CN102668899A - Crop planting mode recognition method - Google Patents
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Abstract
The invention discloses a crop planting mode recognition method which comprises the following steps: inputting remote-sensing vegetation index time series data, and determining the vegetation growth threshold; optimizing parameters by selecting training samples to obtain the shortest length of the growing season of the crops, the longest length of the growing season of the crops and the smallest growth magnitude of the crops; extracting the vegetation growth information which is the number of the growing season of the crops in one year, the length of each growing season of the crops and the growth magnitude of the crops, and eliminating the non-crops; calculating the number of the growing season of the crops to obtain the multiple cropping index in one year; and finally determining the crop planting mode comprehensively according to the multiple cropping indexes of the crops in the last year, current year and next year. Due to the adoption of the technical scheme, the crop planting mode can be recognized directly according to the remote-sensing vegetation index time series data, and the problems the experience of calculating parameters is deficient, and the peak numbers do not correspond to the crop planting modes are solved. The crop planting mode recognition method has the advantages of strong noise resisting capability, less required parameters and strong versatility.
Description
Technical field
The present invention relates to the agricultural remote sensing technical field, relate in particular to a kind of crop growing mode recognition methods.
Background technology
(Cropping pattern is the space expression of shift of crops CP) to cropping pattern, is the summary to stubble order before and after the crops.Crop planting model is related to making full use of of resources such as water, heat, light, soil fertility, is the important technical links that improves field piece per unit area yield and gross output, has very important significance for efficient, controlled agricultural management.Be accompanied by continuing to increase and minimizing trend that the arable land storage is potential of the redistributing of agricultural natural resources such as world wide light, heat, water, world population, cropping pattern is also taking place to change.In time, accurately monitor agricultural planting pattern and change in time and space thereof, help predicting grain yield and variation thereof and formulation agricultural development policy.Reasonably cropping pattern should help the most effectively utilizing of various resources such as soil, sunlight, heat and water, obtains crops produce under the prevailing condition best society, economy and environmental benefit, and can develop sustainably.Unsuitable planting patterns can be in time found and corrected in the crop growing mode monitoring, keeps the sustainable development potentiality in arable land, and then ensures grain security.
Chinese scholars attempt to adopt meteorological data or statistical data, be minimum research unit reflecting regional and global cropping pattern with the administrative area, the spatial variations of showing cropping pattern from macroscopic view.But agriculture natural conditions have the characteristics of local microclimate, and internal diversity can not ignore.Make ecological region planting pattern complicacy various such as the plantation management mode that with the peasant household is the unit, this method can not be described the space characteristics of cropping pattern exactly.In addition, also there is certain hysteresis quality in obtaining of meteorological data and statistical data, and there is certain error in statistical data itself, has brought uncertainty to result of study.For big, the ageing demanding cropping pattern research of spatial dimension, this method is difficult to reach requirement.
Satellite remote sensing is the effective means of surveying soil utilization/covering general layout and variation, and remotely-sensed data is the ideal data source that obtains crop growing mode.Crop growing mode remote sensing recognition method has two kinds: a kind of recognition methods that is based on multidate middle high-resolution remote sensing image, a kind of recognition methods that is based on the time series remotely-sensed data.
Utilize the multispectral satellite image identification of multidate, the middle high-resolution crop growing mode in different crops growth season based on the cropping pattern recognition methods of multidate middle high-resolution image.Concrete grammar is in 1 year, each crop growth season all to obtain many scapes middle high-resolution multispectral image, and the image in difference growth season is carried out decipher and classification respectively, extracts the crops in the season of respectively growing; The situation of difference growth season plantation crops is being analyzed, thereby confirmed the crop planting pattern.Owing to receive satellite heavily to visit the influence of cycle and weather conditions, in plant growth season, obtain sufficient, the quality unusual difficulty of middle high-resolution data preferably, limited the application of this method in the large scale cropping pattern is discerned.
The development of satellite remote sensing technology for we provide long-term repeated measures data, makes us can study the long-time variation of things, to find characteristic and rule wherein.Recent two decades has carried out extensive and deep research with the remote sensing time series data to natural vegetation phenology both at home and abroad, and the algorithm and model identification vegetation phenology characteristic of many maturations is arranged.And the crops that go up plantation that plough also embody tangible phenology characteristic in the influence process to factors such as weather, the hydrology, humanities, and this makes that utilizing the remote sensing time series data that cropping pattern is discerned becomes possibility.
The vegetation index of remotely-sensed data inverting can reflect the vegetation growth situation preferably, and the seasonal effect in time series vegetation index then is the sign of vegetation dynamic change monitoring, 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 process of growth of crops, promptly from sowing, emerge, jointing, earing to periodicity situation ripe, harvesting.The arable land vegetation index curve in 1 year one ripe zone was accomplished the dynamic process of a circulation in 1 year, two circulations are accomplished in the arable land in the zone of yielding two crops a year, and three growth cycles will be accomplished in the arable land in 1 year three ripe zone.Therefore, utilize the cyclically-varying of time series vegetation index can accomplish the monitoring of arable land cropping pattern.
Cropping pattern research based on time series data all is to be the basis with remote sensing time series vegetation index data at present, at first adopts various filtering and noise reduction algorithms to obtain comparatively level and smooth crop growth curve, carries out the extraction of cropping pattern then.The method of discrimination of remote sensing cropping pattern mainly comprises classification, spectrum (sequential spectrum) matching method and Peak Intensity Method.Classification is directly to adopt the remote sensing sorting technique to obtain different cropping pattern classifications to the time series vegetation index curve behind the filtering and noise reduction.Spectral matching is earlier to time series vegetation index filtering and noise reduction; Set up cropping pattern calibration curve storehouse according to representative point again; Adopt time series vegetation index curve and the matching degree of cropping pattern calibration curve after the denoising of Spectral matching technique computes then, thereby confirm various cropping pattern classifications.
The basic assumption of Peak Intensity Method is: the peak value of the vegetation index change curve in the number of times of kind plant and arable land was more identical within last one year of ploughing; The vegetation index curve of promptly 1 year one ripe cropping pattern forms significantly unimodal within the year, and the vegetation index curve that the two crops a year cropping pattern is ploughed forms bimodal.Therefore, can confirm the cropping pattern of ploughing through the peak value number of monitoring vegetation index curve.Peak Intensity Method is divided into direct comparison method and second difference point-score again.At present, Peak Intensity Method has obtained using the most widely owing to being simple and easy to be used in the cropping pattern monitoring of arable land.
Overview is got up, and the cropping pattern method for distilling based on the remote sensing time series data mainly comprises following three kinds of technical schemes at present:
Technical scheme one: classification.Time series vegetation index data after adopting the remote sensing sorting technique to denoising are classified, and confirm cropping pattern according to the time-serial position of each type crops.Sorting technique can be a supervised classification method, also can be not supervised classification.
Classification requires operating personnel very familiar to the crop growing mode of study area, selects sample to carry out Classification and Identification cropping pattern (supervised classification) thus, or cluster result is carried out cropping pattern differentiate (unsupervised classification).Nicety of grading depends on the selection of sorting technique on the one hand, depends on researcher's experience on the other hand.Therefore, repeatable relatively poor, the regional adaptedness of classification identification cropping pattern is lower.
Technical scheme two: spectral matching.Earlier set up cropping pattern calibration curve storehouse, calculate the matching degree of the vegetation index timing curve and the standard species implant model curve of every pixel then according to the vegetation index timing curve of representative point.Matching degree is meant reasons such as considering phenology, sowing time difference; Time shaft to the vegetation index timing curve carries out relative translation, and the vegetation index timing curve in vegetation index timing curve and standard species implant model storehouse that calculates each pixel is in the locational matching degree of different time.Discern cropping pattern with matching degree as the similitude index, the cropping pattern when choosing the matching degree maximum is as the cropping pattern of pixel to be identified.
Spectral matching requires to set up in advance a complete cropping pattern calibration curve storehouse; Because the variation of remote sensing vegetation index sequential is bigger; Cropping pattern calibration curve storehouse is difficult to all possible cropping pattern calibration curve of limit, and requires the researcher very familiar to the crop growing mode of study area equally.
Technical scheme three: Peak Intensity Method.At first adopt direct comparison method or second difference point-score that the vegetation index time series data is extracted peak value,, confirm the peak value number through certain differentiation.Confirm the cropping pattern of pixel then according to the number of peak value.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 in this interval; So repeatedly, can obtain the quantity and the time distributed points thereof of whole arable land all peak values in growth season.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 value of back, forms the individual new value of N-1; New value to this N-1 is 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 arrays formed, wherein element be-2 and the front and back element to be all 0 point be exactly peak point.
Peak Intensity Method has three deficiencies: the first, and Peak Intensity Method is to noise-sensitive, and is high to the requirement of time series data filtering and noise reduction sound algorithm.Though present filtering method can be eliminated some obvious noise preferably, filtered curve is not perfectly smooth curve, still have some trickle noises, and Peak Intensity Method can all detect each peak value.The second, this method depends on researcher's experience and region characteristic, and the universality of method is not strong.The 3rd, peak value number and cropping pattern are not to concern one to one, are inaccurate with peak value number representative species implant model.What the peak value number reflected is to plough in 1 year, to plant the number of times (cropping index) of plant, but a complete cropping pattern can not be accomplished needs 2 years or more time sometimes in 1 year.Such as in 2 years three ripe districts, cropping pattern is stable, but the peak value number of 1 year and 1 year generally is different.Therefore the variation of the peak value number between 2 years is a kind of " the pseudo-variation ", infers that thus the variation of cropping pattern is unreasonable especially.
In a word, low, the versatility of existing method zone adaptability based on remote sensing vegetation index time series data identification crop growing mode a little less than.
Summary of the invention
The objective of the invention is to propose a kind of crop growing mode recognition methods, input remote sensing vegetation index time series data and a small amount of training sample just can be realized the extraction of crop growing mode, few, the highly versatile of desired parameters.
For reaching this purpose, the present invention adopts following technical scheme:
A kind of crop growing mode recognition methods may further comprise the steps:
A, input remote sensing vegetation index time series data;
B, confirm the vegetation growth threshold value, areal bare area and the vegetation-covered area vegetation index value when difference occurs spring and summer is the earliest confirmed as the vegetation growth threshold value;
C, selection crops training sample;
D, parameter optimization; According to training sample to the study area crops the shortest growth season length, the most long-living long season length and these 3 parameters of minimum growth amplitude make up at random, select the highest parameter value combination of training sample cropping pattern accuracy of identification as optimized parameter;
E, extraction vegetation growth information;
F, the non-agricultural crop of eliminating zone; Judge successively pixel to be identified vegetation growth season length whether between crops between the length of the most long-living long season of the shortest growth season length and crops, whether the vegetation growth amplitude greater than the minimum growth amplitude of crops; As long as there is 1 not satisfy in these 2 conditions, then should be judged to be non-agricultural crop zone by pixel to be identified;
G, according to crop growth season number confirm the crops cropping index in a year;
H, according to the previous year, comprehensively confirm crop growing mode with back 1 year crops cropping index then.
In the step e; The vegetation growth information of extracting comprises vegetation growth season number, length and 3 indexs of growth amplitude in each growth season in a year; Concrete extraction step comprises: the value and the vegetation growth threshold value of all time points of vegetation index time series data are made comparisons in (1); To be 1 more than or equal to 0 time point assignment, the time point assignment less than 0 be 0, thereby obtains a time series of being made up of 0 and 1 value; (2) to being that 1 value adds up continuously in the new time series,, then restart to add up, obtain a time series after adding up if run into 0; (3) to the time series after adding up; The time point at promising 1 value place confirm as the from date in vegetation growth season; All are greater than 0 and first is the Close Date that the time point at 0 value place is confirmed as vegetation growth season following closely; Extract vegetation growth season from date with the Close Date in vegetation growth season alternately, be vegetation growth season from date as if last, then with its deletion; (4) number of times that occurs according to vegetation growth season from date in a year is confirmed vegetation growth season number; Confirm vegetation growth season length according to initial and the Close Date in each growth season, according to vegetation growth season from date and the vegetation index maximum between the Close Date and vegetation growth season from date the difference of vegetation index value confirm the season amplitude of growing.
Among the step H, according to the previous year, comprehensively confirm crop growing mode with back 1 year cropping index then, the cropping index combination in 3 years has 4
3The situation of kind; Concrete step comprises: (1) in all contained 0 combination, 3 years cropping index were to confirm as uncultivated area at 0 o'clock entirely, and cropping index is 0 and have in the previous year or back 1 year and confirm as leisure cultivated land when not being 0 situation then; Contain 0 combination for other; Cropping pattern is then confirmed (cropping index is 1 year one ripe cropping pattern of 1 expression, and cropping index is the double-cropped cropping patterns of 2 expressions, and cropping index is 1 year three ripe cropping patterns of 3 expressions) by cropping index then; (2) all that be left contain 3 combination, and cropping pattern is then confirmed by cropping index then; (3) adopt two kinds of principles to confirm remaining by 1 and 2 combinations that constitute, the one, the homogeny principle as long as any 1 year cropping index is identical in cropping index then and the previous year or back 1 year, is then pressed the definite cropping pattern then of identical cropping index; The 2nd, symmetry principle, to (1,2,1) and (2,1,2) combination, they all are to accomplish proportion of crop planting in two years three times, thus cropping pattern be 2 years three ripe.
Adopted technical scheme of the present invention, can directly extract crop growing mode, had following advantage the remote sensing vegetation index time series data:
(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 crop growing mode.
(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 vegetation growth season from date, growth season length, a growth amplitude sketch map.
Fig. 2 is the flow chart of crop growing mode identification 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 propose a kind of method and a kind of rational crop growing mode recognition methods that can confirm optimized parameter comparatively automatically.The vegetation growth parameter that the present invention adopted comprises vegetation growth season length and growth amplitude.Vegetation growth season, from date was to characterize the time point that vegetation begins to grow, the just time point (Fig. 1) of vegetation index when reach the vegetation growth threshold value spring and summer the earliest.Growth season length is meant the time span of a complete growth cycle of vegetation completion.The growth amplitude is meant that vegetation index maximum in the vegetation growth process is apart from the amplitude of variation of the vegetation index value of vegetation growth threshold value.Because all green vegetations all have such characteristic,, need to confirm the shortest growth season length of study area crops, the most long-living long season length and minimum growth amplitude for effective identification crop growth season.The shortest growth season length of crops is meant that crops are accomplished the shortest time that complete growth cycle is required in the zone; The most long-living long season of crops, length was meant that crops are accomplished the maximum duration that complete growth cycle is required in the zone, and the minimum growth amplitude of crops is meant crops minimum vegetation index amplitude of variation that should reach in process of growth in the zone.
Fig. 2 is the flow chart of crop growing mode identification in the specific embodiment of the invention.Crop growing mode identification process of the present invention may further comprise the steps:
(1) value and the vegetation growth threshold value of all time points of vegetation index time series data being made comparisons, will be 1 more than or equal to 0 time point assignment, and the time point assignment less than 0 is 0, thereby obtain a time series of being made up of 0 and 1 value;
(2) to being that 1 value adds up continuously in the new time series,, then restart to add up, obtain a time series after adding up if run into 0;
(3) to the time series after adding up, the time point at the value of institute promising 1 place is confirmed as the from date in vegetation growth season, all greater than 0 and following closely first time point that is 0 value belongs to confirm as Close Date in vegetation growth season; Extract vegetation growth season from date with the Close Date in vegetation growth season alternately, be vegetation growth season from date as if last, then with its deletion;
(4) number of times that occurs according to vegetation growth season from date in a year is confirmed vegetation growth season number; Confirm vegetation growth season length according to initial and the Close Date in each growth season, according to vegetation growth season from date and the difference of vegetation index maximum between the Close Date and vegetation growth threshold value confirm the season amplitude of growing.
The crops whether step 106, the vegetation growth season length of judge extracting obtain less than step 104 are the shortest growth season length, if, then go to step 107, if not, then go to step 108.
The minimum growth amplitude of crops whether the growth amplitude in step 108, vegetation growth season of judge extracting obtains less than step 104, if, then go to step 109, if not, then go to 110.
The most long-living long season of the crops whether step 110, the growth season length of judge extracting obtain greater than step 104 length, if then go to step 111; If not, then go to 112.
If the length in one of step 111 growth season is greater than length of the most long-living long season of crops, then deletion should growth season, and goes to step 112.
(1) in all contain 0 combination, 3 years cropping index are to confirm as uncultivated area at 0 o'clock entirely; Cropping index is 0 and have in the previous year or back 1 year and confirm as leisure cultivated land when not being 0 situation then; Contain 0 combination for other, cropping pattern is then confirmed (cropping index is 1 year one ripe cropping pattern of 1 expression, and cropping index is the double-cropped cropping patterns of 2 expressions, and cropping index is 1 year three ripe cropping patterns of 3 expressions) by cropping index then.
(2) remaining all are contained 3 combination, expression is 1 year potential triple cropping district, so cropping pattern take 1 year one ripe, yield two crops a year or ripely all be fine in 1 year three, therefore then cropping pattern is confirmed by cropping index then.
(3) to remaining by 1 and 2 combinations that constitute, adopt two kinds of principles definite.Principle one: homogeny principle.As long as it is any 1 year in then cropping index and the previous year or back 1 year with identical, then by the definite cropping pattern then of identical cropping index.Principle two: symmetry principle.(1,2,1) and (2,1,2) combination all be to accomplish proportion of crop planting in two years three times, so cropping pattern be 2 years three ripe.Thus, can confirm the cropping pattern of all combinations, as shown in table 1.
The table 1 cropping pattern table of comparisons
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 crop growing mode recognition methods is characterized in that, may further comprise the steps:
A, input remote sensing vegetation index time series data;
B, confirm the vegetation growth threshold value, areal bare area and the vegetation-covered area vegetation index value when difference occurs spring and summer is the earliest confirmed as the vegetation growth threshold value;
C, selection crops training sample;
D, parameter optimization; According to training sample to the study area crops the shortest growth season length, the most long-living long season length and these 3 parameters of minimum growth amplitude make up at random, select the highest parameter value combination of training sample cropping pattern accuracy of identification as optimized parameter;
E, extraction vegetation growth information;
F, the non-agricultural crop of eliminating zone; Judge successively pixel to be identified vegetation growth season length whether between crops between the length of the most long-living long season of the shortest growth season length and crops, whether the vegetation growth amplitude greater than the minimum growth amplitude of crops; As long as there is 1 not satisfy in these 2 conditions, then should be judged to be non-agricultural crop zone by pixel to be identified;
G, according to crop growth season number confirm the crops cropping index in a year;
H, according to the previous year, comprehensively confirm crop growing mode with back 1 year crops cropping index then.
2. a kind of crop growing mode recognition methods according to claim 1; It is characterized in that in the step e, the vegetation growth information of extraction comprises vegetation growth season number, length and 3 indexs of growth amplitude in each growth season in a year; It is characterized in that, may further comprise the steps:
A, the value and the vegetation growth threshold value of all time points of vegetation index time series data being made comparisons, will be 1 more than or equal to 0 time point assignment, and the time point assignment less than 0 is 0, thereby obtains a time series of being made up of 0 and 1 value;
B, to being that 1 value adds up continuously in the new time series, if run into 0, then restart to add up, obtain a time series after adding up;
C, to the time series after adding up, the time point at the value of institute promising 1 place is confirmed as the from date in vegetation growth season, all greater than 0 and following closely first time point that is 0 value belongs to confirm as Close Date in vegetation growth season; Extract vegetation growth season from date with the Close Date in vegetation growth season alternately, be vegetation growth season from date as if last, then with its deletion.
D, the number of times that occurs according to vegetation growth season from date in a year are confirmed vegetation growth season number; Confirm vegetation growth season length according to initial and the Close Date in each growth season, according to vegetation growth season from date and the difference of vegetation index maximum between the Close Date and vegetation growth threshold value confirm the season amplitude of growing.
3. a kind of crop growing mode recognition methods according to claim 1 is characterized in that, among the step H, according to the previous year, comprehensively confirm crop growing mode with back 1 year cropping index then, the cropping index combination in 3 years has 4
3The situation of kind is characterized in that, may further comprise the steps:
A, in all contain 0 combination; 3 years cropping index are to confirm as uncultivated area at 0 o'clock entirely; Cropping index is 0 and have in the previous year or back 1 year and confirm as leisure cultivated land when not being 0 situation then, contains 0 combination for other, and cropping pattern then confirms that by cropping index then (cropping index is 1 year one ripe cropping pattern of 1 expression; Cropping index is the double-cropped cropping patterns of 2 expressions, and cropping index is 1 year three ripe cropping patterns of 3 expressions);
B, remaining all are contained 3 combination, cropping pattern is then confirmed by cropping index then;
C, adopt two kinds of principles to confirm by 1 and 2 combinations that constitute to remaining, the one, the homogeny principle as long as any 1 year cropping index is identical in cropping index then and the previous year or back 1 year, is then pressed the definite cropping pattern then of identical cropping index; The 2nd, symmetry principle, to (1,2,1) and (2,1,2) combination, they all are to accomplish proportion of crop planting in two years three times, thus cropping pattern be 2 years three ripe.
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