CN115829118A - Forest carbon sink remote sensing monitoring method, device, equipment and storage medium - Google Patents
Forest carbon sink remote sensing monitoring method, device, equipment and storage medium Download PDFInfo
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Abstract
The invention relates to the technical field of remote sensing, and discloses a forest carbon sink remote sensing monitoring method, a device, equipment and a storage medium, wherein the method comprises the following steps: extracting a plurality of forest region images from the shot original remote sensing images of the target forest region; acquiring climate characteristics of a target forest area through a historical weather library of the target forest area and extracting characteristic factors; performing radiation correction on the multiple forest region images according to the characteristic factors, and integrating the corrected multiple forest region images to obtain a target forest region image; and inputting the target forest area image into a preset carbon sink model to obtain a carbon sink amount prediction result. According to the invention, only the remote sensing data are required to be acquired and the historical database data are required to be inquired, so that the intelligent degree is improved, meanwhile, the carbon sink amount is predicted through the preset model, the forest carbon sink is accurately monitored without using fixed calculation methods such as a theoretical formula and the like, and the monitoring efficiency is improved.
Description
Technical Field
The invention relates to the field of remote sensing image processing, in particular to a forest carbon sink remote sensing monitoring method, device, equipment and storage medium.
Background
Carbon sequestration refers to the process, activity or mechanism of reducing greenhouse gases in atmospheric concentrations by absorbing carbon dioxide in the atmosphere through measures such as afforestation, vegetation restoration and the like. The forest is an important economic asset and an environmental asset, can generate economic value as a forestry product, can fix carbon dioxide as a living body, and has important significance for realizing the aim of carbon neutralization by the development of forest carbon sink. With the development of forestry technology, the need for vegetation identification is increasing. However, at present, the calculation of the carbon sink amount of the forest region in a large range needs to monitor the environment and plant related data in the region on site, so that a large amount of time and manpower are consumed, and the intelligent requirement cannot be met.
The above-mentioned contents are only for assisting understanding of the technical solution of the present invention, and do not represent an admission that the above-mentioned contents are related art.
Disclosure of Invention
The invention mainly aims to provide a remote sensing monitoring method, a remote sensing monitoring device, remote sensing monitoring equipment and a storage medium for forest carbon sink, and aims to solve the technical problems that in the prior art, the calculation and monitoring of forest carbon sink amount are time-consuming and labor-consuming, and are not intelligent enough.
In order to achieve the aim, the invention provides a remote sensing monitoring method for forest carbon sink, which comprises the following steps:
extracting a plurality of forest region images from the shot original remote sensing images of the target forest region;
acquiring the climate characteristics of the target forest region through a historical weather library of the target forest region and extracting characteristic factors;
performing radiation correction on the multiple forest region images according to the characteristic factors, and integrating the corrected multiple forest region images to obtain a target forest region image;
and inputting the target forest area image into a preset carbon sink model to obtain a carbon sink amount prediction result.
Optionally, the obtaining the climate characteristics of the target forest region and extracting characteristic factors through the historical weather library of the target forest region includes:
inquiring a regional historical weather database containing the target forest region to obtain the weather characteristics of the target forest region;
and carrying out data standardization processing on the acquired climate characteristics to obtain standardized characteristic factors.
Optionally, the performing radiation correction on the plurality of forest region images according to the characteristic factors, and integrating the corrected plurality of images to obtain a target forest region image includes:
performing atmospheric correction on the plurality of forest region images according to the normalized characteristic factors;
performing radiation correction on the atmosphere corrected image to obtain a plurality of corrected images;
and integrating the plurality of images by adopting an image fusion technology to obtain a target forest region image.
Optionally, the integrating the plurality of images by using an image fusion technology to obtain a target forest region image includes:
extracting the target forest boundary as a regional contour line characteristic on the corrected images;
adopting a matching algorithm to take the corresponding regional contour line characteristics on the plurality of images as control points;
and resampling the plurality of images based on the control point to obtain a target forest area image.
Optionally, the resampling the multiple images based on the control point to obtain a target forest region image includes:
selecting the image with the most regional contour line feature extraction elements in the plurality of images as a reference image;
and resampling the reference image based on the control point to obtain a target forest area image.
Optionally, the inputting the target forest area image into a preset carbon sink model to obtain a carbon sink amount prediction result includes:
selecting a quantitative remote sensing inversion model as a preset carbon sink model according to the geographic position of the target forest region;
and taking the target forest region image as an input variable, and estimating the carbon accumulation of the target forest region by using the preset carbon sink model to obtain a carbon sink prediction result.
Optionally, the extracting a plurality of forest region images from the captured original remote sensing image of the target forest region includes:
acquiring a plurality of original remote sensing images of a target forest region shot at a preset time point based on a preset time interval;
wherein the preset time points are one or more.
In addition, in order to achieve the above object, the present invention further provides a forest carbon sink remote sensing monitoring device, including:
the image extraction module is used for extracting a plurality of forest region images from the shot original remote sensing images of the target forest region;
the characteristic acquisition module is used for acquiring the climate characteristics of the target forest area through the historical weather library of the target forest area and extracting characteristic factors;
the image integration module is used for carrying out radiation correction on the plurality of forest region images according to the characteristic factors and integrating the corrected plurality of forest region images to obtain target forest region images;
and the model prediction module is used for inputting the target forest area image into a preset carbon sink model to obtain a carbon sink amount prediction result.
In addition, in order to achieve the above object, the present invention further provides a forest carbon sink remote sensing monitoring device, including: the remote sensing monitoring method comprises a memory, a processor and a remote sensing monitoring program for forest carbon sink, wherein the remote sensing monitoring program for forest carbon sink is stored in the memory and can run on the processor, and the remote sensing monitoring program for forest carbon sink is configured to realize the steps of the remote sensing monitoring method for forest carbon sink.
In addition, in order to achieve the above object, the present invention further provides a storage medium, where the storage medium stores a remote forest carbon sink monitoring program, and when the remote forest carbon sink monitoring program is executed by a processor, the steps of the remote forest carbon sink monitoring method as described above are implemented.
According to the method, a plurality of forest region images are extracted from a shot original remote sensing image of a target forest region, then the climate characteristics of the target forest region are obtained through a historical weather library of the target forest region, characteristic factors are extracted, then radiation correction is carried out on the plurality of forest region images according to the characteristic factors, the corrected plurality of forest region images are integrated to obtain the target forest region image, and finally the target forest region image is input into a preset carbon sequestration model to obtain a carbon sequestration prediction result. According to the invention, the forest region image is extracted from the original remote sensing image, the characteristic factor of the current region is obtained based on the historical database, the extracted image is processed and then input into the carbon sink model, only the remote sensing data is required to be obtained and the historical database data is required to be inquired, the intelligent degree is improved, meanwhile, the carbon sink amount is predicted through the preset model, the forest carbon sink is accurately monitored without using fixed calculation methods such as a theoretical formula and the like, and the monitoring efficiency is improved.
Drawings
FIG. 1 is a schematic structural diagram of a forest carbon sink remote sensing monitoring device of a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a forest carbon sink remote sensing monitoring method according to a first embodiment of the present invention;
FIG. 3 is a schematic flow chart of a forest carbon sink remote sensing monitoring method according to a second embodiment of the present invention;
FIG. 4 is a schematic flow chart of a forest carbon sink remote sensing monitoring method according to a third embodiment of the present invention;
fig. 5 is a structural block diagram of a forest carbon sink remote sensing monitoring device according to a first embodiment of the invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, fig. 1 is a schematic structural diagram of a forest carbon sink remote sensing monitoring device in a hardware operating environment according to an embodiment of the present invention.
As shown in fig. 1, the remote forest carbon sink monitoring device may include: a processor 1001, such as a Central Processing Unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to implement connection communication among these components. The user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a Wireless interface (e.g., a Wireless-Fidelity (Wi-Fi) interface). The Memory 1005 may be a Random Access Memory (RAM) or a Non-Volatile Memory (NVM), such as a disk Memory. The memory 1005 may alternatively be a storage device separate from the processor 1001.
Those skilled in the art will appreciate that the configuration shown in fig. 1 does not constitute a limitation of the remote forest carbon sink monitoring apparatus and may include more or fewer components than those shown, or some components in combination, or a different arrangement of components.
As shown in fig. 1, a memory 1005 as a storage medium may include an operating system, a network communication module, a user interface module, and a forest remote sensing monitoring program.
In the forest carbon sink remote sensing monitoring device shown in fig. 1, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 of the forest carbon sink remote sensing monitoring device can be arranged in the forest carbon sink remote sensing monitoring device, the forest carbon sink remote sensing monitoring device calls a forest carbon sink remote sensing monitoring program stored in the memory 1005 through the processor 1001, and the forest carbon sink remote sensing monitoring method provided by the embodiment of the invention is executed.
The embodiment of the invention provides a forest carbon sink remote sensing monitoring method, and referring to fig. 2, fig. 2 is a schematic flow diagram of a first embodiment of the forest carbon sink remote sensing monitoring method.
In this embodiment, the forest carbon sink remote sensing monitoring method includes the following steps:
step S10: and extracting a plurality of forest region images from the shot original remote sensing images of the target forest region.
It should be noted that the execution main body of the method of this embodiment may be a computing service device with data processing, network communication, and program running functions, such as a mobile phone, a tablet computer, a personal computer, and the like, or may be other electronic devices capable of implementing the same or similar functions, which is not limited in this embodiment. Various embodiments of the forest carbon sink remote sensing monitoring method of the present invention are described herein by taking a forest carbon sink remote sensing monitoring device (hereinafter referred to as a device) as an example.
It can be understood that the target forest area may be a whole forest area to be obtained with the carbon sink amount and a certain range of area nearby the whole forest area, or may be a partial area of the whole forest selected based on actual monitoring requirements, which is not limited in this embodiment.
It should be noted that the original remote sensing image taken can be obtained from different remote sensing data sources in the network, and the remote sensing data can be divided into optical remote sensing data, microwave radar data and laser radar data according to the difference of the remote sensing technology. The optical remote sensing technology is a spectral passive remote sensing technology, can remotely sense the reflection spectral characteristics of vegetation, and monitors the condition and dynamic change of the vegetation by measuring and calculating the relation curve of the reflection spectral characteristics and the types, chlorophyll content and growth condition of the vegetation; the microwave radar data is from active scanning of a microwave satellite, microwaves can penetrate through vegetation canopies to further observe branches or trunks (i.e. main bodies of living beings), and the current main microwave radar data is from SAR (Synthetic aperture radar), POLSAR (polar Synthetic aperture radar) and the like; the laser radar data is from the active scanning of the laser radar, and the laser signals can pass through the forest canopy and reach the ground directly, so that the three-dimensional structure information of the earth surface can be acquired accurately, and the data can be acquired through airborne or satellite-borne LIDAR (laser radar). The embodiment does not limit the selection of the remote sensing data source. The embodiments of the forest carbon sink remote sensing monitoring method are explained by taking the selection of the optical remote sensing data source as an example.
It should be understood that the plurality of forest area images may be images obtained by collecting a preset number of times in the same size as the original remote sensing image in the remote sensing data source according to a preset size, where the preset number of times may be set as, for example: 3. 5, 7, or matching values from preset gradient values based on the actual size of the forest region, where the matching value may be the resolution of the original remote sensing image, which is not limited in this embodiment.
Step S20: and acquiring the climate characteristics of the target forest region through the historical weather library of the target forest region and extracting characteristic factors.
It should be noted that the historical weather database of the target forest region may be historical weather data of the region collected through a network, and may be specific to a city, a region, and the like where the target region is located, the climate feature data is standardized, and a feature factor is extracted, where the feature factor may be obtained by classifying the occurrence times of different weather phenomena classified according to names in the queried historical weather database based on the names of the different weather phenomena.
In the specific implementation, the equipment collects historical weather data of an area containing the target forest through a network, acquires the climate characteristics of the area, and performs data standardization processing on the acquired climate characteristics to obtain the occurrence frequency of different weather phenomena classified according to names as standardized characteristic factors.
Step S30: and performing radiation correction on the multiple forest area images according to the characteristic factors, and integrating the corrected multiple forest area images to obtain a target forest area image.
It will be appreciated that radiation correction is the process of correcting for systematic, random radiation distortions or distortions due to external factors, data acquisition and transmission systems, and eliminating or correcting for image distortions due to radiation errors.
It should be understood that a radiation correction model based on a target forest region may be preset, a characteristic factor is introduced to perform personalized optimization on the model, the plurality of forest region images are input into the model to obtain a plurality of corrected images, or radiation correction may be performed on each of the plurality of images based on different selected characteristic factors to obtain a plurality of images subjected to different correction processes.
It should be noted that the image stitching technology can be used for integrating a plurality of images, and the image stitching is a technical process of stitching two or more digital images together to form an integral image.
In a specific implementation, the device may perform personalized radiation correction on the multiple forest region images according to the feature factors, and obtain the target forest region image after splicing the multiple corrected forest region images.
Furthermore, in practical application, a plurality of remote sensing images for image splicing may have inconsistent color tones, and a plurality of images participating in splicing can be subjected to color homogenizing treatment in advance through image color homogenizing combined with actual conditions and overall harmony.
Step S40: and inputting the target forest area image into a preset carbon sink model to obtain a carbon sink amount prediction result.
It should be noted that the preset carbon sink model may be a time series analysis model optimized by a characteristic factor, such as a damping trend model, a simple smoothing model, and a temperature multiplication model, which is not limited in this embodiment.
In the specific implementation, the device constructs a data analysis model in advance, introduces characteristic factors into the model as parameters to optimize the model, obtains a preset carbon sink model which can be used for a current target forest region, inputs a target forest region image obtained through integration into the preset carbon sink model, and obtains a carbon sink prediction data result of the target forest region.
According to the method, a plurality of forest region images are extracted from a shot original remote sensing image of a target forest region, then, the climate characteristics of the target forest region are obtained through a historical weather library of the target forest region, characteristic factors are extracted, then, radiation correction is carried out on the plurality of forest region images according to the characteristic factors, the corrected plurality of forest region images are integrated, the target forest region image is obtained, and finally, the target forest region image is input into a preset carbon sequestration model, and a carbon sequestration prediction result is obtained. According to the method, the forest area image is extracted from the original remote sensing image, the characteristic factor of the current area is obtained based on the historical database, the extracted image is processed and then input into the carbon sink model, the remote sensing data only needs to be obtained and the historical database data is inquired, the intelligent degree is improved, meanwhile, the carbon sink amount is predicted through the preset model, accurate monitoring of forest carbon sink is achieved without using fixed calculation methods such as theoretical formulas, and the monitoring efficiency is improved.
Referring to fig. 3, fig. 3 is a schematic flow chart of a forest carbon sink remote sensing monitoring method according to a second embodiment of the present invention.
Based on the first embodiment, in this embodiment, in order to correct and integrate the plurality of forest region images to obtain a target forest region image into which a preset carbon sink model can be input, the step S30 may include:
step S31: and performing atmospheric correction on the plurality of forest region images according to the normalized characteristic factors.
It should be noted that the atmospheric correction is a process of inverting the real surface reflectivity of the ground object by eliminating the radiation error caused by the atmospheric influence, and the atmospheric correction is a process of reflecting the real surface reflectivity of the ground object, wherein the total radiation brightness of the ground object finally measured by the sensor is not a reflection of the real surface reflectivity of the ground object, and the total radiation brightness includes radiation amount errors caused by atmospheric absorption, especially scattering.
Before atmospheric correction, the irregular noise, the stripe loop and the bright spot on the extracted multiple forest region images in the original remote sensing images can be removed by using image processing methods such as Fourier transform, gaussian filter and the like.
In a specific implementation, the time variation characteristics of the atmosphere affect the remote sensing images acquired at different times, and the atmospheric correction method needs the atmospheric data with definite space and time as input, so that the atmospheric data characteristic factors in the standardized characteristic factors are introduced to perform atmospheric correction on a plurality of forest images.
Step S32: and carrying out radiation correction on the atmosphere corrected image to obtain a plurality of corrected images.
It should be appreciated that since the radiometric calibration method requires minimal differences in radiation between the images due to the atmosphere, the remote sensing image is corrected for the atmosphere before the radiometric calibration is performed.
Step S33: and integrating the plurality of images by adopting an image fusion technology to obtain a target forest region image.
The image fusion refers to that image data about the same target collected by a multi-source channel is processed by an image processing and computer technology, etc., so that the final beneficial information of each channel is extracted to the maximum extent, and finally, the image data is synthesized into a high-quality image. The data form of image fusion is a picture containing light and shade, color, temperature, distance and other scene characteristics, and the pictures can be given in a form of one or a column.
Further, step S33 includes:
step S331: and extracting the target forest boundary as a regional contour line characteristic on the corrected images.
Step S332: and adopting a matching algorithm to take the corresponding regional contour line characteristics on the plurality of images as control points.
It should be noted that the matching algorithm may be a feature-based algorithm and a gray-scale-based algorithm that employ an image registration technique, and the feature-based matching algorithm extracts a significant structure in the image for registration.
It can be understood that the target forest boundaries extracted from a plurality of images can be different, and the number and distribution of control points selected by the regional contour line characteristics can also be different.
In the specific implementation, areas with definite boundaries such as color value change points and line intersection points are selected as contour line features of the area of the target forest boundary machine, the number of control points is determined based on the actual size of a plurality of images and the complexity of the images, and then the control points are obtained.
Step S333: and resampling the plurality of images based on the control point to obtain a target forest area image.
In the specific implementation, the image with the most region contour line feature extraction elements in the multiple images is selected as a reference image, namely, the image with the most distributed and concentrated control points is selected as a sampling reference image, and the reference image is resampled according to the control points of the images except the reference image in the multiple images, so that a single target forest region image to be input into the prediction model is obtained.
In the embodiment, after a plurality of forest region images are obtained from an original remote sensing image, characteristic factors are extracted from an existing historical database to perform personalized preprocessing correction on the images, compared with the prior art that weather related influence data of a monitored area needs to be monitored on site, the labor cost is reduced, in addition, in consideration of errors and accidental conditions possibly existing in data analysis based on a single image, the corrected plurality of images are integrated to obtain a single target forest region image with a plurality of image characteristics, and then the image is input into a preset carbon sink model to obtain a carbon sink amount result, so that the accuracy of monitoring data is improved.
Referring to fig. 4, fig. 4 is a schematic flow chart of a forest carbon sink remote sensing monitoring method according to a third embodiment of the present invention.
Based on the foregoing embodiments, in this embodiment, in order to obtain the carbon sequestration amount prediction result, the step S40 may include:
step S41: and selecting a quantitative remote sensing inversion model as a preset carbon sink model according to the geographic position of the target forest region.
It should be noted that the remote sensing inversion is a technology for reversely deducing the electromagnetic wave condition in the formation process according to the remote sensing image characteristics generated by the electromagnetic wave characteristics of the surface features, and the quantitative remote sensing inversion model may be, for example: a correlation regression model established based on a CASA model (a classic model for estimating net primary productivity of vegetation in a land ecosystem) optimized by normalized vegetation parameters, specific vegetation parameters, differential vegetation parameters and soil adjustment vegetation indexes, which is not limited in this embodiment.
In specific implementation, according to the actual geographic position of the current target forest region in the global and regional scale-based carbon cycle research architecture, a quantitative remote sensing inversion model suitable for the current ecological system is selected as a preset carbon sequestration model for carbon sequestration prediction of the target forest region.
Step S42: and taking the target forest region image as an input variable, and estimating the carbon accumulation of the target forest region by using the preset carbon sink model to obtain a carbon sink prediction result.
It should be noted that the carbon sequestration prediction result is a preliminary estimation of the carbon accumulation of the target forest region image in a specific time period, the input time points of the target forest region image are different, and the results of the carbon sequestration prediction by the preset carbon sequestration model may be different.
Thus, step S10 may comprise:
step S11: and acquiring a plurality of original remote sensing images of the target forest region shot at a preset time point based on a preset time interval.
It can be understood that, in addition to the influence of cloud coverage, the date of image selection is also important for the selection of the original remote sensing image, and the determination of the preset time point can take the date and the specific time when the target area can obtain a better remote sensing image into consideration.
It should be noted that the preset time interval may be an interval of days, or an interval of hours based on the same day, which is not limited in this embodiment.
Step S12: wherein the preset time points are one or more.
According to the method, a plurality of forest region images are obtained from original remote sensing images at preset time points based on preset time intervals, after the images are corrected and integrated by using characteristic factors, an existing quantitative remote sensing inversion model suitable for a current ecological system is input to obtain a carbon sequestration prediction result of the current region, personalized characteristics of the current forest region are considered, accurate prediction is carried out by combining the existing model, the labor cost is further reduced, and meanwhile the monitoring efficiency is improved.
Referring to fig. 5, fig. 5 is a structural block diagram of a forest carbon sink remote sensing monitoring device according to a first embodiment of the present invention.
As shown in fig. 5, the forest carbon sink remote sensing monitoring device provided by the embodiment of the present invention includes:
an image extraction module 501, configured to extract a plurality of forest region images from a captured original remote sensing image of a target forest region;
a characteristic obtaining module 502, configured to obtain climate characteristics of the target forest region through a historical weather base of the target forest region and extract a characteristic factor;
an image integration module 503, configured to perform radiation correction on the multiple forest region images according to the feature factors, and integrate the multiple corrected images to obtain a target forest region image;
and the model prediction module 504 is configured to input the target forest region image into a preset carbon sink model to obtain a carbon sink amount prediction result.
According to the method, a plurality of forest region images are extracted from a shot original remote sensing image of a target forest region, then, the climate characteristics of the target forest region are obtained through a historical weather library of the target forest region, characteristic factors are extracted, then, radiation correction is carried out on the plurality of forest region images according to the characteristic factors, the corrected plurality of forest region images are integrated, the target forest region image is obtained, and finally, the target forest region image is input into a preset carbon sequestration model, and a carbon sequestration prediction result is obtained. According to the method, the forest area image is extracted from the original remote sensing image, the characteristic factor of the current area is obtained based on the historical database, the extracted image is processed and then input into the carbon sink model, the remote sensing data only needs to be obtained and the historical database data is inquired, the intelligent degree is improved, meanwhile, the carbon sink amount is predicted through the preset model, accurate monitoring of forest carbon sink is achieved without using fixed calculation methods such as theoretical formulas, and the monitoring efficiency is improved.
Other embodiments or specific implementation manners of the forest carbon sink remote sensing monitoring device can refer to the above method embodiments, and are not described herein again.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of other like elements in a process, method, article, or system comprising the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present invention or portions thereof contributing to the prior art may be embodied in the form of a software product, which is stored in a storage medium (such as a rom/ram, a magnetic disk, and an optical disk) and includes instructions for enabling a terminal device (such as a mobile phone, a computer, a server, or a network device) to execute the methods according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A remote sensing monitoring method for forest carbon sink is characterized by comprising the following steps:
extracting a plurality of forest region images from the shot original remote sensing images of the target forest region;
acquiring the climate characteristics of the target forest region through a historical weather library of the target forest region and extracting characteristic factors;
performing radiation correction on the multiple forest region images according to the characteristic factors, and integrating the corrected multiple forest region images to obtain a target forest region image;
and inputting the target forest area image into a preset carbon sink model to obtain a carbon sink amount prediction result.
2. A remote sensing monitoring method for forest carbon sink according to claim 1, wherein the obtaining of the climate characteristics of the target forest region and the extraction of characteristic factors through the historical weather base of the target forest region comprises:
inquiring a regional historical weather database containing the target forest region to obtain the weather characteristics of the target forest region;
and carrying out data standardization processing on the acquired climate characteristics to obtain standardized characteristic factors.
3. A forest carbon sink remote sensing monitoring method as claimed in claim 2, wherein the performing radiation correction on the plurality of forest region images according to the characteristic factors and integrating the corrected plurality of images to obtain a target forest region image comprises:
performing atmospheric correction on the plurality of forest region images according to the normalized characteristic factors;
performing radiation correction on the atmosphere corrected image to obtain a plurality of corrected images;
and integrating the plurality of images by adopting an image fusion technology to obtain a target forest region image.
4. A forest carbon sink remote sensing monitoring method as claimed in claim 3, wherein said integrating said plurality of images using image fusion techniques to obtain a target forest area image comprises:
extracting the target forest boundary as a regional contour line characteristic on the corrected images;
adopting a matching algorithm to take the corresponding regional contour line characteristics on the plurality of images as control points;
and resampling the plurality of images based on the control point to obtain a target forest area image.
5. A forest carbon sink remote sensing monitoring method as claimed in claim 4, wherein resampling the plurality of images based on the control points to obtain a target forest area image comprises:
selecting the image with the most regional contour line feature extraction elements in the plurality of images as a reference image;
and resampling the reference image based on the control point to obtain a target forest area image.
6. A remote sensing monitoring method for forest carbon sequestration as claimed in any one of claims 1-5, wherein said inputting said target forest area image into a preset carbon sequestration model to obtain a carbon sequestration prediction result comprises:
selecting a quantitative remote sensing inversion model as a preset carbon sink model according to the geographic position of the target forest region;
and taking the target forest region image as an input variable, and estimating the carbon accumulation of the target forest region by using the preset carbon sink model to obtain a carbon sink prediction result.
7. A forest carbon sink remote sensing monitoring method as claimed in claim 1, wherein said extracting a plurality of forest area images from a captured raw remote sensing image of a target forest area comprises:
acquiring a plurality of original remote sensing images of a target forest region shot at a preset time point based on a preset time interval;
wherein the preset time points are one or more.
8. The utility model provides a forest carbon sink remote sensing monitoring devices which characterized in that, forest carbon sink remote sensing monitoring devices includes:
the image extraction module is used for extracting a plurality of forest region images from the shot original remote sensing images of the target forest region;
the characteristic acquisition module is used for acquiring the climate characteristics of the target forest area through the historical weather library of the target forest area and extracting characteristic factors;
the image integration module is used for carrying out radiation correction on the plurality of forest region images according to the characteristic factors and integrating the corrected plurality of images to obtain target forest region images;
and the model prediction module is used for inputting the target forest area image into a preset carbon sink model to obtain a carbon sink amount prediction result.
9. A remote forest carbon sink monitoring device, characterized in that the device comprises a memory, a processor and a remote forest carbon sink monitoring program stored on the memory and operable on the processor, the remote forest carbon sink monitoring program being configured to carry out the steps of the remote forest carbon sink monitoring method according to any one of claims 1 to 7.
10. A storage medium, characterized in that the storage medium has stored thereon a remote forest carbon sink monitoring program, which when executed by a processor performs the steps of the remote forest carbon sink monitoring method according to any one of claims 1 to 7.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116567187A (en) * | 2023-07-10 | 2023-08-08 | 江苏省地质调查研究院 | Method and device for transmitting and displaying remote sensing image in real time |
| 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 |
| CN117237780A (en) * | 2023-11-15 | 2023-12-15 | 人工智能与数字经济广东省实验室(深圳) | Multidimensional data feature graph construction method, multidimensional data feature graph construction system, intelligent terminal and medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| 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 |
| CN116567187A (en) * | 2023-07-10 | 2023-08-08 | 江苏省地质调查研究院 | Method and device for transmitting and displaying remote sensing image in real time |
| CN116567187B (en) * | 2023-07-10 | 2023-09-26 | 江苏省地质调查研究院 | Method and device for transmitting and displaying remote sensing image in real time |
| CN117237780A (en) * | 2023-11-15 | 2023-12-15 | 人工智能与数字经济广东省实验室(深圳) | Multidimensional data feature graph construction method, multidimensional data feature graph construction system, intelligent terminal and medium |
| CN117237780B (en) * | 2023-11-15 | 2024-03-15 | 人工智能与数字经济广东省实验室(深圳) | Multidimensional data feature graph construction method, multidimensional data feature graph construction system, intelligent terminal and medium |
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