CN104239884A - Abnormal submerging area detection method based on remote sensing vegetation index time sequence - Google Patents
Abnormal submerging area detection method based on remote sensing vegetation index time sequence Download PDFInfo
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Abstract
The invention discloses an abnormal submerging area detection method based on a remote sensing vegetation index time sequence. According to the technical scheme, abnormal (irregular) submerging areas are directly detected from a vegetation index time sequence image, and submerging area extraction is not needed. The abnormal submerging area detection method includes the steps that firstly, time sequence data are divided into two sections including a historical time sequence and a monitoring period time sequence, wherein a to-be-detected abnormal submerging time period is included in a monitoring period; secondly, a sequence section which is stable and free of abnormal changes is found at the tail of the historical time sequence to serve as a stable historical time sequence, and fitting modeling is carried out on the stable historical time sequence; thirdly, a stable historical time sequence fitting model is used for conducting data forecasting on the monitoring period, a monitoring period time sequence forecasting value is compared with a monitoring period time sequence observation value, and the abnormal change level is accordingly calculated; finally, under the constraints of a certain vegetation index threshold value, the abnormal change level is compared with a preset abnormal change level threshold value, whether abnormal submerging occurs in the monitoring period or not is judged, and the abnormal submerging areas are finally detected.
Description
Technical field
The invention belongs to remote sensing image processing and information extraction field, relate to Remote Sensing Imagery Change Detection and method for flood submerged area extraction, be specifically related to a kind of method of based on remote sensing time series image, abnormal flooded area being carried out to direct-detection.
Background technology
The temporal and spatial orientation of flood is vital for the Distribution of biotic population and nutrient.Under normal circumstances, as occurred in the Seasonal flood in river, lake, flood is stretched and is disappeared in relatively little spatial dimension.But when there is great flood, flood will flood bulk zone, and the impact of increased surface covering and bringing on a disaster property of mankind's activity may be given.Whether in the past few years according to region same time section flooding by systematicness, flooded area can be divided into two large classes: systematicness flooding area and irregularity flooding area.Wherein, systematicness flooding area mainly comprises permanent flooding area (as some rivers, pond and lake) and seasonal flooding area (as paddy field, riverbed and loke shore), and irregularity flooding area refer to those due to precipitation, extraodinary flood, to breach a dyke or heavy irrigation etc. causes relatively short time period is by the region extremely flooded, irregularity flooding area is also called abnormal flooding area in this description.
Utilizing remote sensing image to carry out, flooding area extracts is at present conventional means because remote sensing image have wide coverage, can periodically repeated measures, lower-price characteristic.The remote sensing image being widely used in flooding area or Clean water withdraw at present comprises space resolution multi-spectral image, AVHRR and MODIS low spatial resolution high time resolution image etc. in ENVISAT and JERS-1 Synthetic Aperture Radar images, SPOT-5 high spatial resolution multi-spectral image, Landsat, and the comprehensive utilization of multiple remote sensing image.First existing flooding area analytical approach is carry out Clean water withdraw to remote sensing image by scape, then floods figure to these and carries out changing the analysis detecting and flood type.But these class methods mainly contain two broad aspect defects.The first, identifying water boy precision is unstable.Existing conventional water area extraction method, comprises single band threshold method, two waveband Ratio index method, linear solution mixes method, classification and integrated approach, have obvious identifying water boy precision instability problem.Such as, because the water body of different colours or turbidity has visibly different spectral signature, for different water bodys, optimal classification threshold value is different, and same class water body its optimal classification threshold value in the remote sensing image of different scape is also different.For another example, because the atural object (urban road, shade etc.) in remote sensing image with low surface albedo background shows similar features with water body in multispectral image, easily its mistake is divided into water body.The second, flood dynamic change difficulty characterizes.Satellite (as Landsat and HJ has 16 days revisiting period) the more difficult image capturing flood peak period of low observing frequency.In addition, cloud covering, cloud shade and low air quality can reduce quality and the utilizability of image.Therefore, the utilized remote sensing image that temporal resolution is low is difficult to the dynamic change characterizing real maximum submergence ratio and flood.3rd, detection efficiency is low on a large scale.When on large spatial scale, (as the Yangtze river basin, the whole nation even continent) detects abnormal flooded area, under the prioris such as the time period occurred not having flood and geographic range, if first carry out high precision identifying water boy to the remote sensing image of large space scope by scape, then carry out flooding type analysis, to waste time and energy, especially detect based on high time resolution or high spatial resolution remote sense image.
As can be seen here, in correlation technique the identification of flooded area and analytical approach have that identifying water boy precision is unstable, flood dynamic change difficulty characterizes, the problem of detection efficiency this three aspect low on a large scale.Further, in correlation technique not to the method that abnormal flooded area is directly detected.
Summary of the invention
In view of this, the present invention proposes a kind of abnormal flooded area detection method based on remote sensing vegetation index time series, the method utilizes the temporal change characteristic of remote sensing vegetation index time series to judge abnormal generation of flooding situation, without the need to carrying out identifying water boy to by scape remote sensing image, can directly detect abnormal flooded area.This method avoid the problem of the identifying water boy precision instability in art methods, flood dynamic change difficulty sign, be applicable to large-scale abnormal flooded area simultaneously and detect.
A kind of abnormal flooded area detection method basic ideas based on remote sensing vegetation index time series of the present invention are: first remote sensing vegetation index time series data is divided into two sections, be historical time sequence and monitoring phase time series respectively, abnormal flooding time section wherein to be detected was included in the monitoring phase.Secondly, find out the time series of a section steady (change without exception) as steady historical time sequence at historical time sequence end automatic seeking, and matching modeling is carried out to it.Then, utilize above-mentioned steady historical time sequence fit model to carry out data prediction to the monitoring phase, monitoring phase time series forecasting value and monitoring phase time series observed reading are compared, therefrom calculates ANOMALOUS VARIATIONS level.Finally, under certain vegetation index threshold value constraint, ANOMALOUS VARIATIONS level and the ANOMALOUS VARIATIONS level thresholds preset are compared, judge whether occur in the monitoring phase extremely flooding situation, finally detect abnormal flooded area.
Technical scheme: realize the technical scheme flow process of the inventive method as shown in Figure 1, technical scheme is described below:
A. from remote sensing vegetation index time series image, extract vegetation index time series observed reading data by pixel, vegetation index time series observed reading is expressed as { Y
t: t=1,2 ..., n, n+1 ..., N ..., seasonal effect in time series length is more than or equal to N;
B. from vegetation index time series observed reading Y
tin be partitioned into time series observed reading before the phase to be monitored, namely historical time sequence observed reading, is expressed as
C. from vegetation index time series observed reading Y
tin be partitioned into the time series observed reading in phase interval to be monitored, namely monitor phase time series observed reading, be expressed as
D. find out the time series of a section steady (change without exception) as steady historical time sequence at historical time sequence observed reading end automatic seeking, be expressed as
and data fitting modeling is carried out to steady historical time sequence, obtain steady historical time series model
E. by steady historical time series model
dope the time series data in monitoring phase interval, namely monitor phase time series forecasting value, be expressed as
F. by the minimum value VI in monitoring phase time series observed reading
mincompare, if VI with presetting vegetation index threshold value TVI
min< TVI, be for further processing (step G), otherwise be non-abnormal flooded area (other region) by the territorial classification represented by this time series;
G. according to monitoring phase time series observed reading
And predicted value
Between difference, calculate the horizontal CL of time series variation in the monitoring phase;
H. judge whether change level CL is less than presetting change level threshold value TCL, be then if so, abnormal flooded area by the territorial classification represented by this time series, otherwise be categorized as other region.
Further, in step, institute's usage data vegetation index (VI) can be normalization difference vegetation index (NDVI), also can be enhancement mode meta file (EVI).
Further, in step D, time series model of fit can be season-Trend Decomposition model, that is: Y
t=S
t+ T
t+ ε
t, wherein each sum term represents seasonal effect in time series item in season, trend term and error term respectively.
Further, in step D, steady historical time sequence
automatic selection can adopt error of fitting Cumulate Sum method, i.e. Cumulative Sum (CUSUM) of residuals.
Further, in step D, data fitting modeling is carried out to steady historical time sequence, linear trend-harmonic wave model of fit in season can be adopted, that is:
Wherein overtone order K can value be 3.
Further, in step F, presetting vegetation index threshold value TVI can be 0.1, and the object of setting vegetation index threshold value is to get rid of the time series data belonging to non-submersion region as much as possible.
Further, in step G, the horizontal CL of the time series variation in the monitoring phase can by calculating monitoring phase time series observed reading
With predicted value
Difference median obtain, that is:
Further, in steph, for different geographic areas and different monitoring phases, there is notable difference in change level threshold value TCL.TCL can calculate CL approximate range by selecting from abnormal flooding area sample time-series to carry out above step and obtains, and also can rule of thumb set.For abnormal situation of flooding, TCL should be negative value.The object of setting change level threshold value is the error in order to suppress caused by less change level, less change level owing to same or similar atural object year border vegetation index difference and the irregular fluctuation etc. of water body vegetation index time series.
By means of technical scheme provided by the invention, by presetting monitoring phase, vegetation index threshold value and change level threshold value, automatically abnormal flooded area can be detected according to vegetation index time series variation characteristic.In the inventive solutions, do not need to carry out by scape identifying water boy to remote sensing image, do not need to distinguish different water body classification or water quality, the priori of true submergence ratio (namely detect and be not limited in submergence ratio, and abnormal flooded area can be gone out at zone of ignorance direct-detection) is not needed yet.Abnormal flooded area detection method based on remote sensing vegetation index time series of the present invention, simply, geographic area and type of ground objects applicability by force, can be applicable to large-scale abnormal flooded area and detect for data processing and calculating.
Accompanying drawing explanation
Accompanying drawing is used to provide a further understanding of the present invention, and forms the part of this instructions, together with embodiments of the present invention for explaining the present invention, is not construed as limiting the invention.In the accompanying drawings:
Fig. 1 is the abnormal flooded area detection method process flow diagram based on remote sensing vegetation index time series of the present invention;
Fig. 2 is that the three kinds of typical cases being applied to the embodiment of the present invention are flooded NDVI time series data corresponding to type and extremely flood testing process;
Fig. 3 is 16 days synthesis MODIS NDVI images when being applied to the Poyang Lake Zone, test site of the embodiment of the present invention summer, maximum flood flooded in 2010;
Fig. 4 is 16 days synthesis MODIS NDVI time series images in Poyang Lake Zone, the test site end of the year in February, 2000 to 2010 being applied to the embodiment of the present invention;
Fig. 5 is test site monitoring phase in 2010 " change level CL " the result figure according to the embodiment of the present invention;
Fig. 6 is that the abnormal flooded area detection method based on remote sensing vegetation index time series according to the present invention is to the testing result figure of abnormal flooded area in 2010, test site.
Embodiment
Now by reference to the accompanying drawings and specific embodiment, illustrate the present invention further, specific embodiment should be understood and be only not used in restriction the present invention for illustration of the present invention, the application's claims limited range is all fallen within to the amendment of the various equivalent form of value of the present invention.
Fig. 1 is the abnormal flooded area detection method process flow diagram based on remote sensing vegetation index time series of the present invention, and in conjunction with the specific descriptions in " summary of the invention ", the specific embodiment of the present invention comprises the following steps:
A. from remote sensing vegetation index time series image, extract vegetation index time series observed reading data by pixel, vegetation index time series observed reading is expressed as { Y
t: t=1,2 ..., n, n+1 ..., N ..., seasonal effect in time series length is more than or equal to N;
B. from vegetation index time series observed reading Y
tin be partitioned into time series observed reading before the phase to be monitored, namely historical time sequence observed reading, is expressed as
C. from vegetation index time series observed reading Y
tin be partitioned into the time series observed reading in phase interval to be monitored, namely monitor phase time series observed reading, be expressed as
D. find out the time series of a section steady (change without exception) as steady historical time sequence at historical time sequence observed reading end automatic seeking, be expressed as
and data fitting modeling is carried out to steady historical time sequence, obtain steady historical time series model
E. by steady historical time series model
dope the time series data in monitoring phase interval, namely monitor phase time series forecasting value, be expressed as
F. by the minimum value VI in monitoring phase time series observed reading
mincompare, if VI with presetting vegetation index threshold value TVI
min< TVI, be for further processing (step G), otherwise be non-abnormal flooded area (other region) by the territorial classification represented by this time series;
G. according to monitoring phase time series observed reading
And predicted value
Between difference, calculate the horizontal CL of time series variation in the monitoring phase;
H. judge whether change level CL is less than presetting change level threshold value TCL, be then if so, abnormal flooded area by the territorial classification represented by this time series, otherwise be categorized as other region.
Further, in step, institute's usage data vegetation index (VI) can be normalization difference vegetation index (NDVI), also can be enhancement mode meta file (EVI).
Further, in step D, time series model of fit can be season-Trend Decomposition model, that is: Y
t=S
t+ T
t+ ε
t, wherein each sum term represents seasonal effect in time series item in season, trend term and error term respectively.
Further, in step D, steady historical time sequence
automatic selection can adopt error of fitting Cumulate Sum method, i.e. Cumulative Sum (CUSUM) of residuals.
Further, in step D, data fitting modeling is carried out to steady historical time sequence, linear trend-harmonic wave model of fit in season can be adopted, that is:
Wherein overtone order K can value be 3.
Further, in step F, presetting vegetation index threshold value TVI can be 0.1, and the object of setting vegetation index threshold value is to get rid of the time series data belonging to non-submersion region as much as possible.
Further, in step G, the horizontal CL of the time series variation in the monitoring phase can by calculating monitoring phase time series observed reading
With predicted value
Difference median obtain, that is:
Further, in steph, for different geographic areas and different monitoring phases, there is notable difference in change level threshold value TCL.TCL can calculate CL approximate range by selecting from abnormal flooding area sample time-series to carry out above step and obtains, and also can rule of thumb set.For abnormal situation of flooding, TCL should be negative value.The object of setting change level threshold value is the error in order to suppress caused by less change level, less change level owing to same or similar atural object year border vegetation index difference and the irregular fluctuation etc. of water body vegetation index time series.
Fig. 2 is that the three kinds of typical cases being applied to the embodiment of the present invention are flooded NDVI time series data corresponding to type and extremely flood testing process, is described in detail below according to embodiment and Fig. 2 to the specific embodiment of the present invention:
A. from NDVI time series image (20 scapes in 2000 of the synthesis in MODIS16 days in the end of the year in February, 2000 to 2010 of the Chinese Poyang Lake Zone of covering, all the other annual 23 scapes, totally 250 scapes) in choose three kinds of typical cases and flood NDVI time series observed reading corresponding to type (abnormal flooding area, seasonal flooding area and permanent flooding area), be expressed as { Y
t: t=1,2 ..., 250}, length of time series is N=250;
B. from NDVI time series observed reading Y
tin be partitioned into time series (totally 227 observed readings) before 2010, namely historical time sequence observed reading, is expressed as
as the solid line time series on the left of vertical dotted line in Fig. 2;
C. from NDVI time series observed reading Y
tin be partitioned into the time series of 2010, namely monitor phase time series observed reading, be expressed as
as the solid line time series on the right side of vertical dotted line in Fig. 2;
D. by season-trend fitting method and error of fitting Cumulate Sum method (CUSUM), at historical time sequence observed reading end, automatic seeking finds out steady history and matching, obtains steady historical time series model
Wherein overtone order K value is 3, and time series frequency f value is 23, and after steady history matching, data are as the dotted line time series on the left of vertical dotted line in Fig. 2;
E. by steady historical time series model
dope monitoring phase time series data, namely monitor phase time series forecasting value
as the dotted line time series on the right side of vertical dotted line in Fig. 2;
F. by the minimum value VI in monitoring phase time series observed reading
mincompare, if VI with presetting a certain NDVI threshold value TVI=0.1
min< TVI, goes to step G, otherwise is non-abnormal flooded area (other region) by the territorial classification represented by this time series;
G. according to monitoring phase time series observed reading
And predicted value
Between difference (gray area as in the monitoring phase in Fig. 2), calculate the horizontal CL of time series variation in the monitoring phase;
H. judge whether change level CL is less than presetting a certain change level threshold value TCL=-0.2, be then if so, abnormal flooded area by the territorial classification represented by this time series, otherwise be categorized as other region.
Below the embodiment that the inventive method is applied to test site is described.
Chinese Poyang Lake Zone is selected in test site.Poyang Lake is that seasonality floods lake, lake water submergence ratio is obvious with seasonal variations within the year, summer " flood a slice ", winter " low water one line ", and the impact owing to being subject to quantity of precipitation and the Changjiang river and other river between different year also can present different submergence ratio.Summer in 2010, there is extraodinary flood in Poyang Lake, is flood maximum between 2000 to 2014.In the method for flood submerged area of 2010, comprise permanent flooding area, seasonal flooding area and irregularity flooding area (abnormal flooding area).The abnormal flooding area of the embodiment of the present invention to Poyang Lake Zone in 2010 is detected.
Fig. 3 is 16 days synthesis MODIS NDVI images when being applied to the Poyang Lake Zone, test site of the embodiment of the present invention summer, maximum flood flooded in 2010.Fig. 4 is 16 days synthesis MODIS NDVI time series images in Poyang Lake Zone, the test site end of the year in February, 2000 to 2010 being applied to the embodiment of the present invention, wherein totally 20 scape images in 2000, all the other annual 23 scapes, totally 250 scapes.
In embodiments of the invention, namely apply the abnormal flooded area detection method based on remote sensing vegetation index time series of the present invention and detect Poyang Lake Zone at the abnormal submergence ratio of 2010, the major parameter related in testing process as shown in Table 1.
Table one
| Parameter place step | Parameter | Value | Explanation |
| B | n | 227 | Historical time sequence data 1 to n |
| C | N | 250 | Monitoring phase time series data n+1 to N |
| D | K | 3 | Season-overtone order of Trend Decomposition model of fit |
| F | TVI | 0.1 | The vegetation index threshold value in non-submersion region is got rid of for part |
| H | TCL | -0.2 | Represent abnormal monitoring phase time series variation level thresholds of flooding |
Within 2010, the phase " change level CL " is monitored as shown in Figure 5 according to Poyang Lake Zone in the testing process of the inventive method.Fig. 6 is the Poyang Lake Zone abnormal submergence ratio testing result in 2010 based on MODIS NDVI time series image according to the embodiment of the present invention.
These are only the preferred embodiments of the present invention, be not limited to the present invention.For a person skilled in the art, the present invention can have various modifications and variations.In every case any amendment done within basic ideas of the present invention and principle, equivalent replacement and improvement etc., all should be included within protection scope of the present invention.
Claims (7)
1., based on an abnormal flooded area detection method for remote sensing vegetation index time series, the method can go out abnormal flooded area and without the need to carrying out submergence ratio extraction or identification, it is characterized in that by direct-detection from remote sensing time series image, comprises the following steps:
A. from the remote sensing vegetation index time series image obtained, vegetation index time series observed reading data are extracted by pixel;
B. from vegetation index time series observed reading, the time series observed reading before the phase to be monitored is partitioned into, i.e. historical time sequence observed reading;
C. from vegetation index time series observed reading, be partitioned into the time series observed reading in phase interval to be monitored, namely monitor phase time series observed reading;
D. find out the time series of one section of change steady, without exception as steady historical time sequence at historical time sequence observed reading end automatic seeking, and data fitting modeling is carried out to steady historical time sequence, obtain steady historical time series model;
E. doped the time series data in monitoring phase interval by steady historical time series model, namely monitor phase time series forecasting value;
F. by the minimum value VI in monitoring phase time series observed reading
mincompare, if VI with presetting vegetation index threshold value TVI
min< TVI, carries out step G, otherwise is non-abnormal flooded area by the territorial classification represented by this time series;
G. according to the difference between monitoring phase time series observed reading and predicted value and predicated error, the horizontal CL of time series variation in the monitoring phase is calculated;
H. judge whether change level CL is less than presetting change level threshold value TCL, be then if so, abnormal flooded area by the territorial classification represented by this time series, otherwise be categorized as non-abnormal flooded area.
2. abnormal flooded area according to claim 1 detection method, it is characterized in that, in step, institute usage data vegetation index (VI) is conventional normalization difference vegetation index (NDVI) or enhancement mode meta file (EVI), or is the vegetation index of other type.
3. abnormal flooded area according to claim 1 detection method, is characterized in that, in step D, time series model of fit be season-trend model, that is: Y
t=S
t+ T
t+ ε
t, wherein each sum term represents seasonal effect in time series item in season, trend term and error term respectively.
4. abnormal flooded area according to claim 1 detection method, is characterized in that, in step D, the automatic selection of steady historical time sequence adopts error of fitting Cumulate Sum method or adopts the stationary time series extracting method of other form.
5. abnormal flooded area according to claim 1 detection method, is characterized in that, in step F, presetting vegetation index threshold value TVI is empirical value, and TVI selects the vegetation index distinguishing flooding area and non-submersion district.
6. abnormal flooded area according to claim 1 detection method, is characterized in that, in step G, the horizontal CL of the time series variation in the monitoring phase is obtained by the median of computational prediction error, or is obtained by other quantification manner.
7. abnormal flooded area according to claim 1 detection method, is characterized in that, in steph, change level threshold value TCL is empirical value, or estimates TCL according to the CL scope that sample time-series calculates, and TCL is negative value.
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| CN110472525A (en) * | 2019-07-26 | 2019-11-19 | 浙江工业大学 | A kind of noise detecting method of time series remote sensing vegetation index |
| CN110569733A (en) * | 2019-08-09 | 2019-12-13 | 中国科学院南京地理与湖泊研究所 | Lake long time sequence continuous water area change reconstruction method based on remote sensing big data platform |
| CN110569733B (en) * | 2019-08-09 | 2022-02-01 | 中国科学院南京地理与湖泊研究所 | Lake long time sequence continuous water area change reconstruction method based on remote sensing big data platform |
| CN113343180A (en) * | 2021-06-17 | 2021-09-03 | 北京市环境保护科学研究院 | Vegetation ecological environment mutation monitoring method and system |
| CN114219847A (en) * | 2022-02-18 | 2022-03-22 | 清华大学 | Method and system for determining crop planting area based on phenological characteristics and storage medium |
| CN114219847B (en) * | 2022-02-18 | 2022-07-01 | 清华大学 | Method and system for determining crop planting area based on phenological characteristics and storage medium |
| CN115964620A (en) * | 2023-03-15 | 2023-04-14 | 阿里巴巴（中国）有限公司 | Data processing method, storage medium and electronic device |
| CN115964620B (en) * | 2023-03-15 | 2023-12-12 | 阿里巴巴（中国）有限公司 | Data processing method, storage medium and electronic device |
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