CN114648705A - Carbon sink monitoring system and method based on satellite remote sensing - Google Patents

Carbon sink monitoring system and method based on satellite remote sensing Download PDF

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CN114648705A
CN114648705A CN202210309530.9A CN202210309530A CN114648705A CN 114648705 A CN114648705 A CN 114648705A CN 202210309530 A CN202210309530 A CN 202210309530A CN 114648705 A CN114648705 A CN 114648705A
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王大成
杨邦会
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Abstract

The invention discloses a carbon sink monitoring system and method based on satellite remote sensing, which comprises a remote sensing module, a processing module and a monitoring module; the remote sensing module, the processing module and the monitoring module are connected in sequence; the remote sensing module is used for acquiring remote sensing data; the processing module is used for selecting the remote sensing data, identifying the extracted remote sensing data and acquiring carbon storage monitoring data; the monitoring module is used for predicting carbon storage monitoring data through a deep learning model to obtain carbon reserves, and carbon sink is calculated based on the carbon reserves. The method can acquire multi-aspect data from the remote sensing data, and effectively and accurately calculate and monitor the carbon sink amount intelligently through multi-aspect data fitting analysis.

Description

Carbon sink monitoring system and 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 system and method based on satellite remote sensing.
Background
Carbon sequestration refers to a process, activity or mechanism of reducing the concentration of greenhouse gases in the atmosphere by using plant photosynthesis to absorb carbon dioxide in the atmosphere and fixing it in vegetation and soil through measures such as afforestation, forest management, vegetation restoration and the like. In the prior art, related calculation and monitoring are carried out on carbon sinks, wherein most methods need to carry out on-site monitoring on environment and plant related data in a monitored area, and data fitting calculation is carried out according to the monitored data.
Disclosure of Invention
In order to solve the problem that the carbon sink monitoring in the prior art is not intelligent enough, the invention provides a carbon sink monitoring system and method based on satellite remote sensing, which are used for processing remote sensing data, acquiring multi-aspect data from the remote sensing data, and effectively and accurately calculating and monitoring the carbon sink amount intelligently through multi-aspect data fitting analysis.
In order to achieve the technical purpose, the invention provides the following technical scheme:
a carbon sequestration monitoring system based on satellite remote sensing, comprising:
the remote sensing module, the processing module and the monitoring module; the remote sensing module, the processing module and the monitoring module are connected in sequence;
the remote sensing module is used for acquiring remote sensing data;
the processing module is used for selecting the remote sensing data, identifying the selected remote sensing data and acquiring carbon storage monitoring data;
the monitoring module is used for predicting carbon storage monitoring data through a deep learning model to obtain carbon reserves, and carbon sink is calculated based on the carbon reserves.
Optionally, the remote sensing module includes an acquisition module and a preprocessing module;
the acquisition module is connected with the preprocessing module;
the acquisition module is used for acquiring initial remote sensing data, wherein the initial remote sensing data is a multispectral remote sensing image;
the preprocessing module is used for preprocessing the multispectral remote sensing image to obtain remote sensing data, wherein the preprocessing comprises image addition and correction.
Optionally, the identification module includes a selection module and an analysis module;
the selection module is connected with the analysis module;
the selection module is used for selecting the remote sensing data according to different wave bands to obtain the remote sensing data under different wave bands, and the remote sensing data under different wave bands comprise the remote sensing data under red wave bands, green wave bands and infrared wave bands;
the analysis module is used for analyzing and calculating the remote sensing data under different wave bands respectively to obtain carbon storage monitoring data, wherein the carbon storage monitoring data comprise vegetation coverage area, vegetation type, vegetation condition and earth surface characteristics.
Optionally, the monitoring module includes a prediction module and a calculation module;
the prediction module is connected with the calculation module;
the prediction module predicts the carbon storage monitoring data through a deep learning model, wherein the deep learning model adopts a convolutional neural network model;
and the calculation module calculates the difference of the carbon reserves based on the conversion coefficient to obtain the carbon sink.
Optionally, the system further includes a training module, and the training module is connected to the monitoring module;
the training module is used for acquiring historical carbon storage monitoring data, performing data filling and data integration on the historical carbon storage monitoring data to obtain data after the data are filled and integrated, training and verifying the deep learning model through ten-fold cross validation based on the data after the data are filled and integrated to obtain a trained deep learning model, and predicting the carbon storage monitoring data through the trained deep learning model.
In order to better achieve the technical purpose, the invention also provides a carbon sink monitoring method based on satellite remote sensing, which comprises the following steps:
acquiring remote sensing data, selecting the remote sensing data, identifying the extracted remote sensing data, and acquiring carbon storage monitoring data;
and predicting the carbon storage monitoring data through a deep learning model to obtain the carbon storage amount, and calculating to obtain the carbon sink amount based on the carbon storage amount.
Optionally, the process of obtaining the remote sensing data includes: acquiring initial remote sensing data, wherein the initial remote sensing data is a multispectral remote sensing image; and preprocessing the multispectral remote sensing image to obtain remote sensing data, wherein the preprocessing comprises image addition and correction.
Optionally, the acquiring process of the carbon storage monitoring data includes: the remote sensing data are selected according to different wave bands to obtain the remote sensing data under the different wave bands, the remote sensing data under the different wave bands comprise the remote sensing data under red wave bands, green wave bands and infrared wave bands, the remote sensing data under the different wave bands are analyzed and calculated respectively to obtain carbon storage monitoring data, and the carbon storage monitoring data comprise vegetation coverage area, vegetation type, vegetation condition and earth surface characteristics.
Optionally, the obtaining process of the carbon sink amount includes: predicting the carbon storage monitoring data through a deep learning model, wherein the deep learning module adopts a convolutional neural network model; and calculating the difference of the carbon reserves based on the conversion coefficient to obtain the carbon sink amount.
Optionally, before predicting the carbon storage monitoring data through the deep learning model, the method further includes: the method comprises the steps of obtaining historical carbon storage monitoring data, conducting data filling and data integration on the historical carbon storage monitoring data to obtain data after filling and integration, training and verifying a deep learning model through ten-fold cross validation based on the data after filling and integration to obtain a trained deep learning model, and predicting the carbon storage monitoring data through the trained deep learning model.
The invention has the following technical effects:
according to the method, the satellite remote sensing data is obtained, different data in the satellite remote sensing data are analyzed respectively, carbon storage monitoring data capable of reflecting information in various aspects are extracted, the monitoring data are fitted through a deep learning model, and finally the carbon sink amount is calculated accurately and effectively. According to the method, satellite remote sensing data only need to be acquired in real time, local data collection is not needed, the intelligent degree is improved, meanwhile, fixed calculation methods such as theoretical formulas are not needed, the fitting relation between the carbon storage monitoring data and the carbon storage is learned through a deep learning model, the carbon storage is accurately predicted, and then accurate monitoring of the carbon sink is achieved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a schematic diagram of a system according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of a method according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example one
As shown in fig. 1, the present invention provides a carbon sink monitoring system based on satellite remote sensing, which includes: the remote sensing module, the processing module and the monitoring module; the remote sensing module, the processing module and the monitoring module are connected in sequence;
the remote sensing module is used for acquiring remote sensing data; the remote sensing module comprises an acquisition module and a preprocessing module; the acquisition module is connected with the preprocessing module; the acquisition module is used for acquiring initial remote sensing data, wherein the initial remote sensing data is a multispectral remote sensing image; the preprocessing module is used for preprocessing the multispectral remote sensing image to obtain remote sensing data, wherein the preprocessing comprises image adding and correcting. The method comprises the steps that initial remote sensing data are connected with the Internet or other data software through a remote sensing module to be downloaded, the initial remote sensing data are processed through ENVI software, image enhancement is carried out through a histogram equalization method, correction is carried out through correction methods such as radiation correction and geometric correction, and clear and more obvious remote sensing data are obtained through the technical scheme.
The processing module is used for selecting the remote sensing data, identifying the extracted remote sensing data and acquiring carbon storage monitoring data; the identification module comprises a selection module and an analysis module; the selection module is connected with the analysis module; the selection module is used for selecting the remote sensing data according to different wave bands to obtain the remote sensing data under different wave bands, and the remote sensing data under different wave bands comprise the remote sensing data under red wave bands, green wave bands and infrared wave bands; the analysis module is used for analyzing and calculating the remote sensing data under different wave bands respectively to obtain carbon storage monitoring data, wherein the carbon storage monitoring data comprise vegetation coverage area, vegetation type, vegetation condition, plant productivity and earth surface characteristics. And the processing module performs related extraction on the multispectral satellite remote sensing data through the wave band to extract the remote sensing data with related information under different wave bands.
In the embodiment, remote sensing data under red, green and infrared bands are obtained, the remote sensing data under red band is processed, the category and the condition of vegetation are calculated, the category of vegetation can be identified by the brightness and darkness of color, the category of vegetation comprises forest, short forest, grassland and the like, in the categories, different vegetation categories correspond to different brightness and darkness in the remote sensing data, the category is judged according to the brightness, in the vegetation category judgment process, the remote sensing data under red band can be identified by constructing a trained convolutional layer neural network, different categories are set as output, the category judgment result is recorded, after recording, the inspection can be carried out according to the geographical position of an area, if the remote sensing data belong to the category which accords with the geographical position, the vegetation category data is retained, if the remote sensing data do not accord with the geographical position, the remote sensing data under red band, the remote sensing data under infrared band are identified by manpower, the category is identified, and continuing to train the neural network. Calculating a vegetation normalization index as plant condition data according to the image under the red wave band, and evaluating plant productivity through the brightness and darkness of the green wave band color, wherein the plant productivity can be used as the input of a deep learning model according to the gray value in the image; ground object heat radiation is identified under an infrared band, ground surface features such as crop and forest distinguishing, water bodies, rocks and the like are identified according to radiant heat difference, edges of panchromatic bands are extracted, different regions are divided, corresponding regions in the panchromatic band regions are extracted according to the regions where vegetation types determined by the red band are located, vegetation geometric features are obtained, and area calculation is carried out according to the geometric features to obtain vegetation areas. The input of the deep learning model is obtained through respective operation under different wave bands, the data are obtained through remote sensing data, and data are not required to be acquired on site.
The monitoring module is used for predicting carbon storage monitoring data through a deep learning model to obtain carbon reserves, and carbon sink is calculated based on the carbon reserves. The monitoring module comprises a prediction module and a calculation module; the prediction module is connected with the calculation module; the prediction module predicts the carbon storage monitoring data through a deep learning model, wherein the deep learning model adopts a convolutional neural network model; and the calculation module calculates the difference value of the carbon reserves based on the conversion coefficient to obtain the carbon sink amount. The method comprises the steps of constructing a deep learning model in a monitoring module, wherein the deep learning module adopts a convolution neural network model, the model structure comprises three convolution-pooling layers and two full-connection layers which are sequentially connected, an activation function adopts a Sigmoid function, the fitting relation between carbon storage monitoring data and carbon storage is reflected through the neural network, the accuracy of a final numerical value is improved, after the carbon storage is calculated, difference calculation is carried out on the carbon storage at different time, the product of the difference and the conversion coefficient of carbon dioxide and carbon is calculated, the carbon sink is obtained, if the carbon sink is positive, the area is judged to be carbon sink, and if the carbon sink is negative, the area is judged to be carbon emission. The monitoring module is connected with related display equipment to display data in the monitoring module, and visual monitoring is achieved.
The system also comprises a training module, wherein the training module is connected with the monitoring module; the training module is used for acquiring historical carbon storage monitoring data, performing data filling and data integration on the historical carbon storage monitoring data to obtain data after the data are filled and integrated, training and verifying the deep learning model through ten-fold cross validation based on the data after the data are filled and integrated to obtain a trained deep learning model, and predicting the carbon storage monitoring data through the trained deep learning model. The deep learning model is set to have the learning rate of 0.001, the number of training rounds of 500 and the batch size of 24, and the deep learning model is more accurate in relation fitting through the setting of the technical scheme.
Example two
As shown in fig. 2, in order to better achieve the above technical objects, the present invention provides a carbon sink monitoring method based on satellite remote sensing, which comprises,
acquiring remote sensing data, selecting the remote sensing data, identifying the extracted remote sensing data, and acquiring carbon storage monitoring data; and predicting the carbon storage monitoring data through a deep learning model to obtain the carbon storage amount, and calculating to obtain the carbon sink amount based on the carbon storage amount.
Optionally, the process of obtaining the remote sensing data includes: acquiring initial remote sensing data, wherein the initial remote sensing data is a multispectral remote sensing image; and preprocessing the multispectral remote sensing image to obtain remote sensing data, wherein the preprocessing comprises image addition and correction.
Optionally, the acquiring process of the carbon storage monitoring data includes: the remote sensing data are selected according to different wave bands to obtain remote sensing data under different wave bands, the remote sensing data under different wave bands comprise remote sensing data under red wave bands, green wave bands and infrared wave bands, the remote sensing data under different wave bands are analyzed and calculated respectively to obtain carbon storage monitoring data, and the carbon storage monitoring data comprise vegetation coverage area, vegetation type, vegetation condition and earth surface characteristics.
Optionally, the obtaining process of the carbon sink amount includes: predicting the carbon storage monitoring data through a deep learning model, wherein the deep learning module adopts a convolutional neural network model; and calculating the difference of the carbon reserves based on the conversion coefficient to obtain the carbon sink amount.
Optionally, before predicting the carbon storage monitoring data through the deep learning model, the method further includes: the method comprises the steps of obtaining historical carbon storage monitoring data, conducting data filling and data integration on the historical carbon storage monitoring data to obtain data after filling integration, training and verifying a deep learning model through ten-fold cross verification based on the data after filling integration to obtain a trained deep learning model, and predicting the carbon storage monitoring data through the trained deep learning model. The method of the present invention corresponds to the technical content of the system, and is not described herein again.
The foregoing illustrates and describes the principles, general features, and advantages of the present invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims (10)

1. A carbon sink monitoring system based on satellite remote sensing, comprising:
the system comprises a remote sensing module, a processing module and a monitoring module; the remote sensing module, the processing module and the monitoring module are connected in sequence;
the remote sensing module is used for acquiring remote sensing data;
the processing module is used for selecting the remote sensing data, identifying the selected remote sensing data and acquiring carbon storage monitoring data;
the monitoring module is used for predicting the carbon storage monitoring data through the deep learning model to obtain the carbon reserves, and the carbon sink is calculated based on the carbon reserves.
2. The satellite remote sensing-based carbon sequestration monitoring system according to claim 1, characterized in that:
the remote sensing module comprises an acquisition module and a preprocessing module;
the acquisition module is connected with the preprocessing module;
the acquisition module is used for acquiring initial remote sensing data, wherein the initial remote sensing data is a multispectral remote sensing image;
the preprocessing module is used for preprocessing the multispectral remote sensing image to obtain remote sensing data, wherein the preprocessing comprises image addition and correction.
3. The satellite remote sensing-based carbon sink monitoring system according to claim 1, wherein:
the identification module comprises a selection module and an analysis module;
the selection module is connected with the analysis module;
the selection module is used for selecting the remote sensing data according to different wave bands to obtain the remote sensing data under different wave bands, and the remote sensing data under different wave bands comprise the remote sensing data under red wave bands, green wave bands and infrared wave bands;
the analysis module is used for analyzing and calculating the remote sensing data under different wave bands respectively to obtain carbon storage monitoring data, wherein the carbon storage monitoring data comprise vegetation coverage area, vegetation type, vegetation condition and earth surface characteristics.
4. The satellite remote sensing-based carbon sink monitoring system according to claim 1, wherein:
the monitoring module comprises a prediction module and a calculation module;
the prediction module is connected with the calculation module;
the prediction module predicts the carbon storage monitoring data through a deep learning model, wherein the deep learning model adopts a convolutional neural network model;
and the calculation module calculates the difference of the carbon reserves based on the conversion coefficient to obtain the carbon sink.
5. The satellite remote sensing-based carbon sink monitoring system according to claim 1, wherein:
the system also comprises a training module, wherein the training module is connected with the monitoring module;
the training module is used for acquiring historical carbon storage monitoring data, performing data filling and data integration on the historical carbon storage monitoring data to obtain data after the data are filled and integrated, training and verifying the deep learning model through ten-fold cross validation based on the data after the data are filled and integrated to obtain a trained deep learning model, and predicting the carbon storage monitoring data through the trained deep learning model.
6. A carbon sink monitoring method based on satellite remote sensing is characterized by comprising the following steps:
acquiring remote sensing data, selecting the remote sensing data, identifying the extracted remote sensing data, and acquiring carbon storage monitoring data;
and predicting the carbon storage monitoring data through a deep learning model to obtain the carbon storage amount, and calculating to obtain the carbon sink amount based on the carbon storage amount.
7. The method for monitoring the carbon sink based on the satellite remote sensing according to claim 6, wherein:
the process of acquiring the remote sensing data comprises the following steps: acquiring initial remote sensing data, wherein the initial remote sensing data is a multispectral remote sensing image; and preprocessing the multispectral remote sensing image to obtain remote sensing data, wherein the preprocessing comprises image addition and correction.
8. The method for monitoring the carbon sink based on the satellite remote sensing according to claim 6, wherein:
the carbon storage monitoring data acquisition process comprises the following steps: the remote sensing data are selected according to different wave bands to obtain remote sensing data under different wave bands, the remote sensing data under different wave bands comprise remote sensing data under red wave bands, green wave bands and infrared wave bands, the remote sensing data under different wave bands are analyzed and calculated respectively to obtain carbon storage monitoring data, and the carbon storage monitoring data comprise vegetation coverage area, vegetation type, vegetation condition and earth surface characteristics.
9. The satellite remote sensing-based carbon sink monitoring method according to claim 6, wherein:
the carbon sink amount obtaining process comprises the following steps: predicting the carbon storage monitoring data through a deep learning model, wherein the deep learning module adopts a convolutional neural network model; and calculating the difference of the carbon reserves based on the conversion coefficient to obtain the carbon sink amount.
10. The method for monitoring the carbon sink based on the satellite remote sensing according to claim 6, wherein:
before the carbon storage monitoring data is predicted through the deep learning model, the method further comprises the following steps: the method comprises the steps of obtaining historical carbon storage monitoring data, conducting data filling and data integration on the historical carbon storage monitoring data to obtain data after filling integration, training and verifying a deep learning model through ten-fold cross verification based on the data after filling integration to obtain a trained deep learning model, and predicting the carbon storage monitoring data through the trained deep learning model.
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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115235560A (en) * 2022-09-19 2022-10-25 北京英视睿达科技股份有限公司 Method, device, equipment and storage medium for evaluating carbon sink capacity of ecosystem
CN116563716A (en) * 2023-07-07 2023-08-08 吉林省林业科学研究院(吉林省林业生物防治中心站) GIS data processing system for forest carbon sink data acquisition
CN117153291A (en) * 2023-10-31 2023-12-01 水利部交通运输部国家能源局南京水利科学研究院 Method and system for calculating carbon exchange value of paddy field in irrigation area

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140156549A1 (en) * 2012-11-19 2014-06-05 Syracuse University Method for estimating carbon sequestration in grasslands
CN104361338A (en) * 2014-10-17 2015-02-18 中国科学院东北地理与农业生态研究所 Peat bog information extracting method based on ENVISAT ASAR, Landsat TM and DEM data
CN109344550A (en) * 2018-11-26 2019-02-15 国智恒北斗科技集团股份有限公司 A kind of forest reserves inversion method and system based on domestic high score satellite remote sensing date
CN110750904A (en) * 2019-10-22 2020-02-04 南京信大气象科学技术研究院有限公司 Regional carbon reserve space pattern monitoring system and method based on remote sensing data
CN112836610A (en) * 2021-01-26 2021-05-25 平衡机器科技(深圳)有限公司 Land use change and carbon reserve quantitative estimation method based on remote sensing data
CN112884088A (en) * 2021-04-08 2021-06-01 兰州交通大学 Forest carbon reserve calculation method based on neural network model

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140156549A1 (en) * 2012-11-19 2014-06-05 Syracuse University Method for estimating carbon sequestration in grasslands
CN104361338A (en) * 2014-10-17 2015-02-18 中国科学院东北地理与农业生态研究所 Peat bog information extracting method based on ENVISAT ASAR, Landsat TM and DEM data
CN109344550A (en) * 2018-11-26 2019-02-15 国智恒北斗科技集团股份有限公司 A kind of forest reserves inversion method and system based on domestic high score satellite remote sensing date
CN110750904A (en) * 2019-10-22 2020-02-04 南京信大气象科学技术研究院有限公司 Regional carbon reserve space pattern monitoring system and method based on remote sensing data
CN112836610A (en) * 2021-01-26 2021-05-25 平衡机器科技(深圳)有限公司 Land use change and carbon reserve quantitative estimation method based on remote sensing data
CN112884088A (en) * 2021-04-08 2021-06-01 兰州交通大学 Forest carbon reserve calculation method based on neural network model

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
MOSTAFA EMADI ET AL.: "Predicting and mapping of soil organic carbon using machine learning algorithm in northern iran", 《REMOTE SENSING 2020》 *
巫明焱等: "川西米亚罗自然保护区森林地上碳储量遥感估算", 《生态学报》 *

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115235560A (en) * 2022-09-19 2022-10-25 北京英视睿达科技股份有限公司 Method, device, equipment and storage medium for evaluating carbon sink capacity of ecosystem
CN116563716A (en) * 2023-07-07 2023-08-08 吉林省林业科学研究院(吉林省林业生物防治中心站) GIS data processing system for forest carbon sink data acquisition
CN116563716B (en) * 2023-07-07 2023-09-08 吉林省林业科学研究院(吉林省林业生物防治中心站) GIS data processing system for forest carbon sink data acquisition
CN117153291A (en) * 2023-10-31 2023-12-01 水利部交通运输部国家能源局南京水利科学研究院 Method and system for calculating carbon exchange value of paddy field in irrigation area
CN117153291B (en) * 2023-10-31 2024-01-02 水利部交通运输部国家能源局南京水利科学研究院 Method and system for calculating carbon exchange value of paddy field in irrigation area

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