CN104376204A - Method for inverting vegetation coverage by adopting improved pixel dichotomy - Google Patents
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Abstract
The invention relates to a method for inverting the vegetation coverage by adopting an improved pixel dichotomy. The method includes the following steps that S1 the NDVI of videos in a whole area is calculated; S2 effective remote sensing image areas in the whole area are determined; S3 the effective remote sensing image areas are counted through an NDVI unit value obtained through calculation, and the upper limit value and the lower limit valve of a confidence interval are obtained and serve as NDVIveg and NDVIsoil; S4 the vegetation coverage is worked out by using the pixel dichotomy. The improved pixel dichotomy is adopted in the method and is most widely applied because the improved pixel dichotomy is simple, practicable and high in precision and the number of needed parameters is small. Influences of image background values can be eliminated when pure NDVIveg and pure NDVIsoi are determined through the pixel dichotomy, the method is more reliable than a method that the upper limit value and the lower limit valve of a confidence interval are preset for determining pure NDVIveg and pure NDVIsoi, and therefore precision of the vegetation coverage obtained through inversion according to the pixel dichotomy is higher.
Description
Technical field
The present invention relates to a kind of earth system science field, particularly relate to a kind of method adopting the pixel dichotomy inverting vegetation coverage of improvement.
Background technology
Vegetation is most basic part in terrestrial ecosystems, and every other biology all depends on vegetation and gives birth to.The type of vegetation, quality and quantity change profound influence terrestrial ecosystems, its root system gos deep into soil, branches and leaves ingress of air, distinctive transpiration and photosynthesis make the natural geography factors such as soil, air, moisture connect each other, interact, and achieve Exchange of material and energy and the biochemical cycle of terrestrial ecosystems.Vegetation coverage is the important parameter portraying vegetation cover, is also basic, the objective indicator of instruction variation of ecology and environment, all in occupation of consequence in air sphere at the earth's surface, pedosphere, hydrosphere and biosphere.Vegetation coverage is an important biophysical parameters in soil-vegetation-atmospheric transfer model simulation earth's surface and atmospheric boundary layer exchange, and needs to give to estimate accurately in surficial process and climate change, weather forecast numerical simulation.From application, vegetation coverage all has a wide range of applications in fields such as agricultural, forestry, resource and environmental management, Land_use change, the hydrology, calamity source monitoring, draught monitors.
Vegetation coverage measuring method has earth's surface measurement method and remote sensing survey method two kinds usually.Because vegetation coverage has significant Spatio-temporal Distribution, although so based on discrete point earth's surface measurement method may when local cell domain measurement precision higher, there is when being generalized on a large scale very large uncertainty; Remote-sensing monitoring method, based on Spatial continual data, has some superiority at big-and-middle dimensional area estimation vegetation coverage, receives much concern at present.
The method utilizing remotely-sensed data to extract vegetation coverage mainly contains empirical model method, vegetation index and Decomposition of Mixed Pixels method.Empirical model method utilizes certain single wave band, band combination or the vegetation index calculated and actual measurement vegetation coverage to set up regression model, then asks for the vegetation coverage compared with large regions.Near remote sensing image is compared when regression model is applicable to, for time image comparatively early, as 10 years or image data decades ago, due to the change of vegetation cover, usually cannot obtain the sample district actual measurement vegetative coverage degrees of data in corresponding time, the application of the method is subject to certain restrictions in time; Regression model has degree of precision at regional area, but has limitation in the application of space, is only applicable to specific region and specific vegetation pattern, is not of universal significance.Decomposition of Mixed Pixels sources of law are in the linear spectral mixture model of quantitative remote sensing, and basic ideas are vegetation information and non-vegetation information two parts by the pixel analysis of remote sensing image, estimates the proportion of wherein vegetation information, i.e. vegetation coverage.Wherein pixel dichotomy is most widely used in Decomposition of Mixed Pixels method, and it does not need to set up regression model, relies on less, have more universal significance relative to regression model method to land-ground measurement data, extends to area on a large scale after empirical tests.These advantages make pixel dichotomy be one of method be most widely used in vegetation coverage remote-sensing inversion.
Pixel dichotomy supposes that a pixel is made up of vegetation and soil two parts, and the standard difference vegetation index (Normalized Difference Vegetation Index, NDVI) of this pixel is pure vegetation NDVI (NDVI
veg) and pure soil NDVI (NDVI
soil) according to the weighted sum of area.Then vegetation coverage (F
c) be expressed as:
F
c＝(NDVI-NDVI
soil)/(NDVI
veg-NDVI
soil)
If have research to think there is pure vegetation pixel and pure soil in image, then think that the NDVI maximal value in image is NDVI
veg, NDVI minimum value is NDVI
soil.And some scholar thinks the impact should removing air and the noise of image own, to the given fiducial interval of the value of NDVI, get the NDVI at a certain upper limit place of fiducial interval as NDVI
veg, get the NDVI at a certain lower limit place of fiducial interval as NDVI
soil.Thisly determine that the method for pure vegetation and pure soil NDVI is more scientific and reasonable by fiducial interval.But the NDVI that when determining fiducial interval upper and lower bound, as shown in Figure 1, current method makes statistics obtain because of the existence of background value outside image effective coverage
vegand NDVI
soilthere is certain error, have impact on inversion accuracy and the reliability of vegetation coverage.Need a kind of method badly can overcome and asking for NDVI
vegand NDVI
soilthe impact of background value in process.
Summary of the invention
(1) technical matters that will solve
The object of this invention is to provide a kind of method can eliminating the employing pixel dichotomy inverting vegetation coverage of background value region growing, thus the vegetation coverage precision that the inverting of pixel dichotomy is obtained is higher.
(2) technical scheme
The present invention is achieved by the following technical solutions:
Adopt a method for the pixel dichotomy inverting vegetation coverage of improvement, comprise the following steps:
S1 calculates the NDVI pixel value of whole area image;
S2 judges the effective remote sensing image region in whole region;
S3 is added up by the NDVI pixel value calculated effective remote sensing image region, obtains higher limit and the lower limit of fiducial interval, as NDVI
vegand NDVI
soil;
S4 utilizes pixel dichotomy to calculate vegetation coverage.
Wherein, described step S2 comprises following concrete steps:
With the initial growth chosen point for starting point, adopt neighborhood growing method: if the numerical value of each wave band of some pixels is all 0 in starting point neighborhood, then be labeled as new growing point, and eight neighborhood regrowth is carried out centered by the growing point that this is new, until do not have new pixel to be labeled, the border of terminating point composition is the border in effective remote sensing image region.The technique effect of this technical scheme is, comprehensively can determine the border in effective remote sensing image region fast.
Further, described neighborhood to comprise centered by growing point up and down, upper left, lower-left, upper right, the location of pixels in eight directions, bottom right, thus the border determining effective remote sensing image region more comprehensively.
Wherein, the choosing method of described initial growth point is as follows:
The frontier point of four angle points and four edges of choosing remote sensing image grows starting point as preliminary election, then the frontier point of these four angle points and four edges is differentiated, if each wave band numerical value of this point is all 0, then this point is labeled as growth starting point, if ineligible, then not to its mark.The beneficial effect of this technical scheme is, can quick and precisely determine to grow starting point.
Wherein, in described step S4, pixel dichotomy is utilized to calculate in vegetation coverage,
The computing formula of described pixel dichotomy is:
F
c＝(NDVI-NDVI
soil)/(NDVI
veg-NDVI
soil)；
F
cfor vegetation coverage, NDVI
vegfor the NDVI value of pure vegetation; NDVI
soilfor pure soil NDVI value; NDVI is NDVI
vegand NDVI
soilaccording to the weighted sum of area, thus accurately calculate the coverage of vegetation.
Further, in described step S3, comprise following concrete steps:
S31 is arranged in order NDVI pixel value;
After S32 calculates sequence, the accumulation pixel number at each NDVI pixel value place accounts for the accumulation pixel number percent of pixel sum in image effective range;
If the accumulation pixel number percent at certain NDVI value place of S33, deduct compared with degree of confidence parameter institute value with 1, difference is minimum, then this NDVI value is NDVI
soil;
If the accumulation pixel number percent at certain NDVI value place and the difference of degree of confidence parameter minimum, then this NDVI value is NDVI
veg.The technique effect of this technical scheme is to determine NDVI by accurate quick
soiland NDVI
vegnumerical value.
Preferably, in described step S33, degree of confidence parameter values scope is 1%-5%, thus effectively avoids the NDVI produced due to noise effect too low or too high.
Wherein, the computing method of the NDVI pixel value of whole area image are:
NDVI＝(p(nir)-p(red))/(p(nir)+p(red))
Wherein p (nir) represents spectral reflectivity or the gray-scale value of near-infrared band, and p (red) represents spectral reflectivity or the gray-scale value of visible red wave band.The technique effect of this technical scheme is, accurately can be recorded the image NDVI pixel value in whole region by the computing method of NDVI pixel value.
(3) beneficial effect
Compare with product with prior art, the present invention has the following advantages:
The present invention adopts the pixel dichotomy of improvement, is most widely used because it is simple, desired parameters is less, precision is higher.Can be eliminated in pixel dichotomy by the present invention and determine pure vegetation NDVI
vegwith pure soil NDVI
soiltime image background value affect problem, make the pure vegetation NDVI that determined by the method for given fiducial interval upper and lower bound and pure soil NDVI more reliable, thus the vegetation coverage precision that the inverting of pixel dichotomy is obtained is higher.
Accompanying drawing explanation
Fig. 1 is the NDVI striograph considering background value in background technology;
Fig. 2 is the NDVI striograph not considering background value provided by the invention;
Fig. 3 is the step schematic diagram of algorithm of region growing provided by the invention;
Fig. 4 is the pure vegetation NDVI in pixel dichotomy provided by the invention
vegwith pure soil NDVI
soilcomputing method step schematic diagram;
Fig. 5 is the step schematic diagram adopting the method for the pixel dichotomy inverting vegetation coverage improved provided by the invention.
Embodiment
Understand for the ease of those of ordinary skill in the art and implement the present invention, below in conjunction with the drawings and the specific embodiments, the present invention is described in further detail.
As shown in Figure 5, the present embodiment provides a kind of method adopting the pixel dichotomy inverting vegetation coverage of improvement, comprises the following steps:
S1 calculates the NDVI of whole image (containing inactive area) by NDVI computing formula.This NDVI is used for the NDVI sequence in determining step S3, determines NDVI
soiland NDVI
veg, NDVI computing formula is as follows:
NDVI＝(p(nir)-p(red))/(p(nir)+p(red))
Wherein p (nir) represents spectral reflectivity or the gray-scale value of near-infrared band, and p (red) represents spectral reflectivity or the gray-scale value of visible red wave band.
S2 judges the effective remote sensing image region in whole region.
The method of the effective remote sensing range of influence of judgement of concrete employing as shown in Figure 3, the frontier point of four angle points and four edges of choosing remote sensing image grows starting point as preliminary election, then the frontier point of four angle points and four edges is differentiated, if each wave band numerical value of this point is all 0, then this point is labeled as growth starting point, if ineligible, then not to its mark, thus, comprehensively can determine the border in effective remote sensing image region fast.
With the growth starting point chosen for starting point, (eight neighborhood to refer to centered by this pixel up and down to carry out eight neighborhood, upper left, lower-left, upper right, the location of pixels in eight directions, bottom right) growth, if the numerical value of each wave band of some pixels is all 0 in starting point eight neighborhood, then be designated as new growing point, and carried out eight neighborhood regrowth centered by this point, until do not have new pixel to be labeled.The border of terminating point composition is the effective remote sensing image border be of practical significance.The process of whole region growing as shown in Figure 3.
S3, by calculating the NDVI value in effective remote sensing image region and adding up, obtains the upper and lower bound value of fiducial interval, as NDVI
vegand NDVI
soil.
In step S3, concrete steps as shown in Figure 4:
S31 is arranged in order NDVI cell value;
After S32 calculates sequence, the accumulation pixel number at each NDVI value place accounts for the accumulation pixel number percent of pixel sum in image effective range;
If the accumulation pixel number percent at certain NDVI value place of S33 is minimum with the difference of (1-degree of confidence parameter), then this NDVI value is NDVI
soil;
If the accumulation pixel number percent at certain NDVI value place and the difference of degree of confidence parameter minimum, then this NDVI value is NDVI
veg.
S4 utilizes pixel dichotomy to calculate vegetation coverage.By the concrete grammar of above-mentioned steps S3, NDVI can be determined by accurate quick
soiland NDVI
vegnumerical value.
Wherein, the computing formula of described pixel dichotomy is:
F
o＝(NDVI-NDVI
soil)/(NDVI
veg-NDVI
soil)；
F
cfor vegetation coverage, NDVI
vegfor the NDVI of pure vegetation; NDVI
soilfor pure soil NDVI; NDVI is pure vegetation NDVI
vegwith pure soil NDVI
soilaccording to the weighted sum of area.
Vegetation coverage is basic, the objective indicator of instruction variation of ecology and environment, be an important biophysical parameters in soil-vegetation-atmospheric transfer model simulation earth's surface and atmospheric boundary layer exchange, be all widely used in fields such as agricultural, forestry, resource and environmental management, Land_use change, the hydrology, calamity source monitoring, draught monitors.As the pixel dichotomy of one of remote-sensing inversion vegetation coverage method, be most widely used because it is simple, desired parameters is less, precision is higher.
As shown in Figure 2, can be eliminated in pixel dichotomy by the method for the pixel dichotomy inverting vegetation coverage adopting improvement in the present embodiment and determine pure vegetation NDVI
vegwith pure soil NDVI
soiltime image background value affect problem, make the pure vegetation NDVI determined by the method for given fiducial interval upper and lower bound
vegwith pure soil NDVI
soilmore reliable, thus the vegetation coverage precision that the inverting of pixel dichotomy is obtained is higher.
According to the range size of certain test site and the resolution of image, the NDVI produced for avoiding noise is too low or cross high level, and preferably establishing degree of confidence parameter values scope is 1%-5%.As Fig. 1 considers the NDVI image of background value, can obtain in table 1 according to the NDVI that traditional method is chosen
vegand NDVI
soil.As Fig. 2 does not consider the NDVI image of background value, can obtain in table 2 according to the NDVI that algorithm of region growing is chosen
vegand NDVI
soil.Both still have obvious gap, and this is apparent on the impact of vegetation coverage inverting.
The NDVI that table 1 is chosen according to traditional method
vegand NDVI
soil
The NDVI of table 2 for choosing according to algorithm of region growing
vegand NDVI
soil
Above embodiment is only one embodiment of the present invention, and it describes comparatively concrete and detailed, but therefore can not be interpreted as the restriction to the scope of the claims of the present invention.Its concrete structure and size can adjust according to actual needs accordingly.It should be pointed out that for the person of ordinary skill of the art, without departing from the inventive concept of the premise, can also make some distortion and improvement, these all belong to protection scope of the present invention.
Claims (8)
1. adopt a method for the pixel dichotomy inverting vegetation coverage of improvement, it is characterized in that, comprise the following steps:
S1 calculates the NDVI pixel value of whole area image;
S2 judges the effective remote sensing image region in whole region;
S3 is added up by the NDVI pixel value calculated effective remote sensing image region, obtains higher limit and the lower limit of fiducial interval, as NDVI
vegand NDVI
soil;
S4 utilizes pixel dichotomy to calculate vegetation coverage.
2. the method adopting the pixel dichotomy inverting vegetation coverage improved according to claim 1, it is characterized in that, described step S2 comprises following concrete steps:
With the initial growth chosen point for starting point, adopt neighborhood growing method: if the numerical value of each wave band of some pixels is all 0 in starting point neighborhood, then be labeled as new growing point, and eight neighborhood regrowth is carried out centered by the growing point that this is new, until do not have new pixel to be labeled, the border of terminating point composition is the border in effective remote sensing image region.
3. according to claim 2ly adopt the method for pixel dichotomy inverting vegetation coverage improved, it is characterized in that, described neighborhood to comprise centered by growing point up and down, upper left, lower-left, upper right, the location of pixels in eight directions, bottom right.
4. the method adopting the pixel dichotomy inverting vegetation coverage improved according to claim 2, it is characterized in that, the choosing method of described initial growth point is as follows:
The frontier point of four angle points and four edges of choosing remote sensing image grows starting point as preliminary election, then the frontier point of these four angle points and four edges is differentiated, if each wave band numerical value of this point is all 0, then this point is labeled as growth starting point, if ineligible, then not to its mark.
5. the method adopting the pixel dichotomy inverting vegetation coverage improved according to claim 1, is characterized in that, in described step S4, utilize pixel dichotomy to calculate in vegetation coverage,
The computing formula of described pixel dichotomy is:
F
c＝(NDVI-NDVI
soil)/(NDVI
veg-NDVI
soil)；
F
cfor vegetation coverage, NDVI
vegfor the NDVI value of pure vegetation; NDVI
soilfor pure soil NDVI value; NDVI is NDVI
vegand NDVI
soilaccording to the weighted sum of area.
6. the method adopting the pixel dichotomy inverting vegetation coverage improved according to claim 1, is characterized in that, in described step S3, comprise following concrete steps:
S31 is arranged in order NDVI pixel value;
After S32 calculates sequence, the accumulation pixel number at each NDVI pixel value place accounts for the accumulation pixel number percent of pixel sum in image effective range;
If the accumulation pixel number percent at certain NDVI value place of S33, deduct compared with degree of confidence parameter institute value with 1, difference is minimum, then this NDVI value is NDVI
soil;
If the accumulation pixel number percent at certain NDVI value place and the difference of degree of confidence parameter minimum, then this NDVI value is NDVI
veg.
7. the method adopting the pixel dichotomy inverting vegetation coverage improved according to claim 6, it is characterized in that, in described step S33, degree of confidence parameter values scope is 1%-5%.
8. the method for the pixel dichotomy inverting vegetation coverage that the employing according to any one of claim 1 ~ 7 improves, it is characterized in that, the computing method of the NDVI pixel value of whole area image are:
NDVI＝(p(nir)-p(red))/(p(nir)+p(red))
Wherein p (nir) represents spectral reflectivity or the gray-scale value of near-infrared band, and p (red) represents spectral reflectivity or the gray-scale value of visible red wave band.
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Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106600637A (en) * | 2016-12-08 | 2017-04-26 | 中国科学院海洋研究所 | Remote sensing image-based wild animal population quantity observation method |
| CN107909607A (en) * | 2017-12-11 | 2018-04-13 | 河北省科学院地理科学研究所 | A kind of year regional vegetation coverage computational methods |
| CN109269448A (en) * | 2018-09-26 | 2019-01-25 | 中国农业大学 | A kind of vegetation coverage measurement method and device based on infrared temperature image |
| CN109359411A (en) * | 2018-11-01 | 2019-02-19 | 中国科学院东北地理与农业生态研究所 | A kind of Marsh Wetland vegetation fraction estimation method under climate change effect |
| CN110059553A (en) * | 2019-03-13 | 2019-07-26 | 中国科学院遥感与数字地球研究所 | The method for knowing potential landslide stage vegetation anomalies feature is sentenced using optical remote sensing image |
| CN110503018A (en) * | 2019-08-12 | 2019-11-26 | 华中农业大学 | A kind of slope collapse active degree method of discrimination based on vegetation cover degree |
| CN110929423A (en) * | 2019-12-17 | 2020-03-27 | 山东科技大学 | Soil water content inversion method of comprehensive drought model |
| CN113553549A (en) * | 2021-07-26 | 2021-10-26 | 中国科学院西北生态环境资源研究院 | Method and device for inversion of plant coverage, electronic equipment and storage medium |
| CN114689545A (en) * | 2022-03-02 | 2022-07-01 | 珠江水利委员会珠江水利科学研究院 | Vegetation coverage layered estimation method and medium based on DSM (digital surface model) contour slices |
| CN114842325A (en) * | 2022-03-16 | 2022-08-02 | 北京四象爱数科技有限公司 | Ground temperature inversion method based on single-waveband medium-wave infrared satellite remote sensing data |
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-
2014
- 2014-11-06 CN CN201410636780.9A patent/CN104376204A/en active Pending
Non-Patent Citations (4)
| Title |
|---|
| 姜烨等: "基于像元二分模型的植被覆盖度遥感信息提取", 《科技信息》 * |
| 李攀等: "北方农牧交错带植被覆盖变化遥感监测研究-以河北省沽源县为例", 《国土资源遥感》 * |
| 杨晓鹏等: "手背静脉图像预处理算法研究", 《中国医疗设备》 * |
| 郭伟伟等: "基于 NDVI 的植被覆盖度变化的研究与分析-以河北省张家口市为例", 《测绘与空间地理信息》 * |
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|---|---|---|---|---|
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| CN106600637B (en) * | 2016-12-08 | 2019-04-09 | 中国科学院海洋研究所 | A method of wild animal population quantity is observed using remote sensing image |
| CN107909607A (en) * | 2017-12-11 | 2018-04-13 | 河北省科学院地理科学研究所 | A kind of year regional vegetation coverage computational methods |
| CN109269448A (en) * | 2018-09-26 | 2019-01-25 | 中国农业大学 | A kind of vegetation coverage measurement method and device based on infrared temperature image |
| CN109359411A (en) * | 2018-11-01 | 2019-02-19 | 中国科学院东北地理与农业生态研究所 | A kind of Marsh Wetland vegetation fraction estimation method under climate change effect |
| CN110059553B (en) * | 2019-03-13 | 2020-11-10 | 中国科学院遥感与数字地球研究所 | Method for judging and identifying vegetation abnormal features in landslide stage by using optical remote sensing image |
| CN110059553A (en) * | 2019-03-13 | 2019-07-26 | 中国科学院遥感与数字地球研究所 | The method for knowing potential landslide stage vegetation anomalies feature is sentenced using optical remote sensing image |
| CN110503018A (en) * | 2019-08-12 | 2019-11-26 | 华中农业大学 | A kind of slope collapse active degree method of discrimination based on vegetation cover degree |
| CN110503018B (en) * | 2019-08-12 | 2022-06-24 | 华中农业大学 | Ridge collapse activity degree judging method based on vegetation coverage |
| CN110929423A (en) * | 2019-12-17 | 2020-03-27 | 山东科技大学 | Soil water content inversion method of comprehensive drought model |
| CN110929423B (en) * | 2019-12-17 | 2023-04-14 | 山东科技大学 | Soil water content inversion method of comprehensive drought model |
| CN113553549A (en) * | 2021-07-26 | 2021-10-26 | 中国科学院西北生态环境资源研究院 | Method and device for inversion of plant coverage, electronic equipment and storage medium |
| CN113553549B (en) * | 2021-07-26 | 2023-04-14 | 中国科学院西北生态环境资源研究院 | Method and device for inversion of coverage degree of planting, electronic equipment and storage medium |
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| CN114842325A (en) * | 2022-03-16 | 2022-08-02 | 北京四象爱数科技有限公司 | Ground temperature inversion method based on single-waveband medium-wave infrared satellite remote sensing data |
| CN116821589A (en) * | 2023-08-29 | 2023-09-29 | 生态环境部卫星环境应用中心 | Vegetation coverage recovery upper limit calculation method for promoting ecological service function improvement |
| CN116821589B (en) * | 2023-08-29 | 2023-11-14 | 生态环境部卫星环境应用中心 | Vegetation coverage recovery upper limit calculation method for promoting ecological service function improvement |
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