CN105758806A - Spectral characteristic based remote sensing monitoring method of plastic film mulched farmland - Google Patents
Spectral characteristic based remote sensing monitoring method of plastic film mulched farmland Download PDFInfo
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- CN105758806A CN105758806A CN201610077765.4A CN201610077765A CN105758806A CN 105758806 A CN105758806 A CN 105758806A CN 201610077765 A CN201610077765 A CN 201610077765A CN 105758806 A CN105758806 A CN 105758806A
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Abstract
The invention discloses a spectral characteristic based remote sensing monitoring method of plastic film mulched farmland. The spectral characteristic based remote sensing monitoring method of the plastic film mulched farmland comprises a step S1 of carrying out preprocessing on a remote sensing image, which comprises S1, radiation correction, atmospheric correction and the carrying out of inlaying and cutting processing on an image to obtain a research-area image, on the remote sensing image; a step S2 of establishing a plastic film mulched farmland remote sensing monitoring classification system, so as to distinguish the plastic film mulched farmland and other ground objects; a step S3 of collecting irregular polygonal samples of different ground object types in the classification system through visually interpreting a Google Earth image with a time phase same with that of the research-area image, and then delineating regular polygonal samples of a predetermined-size picture element anew in irregular polygons through visual interpretation; a step S4 of carrying out analysis on the separability of the different ground objects in the research-area image, so as to select a separable wave band; a step S5 of classifying the research-area image by utilizing the regular polygonal samples and using a classifier. The invention provides a new method for monitoring the plastic film mulched farmland.
Description
Technical field
The present invention relates to remote sensing monitoring technology, more particularly, to the covering with ground sheeting farmland remote-sensing monitoring method based on spectral signature.
Background technology
Plastic film mulching cultivation can be obviously improved the habitat conditions such as farmland temperature, light, water, gas, fertilizer, improve soil moisture content, promote crop growth, shorten period of duration, avoid the natural disaster such as later stage pest and disease damage and dry, heat, wind, crop yield is greatly improved, and can list in advance, improve income, it is Arid&semi-arid area, one of Key Cultivation Technology of low temperature water-deficient area, temperature and changes and precipitation amplitude and area differentiation larger area.
But, after crops harvesting, in farmland, the mulch film of residual can cause following harmful effect: causes environmental pollution (field white pollution)；Soil permeability, moisture and nutrient transporting, soil fertility reduce；Every fertile water proof, affect fertilizer efficiency；Crop root is grown, yield declines；Change the energy balance between ground vapour: greenhouse gas emission；Regional evaportranspiration.
These harmful effects await reducing or eliminating, then depend on the collection of mulch film data, analysis.But, the Spatial Distribution Pattern in current China's covering with ground sheeting farmland, distribution area and variation characteristic thereof are unclear.Therefore, just cannot produce for mulch film, use and the planning of science activities management of used plastic collection improvement etc. provides foundation, reference frame can not be provided for alleviating the negative effect that film-mulching technique brings and the effective way etc. finding solution problem.More cannot provide basic data for other researchs (transition of crop phenology, earth's surface humiture, evapotranspiration etc.).Therefore, mulch film covering farm land is monitored by the method that is currently needed for.
Summary of the invention
For the problem in background technology, the present invention proposes a kind of covering with ground sheeting farmland remote-sensing monitoring method based on spectral signature, including:
Step S1, carries out pretreatment to remote sensing image, including:
1) radiant correction；2) atmospheric correction；With 3) image is inlayed, cutting process to obtain study area image；
Step S2, sets up covering with ground sheeting farmland remote sensing monitoring taxonomic hierarchies, to distinguish covering with ground sheeting farmland and other atural objects；
Step S3, GoogleEarth image by visual interpretation phase identical with described study area image, gather the polygon sample of different types of ground objects in described taxonomic hierarchies, then again through study area image described in visual interpretation, in polygon, the regular polygon sample of preliminary dimension pixel is again delineated；
Step S4, utilizes described regular polygon sample, and different atural objects are analyzed in the separability of described study area image, to select the wave band being suitable for, using the reflectance of selected wave band as spectral signature；
Step S5, based on the spectral signature in the training sample gathered in step S3 and S4, utilizes grader that described study area image is classified, to obtain the spatial distribution of covering with ground sheeting farmland and other atural objects in the remote sensing monitoring taxonomic hierarchies of described covering with ground sheeting farmland.
The present invention proposes a kind of new method to monitor covering with ground sheeting farmland, and can reach at a relatively high precision by verifying.
Accompanying drawing explanation
Fig. 1 shows the spectral reflectivity figure of 5 kinds of plastics.
Fig. 2 shows ASTER vegetation spectral reflectivity curve chart.
Fig. 3 shows ASTER soil spectrum reflectance curve figure.
Fig. 4 shows covering with ground sheeting farmland ASD measured spectra reflectance curve.
Fig. 5 shows soil ASD measured spectra reflectance curve.
Fig. 6 is the flow chart of an embodiment of the method for the present invention.
Fig. 7 shows the crops phenological calendar of a study area.
Fig. 8 shows different atural object Landsat8OLI spectral reflectivity curve chart.
Fig. 9 shows the different IPs function expression of support vector machine.
Figure 10 shows the covering with ground sheeting farmland spatial distribution map based on spectral signature.
Detailed description of the invention
Describing embodiments of the present invention with reference to the accompanying drawings, wherein identical parts are presented with like reference characters.
Monitoring for covering with ground sheeting farmland, applicant is to USGS (UnitedStatesGeologicalSurvey, United States Geological Survey), US National Aeronautics and Space Administration ASTER (AdvancedSpaceborneThermalEmissionReflectionRadiometer) spectrum database data and ASD (AnalyticalSpectralDevices, object spectrum instrument) spectrogrph measured spectra data carry out the spectral reflectivity curve shape feature of relevant type of ground objects and reflectance value scope is analyzed.
Fig. 1 figure shows the spectral reflectivity of 5 kinds of plastics, including: HDPE (high density polyethylene (HDPE)), LDPE (Low Density Polyethylene), PETE (polyethylene terephthalate) and PVC (polrvinyl chloride).Fig. 2 shows ASTER vegetation spectral reflectivity curve chart.Fig. 3 shows ASTER soil spectrum reflectance curve figure.Fig. 4 shows covering with ground sheeting farmland ASD measured spectra reflectance curve.Fig. 5 shows soil ASD measured spectra reflectance curve.
Finding out from Fig. 1-5, different atural objects present different spectral profile shapes and different reflectance value scopes in different wavelength range.Can be seen that from USGS and ASTER spectrum database data, different atural objects have wave spectrum reflectance curve and the reflectance value scope of significantly different shape within the scope of visible ray-near-infrared and short infrared wave band.Same from ASD measured spectra data it can also be seen that this category feature.The analysis of these data can provide foundation for the selection of remote sensing image data, and namely the remote sensor data of same or like ripple width design can provide effective data source for covering with ground sheeting farmland remote sensing monitoring.
Utilize remotely-sensed data spectral signature that mulch film covering farm land is monitored, there is also following technical barrier:
1, time factor: different regions, the film mulching method of Different Crop, overlay film time and overlay film time span (plant growth early stage, the time of infertility overlay film etc.) are different.Such as crop grow from mulch film after the analysis difficulty of remote sensing image data, big when not growing than crop, it is possible to cause monitoring inaccurate.
2, spectral signature: spectral signature is by the impact of soil and crop under mulch film color, density, thickness and film, and the dynamic variability of its spectral signature is strong, stability is weak.
To this, the selection of remote sensing image data Optimum temoral is a need for.Overlay film farmland has obvious phenology and rhythm and pace of moving things change, it is determined that remote sensing image data Optimum temoral is the basis in accurate remote sensing monitoring overlay film farmland.Can implement according to target monitoring district chief crop phenological calendar data and covering with ground sheeting, retain, the information such as farming operation, it is determined that covering with ground sheeting farmland the best remote sensing monitoring period.After having had theory support, as shown in Figure 6, the covering with ground sheeting farmland monitoring method of the present invention includes:
Step S1, carries out pretreatment to the remote sensing image data of study area.
Wherein, the selection of remote sensing image data, the spectral signature according to mulch film Yu other atural objects, select suitable and the monitoring of covering with ground sheeting farmland remotely-sensed data.In the following example, the present invention selects Landsat8OLI remote sensing image that mulch film covering farm land is monitored, but the adoptable remotely-sensed data of the present invention is not limited to this.
Preferably, the remote sensing image data of the best monitoring phase in the covering with ground sheeting farmland in Selecting research district, described best monitoring phase refers to crop sowing time to the seeding stage.
In an example, Fig. 7 shows the crops phenological calendar of the trial zone of Jizhou City of Hebei province.Determine that this covering with ground sheeting farmland, region is the best monitoring phase in crop sowing time to the seeding stage, and then have selected corresponding best period Landsat8OLI remote sensing image on April 29th, 2014 as remote sensing monitoring data source.
More specifically, described pretreatment specifically includes:
(1) data are carried out radiant correction
Impact due to the outside environmental elements such as electro-optical system feature and air, landform, altitude of the sun of remote sensor itself, measured value that remote sensor obtains and Target scalar truly reflects or there is discordance between the physical quantity such as radiation, i.e. the distortion phenomenon of spectral characteristic of ground.Radiant correction and atmospheric correction in order that eliminate these distortions, obtain more real ground return value.Wherein radiant correction is that the digital measured value (DigitalNumber) obtained by remote sensor converts remote sensor radiation value to.Below computing formula:
Lλ=Gain*Pixelvalue+Offset
Wherein, LλRepresenting remote sensor radiation value, Pixelvalue represents pixel digital measured value, and Gain represents gain, and offset represents side-play amount.
Remote sensing image processing software (such as Envi5.1) radiation calibration module (Radiometriccalibration) such as can be utilized to carry out radiant correction.
(2) atmospheric correction (FLAASH)
The purpose of atmospheric correction is to eliminate the impact of atmospheric factor, converts reflectance value to by remote sensor radiation value.The equally possible atmospheric correction module FastLine-of-sightAtmosphericAnalysisofHypercubes (FLAASH) utilized in remote sensing image processing software (such as Envi5.1) carries out atmospheric correction, obtains Reflectivity for Growing Season data.
(3) image is inlayed, cutting to be to obtain study area image
Administrative line figure according to study area, utilizes remote sensing image processing software (such as Envi5.1) data cutting module (subsetviaregionofinterest), carries out cutting process, to obtain the image data of this survey region.
With reference to Fig. 6, after pretreatment, in step S2, set up covering with ground sheeting farmland remote sensing monitoring taxonomic hierarchies, to distinguish covering with ground sheeting farmland and other atural objects (increased surface covering).
In an example, according to study area Land cover types, set up covering with ground sheeting farmland, impermeable stratum, vegetation, water body, this five classes atural object of exposed soil.Table 1 shows this covering with ground sheeting farmland remote sensing monitoring taxonomic hierarchies.Other kind of taxonomic hierarchies can also be set up, it is an object of the invention to extract covering with ground sheeting farmland, so taxonomic hierarchies is to distinguish covering with ground sheeting farmland and other atural objects.In the present invention, impermeable stratum, vegetation, water body, exposed soil are merged into non-covering with ground sheeting farmland the most at last.So, then on final covering with ground sheeting farmland spatial distribution map, only need to mark covering with ground sheeting farmland and non-covering with ground sheeting farmland two types.
Table 1 covering with ground sheeting farmland remote sensing monitoring taxonomic hierarchies
With reference to Fig. 6, in step S3, more high spatial resolution remote sense image (such as Googleearth image) by visual interpretation phase identical with selected remote sensing image, gather the polygon sample (general collection larger area polygon sample) of five kinds of types of ground objects, then again through visual interpretation for covering with ground sheeting farmland monitoring remote sensing image (preferably, select Landsat8OLI remote sensing image, more preferably, select the SWIR2 of Landsat8OLI data, NIR, RED wave band), again the regular polygon sample of less area of 3*3 pixel (can also be the pixel of 5*5) is delineated in described larger area polygon sample, to ensure the representativeness of sample.
Refer again to Fig. 6, step S4, utilize described regular polygon sample, different atural objects are analyzed in the separability of described study area image, to select the suitable wave band for classifying, and using the reflectance of selected wave band as spectral signature；
Specifically, utilize sample region of interest to extract remote sensing image reflectance average, analyze five kinds of atural objects separability in OLI data by the sample reflectance average extracted, in order to carry out waveband selection.Fig. 8 shows the spectral reflectance rate curve of five kinds of atural objects, it can be seen that can be distinguished (different atural object reflectance curve shapes and codomain are different with the change of wavelength) by five kinds of atural objects of spectral reflectivity feature.In the present invention, the classification of five kinds of atural objects is all had certain values by seven wave bands of Landsat8OLI remote sensing image, so seven wave bands are all chosen as spectral signature parameter.
Refer again to Fig. 6, in step S5, with the polygon training sample in step S3 and the spectral signature in step S4, with different graders, the taxonomic hierarchies in step S2 is carried out terrain classification.
Step S5, utilizes the regular polygon sample grader gathered in step S3 that described study area image is classified, to obtain the spatial distribution of covering with ground sheeting farmland and other atural objects in the remote sensing monitoring taxonomic hierarchies of described covering with ground sheeting farmland.
Wherein said grader can be support vector machine (SVM), method of maximum likelihood, knearest neighbour method etc..In an example, mulch film covering farm land, impermeable stratum, vegetation, water body, this five classes atural object of exposed soil are classified.The sort module in remote sensing image processing software (such as Envi5.1) such as can be utilized to classify, and input data are 7 wave band reflectivity datas of Landsat8OLI remote sensing image, and output is classification results.Fig. 9 lists several kernel function expression formulas of support vector machine (SVM).
Figure 10 shows the covering with ground sheeting farmland spatial distribution map based on spectral signature, can clearly distinguish covering with ground sheeting farmland and other atural objects in figure.
It practice, the method for the present invention have passed through checking.Verification method is as follows: the sample in step S3 is divided into training sample and checking sample.Table 2 shows a classification samples example.Wherein training sample is for the classification of step S5, and checking sample is used as the checking of classification results.Remote sensing image processing software (such as Envi5.1) can be utilized to calculate confusion matrix, obtain overall accuracy, cartographic accuracy, user's precision, and then carry out classification of assessment device nicety of grading.Table 3 shows the precision of different sorting technique.
Table 2 classification samples table
Table 3 nicety of grading
As seen from Table 3, support vector machine different IPs function is all more satisfactory in covering with ground sheeting farmland remote sensing monitoring precision, overall accuracy is all higher than 92.7% (SVM-S), to mulch film covering farm land, cartographic accuracy is all higher than 89.99% (SVM-S), and user's precision is higher than 89.56% (SVM-S).Wherein the highest overall accuracy reaches 93.57% (SVM-L linear kernel function), and the highest cartographic accuracy and user's precision reach 90.38% (SVM-L).Maximum likelihood (MLC) and beeline (MDC) are also provided that good result, but the stability of its nicety of grading is not as support vector machine.Especially between cartographic accuracy and user's precision, there is some difference.So, when utilizing Landsat8OLI data light spectrum signature to carry out covering with ground sheeting farmland remote sensing monitoring, linear kernel function support vector machine provides maximally effective classification.
The techniqueflow of method a kind of covering with ground sheeting farmland remote sensing monitoring based on spectral signature of deduction of the present invention.The method take into account mulch film covering farm land remote sensing separability, the impact of covering with ground sheeting farmland the best remote sensing monitoring phase, and SVM different IPs function, to the application in mulch film covering farm land remote sensing monitoring, is all the innovation of the present invention.
Embodiment described above, the simply present invention more preferably detailed description of the invention, the usual variations and alternatives that those skilled in the art carries out within the scope of technical solution of the present invention all should be included in protection scope of the present invention.
Claims (9)
1. the covering with ground sheeting farmland remote-sensing monitoring method based on spectral signature, it is characterised in that including:
Step S1, carries out pretreatment to remote sensing image, including:
1) radiant correction；2) atmospheric correction；With 3) image is inlayed, cutting process to obtain study area image；
Step S2, sets up covering with ground sheeting farmland remote sensing monitoring taxonomic hierarchies, to distinguish covering with ground sheeting farmland and other atural objects；
Step S3, GoogleEarth image by visual interpretation phase identical with described study area image, gather the irregular polygon sample of different types of ground objects in described taxonomic hierarchies, then again through study area image described in visual interpretation, again delineating the regular polygon sample of preliminary dimension pixel in irregular polygon, the size of wherein said regular polygon sample is less than described irregular polygon sample；
Step S4, utilizes described regular polygon sample, and different atural objects are analyzed in the separability of described study area image, to select separable wave band, using the reflectance of selected wave band as spectral signature；
Step S5, utilize the spectral signature in the regular polygon sample and S4 gathered in step S3, with grader, described study area image is classified, to obtain the spatial distribution of covering with ground sheeting farmland and other atural objects in the remote sensing monitoring taxonomic hierarchies of described covering with ground sheeting farmland.
2. the covering with ground sheeting farmland remote-sensing monitoring method based on spectral signature according to claim 1, it is characterised in that the selection of described remote sensing image, is the spectral signature according to mulch film Yu other atural objects, selects suitable and the monitoring of covering with ground sheeting farmland remotely-sensed data.
3. the covering with ground sheeting farmland remote-sensing monitoring method based on spectral signature according to claim 2, it is characterised in that
In step S1, the Landsat8OLI remote sensing image of the best monitoring phase in the covering with ground sheeting farmland in Selecting research district, described best monitoring phase refers to crop sowing time to the seeding stage；
In step s3, SWIR2, NIR and the RED wave band that described study area image is Landsat8OLI data of visual interpretation.
4. the covering with ground sheeting farmland remote-sensing monitoring method based on spectral signature according to claim 3, it is characterised in that in the 3 of step S1) in, the administrative line data according to study area, image is carried out cutting to obtain study area image.
5. the covering with ground sheeting farmland remote-sensing monitoring method based on spectral signature according to claim 1, it is characterised in that in step s3, the size of described regular polygon sample is less than described irregular polygon sample.
6. the covering with ground sheeting farmland remote-sensing monitoring method based on spectral signature according to claim 5, it is characterised in that described preliminary dimension pixel is 3*3 pixel.
7. the covering with ground sheeting farmland remote-sensing monitoring method based on spectral signature according to claim 1, it is characterised in that in step s 2, described taxonomic hierarchies includes: covering with ground sheeting farmland, impermeable stratum, vegetation, water body and exposed soil.
8. the covering with ground sheeting farmland remote-sensing monitoring method based on spectral signature according to claim 1, it is characterised in that in step s 5, described grader is support vector machine, method of maximum likelihood or knearest neighbour method.
9. the covering with ground sheeting farmland remote-sensing monitoring method based on spectral signature according to claim 1, it is characterised in that also include: the sample in step S3 is divided into training sample and checking sample, utilizes checking sample that classification results is carried out precision test.
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| CN111985433A (en) * | 2020-08-28 | 2020-11-24 | 中国科学院地理科学与资源研究所 | Rice remote sensing information extraction method and system |
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| CN106644939A (en) * | 2016-12-08 | 2017-05-10 | 塔里木大学 | Measurement method and system of residual amount of residual plastic film on farmland soil surface layer |
| CN106644939B (en) * | 2016-12-08 | 2019-03-15 | 塔里木大学 | A kind of measurement method and system of agricultural land soil surface layer residual film residual quantity |
| CN107945228A (en) * | 2017-12-14 | 2018-04-20 | 北京市遥感信息研究所 | A kind of method based on single width satellite image extraction tank elevation |
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| CN109214287A (en) * | 2018-08-02 | 2019-01-15 | 九江学院 | Crops decomposition method and system based on RapidEye satellite remote-sensing image |
| CN109886142A (en) * | 2019-01-28 | 2019-06-14 | 中科光启空间信息技术有限公司 | A kind of crops decomposition method based on SAR technology |
| CN109886142B (en) * | 2019-01-28 | 2022-12-02 | 中科光启空间信息技术有限公司 | Crop interpretation method based on SAR technology |
| CN111985433A (en) * | 2020-08-28 | 2020-11-24 | 中国科学院地理科学与资源研究所 | Rice remote sensing information extraction method and system |
| CN111985433B (en) * | 2020-08-28 | 2023-01-20 | 中国科学院地理科学与资源研究所 | Rice remote sensing information extraction method and system |
| CN113591775A (en) * | 2021-08-11 | 2021-11-02 | 武汉工程大学 | Multispectral remote sensing image specific ground object extraction method combining hyperspectral features |
| CN113591775B (en) * | 2021-08-11 | 2022-08-02 | 武汉工程大学 | Multispectral remote sensing image specific ground object extraction method combining hyperspectral features |
| CN114486764A (en) * | 2022-01-26 | 2022-05-13 | 安徽新宇环保科技股份有限公司 | Agricultural non-point source pollution monitoring system based on full-spectrum water quality analyzer |
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