CN117095290A - Carbon sink monitoring method based on satellite remote sensing - Google Patents

Carbon sink monitoring method based on satellite remote sensing Download PDF

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CN117095290A
CN117095290A CN202311063728.4A CN202311063728A CN117095290A CN 117095290 A CN117095290 A CN 117095290A CN 202311063728 A CN202311063728 A CN 202311063728A CN 117095290 A CN117095290 A CN 117095290A
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王野
邰贺
吕东洋
王波
赵特
彭敏
冯鑫
刘佳奇
解意
蔡晨
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Abstract

The invention discloses a carbon sink monitoring method based on satellite remote sensing, which relates to the technical field of satellite remote sensing, and comprises the following steps: acquiring remote sensing image data of a monitoring area by utilizing a satellite remote sensing technology, and preprocessing the remote sensing image data; extracting vegetation index from the processed remote sensing image data, and calculating the vegetation index value of each pixel by using image processing software; extracting land texture features from the processed remote sensing image data, and dividing a monitoring area into different land utilization types according to the land texture features; establishing a carbon reserve estimation model by combining the vegetation index value, the land utilization type and the meteorological parameters in the monitoring time period, and estimating the carbon reserve of the monitoring area; evaluating carbon sink absorption and release; and visualizing the carbon exchange change diagram and making carbon exchange management measures. According to the invention, through carrying out atmospheric correction on the remote sensing data, the data error can be reduced, and the accuracy and reliability of carbon sink monitoring are improved.

Description

Carbon sink monitoring method based on satellite remote sensing
Technical Field
The invention relates to the technical field of satellite remote sensing, in particular to a carbon sink monitoring method based on satellite remote sensing.
Background
Carbon sink refers to natural or artificial systems that act to slow down climate change and control greenhouse gas concentration in the earth's atmosphere, land and sea systems by absorbing, converting and storing carbon dioxide in the atmosphere. Briefly, carbon sink refers to the absorption and fixation of carbon dioxide in the atmosphere by the ecosystem as part of organisms and soil organisms. The amount of carbon sink is measured in terms of the size of the carbon reserves. Carbon reserves refer to the total amount of carbon stored in natural and artificial systems. Carbon reserves in natural systems mainly include forests, grasslands, wetlands, oceans, etc., while carbon reserves in artificial systems mainly include forests, land utilization changes, etc.
Carbon sink has a great effect on controlling the concentration of carbon dioxide in the atmosphere, and because carbon dioxide is one of greenhouse gases, can absorb and radiate infrared radiation on the surface of the earth, and thus the temperature of the earth rises, the carbon sink can be increased to slow down the speed of climate change, and sustainable development is promoted. The carbon sink monitoring can provide scientific basis for realizing carbon emission reduction, coping with climate change, promoting sustainable development and the like, and has important strategic significance. The carbon sink monitoring method based on satellite remote sensing has the advantages of rapidness, comprehensiveness and accuracy, and becomes one of the important means for current carbon sink monitoring. However, the monitoring of carbon sinks by satellite remote sensing technology in the prior art still has some drawbacks, which are mainly manifested in the following aspects:
(1) The satellite remote sensing data is easily affected by cloud coverage, atmospheric interference and the like, and the quality of the data is possibly reduced or related data cannot be acquired;
(2) The satellite remote sensing data can only acquire parameters such as vegetation index, vegetation coverage and the like, can not directly acquire carbon reserves of an ecological system, and needs to be estimated by combining other parameters;
(3) The satellite remote sensing data has limited land utilization classification, and the influence of the land utilization type on the carbon sink reserves is difficult to accurately describe.
For the problems in the related art, no effective solution has been proposed at present.
Disclosure of Invention
The invention mainly aims to provide a carbon sink monitoring method based on satellite remote sensing, which aims to overcome the technical problems existing in the prior art.
For this purpose, the invention adopts the following specific technical scheme:
a carbon sink monitoring method based on satellite remote sensing comprises the following steps:
s1, acquiring remote sensing image data of a monitoring area by utilizing a satellite remote sensing technology, and preprocessing the remote sensing image data;
s2, extracting a vegetation index from the processed remote sensing image data, and calculating a vegetation index value of each pixel by using image processing software;
s3, extracting land texture features from the processed remote sensing image data, and dividing the monitoring area into different land utilization types according to the land texture features;
s4, establishing a carbon reserve estimation model by combining the vegetation index value, the land utilization type and the meteorological parameters in the monitoring time period, and estimating the carbon reserve of the monitoring area;
s5, evaluating carbon sink absorption and release based on the carbon reserves of the monitoring area;
and S6, drawing a carbon sink change chart, visualizing the carbon sink change chart, and making carbon sink management measures.
Optionally, the acquiring remote sensing image data of the monitoring area by using the satellite remote sensing technology and preprocessing the remote sensing image data includes the following steps:
s11, determining a monitoring area and monitoring time, and acquiring remote sensing image data of the monitoring area by using a remote sensing satellite;
s12, removing cloud, shadow and edge effect processing is carried out on the remote sensing image data;
s13, calculating atmospheric parameters, and carrying out atmospheric correction on the remote sensing image data according to the atmospheric parameters;
the atmospheric parameters comprise the transparency of the atmosphere, the type of aerosol and the content of a water vapor column in the atmosphere;
s14, performing image enhancement and space analysis processing on the remote sensing image data subjected to the atmospheric correction.
Optionally, the calculating the atmospheric parameter and performing atmospheric correction on the remote sensing image data according to the atmospheric parameter includes the following steps:
s131, calculating the intensity of visible light wave band light transmitted through the atmosphere, and estimating the transparency of the atmosphere;
s132, calculating an aerosol reflectivity spectral line in the remote sensing image data, and estimating an aerosol type;
s133, calculating the reflectivity of an infrared radiation band in the remote sensing image data, and estimating the content of the water vapor column in the atmosphere.
Optionally, the extracting the land texture features from the processed remote sensing image data and dividing the monitoring area into different land utilization types according to the land texture features includes the following steps:
s31, acquiring land utilization data from the processed remote sensing image data, and extracting a characteristic spectrum vector from the land utilization data by using a CARS algorithm;
s32, extracting land texture features from land utilization data by using a gray level co-occurrence matrix;
s33, performing PCA dimension reduction fusion on the characteristic spectrum vector and the land texture feature to serve as an optimal characteristic vector of land classification;
s34, dividing land utilization data into different land utilization types by using a classifier based on the optimal feature vector of land classification;
and S35, counting and analyzing each land utilization type, including the area, distribution and change condition of the land utilization type.
Optionally, the obtaining land utilization data from the processed remote sensing image data and extracting the characteristic spectrum vector from the land utilization data by using the CARS algorithm includes the following steps:
s311, selecting a spectrum band related to land utilization data, and selecting a correction set sample of the PLS model through Monte Carlo sampling;
s312, calculating PLS regression coefficients of the correction set samples at each wavelength, and taking absolute values of the PLS regression coefficients as weights;
s313, determining the variable quantity by adopting an attenuation exponential method, excluding wavelength variables with smaller weight, and selecting a subset of the plurality of wavelength variables by utilizing an adaptive weighted sampling method;
s314, selecting a model with the minimum root mean square error of the training set from the plurality of wavelength variable subsets, and determining the optimal characteristic wavelength combination;
s315, extracting characteristic spectrum bands according to the determined characteristic wavelength combination, and constructing a spectrum characteristic vector based on the characteristic spectrum bands.
Optionally, the extracting the land texture feature from the land utilization data by using the gray level co-occurrence matrix includes the following steps:
s321, dividing land utilization data into texture windows, and selecting different directions of the texture windows;
s322, determining gray level according to the brightness range and the color depth of the texture window;
s323, calculating the number of times of gray value occurrence of adjacent pixels within the range of the texture window to obtain a gray level co-occurrence matrix;
s324, calculating land texture features by using the gray level co-occurrence matrix, and carrying out normalization processing on the land texture features;
wherein the ground texture features include second moment, contrast, entropy, and correlation.
Optionally, the calculation formula of the second moment is:
the calculation formula of the contrast ratio is as follows:
wherein L represents the total number of gray levels;
v, b represents a specific value of the pixel gray value, wherein v, b=0, 1,2 … … L-1;
d represents the offset distance in the x-direction or y-direction;
θ represents the direction of generation of the gray level co-occurrence matrix;
n represents a pixel point;
p represents the pixel gray value.
Optionally, the meteorological parameters in the monitoring period include precipitation, temperature, wind speed and sunlight time in the monitoring period.
Optionally, the establishing a carbon reserve estimation model by combining the vegetation index value, the land utilization type and the meteorological parameters in the monitoring time period, and estimating the carbon reserve of the monitoring area comprises the following steps:
s41, taking a vegetation index value, a land type and meteorological parameters in a monitoring period as independent variables, and taking carbon reserves in a monitoring area as independent variables;
s42, determining a quantitative relation between the dependent variable and the independent variable, constructing a multiple linear regression model, and estimating the carbon reserve of the monitoring area by using the multiple linear regression model;
s43, checking the multiple linear regression model by adopting a cross verification mode.
Optionally, the estimating the carbon sink absorption and release based on the monitored carbon reserves includes the steps of:
s51, determining carbon reserve data in a monitoring time period, wherein the carbon reserve data comprises a carbon reserve value of a starting time and a carbon reserve value of a terminating time;
s52, establishing a carbon reserve prediction model, and predicting the carbon reserve of a future time period;
s53, subtracting the carbon reserves in the current time period from the carbon reserves in the future time period to obtain a carbon reserve change value;
s54, if the carbon reserve change value is positive, the carbon sink has an absorption effect, and the absorption amount is calculated;
and S55, if the carbon reserve change value is negative, the carbon sink has a release effect, and the release amount is calculated.
The beneficial effects of the invention are as follows:
1. according to the invention, the atmospheric influence in the remote sensing image data can be removed by carrying out atmospheric correction on the satellite remote sensing image data, so that the remote sensing data is more accurate and fine, the earth surface spectral characteristics and the land change condition are better reflected, the interpretation and analysis capacity of the remote sensing data is improved, the earth surface vegetation is required to be quantitatively analyzed by carbon sink monitoring, the spectral reflection characteristics of the vegetation are influenced by atmospheric scattering and absorption, and further, the data error can be reduced and the accuracy and reliability of the carbon sink monitoring are improved by carrying out atmospheric correction on the remote sensing data.
2. According to the invention, by combining the vegetation index value, land utilization characteristics, meteorological parameters and other changing factors influencing carbon sink monitoring, the carbon reserves of the monitoring area can be effectively estimated, and the carbon reserves of the monitoring area can be more comprehensively and accurately reflected, so that the value and application prospect of monitoring data are improved, and the carbon reserves estimation model can be established by utilizing remote sensing data, meteorological data and other multi-source data, so that the rapid and comprehensive estimation of the carbon reserves in a large area can be realized, and the efficiency and cost benefit of carbon sink monitoring are further improved.
3. According to the invention, the land texture characteristics are extracted, and the monitoring area is divided into different land types, so that the carbon reserve change situation of the different land types can be better reflected, the land utilization type is one of key factors influencing the carbon reserve, the monitoring area is divided into the different land utilization types by using the classifier, the change situation of the carbon reserve can be more comprehensively analyzed, and a scientific basis is provided for formulating a carbon assembly management strategy.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention. In the drawings:
fig. 1 is a flowchart of a carbon sink monitoring method based on satellite remote sensing according to an embodiment of the present invention.
Detailed Description
It should be noted that the following detailed description is illustrative and is intended to provide further explanation of the invention. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein.
As described in the background art, in the prior art, satellite remote sensing data is easily affected by cloud coverage, atmospheric interference and the like, and the data quality is possibly reduced.
The invention will now be further described with reference to the accompanying drawings and detailed description, as shown in fig. 1, a method for monitoring a carbon sink based on satellite remote sensing according to an embodiment of the invention, the method comprising the steps of:
s1, acquiring remote sensing image data of a monitoring area by utilizing a satellite remote sensing technology, and preprocessing the remote sensing image data.
The remote sensing image data of the monitoring area is obtained by utilizing a satellite remote sensing technology, and the remote sensing image data is preprocessed, which comprises the following steps:
s11, determining a monitoring area and monitoring time, and acquiring remote sensing image data of the monitoring area by using a remote sensing satellite;
s12, removing cloud, shadow and edge effect processing is carried out on the remote sensing image data;
s13, calculating the atmospheric parameters, and carrying out atmospheric correction on the remote sensing image data according to the atmospheric parameters.
The atmospheric parameters include the transparency of the atmosphere, the type of aerosol and the content of the water vapor column in the atmosphere.
The method for calculating the atmospheric parameters and carrying out atmospheric correction on the remote sensing image data according to the atmospheric parameters comprises the following steps:
s131, calculating the intensity of visible light wave band light transmitted through the atmosphere, and estimating the transparency of the atmosphere.
It should be noted that, the gas and the particulate matters in the atmosphere layer scatter the light in the visible light band, so that the light is gradually weakened in the transmission process. According to the physical model of atmospheric scattering, the scattering loss of the light rays in the process of transmitting in the atmosphere is calculated, and then the intensity of the light rays penetrating through the atmosphere is calculated according to the optical thickness of the atmosphere and the atmospheric components.
S132, calculating an aerosol reflectivity spectral line in the remote sensing image data, and estimating the type of the aerosol.
The aerosol reflectivity spectral line is obtained by carrying out inversion calculation on the remote sensing image data, the inversion algorithm comprises least square inversion, inversion based on a statistical model, partial differential equation inversion and the like or the aerosol reflectivity spectral line is calculated through an aerosol optical model, and then the calculation result is compared with the remote sensing image data to obtain the aerosol type which is most in line with the actual situation.
S133, calculating the reflectivity of an infrared radiation band in the remote sensing image data, and estimating the content of the water vapor column in the atmosphere.
The method for estimating the content of the water vapor column in the atmosphere is based on the absorption characteristics of an infrared radiation band and the absorption band of water vapor in the atmosphere and comprises a DTP method, wherein the DTP method is based on a differential comparison method and an atmosphere radiation transmission model, and the content of the water vapor column in the atmosphere is estimated through the difference between the infrared radiation brightness temperatures of a cloud area and a non-cloud area in remote sensing image data.
S14, performing image enhancement and space analysis processing on the remote sensing image data subjected to the atmospheric correction.
It should be noted that, by reassigning the gray level of the image, the contrast and detail of the image are enhanced; removing noise and smoothing the image by performing operations such as low-pass filtering, high-pass filtering, median filtering and the like on the image; and analyzing the spatial relationship between different objects in the remote sensing image data to extract the interaction and the association between the objects.
The atmospheric correction in remote sensing monitoring of carbon sinks needs to take into account the specific nature of the carbon sink and the monitoring target. The atmospheric correction is to eliminate the interference of the atmosphere on the remote sensing data so as to obtain the accurate value of the surface reflectivity, and is specifically expressed as follows:
atmospheric correction can eliminate the effects of the atmosphere on the remote sensing data, including absorption and scattering of light. This will make the calculation of the vegetation index more accurate. By eliminating atmospheric effects, the extent of vegetation coverage and the status of vegetation activity can be more accurately estimated, thereby better understanding and monitoring the dynamic changes of the vegetation ecosystem.
The quality and accuracy of the remote sensing image data can be improved through atmospheric correction. When the land utilization types are classified, the atmospheric correction can reduce the interference of atmospheric influence on remote sensing data, so that the distribution of the land utilization types is more accurate. This is very important in the fields of land management, resource monitoring, environmental research, etc., and can provide more reliable land utilization information, supporting decision making and planning.
The calculation of the atmospheric parameters is based on inversion of the remote sensing image data, the atmospheric transparency, the aerosol type and the content of the water vapor column in the atmosphere are calculated through a remote sensing method, and the atmospheric parameters are calculated by adopting a more efficient technology and algorithm, so that the accuracy and the effect of atmospheric correction are improved.
S2, extracting a vegetation index from the processed remote sensing image data, and calculating a vegetation index value of each pixel by using image processing software.
It should be noted that, the method commonly used to extract the vegetation index from the processed remote sensing image data includes normalized vegetation index (NDVI), simplified Vegetation Index (SVI), and Differential Vegetation Index (DVI). The following description will be given of calculating the vegetation index value of each pixel using NDVI as an example:
according to the calculation formula of NDVI: (NIR-RED)/(NIR+RED), where NIR represents the reflectivity in the near infrared band and RED represents the reflectivity in the RED band. The NDVI value of each pixel can be calculated from the reflectivity data of the two bands in the remote sensing image data.
It should be noted that other indexes, such as Leaf Area Index (LAI), photosynthetic effective radiation ratio (FAPAR), forest coverage rate, chlorophyll content, etc., may be used to analyze and estimate forest land biomass.
Leaf Area Index (LAI): LAI is an index reflecting the area density of plant leaves and is related to the growth status and photosynthesis ability of plants. Higher LAI values generally represent denser vegetation coverage, which may mean more carbon sink capacity, and measurement and estimation of LAI may utilize remote sensing techniques, such as by using multispectral or hyperspectral remote sensing data to infer vegetation leaf area density.
Photosynthetically active radiation ratio (FAPAR): FAPAR measures the absorption and utilization capacity of plants to incident light energy and is an index for evaluating photosynthesis activity of plants. Higher FAPAR values indicate that plants more efficiently use light energy for photosynthesis and thus may have higher carbon sink capacity.
Forest coverage: forest coverage refers to the proportion of a region covered by forest, and can be calculated and estimated through remote sensing image data. Higher forest coverage generally means more biomass and vegetation carbon sink capacity.
Chlorophyll content: chlorophyll is a key pigment of photosynthesis in plants, and its content is related to plant growth status and photosynthesis intensity, and higher chlorophyll content may mean healthier and photosynthetically active vegetation, and thus has higher carbon sink capacity.
And S3, extracting land texture features from the processed remote sensing image data, and dividing the monitoring area into different land utilization types according to the land texture features.
The method for extracting the land texture features from the processed remote sensing image data and dividing the monitoring area into different land utilization types according to the land texture features comprises the following steps:
s31, land utilization data are obtained from the processed remote sensing image data, and a CARS algorithm is utilized to extract characteristic spectrum vectors from the land utilization data.
It should be noted that the CARS (Classification and Regression Trees) algorithm is a statistical learning method based on decision trees, and can be used for classification and regression problems. The CARS algorithm progressively divides the data set into smaller subsets, building a decision tree on each subset for predicting classification or regression results for the new data.
The method for acquiring land utilization data from the processed remote sensing image data and extracting the characteristic spectrum vector from the land utilization data by using the CARS algorithm comprises the following steps of:
s311, selecting a spectrum band related to land utilization data, and selecting a positive set sample of the PLS model through Monte Carlo sampling.
It should be noted that, the selection of the positive set sample of the PLS model may be implemented by a method of monte carlo sampling, which specifically includes the following steps:
determining the number of samples: the number of samples to be sampled is determined from the number of samples of the positive set.
Random sampling: and randomly extracting a specified number of samples from all samples by using a Monte Carlo sampling method to serve as a correction set sample. During random sampling, each sample has the same likelihood of being selected.
And (5) repeated sampling: in order to ensure the reliability and stability of the sampling result, multiple random sampling can be performed, and each sampling is performed to obtain a group of different correction set samples. The PLS model was then trained for each set of calibration set samples and the performance of the model was evaluated.
Selecting an optimal sample set: according to the evaluation criteria, the most performing positive set sample is selected as the final positive set sample of the PLS model.
It should be noted that the PLS (Partial Least Squares) model is a commonly used multiple linear regression method, and is mainly used for analyzing the relationship between multiple independent variables and one or more dependent variables. The PLS model can be used to predict, classify and cluster problems. Compared with the traditional multiple linear regression method, the PLS model can effectively solve the problems of high-dimensional data, multiple collinearity and the like.
S312, calculating PLS regression coefficients of the correction set samples at each wavelength, and taking absolute values of the PLS regression coefficients as weights;
s313, determining the variable quantity by adopting an attenuation exponential method, excluding wavelength variables with smaller weight, and selecting a subset of the plurality of wavelength variables by utilizing an adaptive weighted sampling method;
s314, selecting a model with the minimum root mean square error of the training set from the plurality of wavelength variable subsets, and determining the optimal characteristic wavelength combination;
s315, extracting characteristic spectrum bands according to the determined characteristic wavelength combination, and constructing a spectrum characteristic vector based on the characteristic spectrum bands.
S32, extracting land texture features from land utilization data by using the gray level co-occurrence matrix.
Wherein the ground texture features include second moment, contrast, entropy, and correlation.
The calculation formula of the second moment is as follows:
the calculation formula of the contrast ratio is as follows:
the calculation formula of the entropy is as follows:
the calculation formula of the correlation is as follows:
wherein L represents the total number of gray levels;
v, b represents a specific value of the pixel gray value, wherein v, b=0, 1,2 … … L-1;
note that L-1 refers to the number of gray levels, and since the gray levels are counted from 0, not from 1, the number of gray levels is L. If the number of gray levels is L, the gray levels range from 0 to L-1.
d represents the offset distance;
θ represents the direction of generation of the gray level co-occurrence matrix;
n represents a pixel point;
p represents a pixel gray value;
mu represents the average value of the gray level co-occurrence matrix;
beta represents the standard deviation of gray scale;
x and y each represent the offset direction.
S33, performing PCA dimension reduction fusion on the characteristic spectrum vector and the land texture feature to serve as an optimal characteristic vector of land classification.
The feature spectrum vector and the land texture feature are subjected to PCA dimension reduction fusion, so that a group of optimal feature vectors comprehensively reflecting the land features can be obtained, and the precision and the efficiency of land classification are improved. The method comprises the following specific steps: PCA dimension reduction is carried out on the land spectrum feature vector and the land texture feature vector, and the land spectrum feature vector and the land texture feature vector are converted into new feature vectors; and fusing the two obtained new feature vectors to obtain the optimal feature vector comprehensively reflecting the soil features. The fusion method can adopt methods such as simple weighted average, principal component analysis and the like.
S34, based on the optimal feature vector of land classification, the land utilization data are segmented by using a classifier, and the monitoring area is divided into different land utilization types.
It should be noted that, dividing the monitored area into different land utilization types by dividing the land utilization data by using the classifier mainly includes the following steps:
and applying the comprehensive feature vector to a land classification model to perform classification prediction. Various classification algorithms may be used, such as Support Vector Machines (SVMs), decision trees, random forests, etc.;
the selected classifier is trained. Training a classifier to learn the relationship and pattern between different land utilization types by inputting feature vectors of a training set and corresponding land utilization types;
by inputting the feature vectors of the test set, the classifier can classify and predict the test samples according to the modes and the relations learned before;
and verifying the classification result and evaluating the performance of the classifier. The performance of the classifier can typically be evaluated using indexes such as Accuracy (Accuracy), precision (Precision), recall (Recall), etc. The method can be compared with the real land utilization data, and the consistency of the classification result and the real result is calculated.
And S35, counting and analyzing each land utilization type, including the area, distribution and change condition of the land utilization type.
And S4, establishing a carbon reserve estimation model by combining the vegetation index value, the land utilization type and the meteorological parameters in the monitoring time period, and estimating the carbon reserve of the monitoring area.
The meteorological parameters in the monitoring time period comprise precipitation, temperature, wind speed and sunlight time in the monitoring time period.
The precipitation amount is: proper amount of precipitation can promote plant growth, improve plant photosynthesis efficiency and further promote carbon sink absorption, but excessive or insufficient precipitation can influence plant growth, so that carbon sink absorption efficiency is influenced.
Temperature: the proper temperature can improve the growth rate and photosynthesis efficiency of plants, is also beneficial to the metabolic activity of soil microorganisms and further promotes the absorption of carbon sink, but too high or too low temperature can influence the physiological metabolism of plants and the activity of soil microorganisms and further influence the absorption efficiency of carbon sink.
Wind speed: the proper wind speed can promote the gas exchange of plant photosynthesis and the transport of nutrient substances, which is favorable for the absorption of carbon sink, but excessive wind speed can cause mechanical injury and water evaporation to plants, thereby affecting the growth and photosynthesis efficiency of plants and further affecting the absorption efficiency of carbon sink.
Sun exposure time: sufficient sunlight time can improve the photosynthesis efficiency and the plant growth rate of plants, is also beneficial to the metabolic activity of microorganisms in soil, and further promotes the absorption of carbon sink, but too long or too short sunlight time can influence the plant growth and the photosynthesis efficiency, thereby influencing the absorption efficiency of carbon sink.
The method for estimating the carbon reserves in the monitored area comprises the following steps of:
s41, taking a vegetation index value, a land type and meteorological parameters of a monitoring period as independent variables and taking carbon reserves of a monitoring area as independent variables.
It should be noted that the vegetation index is an important index reflecting the growth and the degree of complexity of the vegetation, and a higher vegetation index value generally means more complex and diverse vegetation, higher productivity and better carbon sink absorption capacity. Thus, for forests, grasslands, etc., a higher vegetation index value generally means a higher carbon sink uptake.
It should be noted that the effect of different land use types on carbon sinks is also different. For example, natural vegetation ecosystems such as forests, grasslands, and wetlands typically have higher carbon reserves and thus can help increase carbon sequestration while protecting and restoring these ecosystems. However, in urban, industrial and other human activity areas, there is generally a lower carbon reserve and a higher carbon emission, so that appropriate emission reduction and energy saving measures are required to reduce the carbon emission.
S42, determining a quantitative relation between the dependent variable and the independent variable, constructing a multiple linear regression model, and estimating the carbon reserves of the monitoring area by using the multiple linear regression model.
The mathematical model of the quantitative relationship between the dependent variable and the independent variable is:
in the method, in the process of the invention,all represent regression coefficients, i.e., the influence relationship of the independent variable to the dependent variable;
C 1 ，…，C m all represent independent variables;
e represents a dependent variable;
λ represents an error term;
m represents a variable value, which is a natural number other than 0.
S43, checking the multiple linear regression model by adopting a cross verification mode.
And S5, evaluating the carbon sink absorption and release based on the carbon reserves of the monitoring area.
Wherein, based on the carbon reserves of the monitoring area, the carbon sink absorption and release amount evaluation comprises the following steps:
s51, determining carbon reserve data in a monitoring time period, wherein the carbon reserve data comprises a carbon reserve value of a starting time and a carbon reserve value of a terminating time;
s52, establishing a carbon reserve prediction model, and predicting the carbon reserve of a future time period;
the carbon reserve prediction model is used for predicting the carbon reserve in the future time period, and predicts the future carbon reserve based on the carbon reserve data and other related data in the current time period, so as to predict the change trend and magnitude of the future carbon sink.
S53, subtracting the carbon reserves in the current time period from the carbon reserves in the future time period to obtain a carbon reserve change value;
s54, if the carbon reserve change value is positive, the carbon sink has an absorption effect, and the absorption amount is calculated;
and S55, if the carbon reserve change value is negative, the carbon sink has a release effect, and the release amount is calculated.
It should be noted that the difference between the current time period and the future time period, that is, the carbon reserve change value, may be calculated by monitoring and measuring the carbon reserve. If the carbon reserve change value is positive, indicating that the carbon sink continues to have an absorption effect in the future period, the absorption amount may be calculated by dividing the carbon reserve change value by the time. If the carbon reserve change value is negative, indicating that the carbon sink will release carbon over a future period of time, the released amount may be calculated by multiplying the carbon reserve change value by the carbon emission factor.
And S6, drawing a carbon sink change chart, visualizing the carbon sink change chart, and making carbon sink management measures.
It should be noted that, according to the collected data, a drawing tool is selected to draw a carbon sink change chart, and visualization software (such as tab, power BI, etc.) is used to further process and optimize the carbon sink change chart, such as adding labels, adjusting colors, adding trend lines, etc., so as to improve the readability and the aesthetic property of the chart.
It should be noted that, according to the drawn carbon sink change chart, the trend and the reason of the carbon sink change can be analyzed, and corresponding management measures can be formulated. For example, if the reduction in carbon sequestration is due to overdrawing, appropriate forest protection measures may be taken to promote forest growth and soil carbon storage.
In summary, by means of the technical scheme, the atmospheric correction is performed on satellite remote sensing image data, so that the atmospheric influence in the remote sensing image data can be removed, the remote sensing data is more accurate and fine, the earth surface spectral characteristics and the land change condition are better reflected, the interpretation and analysis capacity of the remote sensing data are improved, the earth surface vegetation is required to be quantitatively analyzed during carbon sink monitoring, the spectral reflection characteristics of the vegetation are influenced by atmospheric scattering and absorption, and further, the data error can be reduced and the accuracy and reliability of carbon sink monitoring are improved through the atmospheric correction on the remote sensing data.
According to the invention, by combining the vegetation index value, land utilization characteristics, meteorological parameters and other changing factors influencing carbon sink monitoring, the carbon reserves of the monitoring area can be effectively estimated, and the carbon reserves of the monitoring area can be more comprehensively and accurately reflected, so that the value and application prospect of monitoring data are improved, and the carbon reserves estimation model can be established by utilizing remote sensing data, meteorological data and other multi-source data, so that the rapid and comprehensive estimation of the carbon reserves in a large area can be realized, and the efficiency and cost benefit of carbon sink monitoring are further improved.
According to the invention, the land texture characteristics are extracted, and the monitoring area is divided into different land types, so that the carbon reserve change situation of the different land types can be better reflected, the land utilization type is one of key factors influencing the carbon reserve, the monitoring area is divided into the different land utilization types by using the classifier, the change situation of the carbon reserve can be more comprehensively analyzed, and a scientific basis is provided for formulating a carbon assembly management strategy.
The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention.

Claims (10)

1. The carbon sink monitoring method based on satellite remote sensing is characterized by comprising the following steps of:
s1, acquiring remote sensing image data of a monitoring area by utilizing a satellite remote sensing technology, and preprocessing the remote sensing image data;
s2, extracting a vegetation index from the processed remote sensing image data, and calculating a vegetation index value of each pixel by using image processing software;
s3, extracting land texture features from the processed remote sensing image data, and dividing the monitoring area into different land utilization types according to the land texture features;
s4, establishing a carbon reserve estimation model by combining the vegetation index value, the land utilization type and the meteorological parameters in the monitoring time period, and estimating the carbon reserve of the monitoring area;
s5, evaluating carbon sink absorption and release based on the carbon reserves of the monitoring area;
and S6, drawing a carbon sink change chart, visualizing the carbon sink change chart, and making carbon sink management measures.
2. The method for monitoring carbon sink based on satellite remote sensing according to claim 1, wherein the steps of obtaining remote sensing image data of a monitored area by using a satellite remote sensing technology and preprocessing the remote sensing image data comprise the following steps:
s11, determining a monitoring area and monitoring time, and acquiring remote sensing image data of the monitoring area by using a remote sensing satellite;
s12, removing cloud, shadow and edge effect processing is carried out on the remote sensing image data;
s13, calculating atmospheric parameters, and carrying out atmospheric correction on the remote sensing image data according to the atmospheric parameters;
the atmospheric parameters comprise the transparency of the atmosphere, the type of aerosol and the content of a water vapor column in the atmosphere;
s14, performing image enhancement and space analysis processing on the remote sensing image data subjected to the atmospheric correction.
3. The method for monitoring carbon sink based on satellite remote sensing according to claim 2, wherein the calculating the atmospheric parameter and performing the atmospheric correction on the remote sensing image data according to the atmospheric parameter comprises the following steps:
s131, calculating the intensity of visible light wave band light transmitted through the atmosphere, and estimating the transparency of the atmosphere;
s132, calculating an aerosol reflectivity spectral line in the remote sensing image data, and estimating an aerosol type;
s133, calculating the reflectivity of an infrared radiation band in the remote sensing image data, and estimating the content of the water vapor column in the atmosphere.
4. The method for monitoring carbon sink based on satellite remote sensing according to claim 1, wherein the steps of extracting land texture features from the processed remote sensing image data and dividing the monitored area into different land utilization types according to the land texture features comprise the following steps:
s31, acquiring land utilization data from the processed remote sensing image data, and extracting a characteristic spectrum vector from the land utilization data by using a CARS algorithm;
s32, extracting land texture features from land utilization data by using a gray level co-occurrence matrix;
s33, performing PCA dimension reduction fusion on the characteristic spectrum vector and the land texture feature to serve as an optimal characteristic vector of land classification;
s34, dividing land utilization data into different land utilization types by using a classifier based on the optimal feature vector of land classification;
and S35, counting and analyzing each land utilization type, including the area, distribution and change condition of the land utilization type.
5. The method for monitoring carbon sink based on satellite remote sensing as set forth in claim 4, wherein the steps of obtaining land utilization data from the processed remote sensing image data and extracting the characteristic spectrum vector from the land utilization data by using CARS algorithm include the steps of:
s311, selecting a spectrum band related to land utilization data, and selecting a correction set sample of the PLS model through Monte Carlo sampling;
s312, calculating PLS regression coefficients of the correction set samples at each wavelength, and taking absolute values of the PLS regression coefficients as weights;
s313, determining the variable quantity by adopting an attenuation exponential method, excluding wavelength variables with smaller weight, and selecting a subset of the plurality of wavelength variables by utilizing an adaptive weighted sampling method;
s314, selecting a model with the minimum root mean square error of the training set from the plurality of wavelength variable subsets, and determining the optimal characteristic wavelength combination;
s315, extracting characteristic spectrum bands according to the determined characteristic wavelength combination, and constructing a spectrum characteristic vector based on the characteristic spectrum bands.
6. The method for monitoring carbon sink based on satellite remote sensing according to claim 5, wherein the extracting the land texture features from the land utilization data by using the gray level co-occurrence matrix comprises the steps of:
s321, dividing land utilization data into texture windows, and selecting different directions of the texture windows;
s322, determining gray level according to the brightness range and the color depth of the texture window;
s323, calculating the number of times of gray value occurrence of adjacent pixels within the range of the texture window to obtain a gray level co-occurrence matrix;
s324, calculating land texture features by using the gray level co-occurrence matrix, and carrying out normalization processing on the land texture features;
wherein the ground texture features include second moment, contrast, entropy, and correlation.
7. The method for monitoring carbon sink based on satellite remote sensing as set forth in claim 6, wherein the second moment is calculated by the formula:
the calculation formula of the contrast ratio is as follows:
wherein L represents the total number of gray levels;
v, b represents a specific value of the pixel gray value, wherein v, b=0, 1,2 … … L-1;
d represents the offset distance in the x-direction or y-direction;
θ represents the direction of generation of the gray level co-occurrence matrix;
n represents a pixel point;
p represents the pixel gray value.
8. The method of claim 7, wherein the meteorological parameters during the monitoring period include precipitation, temperature, wind speed and sunlight time during the monitoring period.
9. The method of claim 8, wherein the steps of combining the vegetation index value, the land use type and the meteorological parameters during the monitoring period, establishing a carbon reserves estimation model, and estimating the carbon reserves in the monitored area include the steps of:
s41, taking a vegetation index value, a land type and meteorological parameters in a monitoring period as independent variables, and taking carbon reserves in a monitoring area as independent variables;
s42, determining a quantitative relation between the dependent variable and the independent variable, constructing a multiple linear regression model, and estimating the carbon reserve of the monitoring area by using the multiple linear regression model;
s43, checking the multiple linear regression model by adopting a cross verification mode.
10. The method for monitoring carbon sink based on satellite remote sensing according to claim 1, wherein the step of estimating the carbon sink absorption and release based on the carbon reserves in the monitored area comprises the steps of:
s51, determining carbon reserve data in a monitoring time period, wherein the carbon reserve data comprises a carbon reserve value of a starting time and a carbon reserve value of a terminating time;
s52, establishing a carbon reserve prediction model, and predicting the carbon reserve of a future time period;
s53, subtracting the carbon reserves in the current time period from the carbon reserves in the future time period to obtain a carbon reserve change value;
s54, if the carbon reserve change value is positive, the carbon sink has an absorption effect, and the absorption amount is calculated;
and S55, if the carbon reserve change value is negative, the carbon sink has a release effect, and the release amount is calculated.
CN202311063728.4A 2023-08-23 2023-08-23 Carbon sink monitoring method based on satellite remote sensing Pending CN117095290A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117788218A (en) * 2024-02-23 2024-03-29 浙电(宁波北仑)智慧能源有限公司 Carbon emission evaluation method and system

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117788218A (en) * 2024-02-23 2024-03-29 浙电(宁波北仑)智慧能源有限公司 Carbon emission evaluation method and system

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