CN109840516A - A kind of water body variation automatic identifying method based on timing remote sensing image - Google Patents
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Abstract
The present invention relates to a kind of, and the water body based on timing remote sensing image changes automatic identifying method.The present invention is primarily based on vegetation index time series data and extracts Vegetation abundance index, brightness is established by K-T Transformation, green degree and humidity index time series data collection, the mean value and standard deviation of research on utilization area wood land index simultaneously, to brightness, green degree and three indexs of humidity are standardized, combining standardized treated brightness, green degree and humidity index, extract Annoyance Index, and then detect Vegetation abundance, Annoyance Index and humidity index whether there is significant change trend, final foundation Vegetation abundance, the variation tendency of Annoyance Index and humidity index, it establishes the water body based on timing remote sensing image and changes automatic identifying method.The present invention has many advantages, such as that space-time expending is good, strong robustness, automatically extracts suitable for the variation of a wide range of water body.
Description
Technical field
The present invention relates to remote sensing information process fields, and in particular to a kind of water body variation based on timing remote sensing image is automatic
Recognition methods.
Background technique
Wetland is important natural resources, is the habitat of numerous animals and plants, occupies importantly in human ecological system
Position.Any life all be unable to do without water.Important lakes some in recent years such as Dongting Lake area of lake reduces phenomenon, causes all circles' height
Degree concern.It, can a wide range of quick obtaining water body distribution and its Variation Features using timing remote sensing image.In order to preferably mention
Water intaking body, numerous scholars propose a series of water body indexes that can effectively highlight water body, such as LSWI (Land Surface
Water Index)、NDWI (Normalized Difference Water Index)、MNDWI (Modified
Normalized Difference Water Index) etc..These water body indexes play in the Clean water withdraws such as lake, river
Important function.But its challenge encountered is: when being applied to a wide range of Clean water withdraw, being easy to appear threshold value setting difficulty and is easy
Technical bottleneck by the secretly object such as shade.
Term is explained:
MODIS data: Moderate Imaging Spectroradiomete data, full name are MODerate resolution Imaging
Spectroradiometer。
Vegetation index: vegetation index is the factor for characterizing vegetation growth state and spacial distribution density.Common vegetation
Index has NDVI and EVI.NDVI is normalized differential vegetation index, and full name is Normalized Difference Vegetation
Index.EVI is enhancement mode meta file, and full name is Enhanced Vegetation Index.The calculation formula of EVI index
Are as follows:, wherein Red, Blue, NIR are respectively feux rouges, blue light and near-infrared
Wave band.
Summary of the invention
In view of this, the purpose of the present invention is to provide a kind of, the water body based on timing remote sensing image changes automatic identification side
Method does not need setting threshold value, can be advantageously applied to a wide range of fast slowdown monitoring of long-term sequence water body region of variation.
To achieve the above object, the present invention adopts the following technical scheme:
A kind of water body variation automatic identifying method based on timing remote sensing image, comprising the following steps:
Step S01: acquisition research area vegetation index time series data over the years, and establish vegetation index time series data collection；
Step S02: according to obtained vegetation data time sequence data set, Vegetation abundance index is extracted year by year；
Step S03: MODIS remote sensing image wave band reflectivity data is combined to according to research area's maximum, is established by K-T Transformation
Brightness, green degree and humidity index time series data collection；
Step S04: according to obtained brightness, green degree and humidity index time series data collection, Annoyance Index time series data collection is established；
Step S05: the variation tendency of Annoyance Index, Vegetation abundance index and humidity index is calculated；
Step S06: it according to the variation tendency of obtained Annoyance Index, Vegetation abundance and humidity index, obtains research area's water body and becomes
Change distribution map.
Further, based on the second quartile Q2 of vegetation index time series data collection, when obtaining enhancement mode meta file
The middle high level region M of ordinal number evidence calculates the average value of the middle high level region M of enhancement mode meta file time series data, as vegetation
Abundance index.
Further, the step S03 specifically: MODIS remote sensing image wave band 1-7 reflection was combined to based on 8 days maximums
Rate data calculate brightness, green degree and three indexs of humidity, ordinal number when establishing brightness, green degree and humidity index by K-T Transformation
According to collection, wherein the calculation formula of brightness B, green degree G and humidity W index are respectively as follows:
B=0.3956*Band1 + 0.4718*Band2 + 0.3354*Band3 + 0.3834*Band4 + 0.3946*
Band5 + 0.3434* Band6+0.2964* Band7
G= -0.3399* Band1 + 0.5952*Band2 - 0.2129*Band3 - 0.2222*Band4 + 0.4617*
Band5- 0.1037* Band6 - 0.4600* Band7
W=0.10839 *Band1+ 0.0912*Band2 + 0.5065*Band3 + 0.4040*Band4 - 0.2410*
Band5- 0.4658* Band6 - 0.5306* Band7
Its medium wave band Band1, Band2, Band3, Band4, Band5, Band6 and Band7 are respectively MODIS image wave band 1-7
Corresponding wave band reflectivity.
Further, the step S04 specifically:
Step S401: pair brightness, it is green degree and humidity index be standardized;
The standardized calculation method of brightness, green degree and humidity index is successively are as follows:
B
r
= (B-B
u
)/ B
ǒ
G
r
= (G-G
u
)/ G
ǒ
W
r
= (W-W
u
)/ W
ǒ
Wherein, Br、GrAnd WrIt is brightness, green degree and the humidity index B after standardization respectively u , G u , W u Respectively study area
Interior brightness, it is green degree, humidity index average value；B ǒ , G ǒ , W ǒ Respectively study the standard of brightness in area, green degree, humidity index
Difference；
Step S402: using brightness, green degree and the humidity index after standardization, Annoyance Index DI is established；
DI=B r -(G r +W r ) 。
Further, the step S05 uses SenShi Slope Method and Mann-Kendall method, detects and interferes by pixel
Index, Vegetation abundance and humidity index variation tendency over the years.
Compared with the prior art, the invention has the following beneficial effects:
1, the present invention utilizes space and time continuous vegetation index time series data collection, by extracting Vegetation abundance index, avoids as far as possible each
Kind weather conditions and sensor bring data noise or other data quality problems, characterization vegetation totally cover situation.
2, particularity of the present invention in view of water body relative to the different atural objects such as vegetation, exposed soil and impervious surface, i.e. vegetation
Abundance and Annoyance Index are minimum, and other atural objects are usually in the lower situation of Vegetation abundance, Annoyance Index with higher,
And then judge whether according to Vegetation abundance, Annoyance Index and in conjunction with humidity index that water body variation occurs
3, the present invention utilizes multi objective secular variation trend, judges whether that water body, which occurs, increases or decreases situation, helps to eliminate
Observe the influence of shade and water body Various Seasonal wave band in image data.
Detailed description of the invention
Fig. 1 is the implementation flow chart of the embodiment of the present invention；
Fig. 2 is the MODIS EVI and Vegetation abundance index timing of the different types of ground objects of 2001-2017 in one embodiment of the invention
Curve graph；
Fig. 3 is the humidity index time-sequence curve chart of the different types of ground objects of 2001-2017 in one embodiment of the invention；
Fig. 4 is the Annoyance Index time-sequence curve chart of the different types of ground objects of 2001-2017 in one embodiment of the invention；
Fig. 5 is Annoyance Index-Vegetation abundance scatter plot in one embodiment of the invention；
Fig. 6 is Annoyance Index-humidity index scatter plot in one embodiment of the invention；
Fig. 7 is that water body changes automatic identifying method techniqueflow chart in one embodiment of the invention；
Fig. 8 is that ground mulching region of variation spatial distribution map in area's is studied in one embodiment of the invention.
Specific embodiment
The present invention will be further described with reference to the accompanying drawings and embodiments.
Please referring to Fig. 1, the present invention provides a kind of water body variation automatic identifying method based on timing remote sensing image, including with
Lower step:
Step S01: vegetation index time series data collection is established.
It is combined to MOD09A1 wave band reflectivity data using 500 meters of 8 days maximums, calculates MODIS EVI.Based on cloudless day
The MODIS EVI time series data being calculated obtains 2001-2017 research area MODIS day by day using linear interpolation method
EVI time series data collection.Then, using Whittaker Smoother data smoothing method, smoothed out 2001- is constructed by pixel
The MODIS EVI time series data collection day by day of research area space and time continuous in 2017.
Step S02: Vegetation abundance index is extracted year by year.
Area's space and time continuous MODIS EVI time series data collection day by day is studied based on 2001-2017, calculates 2001- year by year
The Vegetation abundance of 2017 chronology sign vegetation cover situation.
The second quartile Q2(median of the time vegetation index time series data collection is obtained by pixel year by year), in this base
All data that the time vegetation index time series data collection is greater than or equal to the second quartile are successively extracted on plinth, respectively pair
Answer the middle high level region M of time MODIS EVI time series data collection day by day.And then calculate the time day by day MODIS EVI when ordinal number
According to the average value of the middle high level region M of collection, it is defined as Vegetation abundance.Vegetation abundance is calculated by pixel year by year, obtains 2001-2017
Year Vegetation abundance time series data collection.By taking four kinds of vegetation, impervious surface, water body, bare area types of ground objects as an example, it is formed by 2001-
MODIS EVI in 2017 and Vegetation abundance index time-sequence curve chart are shown in Fig. 2.
Step S03: brightness, green degree and humidity index time series data collection are established by K-T Transformation
It is combined to MODIS remote sensing image wave band 1-7 reflectivity data based on 8 days maximums of 2001-2017, is obtained by K-T Transformation
Obtain brightness, green degree and three indexs of humidity.Wherein the coefficient table of K-T Transformation is shown in Table 1.
The K-T Transformation coefficient table of 1 MODIS of table
| Wave band | Band1 | Band2 | Band3 | Band4 | Band5 | Band6 | Band7 |
| MODIS (nm) | 620~670 | 841~876 | 459~479 | 545~565 | 1230~1250 | 1628~1652 | 2105~2155 |
| Brightness | 0.3956 | 0.4718 | 0.3354 | 0.3834 | 0.3946 | 0.3434 | 0.2964 |
| Green degree | -0.3399 | 0.5952 | -0.2129 | -0.2222 | 0.4617 | -0.1037 | -0.4600 |
| Humidity | 0.10839 | 0.0912 | 0.5065 | 0.4040 | -0.2410 | -0.4658 | -0.5306 |
Brightness (B), the green calculation formula difference for spending (G) and humidity (W) index:
B=0.3956* Band1 + 0.4718* Band2 + 0.3354* Band3 +0.3834* Band4 + 0.3946*
Band5 + 0.3434* Band6+0.2964* Band7
G= -0.3399* Band1 + 0.5952* Band2 - 0.2129* Band3 -0.2222* Band4 +
0.4617*Band5- 0.1037* Band6-0.4600* Band7
W=0.10839 * Band1 +0.0912* Band2+0.5065* Band3 +0.4040*Band4 - 0.2410*
Band5- 0.4658* Band6 - 0.5306* Band7
Its medium wave band Band1, Band2, Band3, Band4, Band5, Band6 and Band7 are respectively MODIS image wave band 1-7
Corresponding wave band reflectivity.
Brightness, green degree and three indexs of humidity are obtained by K-T Transformation according to above-mentioned formula by the phase year by year by pixel, it is raw
At 500 meters of 2001-2017 brightness in 8 days, green degree and humidity index time series data collection.With vegetation, impervious surface, water body, bare area
For four kinds of types of ground objects, it is formed by 2001-2017 humidity index time-sequence curve chart and sees Fig. 3.
Step S04: Annoyance Index time series data collection is established.
For vegetation, green degree and humidity are usually higher, and brightness is lower.And for exposed soil, impervious surface etc. and
Speech, then it is just opposite.In order to eliminate season and different times, different zones image difference bring influences, first by brightness,
Green degree and humidity index time series data collection are standardized.Using the average and standard deviation of wood land, carry out brightness,
The standardization of green degree, humidity index.Wherein brightness, it is green degree and humidity index standardized calculation method successively are as follows:
B
r
= (B-B
u
)/ B
ǒ
G
r
= (G-G
u
)/ G
ǒ
W
r
= (W-W
u
)/ W
ǒ
Wherein, B, G, W are respectively the brightness generated after step S03 K-T Transformation, green degree, humidity index；B u , G u , W u Respectively
For research area wood land in brightness, it is green degree, humidity index average value；B ǒ , G ǒ , W ǒ Respectively study in area wood land
Brightness, it is green degree, humidity index standard deviation.
Using brightness, green degree and the humidity index after standardization, Annoyance Index (abbreviation DI) is established.Its calculation formula is:
DI=B
r
-(G
r
+W
r
)
Wherein, DI is Annoyance Index, Br、GrAnd WrIt is brightness, green degree and the humidity index after standardization respectively.
Annoyance Index is calculated by the phase year by year by pixel, 2001-2017 is generated and studies area's Annoyance Index time series data collection.With
For four kinds of vegetation, impervious surface, water body, bare area types of ground objects, it is formed by 2001-2017 Annoyance Index time-sequence curve chart
See Fig. 4.
Step S05: the variation tendency of Annoyance Index, Vegetation abundance and humidity index is calculated
Using SenShi gradient method, it is based respectively on 2001-2017 Annoyance Index, Vegetation abundance and humidity index time series data
Collection, the variation tendency Q of 2001-2017 Annoyance Index, Vegetation abundance and humidity index is successively calculated by pixel.As Q > 0, table
Show the timing curve in certain ascendant trend；As Q < 0, indicate that the timing curve has certain downward trend.It is based on
Mann-Kendall method further judges whether the variation tendency of the timing curve is significant.According to significance test as a result,
Variation tendency is divided into three kinds of situations: significant positive trend (significant ascendant trend) does not have trend (constant) and significantly bears
Trend (significant downward trend).
Step S06: research area's water body change profile figure is obtained.
Annoyance Index is first depending on the presence or absence of variation tendency, primarily determines potential water body region of variation, and then according to plant
By abundance and humidity index variation tendency, judge that water body decreases or increases actually；
For vegetation, impervious surface, bare area, four kinds of water body different types of ground objects, Annoyance Index numerical value shows obviously
It is different.It is embodied in, the Annoyance Index of water body is minimum, is secondly vegetation, is again impervious surface, the Annoyance Index of exposed soil is most
It is high.If significant changes trend occurs for Annoyance Index, show to change between these four different types of ground objects.
For vegetation, impervious surface, bare area, four kinds of water body different types of ground objects, Vegetation abundance and humidity index
There are notable differences.For Vegetation abundance, secondly the Vegetation abundance highest of vegetation is impervious surface, bare area, the vegetation of water body
Abundance is minimum.For humidity index, from low to high successively are as follows: exposed soil, impervious surface, vegetation, water body.
Therefore, several exposed soils, impervious surface, vegetation, water body are chosen with reference to point, is with Annoyance Index, Vegetation abundance
Transverse and longitudinal coordinate does scatter plot (see figure 5).In four kinds of different types of ground objects, water body close to origin and fall in third and fourth as
Limit (see figure 5).Several exposed soils, impervious surface, vegetation, water body are chosen with reference to point, using Annoyance Index, humidity index as transverse and longitudinal
Coordinate is shown in Fig. 6 as scatter plot.In four kinds of different types of ground objects, the humidity highest of water body, Annoyance Index are smaller and are negative value, water
Body falls in the second quadrant or third quadrant close to origin position (see figure 6).
According to water body Annoyance Index, Vegetation abundance be relatively minimum and humidity index with respect to highest the characteristics of, interference is referred to
Number, Vegetation abundance are in significant downward trend and water body index is judged as that water body increases in the pixel of significant ascendant trend；It will do
It disturbs index, Vegetation abundance and is in significant ascendant trend and water body index is in the pixel of significant downward trend, be judged as that water body is reduced.
Judge whether water body increases or decreases by pixel, ultimately generates research area's water body change profile figure.It is provided according to the present embodiment
Method, by taking Qinghai Province as an example, research area water body change profile figure obtained is shown in Fig. 8.
The foregoing is merely presently preferred embodiments of the present invention, all equivalent changes done according to scope of the present invention patent with
Modification, is all covered by the present invention.
Claims (8)
1. a kind of water body based on timing remote sensing image changes automatic identifying method, which comprises the following steps:
Step S01: acquisition research area vegetation index time series data over the years, and establish vegetation index time series data collection；
Step S02: according to obtained vegetation data time sequence data set, Vegetation abundance index is extracted year by year；
Step S03: MODIS remote sensing image wave band reflectivity data is combined to according to research area's maximum, is established by K-T Transformation
Brightness, green degree and humidity index time series data collection；
Step S04: according to obtained brightness, green degree and humidity index time series data collection, Annoyance Index time series data collection is established；
Step S05: the variation tendency of Annoyance Index, Vegetation abundance index and humidity index is calculated；
Step S06: it according to the variation tendency of obtained Annoyance Index, Vegetation abundance and humidity index, obtains research area's water body and becomes
Change distribution map.
2. a kind of water body based on timing remote sensing image according to claim 1 changes automatic identifying method, feature exists
In: the step S02 specifically: the second quartile Q2 based on vegetation index time series data collection obtains enhanced vegetation and refers to
The middle high level region M of number time series data, calculates the average value of the middle high level region M of enhancement mode meta file time series data, as
Vegetation abundance index.
3. a kind of water body based on timing remote sensing image according to claim 1 changes automatic identifying method, feature exists
In: the step S03 specifically: MODIS remote sensing image wave band 1-7 reflectivity number is combined to based on 8 days maximums of 2001-2017
According to, by K-T Transformation calculate brightness, it is green degree and three indexs of humidity, establish 2001-2017 brightness, green degree and humidity index
Time series data collection, wherein the calculation formula of brightness B, green degree G and humidity W index are respectively as follows:
B=0.3956*Band1 + 0.4718*Band2 + 0.3354*Band3 + 0.3834*Band4 + 0.3946*
Band5 + 0.3434* Band6+0.2964* Band7
G= -0.3399* Band1 + 0.5952*Band2 - 0.2129*Band3 - 0.2222*Band4 + 0.4617*
Band5- 0.1037* Band6 - 0.4600* Band7
W=0.10839 *Band1+ 0.0912*Band2 + 0.5065*Band3 + 0.4040*Band4 - 0.2410*
Band5- 0.4658* Band6 - 0.5306* Band7
Its medium wave band Band1, Band2, Band3, Band4, Band5, Band6 and Band7 are respectively MODIS image wave band 1-7
Corresponding wave band reflectivity.
4. a kind of water body based on timing remote sensing image according to claim 1 changes automatic identifying method, feature exists
In: the step S04 specifically:
Step S401: pair brightness, it is green degree and humidity index be standardized;
The standardized calculation method of brightness, green degree and humidity index is successively are as follows:
B
r
= (B-B
u
)/ B
ǒ
G
r
= (G-G
u
)/ G
ǒ
W
r
= (W-W
u
)/ W
ǒ
Wherein, Br、GrAnd WrIt is brightness, green degree and the humidity index after standardization respectively；B u , G u , W u Respectively study
Brightness in area, green degree, humidity index average value；B ǒ , G ǒ , W ǒ Respectively study area in brightness, it is green degree, humidity index mark
It is quasi- poor；
Step S402: using brightness, green degree and the humidity index after standardization, Annoyance Index DI is established；
DI=B
r
-(G
r
+W
r
)
Wherein, DI is Annoyance Index, and Br, Gr and Wr are brightness, green degree and the humidity index after standardization respectively；By pixel
Annoyance Index is calculated by the phase year by year, 2001-2017 is generated and studies area's Annoyance Index time series data collection.
5. a kind of water body based on timing remote sensing image according to claim 1 changes automatic identifying method, feature exists
Use SenShi Slope Method and Mann-Kendall method in: the step S05, by pixel detection Annoyance Index, Vegetation abundance and
Humidity index variation tendency over the years.
6. a kind of water body based on timing remote sensing image according to claim 1 changes automatic identifying method, feature exists
In: in the step S06, if Annoyance Index presence significantly rises or downward trend, it is judged as potential water body region of variation.
7. a kind of water body based on timing remote sensing image according to claim 1 changes automatic identifying method, feature exists
In: in the step S06, by Annoyance Index, Vegetation abundance be in significant downward trend and water body index in significant ascendant trend
Pixel, be judged as water body increase；By Annoyance Index, Vegetation abundance be in significant ascendant trend and water body index in being remarkably decreased
The pixel of trend is judged as that water body is reduced.
8. changing automatic identification side to a kind of water body based on timing remote sensing image described in 7 any one according to claim 1
Method, it is characterised in that: this method is suitable for timing Remote Sensing Change Detection Technology and its related application field.
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| CN111832506B (en) * | 2020-07-20 | 2023-10-17 | 中国矿业大学 | Remote sensing discrimination method for rebuilding vegetation based on long time sequence vegetation index |
| CN112116242A (en) * | 2020-09-17 | 2020-12-22 | 福州福大经纬信息科技有限公司 | Bare soil change identification method combining multiple remote sensing indexes |
| CN112116242B (en) * | 2020-09-17 | 2022-08-16 | 福州福大经纬信息科技有限公司 | Bare soil change identification method combining multiple remote sensing indexes |
| CN112861810A (en) * | 2021-03-23 | 2021-05-28 | 中国科学院、水利部成都山地灾害与环境研究所 | Artificial forest planting time automatic detection method based on time sequence remote sensing observation data |
| CN112861810B (en) * | 2021-03-23 | 2021-11-02 | 中国科学院、水利部成都山地灾害与环境研究所 | Artificial forest planting time automatic detection method based on time sequence remote sensing observation data |
| CN114120137A (en) * | 2021-10-19 | 2022-03-01 | 桂林理工大学 | Wetland element space-time evolution monitoring method based on time sequence main remote sensing image |
| CN114419463A (en) * | 2022-01-26 | 2022-04-29 | 河南大学 | Cloud platform-based global solar photovoltaic panel remote sensing automatic identification method |
| CN114419463B (en) * | 2022-01-26 | 2022-09-30 | 河南大学 | Cloud platform-based global solar photovoltaic panel remote sensing automatic identification method |
| CN115452759A (en) * | 2022-09-14 | 2022-12-09 | 水利部交通运输部国家能源局南京水利科学研究院 | River and lake health index evaluation method and system based on satellite remote sensing data |
| CN115452759B (en) * | 2022-09-14 | 2023-08-22 | 水利部交通运输部国家能源局南京水利科学研究院 | River and lake health index evaluation method and system based on satellite remote sensing data |
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