CN102435554A - Method for acquiring farmland multiple-cropping index - Google Patents
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Abstract
The invention discloses a method for acquiring a farmland multiple-cropping index. According to the invention, the size of a slide window is defined according to a crop growth period; remote-sensed vegetation index time sequence data is inputted; when the slide window gradually moves forward along the remote-sensed vegetation index time sequence, a maximum value and a minimum value in the data in the current slide window, and the positions of the maximum value and the minimum value in the current slide window are searched for; it is determined whether a data point is a latent peak point or a trough point in a crop growth season; false peak points and false trough points are rejected, such that final peak points and final trough points are obtained; and the farmland multiple-cropping index is determined according to the appearance frequency of the peak points. With the technical scheme provided by the invention, without filtering, farmland multiple-cropping index acquiring can be directly carried out upon pre-treated remote-sensed vegetation index time sequence data. The method has a good noise resistance and a good peak number detection capacity. With the method, the amount of required parameters is small. The versatility of the method is good.
Description
Technical field
The present invention relates to the agricultural remote sensing technical field, relate in particular to a kind of method of extracting the arable land multiple crop index.
Background technology
(Cropping Index CI) is meant to plough for one and plants the number of times of plant in 1 year multiple crop index.Multiple crop index is to weigh the basic index that the cultivated land resource intensification utilizes degree in the cropping system research, also is the important technology index that the macroscopic evaluation cultivated land resource utilizes basal conditions.Multiple crop index extracts has two kinds of methods, a kind of method that is based on statistics, and another kind is based on the method for remote sensing.
Extracting based on the multiple crop index of statistics is a kind of classic method, adopts following formula that the multiple crop index on the administrative division unit is estimated according to the seeding crops area and the cultivated area of statistics:
MCI
r＝A
s/A
c
Wherein, MCI
rBe arable land, zone multiple crop index; A
sBe the annual crop total yield in zone area; A
cBe regional total cultivated area.Can find out that statistical method is calculated fairly simple, but owing to be to adopt statistics to calculate, on the one hand, it is heterogeneous that it has ignored each statistic unit volume inside, can not describe the space characteristics of planting system exactly; Also there is certain hysteresis quality in obtaining of statistics on the other hand.In addition; Receive the interference of Statistical Criteria, range scale and human factor, there is certain error in statistics itself, brings uncertainty to result of study; Especially to big, the ageing demanding multiple crop index research of spatial dimension, the ground statistical method is difficult to reach requirement.
Multiple crop index extraction based on remotely-sensed data is to judge that according to the distinctive spectral signature of green vegetation vegetation growth is dynamic.The vegetation index of remotely-sensed data inverting (Vegetation Index; VI) can reflect the vegetation growth situation preferably; Seasonal effect in time series vegetation index data then are the monitoring signs of vegetation dynamic change, and promptly the timing variations of vegetation index is corresponding to the growth of vegetation and season activity process such as weak.As far as the arable land, the sequential dynamic change of vegetation index has embodied the growth course of arable land crop, promptly from sowing, emerge, jointing, earing to periodicity situation ripe, harvesting.The arable land vegetation index curve in one shortening zone is accomplished a round-robin dynamic process within the year, and two circulations are accomplished in the yielding two crops a year zone, and triple-cropping system will be accomplished three growth cycles.Therefore, utilize the cyclical variation of time series vegetation index can accomplish the monitoring of arable land multiple crop index.
Multiple crop index method for distilling based on remote sensing all is to be the basis with time series vegetation index data basically at present, at first adopts the match of various filtering and noise reduction means to obtain the plant growth curve, carries out the extraction of multiple crop index then.
Receive the interference of remote sensor self-condition (inclination angle, resolution, sensor are aging etc.) and factors such as cloud layer, atmosphere and sun altitude in the remotely-sensed data acquisition process; Make the vegetation index that directly obtains from remote sensing image have much noise, influenced the extraction of multiple crop index.(Maximum Value Composite MVC) generates, and the noise of cloud and atmosphere etc. still can not be eliminated fully, is inappropriate for the extraction of directly carrying out multiple crop index though time series vegetation index commonly used synthesizes through maximal value.
Chinese scholars has developed many technical methods (being referred to as filtering and noise reduction here) that remove cloud processing, noise remove and reconstruct smooth vegetation index curve; These methods totally can be divided into 3 types: threshold value is removed method, based on the smoothing method and the nonlinear fitting method of filtering.Threshold value is removed optimum gradient coefficient intercepting method (the The Best Index Slope Extraction of method with propositions such as Viovy; BISE) be representative; Smoothing method based on filtering mainly comprises Fourier transform, wavelet transformation and Savitzky-Golay filter method etc., but not the linear function fit method mainly comprises Logistic function-fitting method and asymmetric Gaussian function fitting process.
The method of extracting the arable land multiple crop index based on the time series vegetation index of rebuilding also is one of committed step of arable land multiple crop index remote sensing monitoring.Remote sensing multiple crop index method of discrimination mainly comprises classification, intersection fitting process and Peak Intensity Method, and Peak Intensity Method is divided into direct comparison method and second difference point-score.Classification is directly to adopt the remote sensing sorting technique to obtain different shortening classifications to the time series vegetation index curve behind the filtering and noise reduction, confirms multiple crop index according to the shortening classification.The fitting process that intersects is set up shortening typical curve storehouse according to representative point more earlier to time series vegetation index filtering and noise reduction, calculates the degree of fitting that intersects of time series vegetation index curve and shortening typical curve after the denoising then, thus definite multiple crop index.The basic assumption of Peak Intensity Method is: the peak value of the multiple cropping mode in arable land and the vegetation index change curve in arable land is more identical; Promptly 1 year one season, the crop multiple crop index data of ploughing formed tangible unimodal curve within the year, 1 year two season the crop vegetation index of ploughing form bimodal curve.Therefore, can confirm the multiple cropping system of ploughing through the peak value number of monitoring vegetation index change curve.The method of obtaining the peak value frequency commonly used at present is divided into direct comparison method and second difference point-score.
At present, Peak Intensity Method has obtained using the most widely owing to being simple and easy to be used in the multiple crop index monitoring of arable land.Though Peak Intensity Method can effectively be found peak value, may unusual fluctuations for the vegetation index curve of subregion owing to the quality of image and pixel internal influence, thereby " the pseudo-peak value " that occur forming by the noise waves peak.So, only calculate the peak value number merely and possibly cause some errors, need utilize certain constraint condition that the peak value that detects is accepted or rejected.For example in the remote sensing of multiple cropping system is extracted; According to the statistical nature of website climatological observation data confirm that the decision rule of shortening, the parameter in the decision rule comprise that peak value occurs the earliest maybe the time, peak value occur the latest maybe the time, the peak value minimum, two season the crop peak value the difference etc. of minimum interval, maximal value and minimum value.Though these researchs have proposed comparatively reasonably modification method separately, these methods and parameter threshold setting all have certain regional suitability and limitation.Overview is got up, and the multiple crop index method for distilling based on remote sensing mainly comprises following four kinds of technical schemes at present:
Technical scheme one: filtering and noise reduction+classification.At first time series vegetation index data are carried out filtering and noise reduction, the time series data after adopting the remote sensing sorting technique to denoising is again classified, and confirms multiple crop index according to the shortening of each type.Sorting technique can be a supervised classification method, also can be not supervised classification.
Filtering and noise reduction process itself can be infiltrated new noise (as two crests are connected into a spurious peaks, perhaps make two crests not obvious), causes final crest to count calculating and bigger error can occur.Some at present popular filtering algorithms all become two ripe crop curves bimodal unimodal easily, will certainly influence the final extraction result of multiple crop index like this.In addition, filtering and noise reduction relates generally to the process of iterative loop, and is consuming time longer, and operation efficiency can be on the low side to some extent.
Classification requires operating personnel very familiar to the crops shortening of study area, selects sample to classify thus and extracts shortening (supervised classification), or cluster result is carried out shortening differentiate (unsupervised classification).Nicety of grading depends on the selection of sorting technique on the one hand, depends on operating personnel's experience on the other hand, so relatively poor, the regional adaptedness of operability of multiple crop index extraction flow process is lower.
Technical scheme two: filtering and noise reduction+intersection fitting process.Earlier time series vegetation index data are carried out filtering and noise reduction, set up shortening typical curve storehouse according to representative point again, calculate the degree of fitting that intersects of every pixel timing curve and standard shortening curve then.The degree of fitting that intersects is meant reasons such as considering phenology, sowing time difference, and the time shaft of timing curve is carried out relative translation, calculates pixel timing curve and the degree of fitting of standard shortening curve on different time position (wave band position).Discern the pixel shortening with the intersection degree of fitting as the similarity index, the shortening when choosing the degree of fitting maximum is confirmed multiple crop index as the pixel shortening according to shortening.
The shortcoming of filtering and noise reduction is with technical scheme one.The fitting process that intersects requires to set up in advance a complete shortening typical curve storehouse; Because the variation of remote sensing vegetation index time series is bigger; Shortening typical curve storehouse is difficult to all possible shortening typical curve of limit, and requires operating personnel very familiar to the crops shortening of study area equally.
Technical scheme three: filtering and noise reduction+direct comparison method.At first time series vegetation index data are carried out filtering and noise reduction, adopt direct comparison method to extract peak value then,, confirm multiple crop index through certain differentiation.Direct comparison method is to judge at one the vegetation index value of the adjacent several time points of vegetation index value and front and back of each time point to be compared in interval, obtains the time point of vegetation exponential quantity maximum in this interval, is the peak value in this interval; So repeatedly, can reach the quantity and the time distributed points thereof of whole arable land all peak values in growth season.
The shortcoming of filtering and noise reduction is with technical scheme one.Direct comparison method requires at first to set up one and judges intervally, and this needs operating personnel very familiar to the crops shortening of study area, otherwise error appears in the judgement of follow-up time point to peak value and appearance thereof easily; In addition, even given right judgement is interval,, also can be easily error be appearred in the judgement of the time point of the peak value of the less crop type of area and appearance thereof because the phenological calendar of Different Crop is different.
Technical scheme four: filtering and noise reduction+second difference point-score.At first time series vegetation index data are carried out filtering and noise reduction, adopt the method for second order difference to extract multiple crop index again.The second difference point-score forms array in chronological order with N vegetation index of time series vegetation index in a year, at first deducts the vegetation index value of its front with the vegetation index of back, forms N-1 and newly is worth; This N-1 new value carried out assignment again; If negative then is decided to be-1; If positive number then is decided to be 1; Then N-1 value of new assignment carried out first difference again by top method, obtains N-2 by-2,0,2 data formed, wherein element be-2 and the front and back element to be all 0 point be exactly peak point.
The shortcoming of filtering and noise reduction is with technical scheme one.The second difference point-score receives the noise of remote sensing vegetation index time series data easily, and the local maximum (maximum value in non-crop growth season) that is easy to noise is caused is judged as the maximum value in crop growth season, thereby makes multiple crop index higher.
Existing method based on remote sensing time series data extraction multiple crop index all is first to remote sensing time series data filtering and noise reduction, and then calculates the crest number.Because the filtering and noise reduction required time is long, and filtering itself can infiltrate new noise, causes final crest to count calculating and bigger error can occur.In addition, a little less than the existing Peak Intensity Method noise resisting ability, the parameter of correction is many, regional require high, versatility a little less than.
Summary of the invention
The objective of the invention is to propose a kind of method of extracting the arable land multiple crop index; Need not filtering just can be directly extracts the pretreated remote sensing vegetation index time series data multiple crop index of ploughing; Possess stronger noise resisting ability and the ability that detects the crest number; Desired parameters is few, highly versatile.
For reaching this purpose, the present invention adopts following technical scheme:
A kind of method of extracting the arable land multiple crop index may further comprise the steps:
A, according to the size of crop growth period definition moving window;
B, input remote sensing vegetation index time series data;
C, when moving window when remote sensing vegetation index time series progressively moves forward; Search for the maximal value and the minimum value of current sliding window data; And said maximal value and minimum value residing position in said current moving window; If it is wave crest point or the trough point in the potential crop growth season that said maximal value or minimum value, are then judged said data point in the centre position of said current moving window;
If do not have the trough point in the middle of two adjacent potential wave crest points of D; Then delete two less wave crest points of value in the adjacent potential wave crest point; If do not have wave crest point in the middle of two adjacent potential trough points, then delete two trough points that the value in the adjacent potential trough point is bigger;
If the difference between trough point of E and the adjacent wave crest point is then deleted said trough point, and returned step D less than predetermined threshold value,, then go to step F if the difference between trough point and the adjacent wave crest point is not less than predetermined threshold value;
F, obtain final wave crest point and trough point, and confirm the arable land multiple crop index according to the number of times that wave crest point occurs.
In the steps A; Definition moving window
wherein; F is the size of moving window; R is the duration in crop growth cycle; S is a synthesis cycle; Said growth cycle is crops from being seeded into the growth course of harvesting, and final moving window f gets with
immediate odd number and is its value.
In the step e, said predetermined threshold value is the maximal value of minimum crest in the remote sensing vegetation index time series data corresponding with said growth cycle and the difference of minimum value.
Adopted technical scheme of the present invention, need not, just can directly extract the arable land multiple crop index, had following advantage remote sensing vegetation index time series data to remote sensing vegetation index time series data filtering:
(1) principle is simple, and operation efficiency is high, realizes with program language easily.
(2) do not need other auxiliary datas, noise resisting ability is strong, and the result is reliable, stable, is specially adapted to provide in time for agricultural sector or government department the space distribution information of relevant cropping system.
(3) desired parameters is few, has reduced regional applicability requirement, has improved the versatility of method.
(4) human intervention is few, and the degree of automatic operating is high.
Description of drawings
Fig. 1 is the shape synoptic diagram of structural element.
Fig. 2 is the process flow diagram that multiple crop index remote sensing in arable land is extracted in the specific embodiment of the invention.
Embodiment
Further specify technical scheme of the present invention below in conjunction with accompanying drawing and through embodiment.
The main thought of technical scheme of the present invention is to be to seek real wave crest point, and the identification of wave crest point is based on mathematical morphology and the process that decision-making is judged.Mathematical morphology is meant with the structural elements of a known geometries usually seeks signal (technical scheme of the present invention is meant remote sensing vegetation index time series data); Therefrom find out the unique point that is complementary with structural element; In technical scheme of the present invention; The length of structural element is confirmed that by the size of moving window the shape of structural element is meant and meets maximum or all the minimum shapes of moving window central value, and is as shown in Figure 1; Wherein (a) is the partial geometry shape collection of maximal value in the centre position, (b) is the partial geometry shape collection of minimum value in the centre position.
Fig. 2 is the process flow diagram that multiple crop index remote sensing in arable land is extracted in the specific embodiment of the invention.As shown in Figure 2, the flow process that this arable land multiple crop index remote sensing is extracted may further comprise the steps:
For example the winter wheat in the China north generally is in sowing by the end of October, begins to turn green in the March of next year, and early June begins harvesting; Can form two growth crests during this time, a crest is present in to be sowed to period of seedling establishment, and another then is to begin to finish to harvest time from period of seedling establishment; So the time that the single growth crest of winter wheat is covered is about 110 days; So moving window also should be across about 110 days, for 16 days synthetic remote sensing vegetation indexes, its moving window size should be 7.
If do not have the trough point in the middle of two adjacent potential wave crest points of step 107; Then delete two less wave crest points of value in the adjacent potential wave crest point; If do not have wave crest point in the middle of two adjacent potential trough points; Then delete two trough points that the value in the adjacent potential trough point is bigger, and go to step 108.
If the difference between trough point of step 109 and the adjacent wave crest point is then deleted this trough point, and is returned step 106 less than predetermined threshold value.
If the difference between trough point of step 110 and the adjacent wave crest point is not less than predetermined threshold value, then obtains final wave crest point and trough point, and confirm the arable land multiple crop index according to the number of times that wave crest point occurs.
The above; Be merely the preferable embodiment of the present invention, but protection scope of the present invention is not limited thereto, anyly is familiar with this technological people in the technical scope that the present invention disclosed; The variation that can expect easily or replacement all should be encompassed within protection scope of the present invention.Therefore, protection scope of the present invention should be as the criterion with the protection domain of claim.
Claims (3)
1. a method of extracting the arable land multiple crop index is characterized in that, may further comprise the steps:
A, according to the size of crop growth period definition moving window;
B, input remote sensing vegetation index time series data;
C, when moving window when remote sensing vegetation index time series progressively moves forward; Search for the maximal value and the minimum value of current sliding window data; And said maximal value and minimum value residing position in said current moving window; If it is wave crest point or the trough point in the potential crop growth season that said maximal value or minimum value, are then judged said data point in the centre position of said current moving window;
If do not have the trough point in the middle of two adjacent potential wave crest points of D; Then delete two less wave crest points of value in the adjacent potential wave crest point; If do not have wave crest point in the middle of two adjacent potential trough points, then delete two trough points that the value in the adjacent potential trough point is bigger;
If the difference between trough point of E and the adjacent wave crest point is then deleted said trough point, and returned step D less than predetermined threshold value,, then go to step F if the difference between trough point and the adjacent wave crest point is not less than predetermined threshold value;
F, obtain final wave crest point and trough point, and confirm the arable land multiple crop index according to the number of times that wave crest point occurs.
2. a kind of method of extracting the arable land multiple crop index according to claim 1; It is characterized in that; In the steps A; Definition moving window
wherein; F is the size of moving window; R is the duration in crop growth cycle; S is a synthesis cycle, and said growth cycle is crops from being seeded into the growth course of harvesting, and final moving window f gets with
immediate odd number and is its value.
3. a kind of method of extracting the arable land multiple crop index according to claim 2 is characterized in that, in the step e, said predetermined threshold value is the maximal value of minimum crest in the remote sensing vegetation index time series data corresponding with said growth cycle and the difference of minimum value.
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN103500421A (en) * | 2013-10-09 | 2014-01-08 | 福州大学 | Frequency characteristic-based farmland cropping index extraction method |
| CN104346528A (en) * | 2014-10-22 | 2015-02-11 | 中国电子科技集团公司第四十一研究所 | Effective vibration data interception method based on waveform characteristic statistics |
| CN105809632A (en) * | 2014-12-31 | 2016-07-27 | 中国科学院深圳先进技术研究院 | Method for removing noise from radar images of predetermined crops |
| CN103927430B (en) * | 2014-01-23 | 2017-01-11 | 福州大学 | Farmland cropping index automatic extracting method |
| CN108345992A (en) * | 2018-01-31 | 2018-07-31 | 北京师范大学 | A kind of multiple crop index extracting method and device |
| CN108388612A (en) * | 2018-02-08 | 2018-08-10 | 中国矿业大学(北京) | A kind of optimization method of multi-temporal NDVI data sequence |
| CN113589686A (en) * | 2021-06-26 | 2021-11-02 | 中国人民解放军海军工程大学 | GSA-IFCM (generalized likelihood-based inference-based extraction) unit cycle time sequence self-adaptive extraction method |
| WO2023035119A1 (en) * | 2021-09-07 | 2023-03-16 | 山东工商学院 | Method and system for extracting hyperbolic wave from ground penetrating radar image |
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| CN103500421B (en) * | 2013-10-09 | 2017-01-11 | 福州大学 | Frequency characteristic-based farmland cropping index extraction method |
| CN103500421A (en) * | 2013-10-09 | 2014-01-08 | 福州大学 | Frequency characteristic-based farmland cropping index extraction method |
| CN103927430B (en) * | 2014-01-23 | 2017-01-11 | 福州大学 | Farmland cropping index automatic extracting method |
| CN104346528A (en) * | 2014-10-22 | 2015-02-11 | 中国电子科技集团公司第四十一研究所 | Effective vibration data interception method based on waveform characteristic statistics |
| CN104346528B (en) * | 2014-10-22 | 2017-05-10 | 中国电子科技集团公司第四十一研究所 | Effective vibration data interception method based on waveform characteristic statistics |
| CN105809632B (en) * | 2014-12-31 | 2018-11-23 | 中国科学院深圳先进技术研究院 | From the method for the radar image of predetermined crops removal noise |
| CN105809632A (en) * | 2014-12-31 | 2016-07-27 | 中国科学院深圳先进技术研究院 | Method for removing noise from radar images of predetermined crops |
| CN108345992A (en) * | 2018-01-31 | 2018-07-31 | 北京师范大学 | A kind of multiple crop index extracting method and device |
| CN108345992B (en) * | 2018-01-31 | 2021-07-09 | 北京师范大学 | Multiple cropping index extraction method and device |
| CN108388612A (en) * | 2018-02-08 | 2018-08-10 | 中国矿业大学(北京) | A kind of optimization method of multi-temporal NDVI data sequence |
| CN108388612B (en) * | 2018-02-08 | 2020-09-08 | 中国矿业大学(北京) | Optimization method of time sequence NDVI (normalized difference vector) data sequence |
| CN113589686A (en) * | 2021-06-26 | 2021-11-02 | 中国人民解放军海军工程大学 | GSA-IFCM (generalized likelihood-based inference-based extraction) unit cycle time sequence self-adaptive extraction method |
| CN113589686B (en) * | 2021-06-26 | 2023-09-29 | 中国人民解放军海军工程大学 | GSA-IFCM-based unit cycle time sequence self-adaptive extraction method |
| WO2023035119A1 (en) * | 2021-09-07 | 2023-03-16 | 山东工商学院 | Method and system for extracting hyperbolic wave from ground penetrating radar image |
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