CN103310197A - Method for extracting garlic cultivated areas of Huang-Huai-Hai plane terrain by aid of moderate resolution imaging spectroradiometer data - Google Patents
Method for extracting garlic cultivated areas of Huang-Huai-Hai plane terrain by aid of moderate resolution imaging spectroradiometer data Download PDFInfo
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Abstract
The invention provides a method for extracting garlic cultivated areas of a Huang-Huai-Hai plane terrain by the aid of moderate resolution imaging spectroradiometer data. The method includes analyzing spectral characteristics of garlic and wheat in all grown periods; screening proper growth periods to create a classification decision tree by the aid of the MODIS (moderate resolution imaging spectroradiometer) data with high temporal resolution and moderate spatial resolution; extracting the cultivated areas. A decision basis for government departments in main garlic producing areas to guide garlic production and sales is provided by the method. The method has the advantages that free MODIS images with moderate spatial resolution and high temporal resolution are utilized; the problem of interference of wheat and greenhouse vegetables in a garlic cultivated area extracting procedure is solved; the precision of cultivated area data acquired before garlic harvest in the beginning of May is higher than 90%, and the requirement on acquisition of data in large regions can be met.
Description
Technical field
The present invention relates to a kind of intermediate-resolution satellite data (MODIS data) of utilizing and extract the Yellow River and Huai He River sea plain district planting garlic Method for Area, belong to the technical field of satellite remote sensing agricultural.
Background technology
Characteristics such as it is objective, comprehensive, dynamic, quick that remote sensing technology has, as multi-disciplinary intersection and complex arts such as space science, geoscience, information science and agronomy, agricultural remote sensing provides from new method and the means of multidimensional, macroscopic perspective understanding agricultural for the mankind.Begin to utilize remote sensing technology to extract crop acreage from the eighties in 20th century and carried out a large amount of research, and progressively use, but according to the difference of monitoring target, on the selection of remote sensing image and information extracting method, also have certain problem.
1. the influence of precision and expense is extracted in the selection of remote sensing image for area
Comprehensive remote sensing image both at home and abroad extracts discovering of crop acreage, research for crop acreage mostly is large crops such as wheat, paddy rice, corn, cotton, and the remote sensing image of use comprises Earth observation satellite data (SPOT satellite) and the MODIS data of the higher thematic mapper data of spatial resolution (TM data), France.
TM, spot data spatial resolution height, extract precision and be better than the low slightly MODIS data of spatial resolution, but because the image temporal resolution is low, be subjected to weather effect bigger, and expense is higher, should not extract data source as a kind of crop acreage of routine.
2. information extracting method haves much room for improvement
Remote sensing image based on high spatial resolution generally only adopts the first phase data, directly carries out visual interpretation, or exercises supervision after classification or the unsupervised classification auxiliary process by image processing software, manually declares and knows check.Based on the remote sensing image of low spatial resolution, owing to the different spectrum of jljl, the foreign matter existence of spectrum phenomenon together, for same time interference crops spectral analysis deficiency, it is not high to cause area to extract precision.Usually according to the crop growth characteristics, select for a long time the phase remotely-sensed data to set up the categorised decision tree.
3. having now utilizes the agricultural remote sensing technology to obtain the technical deficiency of planting garlic area
Garlic is the important industrial crops in the Yellow River and Huai He River sea plain district, does not see the report that utilizes the agricultural remote sensing technology to obtain the planting garlic area both at home and abroad at present, utilizes remote sensing technology in time to obtain the data support that the planting garlic area can provide science for the garlic trade.But it is very similar with greenhouse vegetable to wheat to distinguish the garlic growth characteristics, utilizes the remote sensing image that temporal resolution is low, spatial resolution is high to be difficult to distinguish.
Summary of the invention
At the deficiencies in the prior art, the invention provides a kind of intermediate-resolution satellite data of utilizing and extract the Yellow River and Huai He River sea plain district planting garlic Method for Area, this method is started with from garlic and the analysis of wheat spectral signature in the time of infertility, utilize the MODIS data of high temporal resolution, medium spatial resolution, screen suitable growthdevelopmental stage and set up the categorised decision tree, extract cultivated area.Method of the present invention is produced and sold for garlic main producing region government department scientific guidance garlic decision-making foundation is provided.
Terminological interpretation:
1.MODIS data: Moderate Imaging Spectroradiomete data.MODIS full name Moderate-resolution Imaging Spectroradiometer.
2.TM data: thematic mapper data.TM full name Thematic Mapper, i.e. thematic mapper.
3.SPOT satellite: a kind of Earth observation satellite system that is French Center For Space Research (CNES) development.SPOT is the abbreviation of French Systeme Probatoired ' Observation dela Tarre, i.e. earth observing system.
4.MYD09Q1 data: by 8 days sinteticses of 250m earth surface reflection rate of MODIS data processing acquisition.
5.NDVI value: normalized differential vegetation index, claim the standardization vegetation index again, full name Normalized Difference Vegetation Index, Index, it is the best indicator of plant growth state and vegetation space distribution density, is linear dependence with the vegetation distribution density.Normalized differential vegetation index (NDVI) is a kind of variation of near infrared and red channel reflectivity ratio (SR=NIR/RED), NDVI=(NIR-R)/(NIR+R).
6.ENVI software: be a complete remote sensing image processing platform, its software processing technology covered view data I/O, image calibration, figure image intensifying, correction, ortho-rectification, inlay, data fusion and various conversion, information extraction, image classification, based on the decision tree classification of knowledge, integration, DEM and terrain information with GIS extract, radar data is handled, the 3 D stereo display analysis.
7. Ya Erbosi (Albers) projection: have another name called " positive axis equivalance secantcone projection ", " secant conic projection ".Conical projection a kind of.For A Baisi (Albers) drafts, so name.Parallel is isocentric circular arc, and warp is radius of a circle, the warp angle with accordingly through the difference be directly proportional.Article two, cut no any distortion after the parallel projection.View field's area keeps and equates on the spot.
8. decision tree classification device: be a typical multistage classifier, it is made of a series of binary decision trees, is used for pixel is belonged to corresponding classification.Each decision tree is declared the knowledge condition according to one the pixel in the image is divided into two classes.Each newly-generated classification can continue classification downwards according to other the knowledge condition of declaring again.The operation result of decision tree is that 0 or 1,0 result is grouped into " No " branch, and 1 result is grouped into " Yes " branch.Declare the knowledge condition and can comprise mathematical operator, relational operator or other functions.
9. region of interest: region of interest (ROI) is an image-region of selecting from image, and this zone is the emphasis that graphical analysis is paid close attention to.
Technical scheme of the present invention is as follows:
A kind of intermediate-resolution satellite data of utilizing is extracted the Yellow River and Huai He River sea plain district planting garlic Method for Area, comprises and utilizes computing machine, carries out according to the following steps:
(1) data are obtained
Late October is carried out the GPS location in garlic, wheat, greenhouse vegetable, trees, cotton (1 year one work), village; Obtain the MYD09Q1 data of mid-September, mid-October, mid-December, mid-April in next year, in the first tenday period of a month May in next year above-mentioned 5 phases, the MYD09Q1 data are carried out the projection conversion, calculate the NDVI value of MYD09Q1 data, extract locating point data, in EXCEL, open, and ask the NDVI mean value of above-mentioned 6 class atural objects in each period;
(2) utilize ground block message before and after the garlic sowing to get rid of the interference of except wheat, greenhouse vegetable same period of atural object
In EXCEL Production Time the sequence garlic and the same period ground object sample mid-September, mid-October, the NDVI broken line graph of 3 phases of mid-December, analyze all kinds of ground object sample NDVI broken lines, by observing the broken line variation tendency, analyze garlic, wheat, greenhouse vegetable is different from the feature of the atural object same period, in ENVI software, set up the categorised decision tree then, image is divided into garlic, wheat, greenhouse vegetable is a class, trees, cotton, village and other atural object are another kind of, and classification results tested, garlic, wheat, when greenhouse vegetable is used as class extraction more than 90%, establish decision tree, get rid of except wheat, outside the greenhouse vegetable same period atural object interference;
(3) interference that utilizes garlic garlic stems extraction in next year, bulb to expand information eliminating in period wheat, greenhouse vegetable, obtain the planting garlic area:
Make the NDVI broken line graph of mid-April in next year and 2 phases of the first tenday period of a month May in next year of garlic, wheat, greenhouse vegetable in time series, analyze the NDVI broken line of above-mentioned garlic, wheat, greenhouse vegetable, by observing the broken line variation tendency, analyze the feature that garlic is different from wheat, greenhouse vegetable, in ENVI software, set up the categorised decision tree, set and extract thresholding, be applied to the classification results of step (2), and area is extracted the result test, establish decision tree when the anchor point garlic is extracted more than 90%, obtain the planting garlic area.
Preferred according to the present invention, the sample GPS location in the described step (1) is to adopt hand-held GPS.The model of described hand-held GPS is Mai Zhelun 600.MODIS data space resolution is 250m * 250m, and in order to guarantee the pure of pixel, anchor point should be positioned at 750m * 750m atural object scope of the same race center at least.
Preferred according to the present invention, the MYD09Q1 data in the described step (1) are by MODIS data sharing platform ftp: //the e4ftl01u.ecs.nasa.gov/ download; Utilize ENVI software to carry out the projection conversion, select Map Convert Map Projection, according to the characteristics of MODIS data self, select Ya Erbosi (Albers) projection for use, adopt the WGS-84 coordinate system, obtain to comprise the Warp file of two wave bands.
Preferred according to the present invention, ask for the NDVI value in the described step (1), in ENVI software, select Spectral〉Spectral Math.In the Enter an expression text box in Spectral Math dialog box, key in (S2-S1)/(S2+S1) and then the wave spectrum value is composed to variable S1, S2; Hit OK continues, Variable to Spectra Pairings dialog box appears, in Variables used in expression text box, select S1-[undefined] click Map Variable to Input File, eject Spectra Math Input File dialog box, in Select Input File text box, select required file, click Spectral Subset button, select to have proofreaied and correct the wave band 1 of Warp file, Warp(Sur Refl B01), click twice OK continuously, finish the assignment of variable S1, get back to Variable to Spectral Pairings dialog box simultaneously, operate equally, to proofread and correct the wave band 2 of Warp file, Warp(Sur Refl B02) compose and give variable S2; Assignment finishes, and selection result file storing path, hit OK begin to calculate.
Preferred according to the present invention, extraction anchor point NDVI value in the described step (1), in ENVI software, open the NDVI file that calculates the September that generates, set up 6 class atural object region of interest by 6 class atural object locating point data, select Basic Tools Region of Interest ROI Tool; ROI Tool dialog box will appear, select ROI-Type〉Point〉Input Points from ASCII, open Enter ASCII Points Filename dialog box, select garlic sample anchor point file dasuan.txt, open Input ASCII File dialog box, select Geographic Lat/Lon hit OK, turn back to ROI Tool dialog box, finish the definition of garlic region of interest Region#1.NDVI data with the garlic anchor point derive then, select File〉Output ROIs to ASCII, open Select Input File for ROI data, select the NDVI file, hit OK, open Output ROIs to ASCII Parameters, in Select ROIs to Output text box, select region of interest Region# 1, select File storing path name dasuan9ndvi.txt and output; Return ROI Tool dialog box, click New Region, define new region of interest successively, derive 6 class atural object anchor point mid-Septembers, mid-October, 3 periods of mid-December NDVI value, and 3 class atural object mid-Aprils in next year of garlic, wheat, greenhouse vegetable, 2 periods of the first tenday period of a month May in next year the NDVI value.
Preferred according to the present invention, in the described step (1), in EXCEl software, ask 6 class atural object NDVI mean values; Open the EXCEL program, file〉open dasuan9ndvi.txt; By the path finding of storage, file type is selected " text ", opens the text import wizard, key in and import initial row 8, data importing is finished in click, and the B1 row are the NDVI value of each anchor point of garlic in the table, uses the function calculating mean value of averaging of toolbar AutoSum button; Try to achieve 6 class atural object mid-Septembers, mid-October, 3 periods of mid-December NDVI mean value successively, and 3 class atural object mid-Aprils in next year of garlic, greenhouse vegetable, wheat, 2 periods of the first tenday period of a month May in next year NDVI mean value.
Preferred according to the present invention, the foundation of knowledge condition is declared in the interference of getting rid of except wheat, greenhouse vegetable same period of atural object in the described step (2), NDVI broken line feature according to 3 periods of 6 class atural objects of drawing in the EXCEL table, the NDVI value in garlic, wheat, greenhouse vegetable plot in the broken line, the NDVI value of mid-September is maximum, the NDVI value of mid-December is taken second place, the NDVI value of mid-October is minimum, and the NDVI value of atural objects such as the trees except garlic, wheat, greenhouse vegetable, cotton, village reduced since mid-September always; In ENVI, open mid-September, mid-October, 3 periods of mid-December NDVI data, the NDVI data in 3 periods are set up decision tree, it is a class that image is divided into garlic, wheat, greenhouse vegetable, trees, cotton, village and other atural object are another kind of; Decision tree is declared the knowledge condition: the NDVI value of mid-September〉the NDVI value of mid-December〉the NDVI value of mid-October, and the NDVI value of mid-September〉a, need to prove and be subjected to climate effect, certain variation can take place in the NDVI value of annual mid-September, therefore the value of a is not fixed, and need revise according to the locating point data in every year; In the ENVI master menu, select Classification〉Decision Tree〉Build New Decision Tree, ENVI Decision Tree window appears, click Node node button, and (s3gt a) for and with declaring knowledge condition (s3gt s5) and (s5gt s4) to import nodename Node1 in Edit Decision Tree Properties dialog box, hit OK, Variable/File Pairings dialog box appears, click { s3} selection NDVI file mid-September, hit OK, with mid-September NDVI file assignment give variable s3, successively with mid-October NDVI file assignment give variable s4, with mid-December NDVI file assignment give variable s5, finish all variable assignments, at ENVI Decision Tree window, operational decisions tree Option〉Execute, Decision Tree Execute window appears, click Memory, click Ok, eject Memory1 classification results figure, the pixel in the image is divided into the class0(trees, cotton, other atural objects such as village) and the class1(garlic, wheat, greenhouse vegetable) two classes.
Preferred according to the present invention, described step is tested to classification results in (2), namely verify the extraction result of anchor point garlic, wheat, greenhouse vegetable, at Memory1 classification results window, Overlay〉Region of Interest opens region of interest window ROI Tool, select ROI-Type〉Point〉Input Points from ASCII, open Enter ASCII Points Filename dialog box, select garlic sample anchor point file dasuan.txt; Select ROI-Type then〉Input Points from ASCII, open Enter ASCII Points Filename dialog box, open wheat anchor point file xiaomai.txt; Select ROI-Type then〉Input Points from ASCII, open Enter ASCII Points Filename dialog box, open greenhouse vegetable anchor point file wenshishucai.txt; So far garlic, wheat, greenhouse vegetable anchor point are defined as a region of interest Region#7, at region of interest window ROI Tool, click Stats, data to Region#7 are carried out the express statistic analysis, eject ROI Statistics Result window, watch DN value and be 1 percentage Percent value, be worth greater than 90 as if this, completing steps (2) then, otherwise continue to adjust a value.
Preferred according to the present invention, set up the decision tree that gets rid of wheat, greenhouse vegetable interference in the described step (3): in ENVI software, select Spectral〉Spectral Math.In the Enter an expression text box in Spectral Math dialog box, key in S6-S7 then with mid-April NDVI file assignment give variable s6, with the first tenday period of a month in May NDVI file assignment give variable s7; Hit OK continues, Variable to Spectra Pairings dialog box appears, in Variables used in expression text box, select S6-[undefined] click Map Variable to Input File, eject Spectra Math Input File dialog box, in Select Input File text box, select NDVI file mid-April, click twice OK continuously, finish the assignment of variable S6, get back to Variable to Spectral Pairings dialog box simultaneously, operate equally, give variable S7 with NDVI value tax in the first tenday period of a month in May; Assignment finishes, and selection result file storing path, hit OK begin to calculate; Return ENVI Decision Tree window, click Class1 button in the decision tree that step (2) sets up by right key, select Add Children to add new node, import nodename Node2 and declare knowledge condition s8gtb, the b value is according to the EXCEL garlic, wheat, greenhouse vegetable mid-April, late May feature broken line analysis result acquisition, give variable s8 hit OK with S6-S7 destination file assignment, in ENVI Decision Tree window, select Options〉Execute, Decision Tree Execution Parameters dialog box appears, select the saving result file path, the hit OK operation, class2 is the planting garlic area.
Preferred according to the present invention, area in the described step (2) extracts product test: in ENVI software, garlic, wheat, greenhouse vegetable locating point data are imported, derive anchor point and extract the result, above-mentioned 3 class atural objects are extracted (class1) as a class, extract precision greater than 90% o'clock, can determine decision tree be get rid of that except wheat, greenhouse vegetable same period, vegetation disturbed declare the knowledge condition, otherwise continue to adjust a value.
Preferred according to the present invention, area in the described step (3) extracts product test: in ENVI, the garlic locating point data is imported, derive anchor point and extract the result, garlic was extracted precision greater than 90% o'clock, can determine decision tree be get rid of that wheat, greenhouse vegetable disturb declare the knowledge condition, otherwise continue to adjust the b value.
Advantage of the present invention is:
The present invention utilizes the MODIS image of free medium spatial resolution, high temporal resolution, solved the interference problem of wheat, greenhouse vegetable in the garlic leaching process, obtain the planting garlic area the first tenday period of a month in May, precision surpasses 90%, can satisfy the requirement that data are obtained in the big zone.
Description of drawings
Fig. 1 is the FB(flow block) of the method for the invention;
Fig. 2 is the 6 class atural objects NDVI broken line graph in 3 periods of step among the embodiment 1 (2);
Fig. 3 be step among the embodiment 1 (3) 2 period garlic, wheat, greenhouse vegetable NDVI broken line graph;
Fig. 4 is the decision tree of a kind of step of embodiment (2) and the decision tree of step (3);
Fig. 5 is that the planting garlic area of step among the embodiment 1 (3) extracts figure, and wherein speckle regions is the planting garlic district that extracts.
Embodiment
The present invention will be further described below in conjunction with embodiment, but be not limited thereto.
Shown in Fig. 1-4.
A kind of intermediate-resolution satellite data of utilizing is extracted the Yellow River and Huai He River sea plain district planting garlic Method for Area, comprises that step is as follows:
(1) data are obtained
Garlic late September-early October plantation, garlic breaking dormancy after 10-15 days, primary leaves expanded then enters the seedling phase; Wheat mid or late October sowing, Dec, early and middle ten days was tillered; Part greenhouse vegetable mid or late October transplants, vegetables ramp behind the Dec slow seedling.The NDVI value of wheat, greenhouse vegetable changes similar garlic, has influenced the extraction precision of garlic area, is the significant obstacle factor that the garlic area extracts.
In order to get rid of the interference of wheat and greenhouse vegetable, in late October, 2008, to garlic, wheat, greenhouse vegetable, village, did the cotton field in 1 year one, trees 6 class atural objects carry out the GPS location.From MODIS data sharing platform ftp: //e4ftl01u.ecs.nasa.gov/, obtain research anchor point in mid-September, 2008, mid-October, mid-December, in mid-April, 2009, the first tenday period of a month in May 5 phase MYD09Q1 data, data are carried out the projection conversion, ask for the NDVI value, extract all kinds of ground object sample anchor point NDVI values.
(2) utilize ground block message before and after the garlic sowing to get rid of the interference of except wheat, greenhouse vegetable same period of atural object
Ask the NDVI mean value in 6 class atural object mid-Septembers of garlic, wheat, greenhouse vegetable, village, exposed cotton field, trees, mid-October, 3 periods of mid-December, draw broken line graph (referring to Fig. 2); The NDVI value as the season progresses of doing cotton, village, trees in 1 year one reduces gradually, the NDVI value in garlic, wheat, greenhouse vegetable plot is higher mid-September, this moment, corn, cotton were multiplely planted in garlic, wheat plot, planting vegetable still in the greenhouse in growing vegetables district; Since garlic harvest time late May early than wheat, the general sowing in results back early ripening maize, or the results protheca grows cotton, the harvest time of early ripening maize, in order to plant garlic suddenly, cotton was often pulled out the edge of a field of banking up in advance in mid or late September; Wheat was gathered in the crops about mid-June, results back sowing summer corn, and mid-September, summer corn was in the pustulation period, and biomass is greater than early ripening maize and cotton, so the NDVI value in wheat plot will be higher than the garlic plot.To mid-October, the summer corn results, wheat begins sowing, and garlic was sowed in late September to early October, emerged early than wheat.Greenhouse vegetable adopts transplanting method, and growth conditions is good, and biomass increases very fast, so the NDVI value greenhouse vegetable in this period is higher than garlic and is higher than winter wheat.To mid-December, wheat is in tillering stage, summit of growth before the winter occurs, and the biomass of garlic, greenhouse vegetable all continues to increase, and NDVI all is higher than mid-October, is lower than mid-September.Based on above-mentioned analysis, get rid of the interference of except wheat, greenhouse vegetable same period of atural object, set up extract declare the knowledge condition: NDVI value, mid-September〉mid-December mid-October, and mid-September 0.48.
(3) garlic is obtained in the interference that utilizes garlic garlic stems extraction in next year, bulb to expand information eliminating in period wheat, greenhouse vegetable
Cultivated area
The variation characteristic of garlic, wheat, greenhouse vegetable is very similar before year, is difficult to distinguish.Temperature rise garlic, wheat are turned green after year, and greenhouse vegetable is growth fast also, and 3 class phytomass all increase.Until April early and middle ten days garlic growing point no longer break up blade, the seedling phase finishes, and enters garlic stems elongating stage, bulb differentiation is simultaneously expanded, the first tenday period of a month in late April to May, garlic stems extracted, the apical growth advantage is removed, and enters bulb and expands the Sheng phase.The increment of this phase root no longer increases, the trend decline; Blade is by green flavescence, and the plant growing way fails, and the nutriment in the blade shifts to the head of garlic, and the head of garlic expands rapidly, and the NDVI value reduces rapidly.Wheat is in jointing latter stage mid-April to the last ten-days period, and biomass has increase slightly, and the NDVI value slightly raises, and is in heading flowering period the first tenday period of a month in May, and biomass continues to increase, but the influence of being bloomed, the NDVI value is constant substantially.Greenhouse vegetable, biomass continue to increase, and the NDVI value increases.Draw the NDVI broken line graph (referring to Fig. 3) in garlic, wheat, greenhouse vegetable mid-April, the first tenday period of a month in May.Utilize the first tenday period of a month in May than mid-April, garlic NDVI value descends rapidly, and wheat NDVI is constant substantially, and what the characteristics that greenhouse vegetable NDVI value continues to raise were set up extraction declares the knowledge condition: the NDVI value, (mid-April-the first tenday period of a month in May) 0.055.Set up decision tree, extract planting garlic area (referring to Fig. 5), 48.12 ten thousand mu of 2008-2009 Jinxiang planting garlic areas, extracting precision is 91%.
Claims (10)
1. one kind is utilized the intermediate-resolution satellite data to extract the Yellow River and Huai He River sea plain district planting garlic Method for Area, comprises and utilize computing machine that it is characterized in that, it is as follows that the method comprising the steps of:
(1) data are obtained
Late October is carried out the GPS location in garlic, wheat, greenhouse vegetable, trees, cotton (1 year one work), village; Obtain the MYD09Q1 data of mid-September, mid-October, mid-December, mid-April in next year, in the first tenday period of a month May in next year above-mentioned 5 phases, the MYD09Q1 data are carried out the projection conversion, calculate the NDVI value of MYD09Q1 data, extract locating point data, in EXCEL, open, and ask the NDVI mean value of above-mentioned 6 class atural objects in each period;
(2) utilize ground block message before and after the garlic sowing to get rid of the interference of except wheat, greenhouse vegetable same period of atural object
In EXCEL Production Time the sequence garlic and the same period ground object sample mid-September, mid-October, the NDVI broken line graph of 3 phases of mid-December, analyze all kinds of ground object sample NDVI broken lines, by observing the broken line variation tendency, analyze garlic, wheat, greenhouse vegetable is different from the feature of the atural object same period, in ENVI software, set up the categorised decision tree then, image is divided into garlic, wheat, greenhouse vegetable is a class, trees, cotton, village and other atural object are another kind of, and classification results tested, garlic, wheat, when greenhouse vegetable is used as class extraction more than 90%, establish decision tree, get rid of except wheat, outside the greenhouse vegetable same period atural object interference;
(3) the planting garlic area is obtained in the interference that utilizes garlic garlic stems extraction in next year, bulb to expand information eliminating in period wheat, greenhouse vegetable
Make the NDVI broken line graph of mid-April in next year and 2 phases of the first tenday period of a month May in next year of garlic, wheat, greenhouse vegetable in time series, analyze the NDVI broken line of above-mentioned garlic, wheat, greenhouse vegetable, by observing the broken line variation tendency, analyze the feature that garlic is different from wheat, greenhouse vegetable, in ENVI software, set up the categorised decision tree, set and extract thresholding, be applied to the classification results of step (2), and area is extracted the result test, establish decision tree when the anchor point garlic is extracted more than 90%, obtain the planting garlic area.
2. a kind of intermediate-resolution satellite data of utilizing according to claim 1 is extracted the Yellow River and Huai He River sea plain district planting garlic Method for Area, it is characterized in that, the sample GPS location in the described step (1) is to adopt hand-held GPS; The model of described hand-held GPS is Mai Zhelun 600; MODIS data space resolution is 250m * 250m, and in order to guarantee the pure of pixel, anchor point should be positioned at 750m * 750m atural object scope of the same race center at least.
3. a kind of intermediate-resolution satellite data of utilizing according to claim 1 is extracted the Yellow River and Huai He River sea plain district planting garlic Method for Area, it is characterized in that, MYD09Q1 data in the described step (1) are by MODIS data sharing platform ftp: //the e4ftl01u.ecs.nasa.gov/ download; Utilize ENVI software to carry out the projection conversion, select Map Convert MapProjection, according to the characteristics of MODIS data self, select Ya Erbosi (Albers) projection for use, adopt the WGS-84 coordinate system, obtain to comprise the Warp file of two wave bands.
4. a kind of intermediate-resolution satellite data of utilizing according to claim 1 is extracted the Yellow River and Huai He River sea plain district planting garlic Method for Area, it is characterized in that, ask for the NDVI value in the described step (1), in ENVI software, select Spectral Spectral Math; In the Enter an expression text box in Spectral Math dialog box, key in (S2-S1)/(S2+S1) and then the wave spectrum value is composed to variable S1, S2; Hit OK continues, Variable toSpectra Pairings dialog box appears, in Variables used in expression text box, select S1-[undefined] click Map Variable to Input File, eject Spectra Math Input File dialog box, in Select Input File text box, select required file, click Spectral Subset button, select to have proofreaied and correct the wave band 1 of Warp file, Warp(Sur Refl B01), click twice OK continuously, finish the assignment of variable S1, get back to Variable to Spectral Pairings dialog box simultaneously, operate equally, to proofread and correct the wave band 2 of Warp file, Warp(Sur Refl B02) compose and give variable S2; Assignment finishes, and selection result file storing path, hit OK begin to calculate.
5. a kind of intermediate-resolution satellite data of utilizing according to claim 1 is extracted the Yellow River and Huai He River sea plain district planting garlic Method for Area, it is characterized in that, extraction anchor point NDVI value in the described step (1), in ENVI software, open the NDVI file that calculates the September that generates, set up 6 class atural object region of interest by 6 class atural object locating point data, select Basic Tools Region of Interest ROI Tool; ROI Tool dialog box will appear, select ROI-Type〉Point〉Input Points from ASCII, open Enter ASCII Points Filename dialog box, select garlic sample anchor point file dasuan.txt, open Input ASCII File dialog box, select Geographic Lat/Lon hit OK, turn back to ROI Tool dialog box, finish the definition of garlic region of interest Region#1; NDVI data with the garlic anchor point derive then, select File〉Output ROIs to ASCII, open Select Input File for ROI data, select the NDVI file, hit OK, open Output ROIs to ASCII Parameters, in Select ROIs to Output text box, select region of interest Region#1, select File storing path name dasuan9ndvi.txt and output; Return ROI Tool dialog box, click New Region, define new region of interest successively, derive 6 class atural object anchor point mid-Septembers, mid-October, 3 periods of mid-December NDVI value, and 3 class atural object mid-Aprils in next year of garlic, wheat, greenhouse vegetable, 2 periods of the first tenday period of a month May in next year the NDVI value.
6. a kind of intermediate-resolution satellite data of utilizing according to claim 1 is extracted the Yellow River and Huai He River sea plain district planting garlic Method for Area, it is characterized in that, in the described step (1), in EXCEl software, asks 6 class atural object NDVI mean values; Open the EXCEL program, file〉open dasuan9ndvi.txt; By the path finding of storage, file type is selected " text ", opens the text import wizard, key in and import initial row 8, data importing is finished in click, and the B1 row are the NDVI value of each anchor point of garlic in the table, uses the function calculating mean value of averaging of toolbar AutoSum button; Try to achieve 6 class atural object mid-Septembers, mid-October, 3 periods of mid-December NDVI mean value successively, and 3 class atural object mid-Aprils in next year of garlic, greenhouse vegetable, wheat, 2 periods of the first tenday period of a month May in next year NDVI mean value.
7. a kind of intermediate-resolution satellite data of utilizing according to claim 1 is extracted the Yellow River and Huai He River sea plain district planting garlic Method for Area, it is characterized in that, get rid of except wheat in the described step (2), outside the greenhouse vegetable same period atural object interference declare the foundation of knowledge condition, NDVI broken line feature according to 3 periods of 6 class atural objects of drawing in the EXCEL table, garlic in the broken line, wheat, the NDVI value in greenhouse vegetable plot, the NDVI value of mid-September is maximum, the NDVI value of mid-December is taken second place, the NDVI value of mid-October is minimum, and except garlic, wheat, trees outside the greenhouse vegetable, cotton, the NDVI value of atural objects such as village reduced since mid-September always; In ENVI, open mid-September, mid-October, 3 periods of mid-December NDVI data, the NDVI data in 3 periods are set up decision tree, it is a class that image is divided into garlic, wheat, greenhouse vegetable, trees, cotton, village and other atural object are another kind of; Decision tree is declared the knowledge condition: the NDVI value of mid-September〉the NDVI value of mid-December〉the NDVI value of mid-October, and the NDVI value of mid-September〉a, need to prove and be subjected to climate effect, certain variation can take place in the NDVI value of annual mid-September, therefore the value of a is not fixed, and need revise according to the locating point data in every year; In the ENVI master menu, select Classification〉Decision Tree〉Build New Decision Tree, ENVI Decision Tree window appears, click Node node button, and (s3gt a) for and with declaring knowledge condition (s3gt s5) and (s5gt s4) to import nodename Node1 in Edit Decision Tree Properties dialog box, hit OK, Variable/File Pairings dialog box appears, click { s3} selection NDVI file mid-September, hit OK, with mid-September NDVI file assignment give variable s3, successively with mid-October NDVI file assignment give variable s4, with mid-December NDVI file assignment give variable s5, finish all variable assignments, at ENVI Decision Tree window, operational decisions tree Option〉Execute, Decision Tree Execute window appears, click Memory, click Ok, eject Memory1 classification results figure, pixel in the image is divided into class0 and class1 two classes, and described class0 comprises trees, cotton, other atural objects such as village; Described class1 comprises garlic, wheat, greenhouse vegetable.
8. a kind of intermediate-resolution satellite data of utilizing according to claim 1 is extracted the Yellow River and Huai He River sea plain district planting garlic Method for Area, it is characterized in that, described step is tested to classification results in (2), namely verify the anchor point garlic, wheat, the extraction result of greenhouse vegetable, at Memory1 classification results window, Overlay〉Region of Interest opens region of interest window ROI Tool, select ROI-Type〉Point〉Input Points from ASCII, open Enter ASCII Points Filename dialog box, select garlic sample anchor point file dasuan.txt; Select ROI-Type then〉Input Points from ASCII, open Enter ASCII Points Filename dialog box, open wheat anchor point file xiaomai.txt; Select ROI-Type then〉Input Points from ASCII, open Enter ASCII Points Filename dialog box, open greenhouse vegetable anchor point file wenshishucai.txt; So far garlic, wheat, greenhouse vegetable anchor point are defined as a region of interest Region#7, at region of interest window ROI Tool, click Stats, data to Region#7 are carried out the express statistic analysis, eject ROI Statistics Result window, watch DN value and be 1 percentage Percent value, be worth greater than 90 as if this, completing steps (2) then, otherwise continue to adjust a value.
9. a kind of intermediate-resolution satellite data of utilizing according to claim 1 is extracted the Yellow River and Huai He River sea plain district planting garlic Method for Area, it is characterized in that, set up the decision tree that gets rid of wheat, greenhouse vegetable interference in the described step (3): in ENVI software, select Spectral〉Spectral Math; In the Enter an expression text box in Spectral Math dialog box, key in S6-S7 then with mid-April NDVI file assignment give variable s6, with the first tenday period of a month in May NDVI file assignment give variable s7; Hit OK continues, Variable to Spectra Pairings dialog box appears, in Variables used in expression text box, select S6-[undefined] click Map Variable to Input File, eject Spectra Math Input File dialog box, in Select Input File text box, select NDVI file mid-April, click twice OK continuously, finish the assignment of variable S6, get back to Variable to Spectral Pairings dialog box simultaneously, operate equally, give variable S7 with NDVI value tax in the first tenday period of a month in May; Assignment finishes, and selection result file storing path, hit OK begin to calculate; Return ENVI Decision Tree window, click Class1 button in the decision tree that step (2) sets up by right key, select Add Children to add new node, import nodename Node2 and declare knowledge condition s8gtb, the b value is according to the EXCEL garlic, wheat, greenhouse vegetable mid-April, late May feature broken line analysis result acquisition, give variable s8 hit OK with S6-S7 destination file assignment, in ENVI Decision Tree window, select Options〉Execute, Decision Tree Execution Parameters dialog box appears, select the saving result file path, the hit OK operation, class2 is the planting garlic area;
Area in the described step (2) extracts product test: in ENVI software, garlic, wheat, greenhouse vegetable locating point data are imported, derive anchor point and extract the result, above-mentioned 3 class atural objects are extracted (class1) as a class, extract precision greater than 90% o'clock, can determine decision tree be get rid of that except wheat, greenhouse vegetable same period, vegetation disturbed declare the knowledge condition, otherwise continue to adjust a value.
10. a kind of intermediate-resolution satellite data of utilizing according to claim 1 is extracted the Yellow River and Huai He River sea plain district planting garlic Method for Area, it is characterized in that, area in the described step (3) extracts product test: in ENVI, the garlic locating point data is imported, derive anchor point and extract the result, garlic was extracted precision greater than 90% o'clock, can determine decision tree be get rid of that wheat, greenhouse vegetable disturb declare the knowledge condition, otherwise continue to adjust the b value.
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