CN112991226A - Remote sensing estimation method for carbon sequestration rate of wetland vegetation based on reconstructed vegetation index time sequence image - Google Patents
Remote sensing estimation method for carbon sequestration rate of wetland vegetation based on reconstructed vegetation index time sequence image Download PDFInfo
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Abstract
The invention discloses a remote sensing estimation method for carbon sequestration rate of wetland vegetation based on reconstructed vegetation index time series images, which comprises the following steps: s1, reconstructing an enhanced vegetation index EVI image time sequence with high space-time continuity in the wetland vegetation distribution area; s2, establishing an optimal wetland vegetation carbon stock remote sensing inversion model based on the measured data and the EVI observed value of the corresponding pixels in the same period; s3, reconstructing an annual dynamic change process of the carbon stock of the wetland vegetation by combining the optimal wetland vegetation carbon stock remote sensing inversion model and the reconstructed EVI time sequence image; and S4, calculating the annual accumulated growth amount of the carbon storage of the wetland vegetation according to the accumulated storage amount method pixel by pixel to obtain the annual carbon sequestration rate of the wetland vegetation. The method is suitable for the wetland ecosystem with frequent air and large water level amplitude in cloud and rainy days, and is simple and convenient to operate, popularize and apply.
Description
Technical Field
The invention relates to the technical field of remote sensing estimation, in particular to a remote sensing estimation method for carbon fixation rate of wetland vegetation based on reconstructed vegetation index time sequence images.
Background
Carbon elements fixed by wetland plants through photosynthesis, namely plant biomass carbon, are the main carbon input in the carbon fixation process of the wetland ecosystem. The carbon sequestration rate of the wetland vegetation directly reflects the carbon sink state of the wetland ecosystem and is a main factor concerned by the wetland ecological process regulation. In recent years, the carbon sequestration function of wetland vegetation is facing a serious challenge under the background that climate change and human activities cause significant changes in wetland hydrological processes. Therefore, scientific estimation of the carbon sequestration rate of wetland vegetation is a technical problem to be solved urgently in the current wetland carbon circulation field.
The traditional field observation is time-consuming and labor-consuming and has higher economic cost, and the remote sensing method for estimating the vegetation carbon sequestration rate of the wetland in a large range has incomparable advantages compared with the traditional field observation. Generally, there are two main methods for remote estimation of vegetation carbon sequestration rate: 1) and interpreting the vegetation space distribution map by adopting a remote sensing means, actually measuring the carbon fixation rate of vegetation at sampling points, and performing spatial extrapolation according to the actually measured value and the vegetation classification map to obtain the carbon fixation rate of vegetation in an area scale. 2) And establishing an ecological process model driven by remote sensing data based on the empirical relationship between each environmental element and the carbon sequestration rate of the vegetation, so as to realize indirect estimation of the carbon sequestration rate of the vegetation in the regional scale. The two methods have great limitations, for example, the spatial extrapolation neglects the difference of carbon fixation rates of the same plant under different environmental influences, the remote sensing data-driven ecological process model is based on excessive theoretical assumptions, and great uncertainty is generated in the ecological mechanism, light energy transmission and conversion processes such as parameterized plant light energy utilization rate and the like.
In view of this, many scholars make an attempt of estimating the Vegetation carbon fixation rate on a regional scale based on remote sensing data, that is, a regression model is constructed by using a remote sensing observation Vegetation Index image (VI) to directly estimate the Vegetation carbon fixation rate on the regional scale. However, the estimation of vegetation carbon rate from a single VI image ignores the fact that the vegetation carbon inventory bio.c is a time-varying quantity, dynamic. The vegetation carbon sequestration rate is the cumulative value of the vegetation carbon inventory per unit time. Therefore, remote sensing estimation of the carbon sequestration rate of annual vegetation requires a time series based on VI images, obtained by high frequency continuous observation of the carbon inventory of vegetation in the vegetation growth period.
However, for the wetland ecosystem, due to the fact that the cloud and the rain are more, and the influence of hydrological processes such as repeated submergence and exposure exists, vegetation index time sequence images obtained by the existing short recurrent period satellite have large noise, and the remote sensing accurate estimation of the carbon sequestration rate of wetland vegetation is greatly influenced. Ideally, the variation trend of the MODIS EVI time series images in the growing season of the vegetation should follow the gradual characteristics of the annual dynamic variation of the vegetation, and the variation trend is represented by a smooth curve with stable increase or decrease. However, under the influence of cloud layer interference, data transmission errors, two-way reflection and the like, a plurality of abnormal low values which do not accord with the vegetation growth rule exist in the MODIS EVI time sequence curve of the Poyang lake wetland, and the estimation of the annual carbon sequestration rate of the wetland vegetation is severely limited.
In view of this, for the sensitive zone of the wetland ecosystem, it is necessary to provide a remote sensing estimation method of carbon fixation rate of wetland vegetation based on reconstructed vegetation index time sequence images, so as to improve the remote sensing estimation precision of carbon fixation rate of wetland vegetation.
Disclosure of Invention
The invention aims to solve the technical problem of providing a remote sensing estimation method for the carbon sequestration rate of wetland vegetation based on reconstructed vegetation index time sequence images, which is suitable for wetland ecosystems with frequent occurrence of air and large water level amplitude in cloud and rainy days, is simple and convenient to operate, and is convenient to popularize and apply.
In order to solve the technical problem, the invention provides a remote sensing estimation method for carbon sequestration rate of wetland vegetation based on reconstructed vegetation index time series images, which comprises the following steps:
s1, reconstructing an enhanced vegetation index EVI image time sequence with high space-time continuity in the wetland vegetation distribution area;
s2, establishing an optimal wetland vegetation carbon stock remote sensing inversion model based on the measured data and the EVI observed value of the corresponding pixels in the same period;
s3, reconstructing an annual dynamic change process of the carbon stock of the wetland vegetation by combining the optimal wetland vegetation carbon stock remote sensing inversion model and the reconstructed EVI time sequence image;
and S4, calculating the annual accumulated growth amount of the carbon storage of the wetland vegetation according to the accumulated storage amount method pixel by pixel to obtain the annual carbon sequestration rate of the wetland vegetation.
Preferably, in step S1, the time series of the enhanced vegetation index EVI images with high spatial-temporal continuity in the reconstructed wetland vegetation distribution area specifically includes: screening and eliminating abnormal values in the EVI time sequence curve by using a generalized quality layer of the EVI image; and interpolating the eliminated abnormal values through S-G filtering to obtain a reconstructed EVI time sequence image with high space-time continuity.
Preferably, the basic operation process of the S-G filtering method is as follows: selecting values of m adjacent points near the EVI image point j to fit a d-order polynomial to obtain a polynomial coefficient C of each pointiThe processing of EVI time series data by S-G filtering to predict the fitted value at point j can be described by the following formula:
in the formula, EVIj *For EVI time series curve fitting values, EVIj+iAs observed on the original EVI timing curve, CiFor S-G filter coefficients, m is the sliding window width and N is the number of convolution operations (N ═ 2m + 1).
Preferably, in step S2, establishing an optimal wetland vegetation carbon stock remote sensing inversion model based on the measured data and the EVI observation value of the corresponding pixel at the same period specifically comprises: establishing a relation model of the carbon storage amount Bio.C of a plurality of actually-measured wetland vegetation and the EVI value of the corresponding coordinate position in the same period by using a plurality of statistical methods to determine the coefficient/goodness of fit R2And selecting R as the fitting effect of the selection standard to each model by taking the root mean square error/effective value RMSE as the selection standard2And taking the model with the highest RMSE as the remote sensing inversion model of the carbon storage of the vegetation of the optimal wetland.
Preferably, in step S3, the step of reconstructing the annual dynamic change process of the carbon storage of the wetland vegetation by combining the remote sensing inversion model of the carbon storage of the optimal wetland vegetation and the reconstructed EVI time-series image specifically comprises: and (4) calculating the reconstructed high-quality EVI time sequence images in the step S1 frame by using the optimal wetland vegetation carbon stock remote sensing inversion model established in the step S2, and effectively reconstructing the annual dynamic change process of the wetland vegetation carbon stock in the research period.
Preferably, in step S4, the annual accumulated growth amount of carbon storage of the wetland vegetation is calculated pixel by pixel according to an accumulated amount method, and the annual carbon sequestration rate of the wetland vegetation is obtained by: on the basis of reconstructing the annual dynamic change process of the carbon inventory of the surface of the wetland vegetation, calculating the annual carbon sequestration rate CSQ of the wetland vegetation by pixels by an accumulation inventory method:
wherein CSQ refers to the annual carbon sequestration rate of wetland vegetation; k is a radical oftThe carbon stock accumulated growth amount of the wetland vegetation between the time t and the time t-1; bio.ctThe instantaneous carbon storage of the wetland vegetation at the time t, and n is the number of acquired images of the EVI time sequence images per year.
The invention has the beneficial effects that: the method can effectively remove the influence of cloud pollution pixels in the remote sensing inversion of the annual dynamic change process of the carbon storage of the wetland vegetation, can also quickly track the rapid change process of the carbon storage of the wetland vegetation under the alternate influence of water surface submergence and exposure, and achieves the purpose of simultaneously removing the remote sensing image error and the influence of high water level amplitude on the remote sensing estimation of the carbon fixation rate of the vegetation of the wetland ecosystem by combining three methods of EVI vegetation index time sequence image quality control, instantaneous carbon storage remote sensing inversion of the wetland vegetation and carbon storage accumulation increment calculation; the operation is simple and convenient, and the required cost is low.
Drawings
FIG. 1 is a schematic flow chart of the method of the present invention.
FIG. 2 is a schematic diagram of the method for reconstructing a high-quality remote sensing observation vegetation index time-series image according to the present invention.
Fig. 3(a) is a schematic diagram of the method for establishing the remote sensing inversion model of the optimal wetland vegetation carbon stock by comparing various statistical methods.
Fig. 3(b) is a schematic diagram of the method for establishing the remote sensing inversion model of the optimal wetland vegetation carbon storage by comparing various statistical methods.
Fig. 3(c) is a schematic diagram of the method for establishing the remote sensing inversion model of the optimal wetland vegetation carbon storage by comparing various statistical methods.
Fig. 3(d) is a schematic diagram of the method for establishing the remote sensing inversion model of the optimal wetland vegetation carbon storage by comparing various statistical methods.
FIG. 4 is a schematic diagram showing the annual dynamic change process of the carbon storage amount of the reconstructed Poyang lake wetland vegetation in the invention.
FIG. 5 is a schematic diagram of the carbon sequestration rate of wetland vegetation in Poyang lake 2016 based on the reconstructed MODIS EVI time sequence image estimation.
FIG. 6 is a comparison of the annual carbon sequestration rate in 2016-Poyang lake wetland and MODIS-related products.
Detailed Description
As shown in fig. 1, a remote sensing estimation method for carbon sequestration rate of wetland vegetation based on reconstructed vegetation index time series images comprises the following steps:
s1, reconstructing an enhanced vegetation index EVI image time sequence with high space-time continuity in the wetland vegetation distribution area;
s2, establishing an optimal wetland vegetation carbon stock remote sensing inversion model based on the measured data and the EVI observed value of the corresponding pixels in the same period;
s3, reconstructing an annual dynamic change process of the carbon stock of the wetland vegetation by combining the optimal wetland vegetation carbon stock remote sensing inversion model and the reconstructed EVI time sequence image;
and S4, calculating the annual accumulated growth amount of the carbon storage of the wetland vegetation according to the accumulated storage amount method pixel by pixel to obtain the annual carbon sequestration rate of the wetland vegetation.
The method specifically comprises the following steps:
aiming at wetland vegetation index time sequence images with high water level amplitude and more cloud pollution pixels, firstly, performing quality control on an original EVI time sequence image by using a general quality image layer and S-G filtering, and reconstructing a high-quality EVI time sequence image with high space-time continuity; establishing an optimal wetland vegetation carbon stock remote sensing inversion model by using the measured data of the carbon stock sample and the corresponding EVI pixel, inverting the reconstructed EVI time sequence image, and effectively reconstructing the annual dynamic change process of the wetland vegetation carbon stock; and finally, calculating the annual accumulated growth amount of the carbon storage of the wetland vegetation pixel by pixel according to an accumulated storage amount method to obtain the annual carbon sequestration rate of the wetland vegetation.
Specifically, the method comprises the following specific technical scheme:
and S1, identifying abnormal values in the original EVI time sequence curve by using the generalized quality graph layer, fitting the original EVI image by using S-G filtering, and interpolating removed values by using the fitted values to reconstruct a high-quality EVI time sequence image with high space-time continuity.
S2, establishing a plurality of wetland vegetation carbon stock remote sensing inversion models based on a linear regression equation, a binomial equation, an exponential model or a random forest algorithm to determine coefficients/goodness of fit (R) based on the reconstructed EVI vegetation index time sequence image and the wetland vegetation carbon stock sample side survey data consistent with the MODIS transit date, taking the actually measured carbon stock as a dependent variable (y) and the EVI value of the corresponding coordinate position at the same period as an independent variable (x)2) And root mean square error/Root Mean Square (RMSE) measures the fit of each model, R is selected2And taking the model with the highest RMSE and the lowest RMSE as the remote sensing inversion model of the vegetation carbon storage of the optimal wetland.
And S3, performing vegetation carbon stock remote sensing inversion on a single image in each reconstructed EVI time sequence image by using the selected optimal wetland vegetation carbon stock remote sensing inversion model to obtain an annual dynamic change process of the vegetation carbon stock in the wetland in the research area.
S4, calculating the annual carbon sequestration rate of the wetland vegetation:
wherein CSQ refers to the annual carbon sequestration rate of wetland vegetation; k is a radical oftThe carbon stock accumulated growth amount of the wetland vegetation between the time t and the time t-1; bio.ctIs the instantaneous carbon storage of the wetland vegetation at the time t. n is the number of images acquired per year of the EVI time series images.
The present invention is further illustrated by the following specific examples.
Example 1
Taking a typical wetland ecosystem Poyang lake wetland as an example.
FIG. 2 is a schematic diagram of a method for reconstructing a high-quality remote sensing observation vegetation index time-series image. According to an AQ value (AQ) of a general Quality image layer (Band Pixel Reliability), eliminating a non-data Pixel (AQ ═ 1), a snow/ice covering Pixel (AQ ═ 2) and a cloud pollution Pixel (AQ ═ 3), and only keeping a high-Quality Pixel and a medium-Quality Pixel of which the AQ values are 0 and 1 in an original EVI image. And fitting the EVI time sequence curve by S-G filtering to complete the interpolation of the removed abnormal value. As can be seen from fig. 2, compared with the original EVI annual variation curve, the reconstructed MODIS EVI time sequence curve describes the dynamic variation of Yanghu wetland vegetation more reasonably, removes numerous noise points, and well captures the one-year-two-season features of the sedge-phalaris community and the one-year-one-season features of the triarrhena sacchariflora-reed community.
Fig. 3(a) - (d) are schematic diagrams for establishing an optimal wetland vegetation carbon stock remote sensing inversion model by using various statistical methods. Wherein, fig. 3(a) is a functional relation between the carbon storage of the wetland vegetation and the EVI index established by the power model, and fig. 3(b), 3(c) and 3(d) are functional relations between the carbon storage of the wetland vegetation and the EVI established by the exponential model, the linear model and the binomial model respectively. The legend marks the parameters of the fitting equation, the determining coefficient R2And root mean square error RMSE. Selecting R in Poyang lake wetland2And (3) taking the binomial model with the highest RMSE and the lowest RMSE as a final remote sensing inversion model of the carbon storage of the wetland vegetation to reconstruct the annual dynamic change process of the carbon storage of the wetland vegetation.
Fig. 4 is the annual dynamic change process of the 2016-year Poyang lake wetland vegetation carbon storage quantity reconstructed based on the optimal wetland vegetation carbon storage quantity remote sensing inversion model. As can be seen from fig. 4, by combining the reconstructed EVI vegetation index time-series image and the optimal wetland vegetation carbon storage remote sensing inversion model, the annual dynamic changes of the carbon storage of the Poyang lake wetland can be well captured, including two main carbon storage accumulation periods in spring and autumn, and two main carbon storage growth inhibition periods in high flood stage in summer and low temperature stage in winter. The annual change process of the reconstructed Poyang lake wetland vegetation carbon storage amount is very consistent with the actual result.
FIG. 5 shows the carbon sequestration rate of wetland vegetation in Poyang lake 2016 estimated based on the reconstructed MODIS EVI time sequence image. As can be seen from fig. 5: the average value of the annual carbon fixing rate of the black pond wetland in the 2016 year Poyang lake is 401g C/m2A, standard deviation 172g C/m2A is calculated. In addition, the carbon fixation rate of the Poyang lake wetland vegetation has an obvious spatial distribution mode, namely a certain gradient distribution rule exists along the direction from water to land, and the distribution rule is consistent with the field observation result.
FIG. 6 is a graph showing the correlation between the estimation result of the present invention and MODIS-related products of the same year. Therefore, the estimation result of the wetland vegetation carbon sequestration rate has good consistency with MODIS related products. In addition, since the spatial resolution of the estimation result of the present invention is 250m and increases with the spatial resolution of the EVI time-series image, the estimation result of the present invention is significantly improved compared to the MODIS product with 1km resolution.
According to the technical scheme, the invention has the following beneficial effects: the influence of cloud pollution pixels on the annual change process of the vegetation carbon storage of the wetland in remote sensing inversion can be effectively removed, the rapid change of the carbon storage of the wetland under the alternate influence of water surface submergence and exposure can be quickly tracked, and the purpose of simultaneously removing the remote sensing observation error and the influence of high water level amplitude on the remote sensing estimation of the carbon fixation rate of the vegetation of the wetland ecosystem is achieved by combining three methods of EVI vegetation index quality control, instantaneous carbon storage remote sensing inversion of the wetland vegetation and accumulated carbon storage calculation in unit time; the operation is simple and convenient, and the required cost is low.
Claims (6)
1. A remote sensing estimation method for carbon sequestration rate of wetland vegetation based on reconstructed vegetation index time series images is characterized by comprising the following steps:
s1, reconstructing an enhanced vegetation index EVI image time sequence with high space-time continuity in the wetland vegetation distribution area;
s2, establishing an optimal wetland vegetation carbon stock remote sensing inversion model based on the measured data and the EVI observed value of the corresponding pixels in the same period;
s3, reconstructing an annual dynamic change process of the carbon stock of the wetland vegetation by combining the optimal wetland vegetation carbon stock remote sensing inversion model and the reconstructed EVI time sequence image;
and S4, calculating the annual accumulated growth amount of the carbon storage of the wetland vegetation according to the accumulated storage amount method pixel by pixel to obtain the annual carbon sequestration rate of the wetland vegetation.
2. The method for remote sensing estimation of carbon sequestration rate of wetland vegetation based on reconstructed vegetation index time-series images according to claim 1, wherein in step S1, the enhanced vegetation index EVI image time-series with high spatial-temporal continuity in the reconstructed wetland vegetation distribution area is specifically: screening and eliminating abnormal values in the EVI time sequence curve by using a generalized quality layer of the EVI image; and interpolating the eliminated abnormal values through S-G filtering to obtain a reconstructed EVI time sequence image with high space-time continuity.
3. The wetland vegetation carbon sequestration rate remote sensing estimation method based on reconstructed vegetation index time series images according to claim 2, characterized in that the S-G filtering method comprises the following basic operation processes: selecting values of m adjacent points near the EVI image point j to fit a d-order polynomial to obtain a polynomial coefficient C of each pointiThe processing of EVI time series data by S-G filtering to predict the fitted value at point j can be described by the following formula:
in the formula, EVIj *For EVI time series curve fitting values, EVIj+iAs observed on the original EVI timing curve, CiFor S-G filter coefficients, m is the sliding window width and N is the number of convolution operations (N ═ 2m + 1).
4. The method for remote sensing estimation of carbon fixation rate of wetland vegetation based on reconstructed vegetation index time-series images according to claim 1, wherein in step S2, establishing an optimal wetland vegetation carbon stock remote sensing inversion model based on measured data and EVI observed values of corresponding pixels in the same period specifically comprises: establishing a relation model of the carbon storage amount Bio.C of a plurality of actually-measured wetland vegetation and the EVI value of the corresponding coordinate position in the same period by using a plurality of statistical methods to determine the coefficient/goodness of fit R2And selecting R as the fitting effect of the selection standard to each model by taking the root mean square error/effective value RMSE as the selection standard2And taking the model with the highest RMSE as the remote sensing inversion model of the carbon storage of the vegetation of the optimal wetland.
5. The method for remote sensing estimation of carbon fixation rate of wetland vegetation based on reconstructed vegetation index time-series images according to claim 1, wherein in step S3, the step of reconstructing an annual dynamic change process of carbon inventory of wetland vegetation by combining the remote sensing inversion model of carbon inventory of optimal wetland vegetation and the reconstructed EVI time-series images specifically comprises: and (4) calculating the reconstructed high-quality EVI time sequence images in the step S1 frame by using the optimal wetland vegetation carbon stock remote sensing inversion model established in the step S2, and effectively reconstructing the annual dynamic change process of the wetland vegetation carbon stock in the research period.
6. The remote sensing estimation method for carbon sequestration rate of wetland vegetation based on reconstructed vegetation index time-series images as claimed in claim 1, wherein in step S4, the annual accumulated growth amount of carbon storage of wetland vegetation is calculated pixel by pixel according to an accumulated storage amount method, and the obtained annual carbon sequestration rate of wetland vegetation specifically comprises: on the basis of reconstructing the annual dynamic change process of the carbon inventory of the surface of the wetland vegetation, calculating the annual carbon sequestration rate CSQ of the wetland vegetation by pixels by an accumulation inventory method:
wherein CSQ refers to the annual carbon sequestration rate of wetland vegetation; k is a radical oftThe carbon stock accumulated growth amount of the wetland vegetation between the time t and the time t-1; bio.ctThe instantaneous carbon storage of the wetland vegetation at the time t, and n is the number of acquired images of the EVI time sequence images per year.
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| CN113806943A (en) * | 2021-09-22 | 2021-12-17 | 河海大学 | Wetland vegetation carbon fixation rate prediction method based on relationship between key water regime variables and vegetation carbon fixation rate |
| CN116070926A (en) * | 2022-01-21 | 2023-05-05 | 武汉大学 | Method for judging feasibility of dynamic monitoring of carbon reserves based on VOD data |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113806943A (en) * | 2021-09-22 | 2021-12-17 | 河海大学 | Wetland vegetation carbon fixation rate prediction method based on relationship between key water regime variables and vegetation carbon fixation rate |
| CN113806943B (en) * | 2021-09-22 | 2024-04-02 | 河海大学 | Wetland vegetation carbon fixation rate prediction method based on relation between key water regime variables and vegetation carbon fixation rate |
| CN116070926A (en) * | 2022-01-21 | 2023-05-05 | 武汉大学 | Method for judging feasibility of dynamic monitoring of carbon reserves based on VOD data |
| CN116070926B (en) * | 2022-01-21 | 2024-06-07 | 武汉大学 | Method for judging feasibility of dynamic monitoring of carbon reserves based on VOD data |
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