determined based on Types of Grassland and the normalization of pure vegetative coverage pixel
Vegetation index；And the vegetation index NDVI on grassland is obtained based on remotely-sensed data；
S112, based on above-mentioned pure exposed soil normalized differential vegetation index, above-mentioned pure vegetative coverage pixel normalized differential vegetation index and
Above-mentioned NDVI obtains the vegetation coverage on grassland by vegetation coverage remote sensing appraising model.
Specifically, in the present embodiment, based on the vegetation index (Normalized obtained in remotely-sensed data
Difference Vegetation Index, NDVI), using Pixel scrambling method, the vegetative coverage on grassland is calculated
It spends (Fractional Vegetation Cover, FVC), shown in calculation formula such as following formula (1):
In above formula, FVC is vegetation coverage, and NDVI is vegetation index, NDVIsoilIt is (or naked for pure exposed soil
It is husky) normalized differential vegetation index, NDVIvegPure vegetative coverage pixel normalized differential vegetation index.
Specifically, in the present embodiment, before step S111 further include:
S110, according to different type, determine the NDVI of each Types of GrasslandsoilAnd NDVIveg, by Pixel scrambling side
Method constructs the vegetation coverage remote sensing appraising model of different Types of Grassland respectively；
By the Types of Grassland on grassland to be monitored, it is based on corresponding vegetation coverage remote sensing appraising model, can directly be led to
It crosses remotely-sensed data and obtains the vegetation coverage on the grassland, it is simple direct.
On the basis of the various embodiments described above, as shown in Fig. 2, obtaining the naked husky area ratio on grassland, tool based on remotely-sensed data
Body includes:
S121, the Pure pixel index in Multi-spectral Remote Sensing Data is obtained based on minimal noise separation transform method MNF
PPI, and pure naked husky endmember, vegetation endmember and soil endmember and above-mentioned naked are obtained based on above-mentioned PPI
Husky endmember, above-mentioned vegetation endmember and the corresponding spectrum of above-mentioned soil endmember；
S122, it is based on established linear spectral mixture model, to above-mentioned naked husky endmember, above-mentioned vegetation endmember
Pixel analysis is carried out with above-mentioned soil endmember, obtains naked husky area ratio.
Specifically, separating transform method (Minimum Noise Fraction Rotation, MNF using minimal noise
Rotation), the pure index of pixel (Pixel Purity Index, PPI) is calculated, based on PPI obtain it is pure it is naked it is husky,
Endmember input linear spectral mixing model obtained is carried out pixel by vegetation, soil three classes endmember and its wave spectrum
It decomposes, obtains the abundance figure and root-mean-square value (RMS) distribution map of each end member component.
In the present embodiment, MNF is substantially the principal component transform being laminated twice.Transformation (making an uproar based on estimation for the first time
Sound covariance matrix) for separating and readjusting the noise in Multi-spectral Remote Sensing Data, this step operation makes transformed noise
The only the smallest of variance of data and without the correlation between wave band.Second step is to noise whitening data (Noise-whitened)
Standard principal component transform.For the processing of further progress wave spectrum, data are determined by checking final characteristic value and associated picture
Inherent dimension.Data space can be divided into two parts: it is a part of related to larger characteristic value and corresponding characteristic image,
Remaining part point is related to approximately uniform characteristic value and the prevailing image of noise.
In the present embodiment, after S121 further include:
It is tested by the naked Shamian Island product comparison linear spectral mixture model of sample-plot survey on the spot, model parameter adjustment,
It is final to realize the naked husky area ratio remote sensing monitoring in grassland.
On the basis of the various embodiments described above, before above-mentioned steps S121 further include:
S120, it is based on Multi-spectral Remote Sensing Data, using naked sand, vegetation, soil as end-member composition, building indicates each end-member composition
The linear spectral mixture model of spectrum and Multi-spectral Remote Sensing Data relationship characteristic；
Based on Multi-spectral Remote Sensing Data, using linear spectral mixture model method, using naked sand, vegetation, soil as end member group
Point, end member feature is analyzed, the relationship characteristic between naked Shamian Island product spectral signature and remotely-sensed data information is determined, constructs the naked Shamian Island in grassland
The high-precision linear spectral mixing model of product ratio.
On the basis of the various embodiments described above, as shown in Fig. 2, based on remotely-sensed data and ground sample data acquisition grassland
Biomass specifically includes:
S130, longitude and latitude and acquisition time based on ground sample data, establish many years sample prescription biomass and corresponding time
Vegetation index NDVI remotely-sensed data collection；
S131, it is based on above-mentioned remotely-sensed data collection, subregion divides Types of Grassland to construct grassland biomass remote sensing monitoring mould respectively
Type；
S132, according to grassland biomass remote sensing monitoring, based on remotely-sensed data and ground sample data acquisition grassland
Biomass.
In the present embodiment, step S130 is based on remotely-sensed data NDVI product and ground sample data, according to ground sample
Longitude and latitude and the sampling time, establish NDVI remotely-sensed data collection of many years sample prescription biomass with the corresponding time.
In step 131, in view of the complexity of soil type, subregion divides Types of Grassland to construct unitary line respectively
The grassland biomass remote sensing monitoring such as property, exponential function, power function；By model debugging, choice of parameters and etc. complete it is each
The modeling process of region grassland biomass remote sensing monitoring.
In the present embodiment, specifically, unitary linear model formula: Y=a × NDVI+b
Exponential Function Model formula: Y=aNDVI×b；
Power function model formula: Y=a × NDVIb；
Wherein, Y is biomass, and a, b are constant.
On the basis of the above embodiments, after S131 further include:
1/4 reserved at random sample prescription data are selected to carry out precision test to grassland biomass remote sensing monitoring, using flat
Equal absolute error and average relative error two indices are evaluated, and finally determine the optimal grassland biomass remote sensing prison in each region
Survey model.
On the basis of the various embodiments described above, before obtaining steppe vegetation composite index GVSI, further includes:
S201, using above-mentioned vegetation coverage, above-mentioned naked husky area ratio and above-mentioned biomass as the factor, building reflection grassland is planted
It is carried out by the vegetation coverage factor of situation, naked Shamian Island product specific factor and biomass factor, and to above-mentioned naked Shamian Island product specific factor
Numerical value reversion goes reversion postfactor to be used as the practical factor；
S202, nondimensionalization processing is carried out to each factor；
S203, the weight coefficient that each factor is determined based on the analytic hierarchy process (AHP) of expert estimation.
In the present embodiment, specifically, step S201 is specifically included:
Predictor selection selects the vegetation coverage, biomass and naked husky three parameters of area ratio on grassland as building model
The factor, and numerical value reversion is carried out to above-mentioned naked Shamian Island product specific factor, reversion postfactor is gone to be used as the practical factor；That is vegetative coverage
Degree, biomass and naked Shamian Island product ratio are respectively as single-factor；
Specifically, the parameter of reflection steppe vegetation situation is numerous, such as vegetation coverage, biomass, vegetation height, naked Shamian Island
Product ratio, floristics etc., according to the research to grass-land deterioration feature, while considering the difficulty or ease of each parameter remote inverting, finally
Select the single-factor of the vegetation coverage, biomass and naked husky three parameters of area ratio on grassland as building model.Wherein, naked sand
Area specific factor is due to being the parameter for reflecting the non-vegetative coverage in grassland, for the realistic meaning of reflection steppe vegetation situation,
The factor of " reversed " effect is served as in GVSI model.Therefore, numerical value reversion is carried out to naked Shamian Island product specific factor (S), negated after turning
Factor SvegThe practical factor of=1-S as building model.
Specifically, step S202 is specifically included:
Min-max standardized method is selected to carry out nondimensionalization processing to each single-factor.Since each single-factor is single in measurement
It is had differences on position, needs to eliminate the influence of different dimensions before constructing GVSI model.
Min-max standardized method is to carry out linear transformation to initial data.If minA and maxA be respectively attribute A most
An original value x of A is mapped to the value x' in section [0,1] by min-max standardization by small value and maximum value；Consider
Unit to vegetation coverage is percentage, the unit of naked husky (soil) cover degree after inverting also is percentage, and the two takes
Value range is all [0,1], therefore, carries out min-max standardization to biomass factor, its original value X is mapped to section
Value X' in [0,1], shown in formula such as following formula (2):
In formula, X' is the mapping value of single-factor after standardization, and X is the original value of single-factor, and Xmin and Xmax are respectively single
The minimum value and maximum value of factor figure layer grid point value.
Specifically, step S203 is specifically included:
The weight that each factor is determined using the analytic hierarchy process (AHP) based on expert estimation, in GVSI model, single-factor weight
The numerical value of each single-factor effect degree in a model and model output result will be directly affected by being arranged, in the present embodiment,
The proportion range of the vegetation coverage factor on grassland is [0.35-0.45], the proportion range of biomass factor is [0.25-
0.35], the proportion range of naked Shamian Island product specific factor is [0.25-0.35]；Preferably, the vegetation coverage factor on grassland, biology
Measure the factor, the weight of naked Shamian Island product specific factor is respectively 0.4127,0.2815,0.3058.
On the basis of the various embodiments described above, before obtaining steppe vegetation composite index GVSI, further includes:
S204, building steppe vegetation composite index GVSI model:
Dimensionless standardization is carried out to steppe vegetation coverage, biomass and naked husky area ratio, assigns weight, building
Steppe vegetation composite index (the Grassland Vegetation with sensitive reflection steppe vegetation ecosystem feature can be stablized
Synthesiszes Index, GVSI) model, shown in model such as formula (3):
In above formula (3), GVSI is steppe vegetation composite index, ViFor the numerical value of steppe vegetation parameter, WiFor the power of each parameter
Weight, n are the number of steppe vegetation parameter.According to steppe vegetation Synthesized Index Model, grassland GVSI value is calculated.
On the basis of the various embodiments described above, after obtaining steppe vegetation composite index GVSI, further includes:
S3, grass-land deterioration degree is classified based on above-mentioned GVSI.
Based on the various embodiments described above, as shown in Fig. 2, by constructing vegetation coverage remote sensing appraising model, biomass in advance
Remote sensing appraising model, naked husky area ratio remote sensing appraising model (i.e. linear spectral mixture model), and establish steppe vegetation synthesis and refer to
Exponential model is based on remotely-sensed data and ground sample data, directly obtains vegetative coverage by vegetation coverage remote sensing appraising model
Degree obtains biomass by biomass remote sensing appraising model, obtains naked husky area ratio by naked husky area ratio remote sensing appraising model,
And steppe vegetation composite index is obtained by steppe vegetation Synthesized Index Model.
Grade classification is carried out by the change rate to GVSI value, formulates degradation criteria, after determining degradation criteria, is calculated each
The change rate of year GVSI value with respect to degradation criteria, the foundation being classified as grass-land deterioration.The degradation level division of setting is shown in Table 1.
1 grass-land deterioration degree GVSI of table classification setting
| Grass-land deterioration grading | GVSI change rate |
| Heavy-degraded | < -38% |
| Gently degraded | - 38%--20% |
| It is slight to degenerate | - 20%--10% |
| It does not degenerate | > -10% |
In the present embodiment, by comprehensively consider steppe vegetation coverage, grassland biomass and naked husky area ratio etc. mostly because
Plain index, more fully reflection steppe vegetation degraded condition.It avoids monofactor and carries out grass-land deterioration remote sensing monitoring evaluation essence
Spend problem low, that stability is poor.
By the grass-land deterioration field investigation data comparison in the type area Liang Ge, grass-land deterioration remote sensing monitoring is calculated
Degradation level and on-site inspection degradation level comparison precision reach 91.3%, obtained good degradation evaluation precision.This hair
The steppe vegetation degeneration remote-sensing monitoring method of bright embodiment combines many factors, and evaluation is more efficient, has more reliability, facilitates
It promotes and applies.
Fig. 3 is a kind of steppe vegetation degeneration remote sensing monitoring device provided in an embodiment of the present invention, including parameter monitoring module
30 and index analysis module 40, in which:
Vegetation coverage and naked husky area ratio of the parameter monitoring module 30 based on remotely-sensed data acquisition grassland, are based on remote sensing number
According to the biomass with ground sample data acquisition grassland；It is distant by constructing vegetation coverage remote sensing appraising model, biomass in advance
Feel appraising model, naked husky area ratio remote sensing appraising model (i.e. linear spectral mixture model), and establishes steppe vegetation composite index
Model is based on remotely-sensed data and ground sample data, directly obtains vegetation coverage by vegetation coverage remote sensing appraising model,
Biomass is obtained by biomass remote sensing appraising model, naked husky area ratio is obtained by naked husky area ratio remote sensing appraising model；
Index analysis module 40 is based on vegetation coverage, naked husky area ratio and biomass in reflection steppe vegetation situation when institute
The weight coefficient accounted for obtains steppe vegetation composite index GVSI.Steppe vegetation coverage, biomass and naked husky area ratio are carried out
Dimensionless standardization assigns weight, and building can stablize plants with the grassland of sensitive reflection steppe vegetation ecosystem feature
It is integrated into index (Grassland Vegetation Synthesiszes Index, GVSI) model；It is comprehensive according to steppe vegetation
Grassland GVSI value is calculated in exponential model；Can also grade classification be carried out to the change rate of GVSI value, formulate degradation criteria,
After determining degradation criteria, change rate of each year GVSI value with respect to degradation criteria is calculated, the foundation as grass-land deterioration classification.
Fig. 4 is the entity structure schematic diagram of electronic equipment provided in an embodiment of the present invention, as shown in figure 4, the electronic equipment
It may include: processor (processor) 810,820, memory communication interface (Communications Interface)
(memory) 830 and communication bus 840, wherein processor 810, communication interface 820, memory 830 pass through communication bus 840
Complete mutual communication.Processor 810 can call the meter that is stored on memory 830 and can run on processor 810
Calculation machine program, to execute the steppe vegetation degeneration remote-sensing monitoring method of the various embodiments described above offer, for example, be based on remote sensing number
According to the vegetation coverage and naked husky area ratio for obtaining grassland, the biology based on remotely-sensed data and ground sample data acquisition grassland
Amount；Based on vegetation coverage, naked husky area ratio and the biomass weight coefficient shared when reflecting steppe vegetation situation, grass is obtained
Former vegetation composite index GVSI.
In addition, the logical order in above-mentioned memory 830 can be realized by way of SFU software functional unit and conduct
Independent product when selling or using, can store in a computer readable storage medium.Based on this understanding, originally
The technical solution of the inventive embodiments substantially part of the part that contributes to existing technology or the technical solution in other words
It can be embodied in the form of software products, which is stored in a storage medium, including several fingers
It enables and using so that a computer equipment (can be personal computer, server or the network equipment etc.) executes the present invention respectively
The all or part of the steps of a embodiment the method.And storage medium above-mentioned includes: USB flash disk, mobile hard disk, read-only memory
(ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic or disk
Etc. the various media that can store program code.
The embodiment of the present invention also provides a kind of non-transient computer readable storage medium, is stored thereon with computer program,
The computer program is implemented to carry out the steppe vegetation degeneration remote sensing monitoring side of the various embodiments described above offer when being executed by processor
Method, for example, vegetation coverage and naked husky area ratio based on remotely-sensed data acquisition grassland are based on remotely-sensed data and ground sample
The biomass on square data acquisition grassland；Based on vegetation coverage, naked husky area ratio and biomass when reflecting steppe vegetation situation
Shared weight coefficient obtains steppe vegetation composite index GVSI.
The embodiment of the present invention also provides the present embodiment and discloses a kind of computer program product, the computer program product packet
The computer program being stored in non-transient computer readable storage medium is included, the computer program includes program instruction, when
When described program instruction is computer-executed, computer is able to carry out such as above-mentioned steppe vegetation degeneration remote-sensing monitoring method, example
Such as include: the vegetation coverage based on remotely-sensed data acquisition grassland and naked husky area ratio, is based on remotely-sensed data and ground sample number
According to the biomass for obtaining grassland；It is shared when reflecting steppe vegetation situation based on vegetation coverage, naked husky area ratio and biomass
Weight coefficient, obtain steppe vegetation composite index GVSI.
In conclusion a kind of steppe vegetation degeneration remote-sensing monitoring method provided in an embodiment of the present invention and device, synthesis is examined
Consider Multiple factors attributes, more fully the reflection steppe vegetations such as the vegetation coverage, biomass and naked husky area ratio on grassland to degenerate
Situation；It avoids monofactor and carries out the problem that grass-land deterioration remote sensing monitoring evaluation precision is low, stability is poor；The degeneration etc. of calculating
Grade is small to ratio error with on-site inspection degradation level, combines many factors, and evaluation is more efficient, has more reliability, helps to push away
Wide application.
The apparatus embodiments described above are merely exemplary, wherein described, unit can as illustrated by the separation member
It is physically separated with being or may not be, component shown as a unit may or may not be physics list
Member, it can it is in one place, or may be distributed over multiple network units.It can be selected according to the actual needs
In some or all of the modules achieve the purpose of the solution of this embodiment.Those of ordinary skill in the art are not paying creativeness
Labour in the case where, it can understand and implement.
Through the above description of the embodiments, those skilled in the art can be understood that each embodiment can
It realizes by means of software and necessary general hardware platform, naturally it is also possible to pass through hardware.Based on this understanding, on
Stating technical solution, substantially the part that contributes to existing technology can be embodied in the form of software products in other words, should
Computer software product may be stored in a computer readable storage medium, such as ROM/RAM, magnetic disk, CD, including several fingers
It enables and using so that a computer equipment (can be personal computer, server or the network equipment etc.) executes each implementation
Method described in certain parts of example or embodiment.
Finally, it should be noted that the above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations；Although
Present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that: it still may be used
To modify the technical solutions described in the foregoing embodiments or equivalent replacement of some of the technical features；
And these are modified or replaceed, technical solution of various embodiments of the present invention that it does not separate the essence of the corresponding technical solution spirit and
Range.
Claims (10)
1. a kind of steppe vegetation degeneration remote-sensing monitoring method characterized by comprising
Vegetation coverage and naked husky area ratio based on remotely-sensed data acquisition grassland, are obtained based on remotely-sensed data and ground sample data
Take the biomass on grassland；
Based on vegetation coverage, naked husky area ratio and the biomass weight coefficient shared when reflecting steppe vegetation situation, obtain
Steppe vegetation composite index GVSI.
2. having the method according to claim 1, wherein obtaining the vegetation coverage on grassland based on remotely-sensed data
Body includes:
The pure exposed soil normalized differential vegetation index and pure vegetative coverage pixel normalized differential vegetation index on grassland are determined based on Types of Grassland；
And the vegetation index NDVI on grassland is obtained based on remotely-sensed data；
Based on the pure exposed soil normalized differential vegetation index, the pure vegetative coverage pixel normalized differential vegetation index and the NDVI,
The vegetation coverage on grassland is obtained by Pixel scrambling method.
3. the method according to claim 1, wherein obtaining the naked husky area ratio on grassland, tool based on remotely-sensed data
Body includes:
The Pure pixel indices P PI in Multi-spectral Remote Sensing Data is obtained based on minimal noise separation transform method MNF, and is based on institute
State PPI obtain pure naked husky endmember, vegetation endmember and soil endmember and the naked husky endmember,
The vegetation endmember and the corresponding spectrum of the soil endmember；
Based on established linear spectral mixture model, to the naked husky endmember, the vegetation endmember and the soil
Earth endmember carries out pixel analysis, obtains naked husky area ratio.
4. according to the method described in claim 3, it is characterized in that, obtaining mostly light based on minimal noise separation transform method MNF
Before Pure pixel indices P PI in spectrum remote sensing data, further includes:
Based on Multi-spectral Remote Sensing Data, using naked sand, vegetation, soil as end-member composition, building indicates each end-member composition spectrum and more
The linear spectral mixture model of spectral remote sensing data relationship feature.
5. the method according to claim 1, wherein based on remotely-sensed data and ground sample data acquisition grassland
Biomass specifically includes:
Longitude and latitude and acquisition time based on ground sample data, establish many years sample prescription biomass and the normalization of corresponding time is poor
Divide the remotely-sensed data collection of vegetation index NDVI；
Based on the remotely-sensed data collection, subregion divides Types of Grassland to construct grassland biomass remote sensing monitoring, the grass respectively
Primary object amount remote sensing monitoring is used to obtain the biomass on grassland based on remotely-sensed data.
6. the method according to claim 1, wherein before obtaining steppe vegetation composite index GVSI, further includes:
Using the vegetation coverage, the naked husky area ratio and the biomass as the factor, building reflection steppe vegetation situation
The vegetation coverage factor, naked Shamian Island product specific factor and biomass factor, and numerical value reversion is carried out to the naked Shamian Island product specific factor,
Reversion postfactor is gone to be used as the practical factor；
Nondimensionalization processing is carried out to each factor, and determines the weight coefficient of each factor based on the analytic hierarchy process (AHP) of expert estimation.
7. the method according to claim 1, wherein after obtaining steppe vegetation composite index GVSI, further includes:
Grass-land deterioration degree is classified based on the GVSI.
8. a kind of steppe vegetation degeneration remote sensing monitoring device characterized by comprising
Parameter monitoring module, for obtained based on remotely-sensed data grassland vegetation coverage and naked husky area ratio, be based on remote sensing number
According to the biomass with ground sample data acquisition grassland；
Index analysis module, for reflecting steppe vegetation situation when institute based on vegetation coverage, naked husky area ratio and biomass
The weight coefficient accounted for obtains steppe vegetation composite index GVSI.
9. a kind of electronic equipment including memory, processor and stores the calculating that can be run on a memory and on a processor
Machine program, which is characterized in that the processor realizes method as described in any one of claim 1 to 7 when executing described program
The step of.
10. a kind of non-transient computer readable storage medium, is stored thereon with computer program, which is characterized in that the calculating
The step of machine program realizes method as described in any one of claim 1 to 7 when being executed by processor.
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| CN110175537A (en) * | 2019-05-10 | 2019-08-27 | 深圳大学 | A kind of method and system merging multi-source remote sensing index evaluation Land degradation status |
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| CN113221829A (en) * | 2021-05-31 | 2021-08-06 | 四川熵智科技有限公司 | Grassland degradation intelligent treatment method and system |
| CN115294460A (en) * | 2022-10-08 | 2022-11-04 | 杭州领见数字农业科技有限公司 | Method for determining degradation degree of phyllostachys praecox forest, medium and electronic device |
| CN115294460B (en) * | 2022-10-08 | 2023-01-17 | 杭州领见数字农业科技有限公司 | Method for determining degradation degree of phyllostachys praecox forest, medium and electronic device |
| CN116090674A (en) * | 2023-04-07 | 2023-05-09 | 中国科学院空天信息创新研究院 | Early warning method, device and system for grassland degradation |
| CN116778303A (en) * | 2023-08-25 | 2023-09-19 | 山东省国土测绘院 | Vegetation coverage measuring method based on unmanned aerial vehicle remote sensing |
| CN116778303B (en) * | 2023-08-25 | 2023-10-31 | 山东省国土测绘院 | Vegetation coverage measuring method based on unmanned aerial vehicle remote sensing |
| CN117875658A (en) * | 2024-01-16 | 2024-04-12 | 广东省六七八控股集团股份公司 | Agricultural digital management method based on Internet of things |
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