CN108388832A - A kind of conceding the land to forestry automatic identifying method based on multiple timings index variation tendency - Google Patents
A kind of conceding the land to forestry automatic identifying method based on multiple timings index variation tendency Download PDFInfo
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Abstract
The present invention relates to a kind of conceding the land to forestry automatic identifying methods based on multiple timings index variation tendency, area's enhancement mode meta file time series data collection day by day for many years is studied by foundation, it is based on enhancement mode meta file time series data, five timing indicators such as extraction abundance, sequential dispersion, growth period length, high score position persistence, low point of position persistence year by year by pixel；The secular variation trend of above-mentioned five timing indicators is detected successively；Based on the variation tendency of five timing indicators, conceding the land to forestry automatic identification flow chart is established, to realize conceding the land to forestry automatic identification.Method proposed by the present invention divides vegetation growth state according to tantile, from the multiple timing indicators of the lateral layouts such as cover time, average state, amplitude of variation, portray the growth characteristics of single cropping crops, more season crops and forest cover, and then indicate the variation from crops to forest using the variation tendency of multiple timings index, to automatic identification conceding the land to forestry region, there is considerable flexibility and expansion.
Description
Technical field
The present invention relates to remote sensing information process fields, and in particular to a kind of conceding the land also based on multiple timings index variation tendency
Woods automatic identifying method.
Background technology
It is raw in all things on earth soil.Arable land is that the mankind depend on for existence and development most basic natural resources.With the growth of population, people
Class constantly asks for the demand aggravation of food to soil.Hilly area is blindly opened up wasteland sowing hillside fields and is brought sternly since arable land is rare
The soil erosion problem of weight.Sandy land in the arid area is cultivated, and is caused the natural calamities such as arid, sandstorm to take place frequently, has been seriously threatened ecology
Safety.Therefore planned, implement multi-line regression model step by step by putting into substantial contribution.
It is reported that implementing over more than 10 years from multi-line regression model, significant effect is achieved.But conceding the land to forestry occurs actually
In which region, it is necessary to carry out implementation region of conceding the land to forestry and monitor in real time on a large scale, and a wide range of fast automatic monitoring is moved back
The spatial distribution for ploughing also woods, is of great significance.Traditional manual research method is not only time-consuming and laborious, but also is difficult to realize cover entirely
Lid is easy to be interfered by subjective factor.Remote sensing image has the characteristics that timeliness is strong, coverage area is big, is examined in land change
It is played an important role in survey.However, the interference of " similitude between heterogeneity, class in class " due to different land cover pattern spectrum,
And the shadow of many factors such as experience, grader, the abundant degree of related data and research area's feature of Classification in Remote Sensing Image personnel
It rings, land cover pattern autodraft precision is caused to be difficult to ensure.
Mostly based on the method for changing detection after classification, the shortcoming of this method exists for traditional land cover pattern variation detection
In：Repeatedly classification brings cumulative errors, and land cover pattern region of variation occurs itself and is smaller, it is easy to cause wrong point or lose
Leak phenomenon.Therefore the land cover pattern change detecting method of directly extraction region of variation is of great interest.Proposed by the invention
Method portrays single cropping crops, more season crops and the forest etc. planted in cultivation area by designing multiple timing indicators
A variety of vegetation patterns, and then according to the variation tendency of multiple timing indicators, disclose single cropping crops or more season crops are changed into
The change procedure of forest finally establishes a kind of conceding the land to forestry automatic identifying method based on multiple timings index variation tendency.
Invention content
The purpose of the present invention is to provide a kind of conceding the land to forestry automatic identifying method based on multiple timings index variation tendency,
To overcome defect existing in the prior art.
To achieve the above object, the technical scheme is that：A kind of conceding the land also based on multiple timings index variation tendency
Woods automatic identifying method, is realized in accordance with the following steps：
Step S1：Time series data collection day by day in research area's enhancement mode meta file year over the years is established by pixel；
Step S2：Set abundance index, sequential dispersion index and growth period length index；
Step S3：Set high score position persistence index, low point of position persistence index；
Step S4：Establish the time series data collection for many years of above-mentioned five timing indicators；
Step S5：Calculate the secular variation trend Q of five timing indicators；
Step S6：It tests to the conspicuousness of the secular variation trend Q of five timing indicators；
Step S7：Establish conceding the land to forestry automatic identification flow chart；
Step S8：Obtain research area's conceding the land to forestry spatial distribution map.
In an embodiment of the present invention, in the step S1, time series data day by day in the enhancement mode meta file year
Collection obtains in accordance with the following steps：
Step S11：By being calculated day by day based on daily MODIS remote sensing images wave band data or by being based on 8 days maximums
The MODIS remote sensing image wave band datas being combined to calculate and linear interpolation obtains time series data collection in original year；
Step S12：Using Whittaker smoother smoothing methods, time series data collection in the original year is put down
Sliding processing obtains in the enhancement mode meta file year time series data collection day by day.
In an embodiment of the present invention, in the step S2, sequential day by day is calculated in the enhancement mode meta file year
Day by day the 50th tantile of time series data in enhancement mode meta file year in data set, and extract and be wherein greater than or equal to the 50th point
All data of place value, and the average value of all data is calculated, as the abundance index.
In an embodiment of the present invention, in the step S2, sequential day by day is calculated in the enhancement mode meta file year
The 75th tantile of time series data day by day in enhancement mode meta file year, the enhanced plant is extracted by pixel successively in data set
By time series data concentration establishes high level region more than or equal to all data of the 75th tantile day by day in index year；It will be described
The product of the very poor and standard deviation in high level region, as the sequential dispersion index.
In an embodiment of the present invention, in the step S2, sequential day by day is calculated in the enhancement mode meta file year
The maximum value Max and minimum M in of time series data day by day in enhancement mode meta file year, calculates maximum value Max in data set
With very poor (Max-Min) of minimum M in, it is greater than or equal to (Min+ (Max-Min)/2) by what is obtained successively in chronological order
The data corresponding duration it is cumulative, as growth period length index.
In an embodiment of the present invention, in the step S3, sequential day by day is calculated in the enhancement mode meta file year
Day by day the 65th tantile of time series data in enhancement mode meta file year in data set, will be through examining successively in chronological order by pixel
Rope is greater than or equal to the maximum value obtained after the duration of the 65th tantile as high score position persistence index.
In an embodiment of the present invention, in the step S3, sequential day by day is calculated in the enhancement mode meta file year
Day by day the 50th tantile of time series data in enhancement mode meta file year in data set, will be through examining successively in chronological order by pixel
Rope is greater than or equal to the second largest value obtained after the duration of the 50th tantile as low point of position persistence index.
In an embodiment of the present invention, in the step S5, the secular variation is obtained by using SenShi Slope Methods
Trend Q.
In an embodiment of the present invention, in the step S6, the significance test of the secular variation trend Q includes sky
Between significance test and sequential significance test and the result of significance test is denoted as：Significantly positive trend, trendless and aobvious
Write the trend of bearing.
In an embodiment of the present invention, in the step S7, the automatic knowledge of the conceding the land to forestry is established in the following way
Other flow chart：
(1) when abundance and growth period length all have notable positive trend, and sequential dispersion shows significantly negative trend, and
And when the equal trendless of high score position persistence, low point of position persistence, then the pixel is changed into forest by single cropping crops, i.e. the pixel
For conceding the land to forestry region；
(2) when high score position persistence, growth period length all have notable positive trend, and low point of position persistence shows significantly
Negative trend, and when the equal trendless of abundance, sequential dispersion, then the pixel is changed into forest by more season crops, i.e. the pixel
For conceding the land to forestry region.
Compared to the prior art, the invention has the advantages that：
(1) by using based on vegetation index time series data quantile, several vegetation growth stages are divided.Due to single cropping agriculture
The vegetation such as crop, more season crops, forest show respective feature in the different vegetation growth stages.Based on several vegetation growths
Stage, by the way that from sides such as cover time, average state, amplitudes of variation, design abundance, sequential dispersion, continuation degree etc. are multiple
Timing indicator, the multi-faceted feature for portraying different vegetation types have considerable flexibility and expansion.
(2) by using the general morphologictrend based on multiple timing indicators, occur to certain time data wrong or different
Situations such as normal, has better fault-tolerant ability.
(3) multiple classification is not depended on, this specific variation pattern of direct automatic identification conceding the land to forestry has concise
The characteristics of.
(4) it can not need human-computer interaction by known training data, do not depend on supervised classification or machine learning side
Method realizes conceding the land to forestry automatic identification.
Description of the drawings
Fig. 1 is the flow chart in one embodiment of the invention.
Fig. 2 is the EVI timing curves of single cropping crops 2001-2016 conceding the land to forestry points in one embodiment of the invention
Figure.
Fig. 3 is the EVI timing curves of double season crops 2001-2016 conceding the land to forestry points in one embodiment of the invention
Figure.
Fig. 4 is five timing indicators of single cropping crops 2001-2016 conceding the land to forestry points in one embodiment of the invention
Trend chart.
Fig. 5 is five timing indicators of double season crops 2001-2016 conceding the land to forestry points in one embodiment of the invention
Trend chart.
Fig. 6 is the spatial distribution that 2001-2016 studies the five timing indicator variation tendencies in area in one embodiment of the invention
Figure.
Fig. 7 is conceding the land to forestry automatic identification flow chart in one embodiment of the invention.
Fig. 8 is that conceding the land to forestry spatial distribution map in area's is studied in one embodiment of the invention.
Specific implementation mode
Below in conjunction with the accompanying drawings, technical scheme of the present invention is specifically described.
The present invention provides a kind of conceding the land to forestry automatic identifying method based on multiple timings index variation tendency, such as Fig. 1 institutes
Show：The enhancement mode meta file time series data collection day by day for many years for initially setting up the research each grid cell in area, then by pixel base
In enhancement mode meta file time series data, extract year by year abundance, sequential dispersion, growth period length, high score position persistence, low point
Position five timing indicators of persistence, the secular variation trend of above-mentioned five timing indicators are detected by pixel, on this basis successively
Based on the variation tendency of five timing indicators, conceding the land to forestry automatic identification flow chart is established, is finally reached the automatic knowledge of conceding the land to forestry
Other target.
In order to allow those skilled in the art to further appreciate that moving back based on multiple timings index variation tendency proposed by the present invention
Also woods automatic identifying method is ploughed, is illustrated with reference to specific steps and embodiment.
In the present embodiment, following steps are specifically included：
Step S01：Research area vegetation index time series data day by day for many years is established by pixel.
In the present embodiment, EVI time series datas collection day by day is established in research area year respectively.MODIS EVI time series datas
It establishes, can day by day be calculated based on daily MODIS remote sensing images wave band data, can also be combined to based on 8 days maximums
MODIS remote sensing image wave band datas are obtained on the basis of being calculated by linear interpolation.Utilize Whittaker smoother
Smoothing method is smoothed time series data collection in original year, to obtain in research area smooth year EVI sequential day by day
Data set, the basis as conceding the land to forestry automatic identification.Such as base EVI time series datas collection day by day within the year, the research area of foundation is moved back
The EVI time-sequence curve charts for ploughing also woods are shown in Fig. 2 and Fig. 3.
In the present embodiment, MODIS data are Moderate Imaging Spectroradiomete data, full name Moderate
Resolution Imaging Spectroradiometer。
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, full name are Enhanced Vegetation Index.The calculation formula of EVI indexes is：Wherein Red, Blue, NIR are respectively feux rouges, blue light and near-infrared wave
Section.
Step S02：Design abundance, sequential dispersion and growth period length index
MODIS EVI time series datas, setting embody several timing indicators of vegetation growth state, packet to base day by day within the year
It includes：Abundance, sequential dispersion and growth period length.The extreme value of time series data and important in extraction MODIS EVI first
Tantile, such as maximum value, minimum value, the 50th tantile, the 65th tantile, the 75th tantile.Then according to these extreme values and point
Time series data in MODIS EVI is divided into several sections for embodying different vegetation growth states by place value, comprehensive on this basis
It closes the time and abundance, sequential dispersion and growth period length index is established in the two aspect design of vegetation index codomain distribution situation.
Further, the 50th tantile based on time series data in MODIS EVI establishes abundance index, calculates stream
Journey is as follows：All data that enhancement mode meta file is greater than or equal to the 50th tantile are extracted, its average value are sought, as abundance
Numerical value.
Further, the 75th tantile based on time series data in MODIS EVI extracts sequential dispersion index,
Calculation process is as follows：Extract all data that enhancement mode meta file is greater than or equal to the 75th tantile successively by pixel, it is corresponding
For the high level region P of time series data in the MODIS EVI in the time.Based on the time enhancement mode meta file time series data
High level region P calculates the very poor and standard deviation SD of data in the P of the high level regionm.According to data in vegetation index high level region
Very poor and standard deviation SDm, build sequential dispersion (Temporal Dispersion) index TD.
Wherein, TD is the product of the very poor and standard deviation of data in high level region in vegetation index, and calculation formula is：TD
=(Max-Quan3)(SDm).Wherein, Max is the maximum value of corresponding time enhancement mode meta file time series data, Quan3For this
The third quantile of time series data, i.e. the 75th tantile, SD in time MODIS EVImFor in time MODIS EVI
Standard deviation in time series data high level region P.
Further, the maximum value Max based on time series data in MODIS EVI and minimum M in, extraction growth period are long
Degree, calculation process are as follows：By the maximum value Max and minimum M in of MODIS EVI time series datas in image element extraction year, seek
Numerical value is reached very poor half and starts the foundation grown as vegetation, and will examined successively in chronological order by very poor (Max-Min)
The data corresponding duration more than or equal to (Min+ (Max-Min)/2) that rope obtains is cumulative, obtains growth period length.
Step S03：Design high score position persistence, low point of position persistence index
Further, the 65th tantile of MODIS EVI time series datas, extraction high score position persistence refer to base day by day within the year
Mark, calculation process are as follows：By pixel, there is the duration more than or equal to the 65th tantile in retrieval successively in chronological order,
It is recorded as t successively1, t2... ..., tn.Seek all high level duration { t1, t2... ..., tnMaximum value, obtain high score
Position persistence index.
Further, the 50th tantile of MODIS EVI time series datas, low point of position persistence of extraction refer to base day by day within the year
Mark, calculation process are as follows：By pixel, there is the duration more than or equal to the 50th tantile in retrieval successively in chronological order,
It is recorded as t successively1, t2... ..., tn.Seek all high level duration { t1, t2... ..., tnSecond largest value, obtain low point
Position persistence index.
Step S04：Establish the time series data collection for many years of five timing indicators
Further, 2001-2016 MODIS EVI time series datas day by day for many years are based on, are extracted successively year by year by pixel
Abundance, sequential dispersion, growth period length, high score position persistence, low point of position persistence index.For each pixel, establish with
Time is step-length five including abundance, sequential dispersion, growth period length, high score position persistence, low point of position persistence
The time series data collection of timing indicator.
In the present embodiment, it is based on the time series data collection, (more season crops become with A (single cropping crops become forest) and B
For forest) for, it is formed by 2001-2016 abundance, sequential dispersion, growth period length, high score position persistence, low point of position
The time-sequence curve chart of persistence index is shown in shown in Fig. 4 and Fig. 5.In comparison, single cropping crops sequential dispersion and high score position
Persistence is higher, but Vegetation abundance, growth period length and low point of position persistence are relatively low.And the vegetation of more season crops is rich
Degree, low point of position persistence are higher, and other three timing indicators are relatively low.The Vegetation abundance of forest cover, high score position persistence
And growth period length is higher, and other two timing indicators are relatively low.Therefore, in arable land vegetation (single cropping crops, more seasons
Crops) when being changed into forest cover, corresponding five timing indicators show corresponding variation tendency.
Step S05：Calculate the secular variation trend Q of five timing indicators.
In the present embodiment, using SenShi Slope Methods, by pixel detection abundance, sequential dispersion, growth period length, height
Divide the secular variation trend of the indexs such as position persistence, low point of position persistence.SenShi Slope Methods can avoid data exception well
Value, interference of the data space distributional pattern to result, reliability is had more for shorter time series trend analysis.Therefore it uses
SenShi Slope Methods calculate separately abundance by pixel, sequential dispersion, growth period length, high score position persistence, low point of position continue
The variation tendency Q of the time series datas for many years such as degree.
Step S06：Carry out the significance test of five timing indicator variation tendency Q.
In the present embodiment, as timing indicator secular variation trend Q>When 0, indicate that the time series becomes in certain rising
Gesture；Work as Q<When 0, indicate that the time series has certain downward trend.The absolute value of variation tendency Q is bigger, indicates variation width
Degree is bigger.In order to judge that the corresponding abundance of each pixel, sequential dispersion, growth period length, high score position persistence, low point of position are held
Whether the variation tendency Q of the timing indicators such as continuous degree is notable, needs further to carry out conspicuousness inspection in terms of space and sequential two
It tests.
Further, in terms of spatial saliency inspection, its purpose is to examine abundance, sequential dispersion, growth period
Whether the variation tendency Q of the timing indicators such as length, high score position persistence, low point of position persistence is notable in residing survey region.
It is illustrated by taking abundance as an example.Assuming that the variation tendency Q of abundance meets normal distribution N (μ, σ in survey region2),
In given α significances, if | Q | >=Q1-α/2, then the variation tendency Q of the abundance index of the pixel is in survey region
Significantly.
Further, in terms of sequential significance test, the purpose is to examine abundance, sequential dispersion, growth period length,
High score position persistence, low point of position persistence this five timing indicator variation tendency Q within the research period whether significantly.
In the present embodiment, sequential significance test uses Mann-Kendall methods.Mann-Kendall methods have
No requirement (NR) is distributed to time series data sequence, it is insensitive to exceptional value the advantages that, therefore can be very well using Mann-Kendall methods
Examine the variation tendency of time series data sequence in ground.
Further, comprehensive SenShi Slope Methods calculate the variation tendency Q obtained, and general space conspicuousness and sequential are notable
Property inspection result, the variation tendency of five timing indicators is divided into three classes：Significantly positive trend (significant ascendant trend), trendless
(constant) and significantly negative trend (significant downward trend).The secular variation trend-monitoring knot of five timing indicators is recorded by pixel
Fruit establishes the spatial distribution map of corresponding variation tendency Q, as shown in Figure 6.
Step S07：Establish conceding the land to forestry automatic identification flow chart.
Further, as shown in fig. 7, abundance, sequential dispersion based on each pixel, growth period length, high score position are held
The variation tendency feature of continuous degree, low point of position persistence, establishes conceding the land to forestry automatic identification flow chart.
For opposite arable land crops vegetation, the abundance of forest is higher, and sequential dispersion is relatively low, and high score position persistence is higher
And low point of position persistence is relatively low, growth period length is higher.Established conceding the land to forestry automatic identification flow chart is as follows：
(1) if abundance and growth period length all have notable positive trend, and sequential dispersion shows significantly negative trend,
And high score position persistence, the low point of equal trendless of position persistence, then the pixel be changed into forest by single cropping crops, i.e. the pixel
For conceding the land to forestry region.
(2) if high score position persistence, growth period length all have notable positive trend, and low point of position persistence show it is aobvious
The trend of bearing, and abundance, the equal trendless of sequential dispersion are write, then the pixel is changed into forest by more season crops, i.e. the pixel
For conceding the land to forestry region.
Step S08：Obtain research area's conceding the land to forestry spatial distribution map.
Based on the conceding the land to forestry automatic identification flow established research is ultimately generated by image element extraction conceding the land to forestry region
Area's conceding the land to forestry spatial distribution map.According to above-mentioned flow, it can be achieved that more accurate concede the land to forestry automatically extracts.Further, such as
Shown in Fig. 8, for by taking Shandong Province as an example, comprehensive study area concedes the land to forestry and automatically extracts the conceding the land to forestry spatial distribution as a result, obtaining
Figure.
The above are preferred embodiments of the present invention, all any changes made according to the technical solution of the present invention, and generated function is made
When with range without departing from technical solution of the present invention, all belong to the scope of protection of the present invention.
Claims (10)
1. a kind of conceding the land to forestry automatic identifying method based on multiple timings index variation tendency, which is characterized in that according to following step
It is rapid to realize：
Step S1：Time series data collection day by day in research area's enhancement mode meta file year over the years is established by pixel；
Step S2：Set abundance index, sequential dispersion index and growth period length index；
Step S3：Set high score position persistence index, low point of position persistence index；
Step S4：Establish the time series data collection for many years of above-mentioned five timing indicators；
Step S5：Calculate the secular variation trend Q of five timing indicators；
Step S6：It tests to the conspicuousness of the secular variation trend Q of five timing indicators；
Step S7：Establish conceding the land to forestry automatic identification flow chart；
Step S8：Obtain research area's conceding the land to forestry spatial distribution map.
2. a kind of conceding the land to forestry automatic identifying method based on multiple timings index variation tendency according to claim 1,
It is characterized in that, in the step S1, time series data collection obtains in accordance with the following steps day by day in the enhancement mode meta file year：
Step S11：By being calculated day by day based on daily MODIS remote sensing images wave band data or by being based on 8 days maximum chemical combination
At MODIS remote sensing image wave band datas calculate and linear interpolation obtains time series data collection in original year；
Step S12：Using Whittaker smoother smoothing methods, time series data collection in the original year is smoothly located
Reason obtains in the enhancement mode meta file year time series data collection day by day.
3. a kind of conceding the land to forestry automatic identifying method based on multiple timings index variation tendency according to claim 1,
It is characterized in that, in the step S2, calculates in the enhancement mode meta file year day by day time series data and concentrate enhanced vegetation
Day by day the 50th tantile of time series data in index year, and all data for being wherein greater than or equal to the 50th tantile are extracted, and
The average value for calculating all data, as the abundance index.
4. a kind of conceding the land to forestry automatic identifying method based on multiple timings index variation tendency according to claim 1,
It is characterized in that, in the step S2, calculates in the enhancement mode meta file year day by day time series data and concentrate enhanced vegetation
Day by day the 75th tantile of time series data in index year extracts in the enhancement mode meta file year sequential day by day successively by pixel
It is greater than or equal to all data of the 75th tantile in data set, establishes high level region；By the very poor of the high level region and mark
The product of quasi- difference, as the sequential dispersion index.
5. a kind of conceding the land to forestry automatic identifying method based on multiple timings index variation tendency according to claim 1,
It is characterized in that, in the step S2, calculates in the enhancement mode meta file year day by day time series data and concentrate enhanced vegetation
Day by day the maximum value Max and minimum M in of time series data in index year, calculates the very poor of maximum value Max and minimum M in
(Max-Min), it is greater than or equal to what is obtained successively in chronological order（Min+(Max-Min)/2）Data it is corresponding continue when
Between add up, as growth period length index.
6. a kind of conceding the land to forestry automatic identifying method based on multiple timings index variation tendency according to claim 1,
It is characterized in that, in the step S3, calculates in the enhancement mode meta file year day by day time series data and concentrate enhanced vegetation
Day by day the 65th tantile of time series data in index year, will be through by pixel, retrieval is greater than or equal to the 65th point successively in chronological order
The maximum value obtained after the duration of place value is as high score position persistence index.
7. a kind of conceding the land to forestry automatic identifying method based on multiple timings index variation tendency according to claim 1,
It is characterized in that, in the step S3, calculates in the enhancement mode meta file year day by day time series data and concentrate enhanced vegetation
Day by day the 50th tantile of time series data in index year, will be through by pixel, retrieval is greater than or equal to the 50th point successively in chronological order
The second largest value obtained after the duration of place value is as low point of position persistence index.
8. a kind of conceding the land to forestry automatic identifying method based on multiple timings index variation tendency according to claim 1,
It is characterized in that, in the step S5, the secular variation trend Q is obtained by using SenShi Slope Methods.
9. a kind of conceding the land to forestry automatic identifying method based on multiple timings index variation tendency according to claim 1,
It is characterized in that, in the step S6, the significance test of the secular variation trend Q includes that spatial saliency is examined with timely
Sequence significance test and the result of significance test is denoted as：Significantly positive trend, trendless and significantly negative trend.
10. a kind of conceding the land to forestry automatic identifying method converting trend based on multiple timings index according to claim 9,
It is characterized in that, in the step S7, establishes the conceding the land to forestry automatic identification flow chart in the following way：
（1）When abundance and growth period length all have notable positive trend, and sequential dispersion shows significantly negative trend, and high
When the equal trendless of point position persistence, low point of position persistence, then the pixel is changed into forest by single cropping crops, i.e., the pixel is to move back
Plough also forest zone domain；
（2）When high score position persistence, growth period length all have notable positive trend, and low point of position persistence shows and significantly bears
Gesture, and when the equal trendless of abundance, sequential dispersion, then the pixel is changed into forest by more season crops, i.e., and the pixel is to move back
Plough also forest zone domain.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111738144A (en) * | 2020-06-19 | 2020-10-02 | 中国水利水电科学研究院 | Surface water product generation method and system based on Google Earth Engine cloud platform |
| CN111860149A (en) * | 2020-06-11 | 2020-10-30 | 中科禾信遥感科技（苏州）有限公司 | Remote sensing identification method and device for overwintering rape and wheat |
| 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 |
| CN113609899A (en) * | 2021-06-24 | 2021-11-05 | 河海大学 | Backing-out land information positioning display system based on remote sensing time sequence analysis |
| NO20221362A1 (en) * | 2022-12-19 | 2024-06-20 | Digifarm As | Methods and systems for assessment of sustainability of agricultural fields and areas |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103679131A (en) * | 2013-01-23 | 2014-03-26 | 福州大学 | Multi-season crop automatic recognition method based on time sequential remote sensing image |
| CN104766070A (en) * | 2015-04-22 | 2015-07-08 | 福州大学 | Forest information remote sensing and automatic extracting method based on vegetation index time series data dispersion measures |
| CN105718936A (en) * | 2016-02-02 | 2016-06-29 | 福州大学 | Forest dynamic change mode automatic extraction method |
| CN106780091A (en) * | 2016-12-30 | 2017-05-31 | 黑龙江禾禾遥感科技有限公司 | Agricultural disaster information remote sensing extracting method based on vegetation index time space statistical nature |
-
2018
- 2018-01-11 CN CN201810025180.7A patent/CN108388832A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103679131A (en) * | 2013-01-23 | 2014-03-26 | 福州大学 | Multi-season crop automatic recognition method based on time sequential remote sensing image |
| CN104766070A (en) * | 2015-04-22 | 2015-07-08 | 福州大学 | Forest information remote sensing and automatic extracting method based on vegetation index time series data dispersion measures |
| CN105718936A (en) * | 2016-02-02 | 2016-06-29 | 福州大学 | Forest dynamic change mode automatic extraction method |
| CN106780091A (en) * | 2016-12-30 | 2017-05-31 | 黑龙江禾禾遥感科技有限公司 | Agricultural disaster information remote sensing extracting method based on vegetation index time space statistical nature |
Non-Patent Citations (1)
| Title |
|---|
| 刘哲等: "2001—2014 年间黄土高原植被覆盖状态时空演变分析", 《国土资源遥感》 * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111860149A (en) * | 2020-06-11 | 2020-10-30 | 中科禾信遥感科技（苏州）有限公司 | Remote sensing identification method and device for overwintering rape and wheat |
| CN111860149B (en) * | 2020-06-11 | 2024-01-12 | 中科禾信遥感科技（苏州）有限公司 | Remote sensing identification method and device for overwintering rape and wheat |
| CN111738144A (en) * | 2020-06-19 | 2020-10-02 | 中国水利水电科学研究院 | Surface water product generation method and system based on Google Earth Engine cloud platform |
| CN111738144B (en) * | 2020-06-19 | 2021-08-31 | 中国水利水电科学研究院 | Surface water product generation method and system based on Google Earth Engine cloud platform |
| 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 |
| CN113609899A (en) * | 2021-06-24 | 2021-11-05 | 河海大学 | Backing-out land information positioning display system based on remote sensing time sequence analysis |
| CN113609899B (en) * | 2021-06-24 | 2024-05-14 | 河海大学 | Remote sensing time sequence analysis-based tilling land information positioning display system |
| NO20221362A1 (en) * | 2022-12-19 | 2024-06-20 | Digifarm As | Methods and systems for assessment of sustainability of agricultural fields and areas |
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