CN107463775A - Vegetation based on more Indices variation tendencies is lost in whereabouts recognition methods - Google Patents
Vegetation based on more Indices variation tendencies is lost in whereabouts recognition methods Download PDFInfo
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Abstract
The invention discloses a kind of vegetation based on more Indices variation tendencies to be lost in whereabouts recognition methods.This method calculates the vegetation index sequential similarity with originating the time over the years using JM distances, generate vegetation index sequential similarity track, region is lost according to vegetation index sequential similarity variable quantity extraction Potential vegetation, and then by vegetation index is remarkably decreased and impervious surface index significantly rises region, clearly region is lost in for vegetation, on this basis, according to water body index, bare soil index variation tendency feature, finally judge that the different vegetation such as urbanization, famineization and wetland is lost in whereabouts.This method determines coupling relationship region by sequential similarity variable quantity, and then judges that vegetation is lost in whereabouts according to a variety of Indices, and threshold value is set independent of manual intervention, has the characteristics that robustness is good, nicety of grading is high, automaticity and strong antijamming capability.
Description
Technical field
The present invention relates to remote sensing information process field, and in particular to one kind is based on sequential similarity variable quantity and Indices
The vegetation of variation tendency is lost in whereabouts automatic identifying method.
Background technology
Land use/covering change is the interactive result of Man ＆ Nature.Over nearest more than 30 years, with China's economic
Develop rapidly, land use/covering change shows the distinguishing feature of following several respects：(1) urbanization, Elements of Urban Land Scale add
Play, take high-quality cultivated land resource；(2) soil famineization, on the one hand, after rural population transfer to city, leave arable land wasted phenomenon is serious；
On the other hand, arid and semiarid region of Northwest China, ecological environment is relatively fragile, although country puts into a large amount of manpower and materials and implements three Norths
The engineering measures such as shelter-forest, but still some some areas desertification can not be avoided.Fast real-time monitoring land use/covering change
For ensuring that ecological safety, grain security etc. are all extremely important.Remote sensing technology plays in land use/covering variation monitoring
More and more important effect.
The variation monitoring method of land use/double phases of covering change remote sensing monitoring generally use, passes through double phases first
Comparison identification region of variation, then again to region of variation carry out land use/cover type remote sensing information extract.This side
Method is disadvantageous in that repeatedly Land Use/Cover Classification error accumulation, data consistency over the years are relatively poor, it is difficult to real
Now a wide range of automatic monitor for continuously for many years.The continuous automatic prison of land use/covering change is carried out based on remote sensing time series data for many years
Survey, it has also become the developing direction of land change scientific research.
The correlation technique that sequential remotely-sensed data carries out land use/covering variation monitoring is currently based on, main sides focus on city
The hot fields such as cityization and forest interference.Variation monitoring technology based on sequential remotely-sensed data, help to disclose multiple period soil
The dynamic change of ground utilization/covering, can continue to monitor change procedure, be effectively prevented from the limitation of double Temporal variation monitoring technology
Property.With flourishing for land use/covering variation monitoring technology is carried out based on sequential remotely-sensed data, the following aspects is still
Need further to be analysed in depth and inquire into：(1) it is waste to be turned to a kind of important land use/covering change procedure, although when very long
Between since attract attention, but make full use of the technical method of sequential remotely-sensed data still rarely seen；(2) disturbed with urbanization, forest
The sequential remote sensing monitoring technique algorithm that monitoring is the theme is relatively complicated, and automaticity has much room for improvement；(3) examine simultaneously at present
The change detecting method of a variety of land uses/covering change type is considered, more based on data driven mode, it is difficult to disclose it well
Change procedure is relative to lack geography meaning.
The content of the invention
A kind of proposed vertical vegetation of the present invention is lost in whereabouts automatic identifying method, can detect simultaneously vegetation urbanization, famineization with
And the loss whereabouts that wetland etc. is different, provide technical support tool for a wide range of automatic detection coupling relationship.
The present invention is realized using following technical scheme：A kind of vegetation based on more Indices variation tendencies is lost in whereabouts and known
Other method, it is characterised in that comprise the following steps：Step S01：Vegetation index, impervious surface index, soil bareness is established to refer to
Number, water body index time series data collection over the years；Step S02：Calculate vegetation index sequential similarity year by year by pixel, establish sequential phase
Like property track；Step S03：Based on sequential similarity track, vegetation index sequential similarity variable quantity is extracted；Step S04：It is based on
Vegetation index sequential similarity variable quantity, obtain Potential vegetation and be lost in region；Step S05：Indices change over the years is calculated to become
Gesture；Step S06：Based on sequential similarity variable quantity and Indices variation tendency, establish vegetation and be lost in whereabouts differentiation flow chart；
Step S07：Carry out vegetation and be lost in whereabouts automatic identification, obtain research area vegetation and be lost in whereabouts distribution map.
Compared to prior art, the invention has the advantages that：
(1) the vegetation index sequential similarity variable quantity that this special models fitting obtains using logic, can disclose plant well
Front and rear amplitude of variation is lost in, is advantageous to eliminate the interference of indivedual time noises, vegetation is preferably extracted and is lost in region of variation.
(2) a variety of Indices secular variation trend are comprehensively utilized, vegetation is formed and is lost in different whereabouts remote-sensing monitoring methods
Frame system, compared with a certain vegetation loss type monitoring is only carried out merely, can preferably avoid different loss types
Mistake point, divide phenomenon by mistake.
The variation tendency of (3) four big basic earth's surface cover types, indicator vegetation change whereabouts can be lost in well, rely on ground
Discipline background of science, there is good geography to explain meaning.
(4) technical method established, it is not necessary to which artificial to set threshold value or man-machine interaction, method is clear, automatically
Change degree is high, as a result reliable and stable independent of other assistance datas in addition to Indices time series data.
Brief description of the drawings
Fig. 1 is the implementation process figure of the embodiment of the present invention.
2001-2016 EVI that Fig. 2 urbanization is formed, BCI, RIBS, LSWI time-sequence curve chart.
2001-2016 EVI that Fig. 3 desertification is formed, BCI, RIBS, LSWI time-sequence curve chart.
2001-2016 EVI that Fig. 4 wetlands are formed, BCI, RIBS, LSWI time-sequence curve chart.
Fig. 5 vegetation index sequential similarities track 1.
Fig. 6 vegetation index sequential similarities track 2.
Fig. 7 obtains the schematic diagram of sequential similarity variable quantity.
Fig. 8 vegetation is lost in whereabouts decision rule.
Fig. 9 research area vegetation is lost in whereabouts distribution map.
Embodiment
Below in conjunction with the accompanying drawings, technical scheme is specifically described.It is below the specific embodiment of the present invention.
The present invention provides a kind of vegetation based on more Indices variation tendencies and is lost in whereabouts recognition methods, as shown in Figure 1：
Research area's vegetation index, impervious surface index, soil bareness index, the time series data collection for many years of water body index are initially set up, so
Extraction coupling relationship region is determined based on vegetation index sequential similarity variable quantity afterwards, and then combines the change of a variety of Indices
Trend feature, automatic identification vegetation are lost in whereabouts.
Further comprise the steps：
Step S01：Establish vegetation index, impervious surface index, soil bareness index, water body index time series data over the years
Collection；
EVI, BCI, RIBS, LSWI are chosen successively, as the Indices for characterizing earth's surface elemental：Vegetation index, no
Permeable facial index, soil bareness index, water body index.In the range of research area, 2001-2016 is established successively year by year by pixel
8 days maximums of EVI, BCI, RIBS, LSWI series Indices are combined to time series data collection for many years.Utilize Whittaker
Smoother smoothing methods, more years time series data smoothing processings of EVI, BCI, RIBS, LSWI are combined to original 8 days maximums, from
And when smooth 2001-2016 EVI in area is studied in acquisition, maximum is combined to for many years within 8 days of BCI, RIBS, LSWI series Indices
Sequence data set, the basis of whereabouts identification is lost in as vegetation.According to a variety of vegetation be lost in whereabouts, as urbanization, famineization,
Wetland, reference point position corresponding to selection, 2001-2016 EVI formed, BCI, RIBS, LSWI Indices for many years when
Overture line chart is shown in Fig. 2, Fig. 3, Fig. 4 successively.
Vegetation index：Vegetation index is to characterize vegetation growth state and the factor of 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 indexes
For：Wherein ρRedρBlue,ρNIRRespectively red spectral band, blue wave band and near
The reflectivity of infrared band.
Water body index：Water body index is the index for characterizing Unified Surface Water Capacity or Humid Status.Conventional water body index Land
SurfaceWater Index (LSWI) calculation formula is：Wherein ρSWIRRepresent that shortwave is red
The reflectivity of wave section.
Impervious surface index：Impervious surface index characterizes the index of urbanization degree.Conventional impervious surface index has life
Thing physics index (Biophysical composition index, BCI), calculation formula is：Wherein TC1、TC2、TC3First principal component, the second master respectively after K-T Transformation
Composition, the 3rd principal component；
Soil bareness index：Soil index (Ratio index for bright soil, RIBS) is normalized, calculation formula is：Wherein
In above-mentioned formula, NDSI is normalizing snow melting body index, and Green and SWIR1 are respectively the green light bands of MODIS images and first short
Ripple infrared band, NDSImax,NDSIminRespectively NDSI maximum and minimum value, TC1max, TC1minRespectively TC1Maximum
Value and minimum value.MODIS data：Moderate Imaging Spectroradiomete data, full name are Moderate Resolution Imaging
Spectroradiometer。
The reference point such as urbanization, famineization, wetland position, with the loss of vegetation, show becoming for vegetation index decline
Gesture.Show as before vegetation is lost in, the usual numerical value of vegetation index is higher and shows vegetation Phenological change in regular year
Feature, and after vegetation is lost in generation, vegetation index shows relatively low level.The amplitude of variation of vegetation index, can be vegetation
Whether generation, which is lost in, provides reference frame.
Although different loss whereabouts, the common trait that vegetation index declines to a great extent, different vegetation stream are showed
Lose in the Indices such as water body index, bare soil index for many years time-sequence curve chart, showing different variation characteristics.Such as：
In urbanization point position, water body index is on a declining curve, and waterproof facial index, soil bareness index are in obvious ascendant trend
(see Fig. 2).And in vegetation famineization point position, water body index, soil bareness index show downward trend, and impervious surface only
Index shows ascendant trend (see Fig. 3).And in wetland point position, impervious surface index, soil bareness index, water body index are equal
(see Fig. 4) in rising trend.Therefore, it can judge that different be lost in of vegetation is gone according further to a variety of Indices variation tendencies
To.
Step S02：Calculate vegetation index sequential similarity year by year by pixel, establish sequential similarity track；
It is similar to the sequential for originating time timing curve in 2001 to calculate annual vegetation index timing curve year by year by pixel
Property, the calculating of sequential similarity uses JM distances, so as to form 2002-2016 and 2001 year vegetation index sequential similarity rail
Mark.So that stable vegetation point position and vegetation are lost in point position (urbanization) as an example, the vegetation index sequential similarity track that is formed
See Fig. 5-6, for stable vegetation region, the amplitude of variation that vegetation is lost in vegetation index sequential similarity track in region is bright
Aobvious increase.Show as before vegetation is lost in and occurred, vegetation index sequential similarity is very high, and JM is apart from very little, as shown in Figure 5.And
After vegetation is lost in and occurred, vegetation index sequential similarity weakens rapidly, and JM distances are also with increase, as shown in Figure 6.
Step S03：Based on sequential similarity track, vegetation index sequential similarity variable quantity is extracted；
Based on 2001-2016 vegetation index sequential similarities track, using Logistic models fittings, when obtaining research
The pixel vegetation index sequential similarity variable quantity in section.The formula of Logistic models is as follows：
Wherein：F represents other times with originating the sequential similarity (JM distances) in 2001 times, and independent variable x is the time,
Represented with the time.There are four parameters, a, b in Logistic models, c, d have certain indicative significance respectively.Wherein parameter a generations
Sequential similarity variable quantity in the table research period, the degree that variable quantity is bigger to represent change are higher.Logistic models and
Implication figure is shown in Fig. 7 corresponding to its model parameter.
In the range of research area, based on sequential similarity track, vegetation index sequential similarity variable quantity is calculated one by one, it is raw
Into research area's vegetation index sequential similarity change spirogram.
Step S04：Based on vegetation index sequential similarity variable quantity, obtain Potential vegetation and be lost in region；
Sequential similarity variable quantity is bigger, represents that the tendency that vegetation is lost in is more obvious.Therefore, when can be according to vegetation index
Sequence similitude variable quantity, it is determined that Potential vegetation is lost in region in research area.Assuming that vegetation index sequential similarity becomes in research area
Change amount M meets normal distribution N (μ, σ2), in given α significances, if M >=Q1-α, then the vegetation index of the pixel
Sequential similarity variable quantity significantly increases, it can be determined that the pixel belongs to Potential vegetation and is lost in region.
Step S05：Calculate Indices variation tendency over the years
Using SenShi gradient methods, 2001-2016 EVI, BCI, RIBS, LSWI timing curve are calculated successively by pixel
General morphologictrend Q for many years.Work as Q>When 0, it is in certain ascendant trend to represent the timing curve；Work as Q<When 0, the sequential is represented
Curve has certain downward trend.Based on Mann-Kendall methods, determining whether the variation tendency of the timing curve is
It is no notable.According to the result of significance test, variation tendency is divided into three kinds of situations：(significant rise becomes significant positive trend
Gesture), without trend (constant) and significant negative trend (significant downward trend).2001- corresponding to each pixel in area will be studied
EVI, BCI, RIBS, LSWI timing curve variation tendency testing result are saved in database successively within 2016.
Step S06：Based on sequential similarity variable quantity and Indices variation tendency, establish vegetation and be lost in whereabouts differentiation stream
Cheng Tu
Summary step, establish vegetation and be lost in whereabouts differentiation flow chart (see Fig. 8).It is first depending on vegetation index sequential phase
Like property variable quantity, the preliminary Potential vegetation that obtains is lost in region, and then according to vegetation index, impervious surface index variation trend, carries
Take vegetation to be lost in the variation tendency such as region, further comprehensive water body index, bare soil index, determine that vegetation is lost in whereabouts.
It is lost in the Potential vegetation according to the extraction of vegetation index sequential similarity variable quantity in regional extent, further by picture
Member judges whether vegetation index, impervious surface index are to be remarkably decreased trend, so as to which further clear and definite Potential vegetation is lost in area
Domain scope.On this basis, with reference to the variation tendency of water body index, bare soil index, obtain vegetation and be lost in whereabouts：If water body refers to
Number, bare soil index show notable ascendant trend, then it is wetland that vegetation, which is lost in whereabouts,；If water body index, bare soil index
Significant downward trend is showed, then it is famineization that vegetation, which is lost in whereabouts,；If water body index shows significant downward trend
And bare soil index shows significant ascendant trend, then it is urbanization that vegetation, which is lost in whereabouts,.
Step S07：Carry out vegetation and be lost in whereabouts automatic identification, obtain research area vegetation and be lost in whereabouts distribution map
Whereabouts is lost according to the vegetation established and differentiates flow chart, judges whether vegetation is lost in by pixel, if hair
Raw vegetation is lost in, and then determines that it is lost in whereabouts, ultimately generates research area vegetation and is lost in whereabouts distribution map.According in the present embodiment
The method of offer, by taking Henan, China as an example, the research area vegetation loss whereabouts distribution map obtained is shown in Fig. 9.
Above is presently preferred embodiments of the present invention, all changes made according to technical solution of the present invention, caused function are made
During with scope without departing from technical solution of the present invention, protection scope of the present invention is belonged to.
Claims (8)
1. a kind of vegetation based on more Indices variation tendencies is lost in whereabouts recognition methods, it is characterised in that including following step
Suddenly：
Step S01：Establish vegetation index, impervious surface index, soil bareness index, water body index time series data collection over the years；
Step S02：Calculate vegetation index sequential similarity year by year by pixel, establish sequential similarity track；
Step S03：Based on sequential similarity track, vegetation index sequential similarity variable quantity is extracted；
Step S04：Based on vegetation index sequential similarity variable quantity, obtain Potential vegetation and be lost in region；
Step S05：Calculate Indices variation tendency over the years；
Step S06：Based on sequential similarity variable quantity and Indices variation tendency, establish vegetation and be lost in whereabouts differentiation flow
Figure；
Step S07：Carry out vegetation and be lost in whereabouts automatic identification, obtain research area vegetation and be lost in whereabouts distribution map.
2. the vegetation according to claim 1 based on more Indices variation tendencies is lost in whereabouts recognition methods, its feature
It is：S01 includes step in detail below：
S011：EVI, BCI, RIBS, LSWI are chosen successively, as the Indices for characterizing earth's surface elemental：It is vegetation index, waterproof
Facial index, soil bareness index, water body index；The calculation formula of EVI indexes is：
Wherein ρRedρBlue,ρNIRThe respectively reflectivity of red spectral band, blue wave band and near infrared band；
Wherein TC1、TC2、TC3First principal component respectively after K-T Transformation,
Two principal components, the 3rd principal component；
WhereinNDSI is normalizing snow melting
Body index, NDSImax、NDSIminRespectively NDSI maximum and minimum value, TC1max、TC1Min is respectively TC11Maximum
And minimum value；Wherein ρSWIRRepresent the reflectivity of short infrared wave band；
S012:In the range of research area, EVI, BCI, RIBS, LSWI series Indices in n are established successively year by year by pixel
8 days maximums be combined to time series data collection for many years, using Whittaker Smoother smoothing methods, original 8 days are maximized
Synthesize more years time series data smoothing processings of EVI, BCI, RIBS, LSWI, so as to obtain research the smooth n in area in EVI, BCI,
8 days maximums of RIBS, LSWI series Indices are combined to time series data collection for many years, and the base of whereabouts identification is lost in as vegetation
Plinth；Whereabouts, reference point position corresponding to selection, n EVI formed, BCI, RIBS, LSWI are lost according to a variety of vegetation
Indices time-sequence curve chart for many years.
3. the vegetation according to claim 1 based on more Indices variation tendencies is lost in whereabouts recognition methods, its feature
It is：In the step S02, based on JM distances, vegetation index timing curve over the years and research starting year period are calculated year by year
The sequential similarity of part, and then establish sequential similarity track.
4. the vegetation according to claim 1 based on more Indices variation tendencies is lost in whereabouts recognition methods, its feature
It is：In the step S03, based on vegetation index sequential similarity track, using Logistic models fittings, studied
The pixel vegetation index sequential similarity variable quantity in period, the formula of Logistic models are as follows：
Wherein：F represents JM distances, and independent variable x is the time, is represented with the time, and a represents the sequential similarity change in the research period
Amount, b are change type, and d is the JM distances before change.
5. the vegetation according to claim 1 changed based on sequential similarity and Indices is lost in whereabouts recognition methods,
It is characterized in that：In the step S04, by the big region of vegetation index sequential similarity variable quantity, it is determined as Potential vegetation
It is lost in region；Assuming that vegetation index sequential similarity variable quantity M meets normal distribution in research area, in given α conspicuousness water
On flat, if M >=Q1-α, then the vegetation index sequential similarity variable quantity of the pixel significantly increase, judge that the pixel belongs to potential
Vegetation is lost in region.
6. the vegetation according to claim 1 changed based on sequential similarity and Indices is lost in whereabouts recognition methods,
It is characterized in that：In the step S05, based on SenShi Slope Methods and Mann-Kendall methods, refer to by pixel detection vegetation
Number, impervious surface index, soil bareness index, water body index variation tendency over the years.
7. the vegetation according to claim 1 changed based on sequential similarity and Indices is lost in whereabouts recognition methods,
It is characterized in that：In the step S06, the Potential vegetation determined in step S04 is lost in region, is according to vegetation index
Trend is now remarkably decreased, and notable ascendant trend is presented in impervious surface index, and further clear and definite Potential vegetation is lost in region model
Enclose.
8. the vegetation according to claim 1 changed based on sequential similarity and Indices is lost in whereabouts recognition methods,
It is characterized in that：In the step S06, become according to sequential similarity variable quantity and vegetation index, impervious surface index
Potential vegetation determined by change trend, according further to water body index, bare soil index variation tendency, judges vegetation in the range of being lost in
It is lost in whereabouts；If water body index, bare soil index show notable ascendant trend, it is wetland that vegetation, which is lost in whereabouts,；Such as
Fruit water body index, bare soil index show significant downward trend, then it is famineization that vegetation, which is lost in whereabouts,；If water body index is in
Reveal significant downward trend and bare soil index shows significant ascendant trend, then it is urbanization that vegetation, which is lost in whereabouts,.
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108875146A (en) * | 2018-05-28 | 2018-11-23 | 福州大学 | A kind of ground mulching consecutive variations detection method considering change procedure |
| CN109446962A (en) * | 2018-10-19 | 2019-03-08 | 中国科学院地理科学与资源研究所 | Land cover interannual variance detection method and system |
| CN109840516A (en) * | 2019-03-06 | 2019-06-04 | 福州大学 | A kind of water body variation automatic identifying method based on timing remote sensing image |
| CN111062368A (en) * | 2019-12-31 | 2020-04-24 | 中山大学 | City update region monitoring method based on Landsat time sequence remote sensing image |
| 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 |
| CN112801007A (en) * | 2021-02-07 | 2021-05-14 | 中国科学院空天信息创新研究院 | Hot melt pond state identification method combining long time sequence data and machine learning algorithm |
| CN113592770A (en) * | 2021-06-23 | 2021-11-02 | 中国科学院南京地理与湖泊研究所 | Algal bloom remote sensing identification method for removing influence of aquatic weeds |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102288954A (en) * | 2011-08-01 | 2011-12-21 | 高吉喜 | Remote sensing estimation method for vegetation coverage of grassland |
| CN103063311A (en) * | 2012-12-24 | 2013-04-24 | 珠江水利委员会珠江水利科学研究院 | Nudity bed rock information extraction method based on soil index |
| CN103971027A (en) * | 2014-05-29 | 2014-08-06 | 福州大学 | Rice automatic mapping method based on ratio index of water body and vegetation index changes |
| CN106446555A (en) * | 2016-09-29 | 2017-02-22 | 福州大学 | Vegetation change occurrence time detection method based on time series similarity |
-
2017
- 2017-07-24 CN CN201710605772.1A patent/CN107463775B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102288954A (en) * | 2011-08-01 | 2011-12-21 | 高吉喜 | Remote sensing estimation method for vegetation coverage of grassland |
| CN103063311A (en) * | 2012-12-24 | 2013-04-24 | 珠江水利委员会珠江水利科学研究院 | Nudity bed rock information extraction method based on soil index |
| CN103971027A (en) * | 2014-05-29 | 2014-08-06 | 福州大学 | Rice automatic mapping method based on ratio index of water body and vegetation index changes |
| CN106446555A (en) * | 2016-09-29 | 2017-02-22 | 福州大学 | Vegetation change occurrence time detection method based on time series similarity |
Non-Patent Citations (1)
| Title |
|---|
| JULIEN Y ET AL: "Changes in land surface temperatures and NDVI values over Europe between 1982 and 1999", 《REMOTE SENS ENVIRON》 * |
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| CN109446962A (en) * | 2018-10-19 | 2019-03-08 | 中国科学院地理科学与资源研究所 | Land cover interannual variance detection method and system |
| CN109446962B (en) * | 2018-10-19 | 2020-12-01 | 中国科学院地理科学与资源研究所 | Land cover annual change detection method and system |
| CN109840516A (en) * | 2019-03-06 | 2019-06-04 | 福州大学 | A kind of water body variation automatic identifying method based on timing remote sensing image |
| CN109840516B (en) * | 2019-03-06 | 2022-10-14 | 福州大学 | Water body change automatic identification method based on time sequence remote sensing image |
| CN111062368A (en) * | 2019-12-31 | 2020-04-24 | 中山大学 | City update region monitoring method based on Landsat time sequence remote sensing image |
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| CN112801007A (en) * | 2021-02-07 | 2021-05-14 | 中国科学院空天信息创新研究院 | Hot melt pond state identification method combining long time sequence data and machine learning algorithm |
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