CN1731216A - A remote sensing detection and evaluation method for the area and production of large-area crop raising - Google Patents

Abstract

The invention provides a remote sensed estimating method of the large area planting area and the output, which establishes a spectrum which has the same growing stage and is correspondent with the field crown layer at the base of doing normal radiation and atmosphere adjustment to the remote sensed image, and computes the distance threshold value of the spectrum and compares the distance threshold value with the image initial wave spectrum and the reference wave spectrum to obtain TM plant unit, it then statistics the quantity of the TM plant unit from each image unit of middle resolution ratio imaging spectrum data MODIS with the same period and region to ascertain the planting number of the MODIS image, it then ascertains the planting area of the MODIS image according to each image unit area of the planting number and the MODIS.

Description

The remote sensing detection and evaluation method of a kind of large tracts of land crops planting area and output thereof

Technical field

The present invention relates to remote sensing detection and evaluation method to large tracts of land monocrop plantation area and output thereof.

Background technology

At present, to the pure pixel identification of monocrop, except on-the-spot investigation method among a small circle, majority all is supervision or the not supervised classification that utilizes the remote sensing video, these methods or relate to a large amount of field work, very inaccurate to defining of mixed pixel, and when selecting supervised classification training area influenced by human factor very big, feature to training field can not be held well, cause the video conversion of different scale in remote sensing application to be difficult to satisfy the requirement of using, this makes the not high traditional classification data of service precision of having in large-scale crop area and output estimation.

Summary of the invention

The purpose of this invention is to provide a kind ofly by detecting the pure pixel of monocrop, the remote sensing that large tracts of land crops planting area and output thereof are estimated detects new method.

Realize that technical scheme of the present invention is:

For the remote sensing estimation method of large tracts of land monocrop plantation area, its estimation steps is as follows:

(1) obtains the TM satellite remote sensing images that estimation is distinguished from satellite earth receiving station;

(2) obtain with reference to wave spectrum: in the detection sample district of the some same crops of ground chosen in advance of estimating the district, obtain the canopy spectra on the spot of respectively surveying the sample district, promptly with reference to wave spectrum, said with reference to wave spectrum by the described crop field survey of respectively surveying the sample district being obtained (canopy spectra of the detected crop on the spot that obtains by synchronous ASD, but need be converted to the reflectance value of the corresponding wave band of TM) or obtaining according to priori and supporting parameter simulation by spectral database by the wave band response function of TM;

(3) the video wave spectrum obtains: find out from remote sensing images with each and survey corresponding crop pixel sample district, sample district, by remote sensing images are carried out pre-service, comprise conventional radiation, atmospheric correction and geometric manipulations, obtain the image average wave spectrum in crop pixel sample district, be reflectivity vector Ri, said reflectivity vector Ri is the vector that each the wave band reflectivity outside the broadband TM heat extraction infrared band is formed;

(4) computed range threshold value: the image wave spectrum and the formed wave spectrum of corresponding reference wave spectrum thereof of each crop sample district pixel that will obtain are right, calculate the right spectrum intervals threshold value of this wave spectrum by following formula:

AV＝SQRT(∑((Ri-Rmi)^2)/N)

In the formula, AV is a distance threshold, Ri is the image wave spectrum reflectivity vector (being exactly the vector of each wave band reflectivity composition of broadband TM here) in crop pixel sample district, Rmi is the reflectance value mean value vector with reference to wave spectrum, and N is wave band number (is visible light, 6 wave bands of near infrared at TM here: i.e. TM1-TM5 and TM7);

(5) comparison of video pixel and distance threshold: with the image wave spectrum of calculated distance threshold value A V of institute and different images pixel with reference to the distance A V ' of wave spectrum relatively, if the image wave spectrum of certain pixel with reference to the distance calculation value of wave spectrum less than distance threshold AV then be defined as pure crop TM pixel, at the image wave spectrum that calculates the different images pixel with reference to the distance A V ' of wave spectrum the time, its computing formula is identical with the formula that calculates spectrum intervals threshold value A V, but Ri is the pixel reflectivity vector of image in the formula;

(6) from the quantity of the same period, determine that the plantation of crop in the MODIS video becomes several with the pure crop pixel of the corresponding TM of statistics every pixel of the intermediate-resolution imaging spectrometer data MODIS in area;

(7) determine the cultivated area of crop among the MODIS according to planting the area that becomes every pixel among number and the MODIS.

Further the method for the detected crop yield of estimation is as follows on the basis of such scheme:

By the pure crop pixel of TM number in identification of image-reference spectra distance threshold method and the every pixel of statistics MODIS, the vegetation index adjusted in conjunction with every TM pixel soil then, determine the vegetation index SAVI of every MODIS pixel accumulation, in order to determine the growing way of every MODIS pixel crop, estimate in conjunction with the area of front again, calculate the output of crop

$\mathrm{SAVI}=\underset{i=1}{\overset{n}{\mathrm{\Σ}}}[1.5*({\mathrm{NIR}}_i-{\mathrm{Red}}_i)/({\mathrm{NIR}}_i+{\mathrm{Red}}_i+0.5)]$

In the following formula, n is the pure pixel number of the TM of each MODIS pixel, and NIR and Red represent the near infrared and the red spectral band reflectivity of satellite respectively.

Fig. 1 is the implementing procedure figure of the present invention to the pure pixel detection method of crop.

Implementation step to this flow process is described as follows:

(1) obtains remote sensing image;

(2) utilize conventional method to carry out the remote sensing image pre-service, obtain the reflectivity video;

(3) on video, seek pure crop pixel sample district, and obtain corresponding video wave spectrum and with reference to wave spectrum to (with reference to wave spectrum can open-air actual measurement synchronously or obtain from the wave spectrum storehouse);

(4) calculate the wave spectrum threshold value of adjusting the distance by the distance threshold formula;

(5) on the video one by one pixel with reference to wave spectrum contrast, and computed range then is defined as pure pixel smaller or equal to above-mentioned threshold value.

The invention is characterized in because the actual measurement end member mean value vector of reaction characters of ground object replaces the mean vector of training field in the traditional classification method, thereby represented target spectral characteristic of ground essence better, remote sensing video wave spectrum and the wave spectrum storehouse wave spectrum that extracts or obtain on the spot has more representativeness to Bizet like this.

Above-mentioned saidly can also obtain by China typical feature wave spectrum storehouse (spl.bnu.edu.cn) direct modeling with reference to wave spectrum; Video end member wave spectrum can be gathered by ground priori (as present landuse map).

The technical solution adopted in the present invention, because it is all very definite with reference to obtaining of wave spectrum and corresponding video end member wave spectrum, computer program is realized automatically, than the time saving and energy saving high efficiency of field method, also avoided traditional supervised classification to determine the shortcoming of mixed pixel by methods such as probability, the automatic, high precision that can realize the pure pixel of crops easily and reliably detects, and can estimate that the needed yardstick conversion of monitoring decision-making and other remote sensing applications provide pure fast and accurately image element information for the crop growing state and the output of low resolution video.

Description of drawings

Fig. 1 is the implementing procedure figure of the present invention to the pure pixel detection method of crop;

Fig. 2 is corn experiment sampled point schematic layout pattern;

Fig. 3 is the corn canopy spectra variation in 12 plot of ground survey;

Fig. 4 is a test block sketch on the 124/34TM satellite orbit;

Fig. 5 is that test block TM image 453 wave bands are false colored synthetic;

Fig. 6 is the corn canopy average wave spectrum in six places;

The main atural object image end member of Fig. 7 for extracting on the TM image;

Fig. 8 is the classification results based on image and measure spectrum distance threshold;

Fig. 9 is the result based on the maximum likelihood classification method in six spectral measurement places;

The position that Figure 10 is the pure crop pixels check of three corns plot on 1: 10000 topomap;

Figure 11 is 1 two kinds of classification results contrasts (left side: A method, the right side: the B method), plot;

Figure 12 is 2 two kinds of classification results contrasts (left side: A method, the right side: the B method), plot;

Figure 13 is 3 two kinds of classification results contrasts (left side: A method, the right side: the B method), plot.

Embodiment

1. experiment place: carry out (as Fig. 2-1) in Chinese Academy of Sciences Luancheng Agro-ecological System testing station, this erect-position is in 37 ° 53 ' of north latitude, 114 ° 40 ' of east longitude, sea level elevation 50.1m, the 3KM place, city east, Luancheng County that is located in the Shijiazhuang City southeast, planting system is winter wheat-summer ripe crop rotation in 1 year two high-withdrawal area.Examination district's area is 5km * 5km, and links in flakes with farmland on every side, is the summer corn of large tracts of land uniformity, helps the accurate synchronous acquisition of spectroscopic data, and guarantees the accurate reliability of instantaneous data.For examination summer corn kind is " Zheng Dan 958 " and " agricultural university 108 ", by local conventional cultivation measure management.

2. the measured spectra data are obtained

For the data sample that makes experimental observation has certain representativeness, it is 5*5 square kilometre that area is implemented in the test block, and six measurement points have been selected on ground.

The observation project comprises canopy spectra measurement, agricultural microclimate observation, crop structure parameter, crop biochemical parameter, soil physical chemistry parameter, the measurement of background spectrum, component spectra, other typical feature spectral measurements, sounding data.

3. remote sensing image obtains: the remotely-sensed data of use is the Landsat TM image data of imaging on September 13 in 2003, and spatial resolution is 30m, use be outer six the wave band (TM1 of visible light, near infrared of heat extraction infrared band, TM2, TM3, TM4, TM5 and TM7).This research has been chosen 5KM * 5KM zone that 6 spectral measurement experiment plot are wherein arranged on ground and has been test site (illustrated scope slightly enlarges).Test wave spectrum and false colored remote sensing image see (Fig. 3, Fig. 4, Fig. 5).

4. with reference to wave spectrum

On the video imaging date, we have obtained the corn canopy and the environmental background spectrum parameter in 12 plot, 6 places, and the atmosphere sounding data.Through handling, the averaged spectrum in this place, 12 plot-6 as shown in Figure 6.From Fig. 6 as seen, especially at near-infrared band, the interval range of its reflectivity wave spectrum value is very obvious, and its absolute value maximum can reach more than 0.2.

5. the image wave spectrum obtains

Based on pretreated image, correction of image ground priori (as topomap, land-use map etc.) and with reference to the ground coordinate of wave spectrum, can on the TM image, find the position of respective objects, thereby extract six target image wave spectrums (Fig. 7), corn, settlement place, nursery, meadow, orchard, water body), and obtain the average wave spectrum such as the table 1 of corn.

The corn image wave spectrum that extracts on the image of table 1 test block

Centre wavelength	Reflectivity
		0.485000	0.0501495
0.560000	0.082455
		0.660000	0.105012167
0.830000	0.3090385
		1.650000	0.179918833
2.220000	0.072699833

6. computed range threshold value:

The image wave spectrum and the formed wave spectrum of corresponding reference wave spectrum thereof of each crop sample district pixel of obtaining is right, calculate the right distance threshold of this wave spectrum by following formula:

AV＝SQRT(∑((Ri-Rmi)^2)/N)……………………(1)

In the formula, Ri is the image wave spectrum reflectivity vector in crop pixel sample district, and Rmi is the reflectance value mean value vector with reference to wave spectrum; To test figure, we obtain this distance threshold is 0.010726.

7. the comparison of video pixel and distance threshold:

With the calculated distance threshold value A V of institute and different pixel videos-relatively with reference to the right distance A V ' of wave spectrum, if the image wave spectrum of certain pixel with reference to the calculated value of the distance A V ' of wave spectrum less than distance threshold AV then be defined as pure crop TM pixel, so just obtained the pure crop pixel figure of test block; The comparison of this result (Fig. 8), show that the distance threshold method more meets surface state with traditional maximum likelihood supervised classification result (Fig. 9).

8. the checking of distance threshold method classification results

By obtaining local test block (about 90 square kilometres) land-use map, we obtain its cultivated area ratio is 87.3%.The milpa area that calculates based on the training area maximum likelihood supervised classification classic method (hereinafter to be referred as the A method) of 6 sampling points is 37.8%; The pure corn crop pixel area that extracts based on 6 sampling point corn wave spectrum storehouse methods (hereinafter to be referred as the B method) is 55.6%, regrettably can not get access to corresponding crop-planting distribution plan and test.Consider the verification method that does not also have pure pixel at present in addition, for this reason, we adopt the true plot of ground investigation to carry out effect, and the result shows that overall nicety of grading reaches 92%.

The checking plot is positioned at two pure crop pixel zones ((Figure 10, three rectangular boxes districts) of 97 mu of near 324 mu of plot (the field survey spectrum sample ground 1), Dong Niu village, test block south and near near Chinese Academy of Sciences's Agro-ecology experiment centres two (the sample ground 6) and 94 mu.

Extract the classification results district in three plot from the figure as a result of two kinds of sorting techniques, we obtain following classification chart and precision contrast table (Figure 11, Figure 12, Figure 13), and the nicety of grading contrast table sees Table 2.

The contrast of table 2 corn monocrop pixel nicety of grading

In addition, to there not being the area of ground wave spectrum correspondence, can solve by the crop wave spectrum that obtains approximate growth period from the wave spectrum storehouse or from the wave spectrum storehouse by the method for simulation.

Though this method is at the pure crop pixel identification of corn TM remote sensing image, but to other crops and image (as long as spectral band is abundant, minimum close) with TM, as long as can obtain the similar crop wave spectrum in growth period of weather conditions, we think and also are suitable for, and support just passable as long as possess complete wave spectrum storehouse.

9. from the quantity of the same period with the pure crop pixel of the corresponding TM of statistics every pixel of the intermediate-resolution imaging spectrometer data MODIS in area.

10. according to the pure crop pixel of the TM number statistics of every pixel among the MODIS, the plantation of obtaining crop in the MODIS video becomes several:

M _iBe the number of the pure pixel of TM in each MODIS pixel, area _Pixel: be the area of each pixel, A _ImageArea for whole image domain.

11. the plantation in each administrative area becomes number and corresponding region area just can determine the crops planting area in each administrative area among the MODIS among the statistics MODIS.

12. the estimation of crop yield:

The calculating of the vegetation index SAVI that the soil that the growing way of each TM pixel crop can utilize forefathers to invent is adjusted comes quantitatively, the accumulation SAVI of crop also can obtain in the MODIS pixel like this, because SAVI provides in satellite image green vegetation coverage rate on each pixel plot, this is important crop growing state information certain period plant growth stage, has important related with crop yield; Thereby, can calculate the output of crop in conjunction with the area estimation of front:

$\mathrm{SAVI}=\underset{i=1}{\overset{n}{\mathrm{\Σ}}}[1.5*({\mathrm{NIR}}_i-{\mathrm{Red}}_i)/({\mathrm{NIR}}_i+{\mathrm{Red}}_i+0.5)].........\left(2\right)$

Yield＝-208830*SAVI ²+136104*SAVI-21667……………………(3)

In the following formula, n is the pure pixel number of the TM of each MODIS pixel, and NIR and Red represent the near infrared and the red spectral band reflectivity of satellite respectively.

According to our the polynomial expression nonlinear regression analysis to 6 place output of test block, Luancheng, Hebei TM video and SAVI, can simulate maize yield mid-September by following formula (3): the correlationship of the SAVI of TM video and output (Yield) reaches 0.68.

Claims (2)

1. the remote sensing estimation method of a large tracts of land crops planting area, its estimation steps is as follows:

(1) obtains the TM satellite remote sensing images that estimation is distinguished from satellite earth receiving station;

(2) obtain with reference to wave spectrum: in the detection sample district of the some same crops of ground chosen in advance of estimating the district, obtain the canopy spectra on the spot of respectively surveying the sample district, promptly with reference to wave spectrum, said with reference to wave spectrum by the described crop field survey of respectively surveying the sample district is obtained or is obtained according to priori and supporting parameter simulation by spectral database;

(3) the video wave spectrum obtains: find out from remote sensing images with each and survey corresponding crop pixel sample district, sample district, by remote sensing images are carried out pre-service, comprise conventional radiation, atmospheric correction and geometric manipulations, obtain the image average wave spectrum in crop pixel sample district, be reflectivity vector Ri, said reflectivity vector Ri is the vector that each the wave band reflectivity outside the broadband TM heat extraction infrared band is formed;

(4) computed range threshold value: the image wave spectrum and the formed wave spectrum of corresponding reference wave spectrum thereof of each crop sample district pixel that will obtain are right, calculate the right spectrum intervals threshold value of this wave spectrum by following formula:

AV＝SQRT(∑((Ri-Rmi)^2)/N)

In the formula, Ri is the image wave spectrum reflectivity vector in crop pixel sample district, and Rmi is the reflectance value mean value vector with reference to wave spectrum, and N is the wave band number;

(5) comparison of video pixel and distance threshold: with the image wave spectrum of calculated distance threshold value A V of institute and different images pixel with reference to the distance A V ' of wave spectrum relatively, if the image wave spectrum of certain pixel with reference to the calculated value of the distance A V ' of wave spectrum less than distance threshold AV, then be defined as pure crop TM pixel, at the image wave spectrum that calculates the different images pixel with reference to the distance A V ' of wave spectrum the time, its computing formula is identical with the formula that calculates spectrum intervals threshold value A V, but Ri is the pixel reflectivity vector of image in the formula;

(6) from the quantity of the same period, determine that the plantation of crop in the MODIS video becomes several with the pure crop pixel of the corresponding TM of statistics every pixel of the intermediate-resolution imaging spectrometer data MODIS in area;

(7) determine the cultivated area of crop among the MODIS according to planting the area that becomes every pixel among number and the MODIS.

2. method according to claim 1, it is characterized in that by the pure crop pixel of TM number in identification of image-reference spectra distance threshold method and the every pixel of statistics MODIS, the vegetation index adjusted in conjunction with every TM pixel soil then, determine the vegetation index SAVI of every MODIS pixel accumulation, in order to determine the growing way of every MODIS pixel crop, estimate in conjunction with the area of front again, calculate the output of crop

$\mathrm{SAVI}=\underset{i=1}{\overset{n}{\mathrm{\Σ}}}[1.5*({\mathrm{NIR}}_i-{\mathrm{Red}}_i)/(\mathrm{NI}{R}_i+\mathrm{Re}{d}_i+0.5)]$

In the following formula, n is the pure pixel number of the TM of each MODIS pixel, and NIR and Red represent the near infrared and the red spectral band reflectivity of satellite respectively.

Images