CN111402169A - Method for repairing remote sensing vegetation index time sequence under influence of coastal tide - Google Patents
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Abstract
The invention discloses a method for repairing remote sensing vegetation index time sequence under the influence of coastal tide, which is characterized by comprising the following steps: acquiring a vegetation index time sequence and a normalized difference water body index, acquiring sample pixels influenced by tides, identifying noise points of the time sequence, constructing a noise point value restoration model and restoring and evaluating the vegetation index time sequence, wherein each sample pixel in an experimental area is traversed, the noise point value restoration model is utilized to calculate the correction amount of the vegetation index noise points, the sum of the vegetation index noise point value and the corresponding vegetation index noise point correction amount value is used as the vegetation index correction value of the vegetation index noise points, and then the restored vegetation index time sequence is obtained; the method has the advantages of wide application range, high automation degree, strong robustness and suitability for different spatial resolutions.
Description
Technical Field
The invention relates to a method for repairing a remote sensing image vegetation index time sequence, in particular to a method for repairing a remote sensing vegetation index time sequence under the influence of tidal bodies on a coast.
Background
In coastal zone areas with closely interacting sea and land, vegetation (saline marsh, mangrove, seaweed and the like) is taken as an important carbon sink, and the 'blue carbon' process of soil is continuously promoted through spontaneous primary production and continuous shoal deposition. Remote sensing (RemoteSensing) provides a vegetation dynamic observation technical means in a large range and a long period, is beneficial to monitoring the primary productivity of the coastal vegetation, dynamically and quantitatively evaluates the carbon sequestration capacity change of the coastal zone ecosystem, and has very important significance. However, due to the periodic influence of ocean tides, the remote sensing of Vegetation on the coastal zones is particularly complicated, the spectral reflectivity of the Vegetation is obviously weakened by the submergence of seawater, noise is introduced into a Vegetation Index (VI) time sequence, and the time sequence characteristics (shape and size) of the Vegetation are further covered. Compared with a land ecosystem, the traditional vegetation index (NDVI, EVI, SAVI and the like) time sequence is low in the whole range of the coastal zone ecosystem, random noise is densely distributed, and time sequence mode distortion is obvious. Under the background, accurate identification and restoration of vegetation index noise under the influence of tides are urgently needed, and a coastal zone vegetation index time sequence is restored, so that the remote sensing technology is effectively applied to dynamic monitoring of a coastal zone ecosystem.
The Water body Index (Water Index, WI) can effectively distinguish the Water body on the remote sensing image from other earth surface coverage types, and provides an opportunity for coastal tidal inundation monitoring. At present, researchers use different image band combination modes to construct various water body indexes so as to meet the requirements of water body identification under different terrain backgrounds, and the method is successfully applied to the aspect of water body extraction in areas and even global areas. Notably, the water body index threshold selection is a prerequisite and key for identifying the water body, and is usually determined by image data of a single period and a short time span (the same month and the same quarter). However, the development of vegetation phenological periods and the difference of imaging time tide heights cause the relative difference between vegetation and seawater to change continuously, the threshold value selection mode in a single period is relatively unilateral, and the accuracy of the periodic tide submergence frequency drawn across multiple periods is poor. How to robustly determine the water body threshold in a time series and accurately identify vegetation index noise is a major current challenge. In addition, on the basis of identifying vegetation index noise, how to quantitatively depict the difference from the noise to the original vegetation index and construct a noise numerical value restoration model is also a technical difficulty to overcome when restoring the coastal vegetation index time sequence.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a method for repairing the remote sensing vegetation index time sequence under the influence of tide on the coast, which has the advantages of wide application range, high automation degree, strong robustness and is suitable for correcting various vegetation index time sequences of remote sensing images with different spatial resolutions.
The technical scheme adopted by the invention for solving the technical problems is as follows: a method for repairing a remote sensing vegetation index time sequence under the influence of coastal tide comprises the following steps:
(1) vegetation Index (NDVI) time series acquisition
Acquiring image pixels (DN) of a designated time and an experimental area through a European space Bureau database, converting image pixel values (DN) into surface reflectivity through radiation correction, calculating a vegetation index (NDVI) of each scene image, removing the pixels covered by clouds, cloud shadows and snow by using a roughly classified product corresponding to each scene image, sequentially arranging vegetation index images according to the sequence of imaging time, and constructing to obtain a vegetation index time sequence;
(2) normalized Differential Water Index (MNDWI) calculation
Calculating the normalized difference water body index (MNDWI) of each scene of image according to an improved normalized difference water body index formula, eliminating pixels covered by clouds, cloud shadows and snow by using a roughly classified product corresponding to each scene of image, and constructing and obtaining a water body index time sequence completely corresponding to the vegetation index time sequence;
(3) tide influenced sample pixel acquisition
Selecting image pixels which are not covered by snow in an experimental area and have cloud coverage rate lower than 10%, and acquiring typical vegetation pixels (which are mostly distributed in pioneer vegetation communities at river mouths, ports and sea sides) which are intermittently covered by seawater (ensuring observation times more than 7, exposing on the water surface at most times and submerging by the seawater at few times) as samples for identifying and repairing noise points;
(4) time series noise point identification
Each sample pixel corresponds to two time sequences of a vegetation index and a water body index, the overall variation trend of the vegetation index time sequence and the water body index time sequence of each sample pixel is observed, the abnormal minimum value of the vegetation index and the positive value of the water body index are recorded sample by sample at the same time, when the vegetation index time sequence and the water body index time sequence occur at the same time, the water body index numerical values marked by all samples are summarized, the minimum value of the water body index numerical values is taken as a water body index threshold value, and the vegetation index corresponding to the water body index numerical value in the water body index time sequence which is greater than the water body index threshold value is taken as a vegetation index noise pointnoise)；
(5) Noise point numerical restoration model construction
For each sample pixel, removing vegetation index noise points, fitting the variation trend of the residual vegetation index in a vegetation index time sequence by adopting a second-order Fourier function, taking the vertical distance from the vegetation index noise points in the sample pixel to a Fourier function fitting value curve as a vegetation index noise point correction quantity, taking the vegetation index noise point correction quantity delta NDVI as a dependent variable, taking a water body index corresponding to the vegetation index noise points as an independent variable, drawing scatter diagrams of the vegetation index noise point correction quantity delta NDVI and the water body index, fitting the relation between the vegetation index noise point correction quantity delta NDVI and the water body index by adopting a polynomial equation, and quantitatively constructing a noise point numerical value restoration model as follows:
ΔNDVI＝-3.84*MNDWI3+3.98*MNDWI2-0.33*MNDWI+0.14；
(6) vegetation index time series remediation assessment
Traversing each sample pixel of the experimental area, calculating the vegetation index noise point correction quantity by using a noise point numerical value restoration model, and taking the sum of the vegetation index noise point numerical value and the corresponding vegetation index noise point correction quantity numerical value as the vegetation index correction value VI of the vegetation index noise pointmAnd further obtaining the vegetation index time series after restoration.
The vegetation index (NDVI) of each scene image in the step (1) is calculated according to the following formula:
wherein rho NIR is the surface spectral reflectivity of the near infrared band, rhoredThe surface spectral reflectivity of the red light wave band.
The improved normalized difference water body index (MNDWI) calculation formula in the step (2) is as follows:
where ρ isgreenSpectral reflectance of the earth's surface in the green band, pswIRThe surface spectral reflectivity of the short wave infrared band.
The formula for fitting the variation trend of the residual vegetation index in the vegetation index time sequence by the second-order Fourier function in the step (5) is as follows: FF (a 0+ a1 Cos (w x) + b1 sin (w x) + a2 Cos (2w x) + b2 sin (2w x), where x is julian day; w is frequency, and for a year's time series values are typically 2 pi/365, w is 0.018, a0 is 0, a1 is 0, a2 is 0, b1 is 0, b2 is 0, and FF represents the fit of the fourier function to the index of the vegetation remaining.
Compared with the prior art, the invention has the advantages that:
firstly, a vegetation index time series noise identification method is provided. By selecting a small amount of vegetation pixels covered by seawater in a targeted manner, the water body index threshold value suitable for annual observation is automatically optimized, the noise of the vegetation index time sequence is accurately identified, and conditions are created for vegetation index time sequence restoration. The method has high identification precision and strong generalization capability, and can be applied to the vegetation index time sequence filtering and denoising process of other land and water interaction frequent areas (river banks and huff-and-puff lakes).
Secondly, a vegetation index time series numerical restoration model is constructed. And the annual variation trend of the vegetation index is fully described by a second-order Fourier function, and the noise correction quantity of the vegetation index is quantitatively measured and calculated. And constructing a polynomial numerical restoration model by depending on the correlation between the noise correction value and the water body index, thereby realizing the global batch correction of the vegetation index time sequence. The innovative method for compensating and correcting the missing data by using the fitting function is not only suitable for the field of remote sensing mapping, but also can provide beneficial reference for numerical restoration and data supplementation of sparse signal sequences in the field of communication.
In summary, the method solves the technical difficulties of identifying noise points, restoring noise values and the like of the time sequence of the vegetation index of the coastal zone under the influence of tide, overcomes the bottleneck that the time sequence of the vegetation index is low in the whole range of the coastal zone and low in applicability due to dense random noise, has high automation degree and strong robustness, is suitable for correcting the time sequences of various vegetation indexes (NDVI, EVI, SAVI and the like) of remote sensing images (MODIS, L andsat, Sentinel and the like) with different spatial resolutions, is expected to serve in the remote sensing time sequence monitoring of the coastal zone ecosystem, and lays a foundation for the effective application of the remote sensing time sequence technology to the dynamic monitoring of the coastal zone ecosystem.
Drawings
FIG. 1 is a time series restoration technique for index vegetation in a coastal zone affected by tide;
FIG. 2 shows a Sentiel-2 MSI image and sample pixel distribution thereof along the coast of Jiangsu;
FIG. 3 is an exponential time series noise point identification of the vegetation in the coastal zone before restoration;
FIG. 4 is a relational expression of a vegetation index noise correction quantity and a water body index based on a polynomial;
FIG. 5 is the sea-shore vegetation index time series noise point identification after the restoration;
FIG. 6 is a distribution of determinant coefficient differences fitted to a vegetation index time series before and after remediation.
Detailed Description
The invention is described in further detail below with reference to the accompanying examples.
The method for repairing the remote sensing vegetation index time sequence under the influence of the coastal tide is further explained by taking the natural protection area of the red-crowned crane in the middle part of Jiangsu along the sea as an experimental area and taking the Normalized Difference Vegetation Index (NDVI) constructed by the multispectral data (Sentinel-2MSI) of the Sentinel satellite No. 2 as experimental data, wherein the flow is shown in FIG. 1, and the specific steps are as follows:
the experimental data are derived from Sentinel 2 multispectral imager images (Sentinel-2MSI) provided by the European and aviation Bureau (https:// sciihub.copernius. eu /), through a designated time and an experimental area, a Sentinel-2MSI L1C product which is imaged in 68 scenes with the corresponding row code number of T51STT in 2018 and subjected to geometric correction is downloaded through experiments, the geometric precision meets the requirements of pixel-level time sequence construction and analysis, on the basis, Sen2Cor software provided by the European and aviation Bureau is adopted for radiation correction in the experiments, the image pixel value (DN) is converted into the surface reflectivity, the vegetation index (NDVI) of each scene image is calculated, and the calculation formula is as follows:
where ρ isNIRSurface spectral reflectance, ρ, for the near infrared bandredThe surface spectral reflectivity of the red light wave band.
In the radiation correction process, a coarse classification product (Scene C L assessment, SC L) of each Scene image is automatically generated, pixels covered by clouds, cloud shadows and snow in each Scene NDVI image are removed through experiments, the NDVI images are sequentially arranged according to the imaging time sequence, and an NDVI time sequence is constructed.
And 2, calculating the normalized difference water body index, on the basis of obtaining the surface reflectivity, calculating the normalized difference water body index (MNDWI) of each scene of image according to an improved normalized water body index formula by an experiment, similarly, removing the pixels covered by clouds, cloud shadows and snow by using an SC L product corresponding to each scene of image, sequentially arranging the MNDWI images according to the imaging time sequence, and constructing and obtaining the MNDWI time sequence completely corresponding to the NDVI time sequence.
Wherein the Modified Normalized Difference Water Index (MNDWI) is calculated as follows:
where ρ isgreenSpectral reflectance of the earth's surface in the green band, pSWIRThe surface spectral reflectivity of the short wave infrared band. MNDWI can highlight the difference of water and vegetation and restrain the confusion of buildings.
And step 3: and collecting sample pixels influenced by tide. In the 68 scenes in 2018, 17 image pixels with no snow coverage and less than 10% cloud coverage are selected and displayed by the standard false color scheme of R-4, G-3 and B-2. Through multi-temporal image contrast observation, the experiment uses the edge of Spartina alterniflora (Spartina alterniflora) salt marsh with frequent sea-land interaction as the target area selected by the vegetation sample. In order to ensure the objectivity and representativeness of sample selection, 39 sample pixels are equidistantly selected in a target area by 500m in the experiment (see the attached figure 2 for details).
And 4, step 4: and (4) identifying time series noise points. In the experiment, Matlab software is used for drawing an NDVI time sequence and an MNDWI time sequence of 39 sample pixels 2018, abnormal minimum values of vegetation indexes and positive values of water indexes, when the vegetation indexes and the water indexes appear at the same time, the water indexes are marked (see the attached figure 3 in detail), the water index values marked by all samples are summarized, and the minimum value of the water indexes is counted to be 0.023 and used as a water index threshold value. On the basis, traversing each pixel of the experimental area, and regarding the vegetation index corresponding to the water body index larger than the threshold value in the water body index time sequence as a vegetation index noise point (VI)noise)。
And 5: and constructing a noise point numerical value restoration model. According to the experiment, vegetation index noise points are removed from a sample pixel by sample pixel, and the variation trend of the residual vegetation index in the vegetation index time sequence is fitted by using a second-order Fourier function (Fourier) of a Matlab software Curve Fitting module. On the basis, the experiment leads vegetation index noise points in sample pixels to Fourier transformThe vertical distance of the function fitting value curve is used as the correction quantity of the vegetation index noise point, the correction quantity delta NDVI of the vegetation index noise point is used as a dependent variable, the water body index corresponding to the vegetation index noise point is used as an independent variable, and a scatter diagram is drawn as shown in an attached figure 4. Fitting the relation between the vegetation index correction quantity and the water body index by adopting a polynomial equation (the order is less than 4), and quantitatively constructing a noise point numerical restoration model: Δ NDVI ═ 3.84 × MNDWI3+3.98*MNDWI2-0.33 × MNDWI +0.14 wherein a second order Fourier Function (FF) is able to adequately characterize the cyclic variations in the coastal vegetation over the years due to seasonal temperature and precipitation environmental factors, which is calculated as follows:
FF (a 0+ a1 cos (w x) + b1 sin (w x) + a2 cos (2w x) + b2 sin (2w x), where x is julian day; w is frequency, and for a year's time series values are typically 2 pi/365, w is 0.018, a0 is 0, a1 is 0, a2 is 0, b1 is 0, b2 is 0, and FF represents the fit of the fourier function to the index of the vegetation remaining.
Step 6: and (4) vegetation index time series restoration evaluation. Traversing each pixel of the experimental area, calculating the vegetation index noise point correction quantity by using a noise point numerical value restoration model, and taking the sum of the vegetation index noise point numerical value and the corresponding vegetation index noise point correction quantity numerical value as a vegetation index correction value (NDVI)m) Thus, the vegetation index time series is repaired (see figure 5 in detail). Determining coefficients (R) by fitting a second order Fourier function2) The difference distribution shows that the effect of the invention is good, the fitting precision of the range of-40% experimental area is improved, especially the fitting precision of the outer edge area of the spartina alterniflora is averagely improved by 0.32, which is shown in figure 6 in detail.
The above description is not intended to limit the present invention, and the present invention is not limited to the above examples. Those skilled in the art should also realize that changes, modifications, additions and substitutions can be made without departing from the true spirit and scope of the invention.
Claims (4)
1. A method for repairing a remote sensing vegetation index time sequence under the influence of coastal tide, which is characterized by comprising the following steps:
(1) vegetation index time series acquisition
Acquiring image pixels of a designated time and an experimental area through a European space Bureau database, converting image pixel values into earth surface reflectivity through radiation correction, calculating vegetation indexes of each image, removing the pixels covered by clouds, cloud shadows and snow by using a roughly classified product corresponding to each image, sequentially arranging the vegetation index images according to the sequence of imaging time, and constructing and obtaining a vegetation index time sequence;
(2) normalized difference water body index calculation
Calculating the normalized difference water body index of each scene image according to an improved normalized difference water body index formula, eliminating pixels covered by clouds, cloud shadows and snow by using a rough classification product corresponding to each scene image, and constructing and obtaining a water body index time sequence completely corresponding to the vegetation index time sequence;
(3) tide influenced sample pixel acquisition
Selecting an image pixel which is not covered by snow in an experimental area and has a cloud coverage rate lower than 10%, and collecting a typical vegetation pixel which is intermittently covered by seawater to serve as a sample for identifying and repairing a noise point;
(4) time series noise point identification
Each sample pixel corresponds to two time sequences of a vegetation index and a water body index, the overall variation trend of the vegetation index time sequence and the water body index time sequence of each sample pixel is observed, the abnormal minimum value of the vegetation index and the positive value of the water body index are recorded sample by sample at the same time, when the vegetation index time sequence and the water body index time sequence occur at the same time, the water body index numerical values marked by all samples are summarized, the minimum value of the water body index numerical values is taken as a water body index threshold value, and the vegetation index corresponding to the water body index numerical value in the water body index time sequence which is greater than the water body index threshold value is taken as a vegetation index noise;
(5) noise point numerical restoration model construction
For each sample pixel, removing vegetation index noise points, fitting the variation trend of the residual vegetation index in a vegetation index time sequence by adopting a second-order Fourier function, taking the vertical distance from the vegetation index noise points in the sample pixel to a Fourier function fitting value curve as a vegetation index noise point correction quantity, taking the vegetation index noise point correction quantity delta NDVI as a dependent variable, taking a water body index corresponding to the vegetation index noise points as an independent variable, drawing scatter diagrams of the vegetation index noise points and the vegetation index noise point correction quantity delta NDVI and the water body index MNDWI, fitting the relation between the vegetation index noise point correction quantity delta NDVI and the water body index MNDWI by adopting a polynomial equation, and quantitatively constructing a noise point numerical value restoration:
ΔNDVI＝-3.84*MNDWI3+3.98*MNDWI2-0.33*MNDWI+0.14；
(6) vegetation index time series remediation assessment
Traversing each sample pixel of the experimental area, calculating the vegetation index noise point correction quantity by using a noise point numerical value restoration model, and taking the sum of the vegetation index noise point numerical value and the corresponding vegetation index noise point correction quantity numerical value as the vegetation index correction value VI of the vegetation index noise pointmAnd further obtaining the vegetation index time series after restoration.
2. The method of repairing a time series of indexes of remote vegetation under the influence of coastal tidal conditions according to claim 1, wherein: the vegetation index calculation formula of each scene image in the step (1) is as follows:
where ρ isNIRSurface spectral reflectance, ρ, for the near infrared bandredThe surface spectral reflectivity of the red light wave band.
3. The method of repairing a time series of indexes of remote vegetation under the influence of coastal tidal conditions according to claim 1, wherein: the improved normalized difference water body index calculation formula in the step (2) is as follows:
where ρ isgreenSpectral reflectance of the earth's surface in the green band, pSWIRThe surface spectral reflectivity of the short wave infrared band.
4. The method of repairing a time series of indexes of remote vegetation under the influence of coastal tidal conditions according to claim 1, wherein: the formula for fitting the variation trend of the residual vegetation index in the vegetation index time sequence by the second-order Fourier function in the step (5) is as follows: FF (a 0+ a1 cos (w x) + b1 sin (w x) + a2 cos (2w x) + b2 sin (2w x), where x is julian day; w is frequency, and for a year's time series values are typically 2 pi/365, w is 0.018, a0 is 0, a1 is 0, a2 is 0, b1 is 0, b2 is 0, and FF represents the fit of the fourier function to the index of the vegetation remaining.
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| CN113591032A (en) * | 2021-07-15 | 2021-11-02 | 清华大学深圳国际研究生院 | Method for calculating optimal growth elevation of mangrove plant |
| CN115035411A (en) * | 2022-06-17 | 2022-09-09 | 宁波大学 | Hyperspectral data-based spartina alterniflora index construction and extraction method |
| CN115082809A (en) * | 2022-06-23 | 2022-09-20 | 宁波大学 | New tidal flat evolution monitoring method based on remote sensing image big data |
| CN115078263A (en) * | 2022-05-27 | 2022-09-20 | 苏州科技大学 | Seaweed remote sensing information extraction method considering tidal influence |
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