CN103971027A - Rice automatic mapping method based on ratio index of water body and vegetation index changes - Google Patents
Rice automatic mapping method based on ratio index of water body and vegetation index changes Download PDFInfo
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Abstract
The invention discloses a rice automatic mapping method based on a ratio index of water body and vegetation index changes. Based on time-series data sets within the year of a water body index and a vegetation index, the method detects a vegetation fast-growing stage corresponding to the vegetation index maximum in each growth cycle pixel by pixel, and establishes the ratio index of water body and vegetation index changes according to change amplitude of the remote sensing water body index and vegetation index from the beginning of growth to the fast-growing stage of vegetation, so as to carry out rice automatic mapping. The method makes full use of the characteristic that relative to other crops and natural vegetation, the change amplitude of the water body index is smaller, and the change amplitude of the vegetation index is larger during the period of time from transplanting to heading of a rice paddy field, and through the design of the ratio index of the water body and vegetation index changes based on the ratio of the two for rice mapping, the method has the characteristics of good robustness, high classification accuracy, high automation degree, strong anti-jamming capability and the like.
Description
Technical field
The present invention relates to remote sensing image information processing technology field, particularly a kind of paddy rice autodraft method based on water body and vegetation index variation Ratio index.
Background technology
Promptly and accurately grasp crops space distribution information for formulating relevant policies and guaranteeing that national food safety is most important.Paddy rice, as one of topmost cereal crops of China, occupies critical role in grain-production.The space distribution scope of fast automatic Monitoring Rice, significant.Traditional manual research method not only wastes time and energy, and is difficult to realize all standing, is easily subject to the interference of subjective factor.Remote sensing image has ageing strong, feature that coverage is large, in crop area monitoring, plays an important role.But, due to the interference of the spectral similarity between different crops, and the impact of the many factors such as the degree of enriching and study area feature of Classification in Remote Sensing Image personnel's experience, sorter, related data, cause paddy rice distribution remote sensing mapping precision to be difficult to ensure.
Because sequential remotely-sensed data can provide the sensor information of Various Seasonal, different times, can effectively strengthen the discrimination between different crops, therefore the crops remote sensing monitoring based on sequential remotely-sensed data has become leading development trend in recent years.The correlative study method of at present paddy rice drawing is taking Xiangming Xiao(2005) method combining with water body index based on vegetation index that proposes is as main, the core concept of the method is: in the rice transplanting phase, owing to conventionally needing flood irrigation, water body index can increase, and paddy rice has just been transplanted, vegetation index is conventionally smaller, therefore can identify the rice transplanting phase by the poor of water body index and vegetation index by judging.As set basis for estimation and be: if meet LSWI+0.05>EVI, this pixel in this moment in the rice transplanting phase.Wherein LSWI is water body index, and EVI is enhancement mode vegetation index.The method makes full use of rice transplanting phase this feature of pouring water, and comprehensive utilization remote sensing vegetation index is carried out paddy rice with water body index and charted, and has certain rationality, has therefore obtained good application in China and even different regions, the world, particularly south east asia.But the weak point of the method is: due to the impact of noise, precipitation and different regions Indices codomain scope may there is some difference etc. various factors, cause the method that directly vegetation index based on certain first phase and water body index original signal carry out to have some limitations.For example: due to the factor of cloud, may cause EVI time series data to occur abnormal low value, even lower than water body index, thereby cause erroneous judgement.Precipitation may directly cause water body index to raise and higher than vegetation index, cause equally erroneous judgement.Therefore need to introduce a kind of paddy rice autodraft method based on water body and vegetation index variation Ratio index.
Summary of the invention
The object of the present invention is to provide a kind of paddy rice autodraft method based on water body and vegetation index variation Ratio index, the method does not need manual intervention, robustness is good and nicety of grading is high.
For achieving the above object, technical scheme of the present invention is: a kind of paddy rice autodraft method based on water body and vegetation index variation Ratio index, comprise the steps,
S01: build level and smooth water body index, vegetation index time series data collection;
S02: reject the non-vegetation in study area and ever green vegetation pixel;
S03: obtain corresponding peak of growing season in each vegetation growth cycle by pixel;
S04: calculate from vegetation and start to grow into water body index between peak of growing season and the amplitude of variation of vegetation index;
S05: build water body and vegetation index and change Ratio index;
S06: change Ratio index according to water body and vegetation index and carry out paddy rice drawing.
In embodiments of the present invention, in described step S3, if find two or more local maximums in vegetation index timing curve within the year simultaneously, the time of being separated by according to two maximal values and relative size and with the gap condition of the local minimum of closing on, filter out each corresponding peak of growing season in vegetation growth cycle.
In embodiments of the present invention, in described step S4, arrive heading required time according to rice transplanting, vegetation growth based on each pixel is contained the phase, infer that by pixel vegetation in each growth cycle starts to grow into peak of growing season during this period of time, for calculating water body index and vegetation index starts to grow into the amplitude of variation in peak of growing season from vegetation, be recorded as respectively C
wand C
v.
In embodiments of the present invention, in described step S5, it is C that water body and vegetation index change Ratio index M
wand C
vratio, its expression formula is: M=C
w/ C
v.
In embodiments of the present invention, in described step S6, carry out paddy rice identification according to the water body of paddy rice and the feature of vegetation index variation Ratio index, water body and vegetation index variation Ratio index are less than certain threshold value ω, are judged as paddy rice.
In embodiments of the present invention, it is 0.25 that threshold value ω value is set, and this threshold value ω suitably adjusts in zones of different practical application in 0.05 scope.
In embodiments of the present invention, the method is applicable to crops or soil utilizes in remote sensing automatic classification field.
Compared to prior art, the present invention has following beneficial effect:
(1) by locking best embody different vegetation growth features, vegetation starts to grow into peak of growing season during this period of time, effectively got rid of the interference of signals in other periods;
(2) by calculate vegetation start to grow into peak of growing season during this period of time in the amplitude of variation of water body index and vegetation index, but not the relative size of both numerical value sometime can be eliminated the interference that noise and precipitation bring to a great extent;
(3) the variation ratio based on water body index and vegetation index, eliminate on the one hand the water body index variation interference that vegetation growth brings, this feature that further fortified water rice field is higher at transplanting time water body index and amplitude of variation is less on the other hand, thus nicety of grading improved;
(4) can be not by other auxiliary datas, anti-noise ability is strong, and result is reliable and stable.
Brief description of the drawings
Fig. 1 is the realization flow figure of the embodiment of the present invention.
Fig. 2 is the interior clock signal figure of MODIS EVI, the LSWI of paddy rice.
Fig. 3 is the interior clock signal figure of MODIS EVI, the LSWI of soybean.
Fig. 4 is the interior clock signal figure of MODIS EVI, the LSWI of the two season crop rotation crops of winter wheat+corn.
Embodiment
Below in conjunction with accompanying drawing, technical scheme of the present invention is specifically described.
A kind of paddy rice autodraft method based on water body and vegetation index variation Ratio index of the present invention, comprises the steps,
S01: build level and smooth water body index, vegetation index time series data collection;
S02: reject the non-vegetation in study area and ever green vegetation pixel;
S03: obtain corresponding peak of growing season in each vegetation growth cycle by pixel;
S04: calculate from vegetation and start to grow into water body index between peak of growing season and the amplitude of variation of vegetation index;
S05: build water body and vegetation index and change Ratio index;
S06: change Ratio index according to water body and vegetation index and carry out paddy rice drawing.
The inventive method is applicable to crops (such as wheat, soybean, corn etc.) or soil utilizes in remote sensing automatic classification field.
Be below specific embodiments of the invention.
A kind of paddy rice autodraft method based on water body and vegetation index variation Ratio index of the present invention, as shown in Figure 1, time series data collection in the year of the LSWI after the smoothing denoising of model study area and EVI index, the non-vegetation in study area and ever green vegetation pixel are being carried out on the basis of mask, obtain the vegetation index maximal value in each growth cycle by pixel, obtain corresponding vegetation growth and contain the phase, calculate remote sensing vegetation index and water body index and start to grow into the amplitude of variation in peak of growing season from vegetation, ratio based on both builds water body and vegetation index changes Ratio index, change Ratio index according to water body and vegetation index and carry out paddy rice identification.
A kind of described paddy rice autodraft method based on water body and vegetation index variation Ratio index, further comprising the steps:
Step S1: the vegetation index, the water body index time series data collection that build smoothing denoising:
The LSWI of model study area based on the earning in a day and original year interior time series data collection of EVI index, can the remote sensing image wave band value based on every day calculate respectively or carry out linear interpolation acquisition according to 8 days synthetic MODIS EVI, LSWI indexes of maximization.Utilize the denoising method that Whittaker smoother, Hants etc. are relevant, original year interior time series data collection carried out to smoothing denoising processing, thereby obtain water body index, vegetation index time series data collection after the smoothing denoising of study area, as the basis of paddy rice drawing.
Step S2: reject the non-vegetation pixel in study area and ever green vegetation pixel:
Based on present landuse map or according to vegetation index codomain distribution characteristics, the non-vegetation such as water body, unused land, building site in survey region and ever green vegetation are carried out to mask process.As the Rule of judgment that non-vegetation is set is: in the vegetation index year after certain pixel smoothing denoising, the maximal value of clock signal is A, if A< θ 1 judges that this pixel is as non-vegetation unit, wherein θ 1 is constant, is 0.2 in the present embodiment.The Rule of judgment of ever green vegetation is: in the vegetation index year after certain pixel smoothing denoising, the minimum value of clock signal is B, variance is C, if meet A> θ 2 and C< θ 3, judge that this pixel is as ever green vegetation, wherein θ 2, θ 3 are constant, are respectively in the present embodiment 0.35,0.2.
Step S3: calculate the vegetation index maximal value in each growth cycle by pixel, obtain corresponding vegetation growth and contain the phase:
For the vegetation area in survey region, ask and calculate the local maximum that in EVI timing curve, numerical value is greater than 0.35 by pixel, if find two or more local EVI maximal values simultaneously, judge successively whether the time that two adjacent local maximums are separated by is greater than 60 days, if be less than 60 days, only retain wherein that larger local maximum of numerical value, difference between the local minimum that this local maximum is further set and close on must be more than 0.15 Rule of judgment, thereby the final all EVI local maximums that satisfy condition that obtain.These EVI local maximums are recorded as to Pn, and wherein n can value be 1,2,3, respectively corresponding natural vegetation or single cropping crop, two season crop and three season crop growth cycle in corresponding vegetation growth contain the phase.
Step S4: calculate remote sensing vegetation index and water body index from starting to grow into the amplitude of variation in peak of growing season:
With D days (in the present embodiment, D is made as 50 days) before the corresponding vegetation growth Sheng phase of each growth cycle, represent that vegetation starts to grow into vegetation growth and contains the phase during this period of time, the amplitude of variation of computing interval remote sensing vegetation index and water body index respectively, is recorded as Cv and Cw(is shown in Fig. 2-Fig. 3).
Step S5: build water body and vegetation index and change Ratio index:
Start to grow into the amplitude of variation of remote sensing water body index and vegetation index in the grown Sheng phase based on vegetation, utilize both ratio, build water body and vegetation index variation Ratio index, be expressed as: M=Cw/Cv.
Step S6: change Ratio index according to water body and vegetation index and carry out paddy rice identification:
Paddy rice needs flood irrigation conventionally at transplanting time, now water body index is relatively high-order, and because the impact of pouring water vegetation index is now very low, it is even negative value, along with the continuous growth of paddy rice is tillered, vegetation index constantly raises, and during to rice ear sprouting period, vegetation index approaches maximal value, and water body index only has small size growth (see figure 2).For non-paddy rice crops or natural vegetation, start period at vegetation growth, vegetation index can be greater than the level in rice terrace transplanting time conventionally, and the water cut of vegetation and soil is all lower in period due to this, its water body index is conventionally lower, the water body index level during well below the rice transplanting phase; Along with the continuous growth of vegetation, vegetation index and water body index all improve constantly, in the time reaching the vegetation growth Sheng phase, conventionally earth's surface is covered by vegetation substantially, now the water cut of vegetation is also the period of enriching very much, vegetation index reaches maximal value, and water body index conventionally also synchronously reaches or approaches maximal value (see figure 3).Therefore relative other vegetation, from vegetation start to grow into peak of growing season during this period of time in, the water body index amplitude of variation of rice terrace is little, and vegetation index vary within wide limits, therefore it is on the low side that water body and vegetation index change Ratio index, can change Ratio index according to water body and vegetation index and carry out paddy rice identification (seeing Fig. 2-Fig. 3).Rule of judgment is: if meet M< ω, this pixel is paddy rice, otherwise is other crops or natural vegetation, and ω is constant, and ω is set as 0.25 in the present embodiment.If adopt the method for Xiao (2005), in the situation that original LSWI clock signal repeatedly occurs being greater than or very approaches EVI, will be mistaken for paddy rice, and adopt method proposed by the invention can judge preferably (Fig. 4).
According to above-mentioned paddy rice identification process, can realize more accurate paddy rice autodraft.
Be more than preferred embodiment of the present invention, all changes of doing according to technical solution of the present invention, when the function producing does not exceed the scope of technical solution of the present invention, all belong to protection scope of the present invention.
Claims (7)
1. the paddy rice autodraft method based on water body and vegetation index variation Ratio index, is characterized in that: comprises the steps,
S01: build level and smooth water body index, vegetation index time series data collection;
S02: reject the non-vegetation in study area and ever green vegetation pixel;
S03: obtain corresponding peak of growing season in each vegetation growth cycle by pixel;
S04: calculate from vegetation and start to grow into water body index between peak of growing season and the amplitude of variation of vegetation index;
S05: build water body and vegetation index and change Ratio index;
S06: change Ratio index according to water body and vegetation index and carry out paddy rice drawing.
2. the paddy rice autodraft method based on water body and vegetation index variation Ratio index according to claim 1, it is characterized in that: in described step S03, if find two or more local maximums in vegetation index timing curve within the year simultaneously, the time of being separated by according to two maximal values and relative size and with the gap condition of the local minimum of closing on, filter out each corresponding peak of growing season in vegetation growth cycle.
3. the paddy rice autodraft method based on water body and vegetation index variation Ratio index according to claim 1, it is characterized in that: in described step S04, arrive heading required time according to rice transplanting, vegetation growth based on each pixel is contained the phase, infer that by pixel vegetation in each growth cycle starts to grow into peak of growing season during this period of time, for calculating water body index and vegetation index starts to grow into the amplitude of variation in peak of growing season from vegetation, be recorded as respectively C
wand C
v.
4. the paddy rice autodraft method based on water body and vegetation index variation Ratio index according to claim 1, is characterized in that: in described step S05, it is C that water body and vegetation index change Ratio index M
wand C
vratio, its expression formula is: M=C
w/ C
v.
5. the paddy rice autodraft method based on water body and vegetation index variation Ratio index according to claim 1, it is characterized in that: in described step S06, carry out paddy rice identification according to the water body of paddy rice and the feature of vegetation index variation Ratio index, be that water body and vegetation index variation Ratio index are less than certain threshold value ω, be judged as paddy rice.
6. the paddy rice autodraft method based on water body and vegetation index variation Ratio index according to claim 5, is characterized in that: it is 0.25 that threshold value ω value is set, and this threshold value ω suitably adjusts in zones of different practical application in 0.05 scope.
7. according to the paddy rice autodraft method based on water body and vegetation index variation Ratio index described in claim 1 to 6 any one, it is characterized in that: the method is applicable to crops or soil utilizes in remote sensing automatic classification field.
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| CN104833637A (en) * | 2015-04-17 | 2015-08-12 | 中国科学院遥感与数字地球研究所 | Vegetation index time-spectrum data-based cyanobacteria bloom monitoring method and device |
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| CN107273797A (en) * | 2017-05-10 | 2017-10-20 | 中山大学 | Paddy rice sub-pixed mapping recognition methods based on the water body index coefficient of variation |
| CN107463775A (en) * | 2017-07-24 | 2017-12-12 | 福州大学 | Vegetation based on more Indices variation tendencies is lost in whereabouts recognition methods |
| CN107463775B (en) * | 2017-07-24 | 2019-11-12 | 福州大学 | Vegetation based on more Indices variation tendencies is lost whereabouts recognition methods |
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| CN111402169A (en) * | 2020-03-23 | 2020-07-10 | 宁波大学 | Method for repairing remote sensing vegetation index time sequence under influence of coastal tide |
| CN111402169B (en) * | 2020-03-23 | 2023-04-11 | 宁波大学 | Method for repairing remote sensing vegetation index time sequence under influence of coastal tide |
| CN111507303A (en) * | 2020-04-28 | 2020-08-07 | 同济大学 | Wetland plant species detection method |
| CN111612777A (en) * | 2020-05-23 | 2020-09-01 | 福州大学 | Soybean mapping method based on leaf aging and water loss index |
| CN111612777B (en) * | 2020-05-23 | 2022-07-22 | 福州大学 | Soybean mapping method based on leaf aging and water loss index |
| CN111652882A (en) * | 2020-07-07 | 2020-09-11 | 中国水利水电科学研究院 | Large-scale surface water product drawing precision evaluation method |
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