CN115908540A - Forest carbon sink amount detection method based on tree growth profile - Google Patents
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Abstract
The invention discloses a forest carbon sink amount detection method based on a tree growth contour, which comprises the following steps of establishing a knowledge base for calculating the single-tree carbon sink rate based on the tree growth contour: establishing growth models corresponding to various tree forms; establishing a template coding tree of the tree shape and a calculation formula of the single-tree carbon sink rate according to the respective corresponding growth forms to form an illumination carbon sink model of the single tree; acquiring a remote sensing image and segmenting out the singletree; calculating the code tree of the single tree, comparing the code tree with the template code tree in the knowledge base to obtain the type of the single tree, and acquiring an illumination carbon sink model of the type; obtaining the estimated quantity of the single-wood carbon sink through an illumination carbon sink model by utilizing the image characteristics of the single wood and combining environmental factors; and calculating the sum of the carbon sequestration estimated quantity of each single wood to obtain the estimated carbon sequestration estimated quantity of the whole forest remote sensing image. The invention is used for solving the dilemma that the step of estimating the carbon sink amount is complicated and the time cost is large; and the detection fineness can be optimized, and the accuracy of carbon sink amount estimation is improved.
Description
Technical Field
The invention belongs to the technical field of forest detection, and particularly relates to a forest carbon sink amount detection method based on a tree growth profile.
Background
The carbon dioxide content in the atmosphere has always been the focus of attention in the field of environmental protection. The practical problems of climate warming, sea level rising and the like are followed up due to the fact that the concentration of carbon dioxide is increased, and great influence is generated on social economy and sustainable development. Forests, an important role of land-based carbon storage, play an irreplaceable role in controlling global warming. Studies have shown that each hectare of forest can absorb 1000 kg of carbon dioxide per day and release 735 kg of oxygen per hectare of forest. Based on this estimate, global forests absorb approximately 30% of fossil fuel emissions per year. Therefore, in recent years, more and more experts and scholars begin to pay attention to the research on the problems related to the plant photosynthetic rate and the like, and aim to estimate the forest carbon sequestration capacity more accurately.
Because of the vast forest area, it is time consuming and impractical to obtain very accurate carbon sink rate values. Therefore, it is of very profound practical significance to accurately and quickly estimate the carbon uptake rate of a piece of forest. On one hand, the carbon sink rate of the forest is accurately estimated, so that the carbon dioxide absorption capacity of the forest in one year can be estimated, and decision support is provided for environmental management and ecological protection. On the other hand, the forest carbon sink rate can be estimated more quickly to reflect the dynamic state of the forest, and inaccuracy of data caused by delay of statistics is avoided.
The existing forest carbon sink amount calculation methods are various, and the mainstream methods comprise a model simulation method and a remote sensing method.
For the model simulation method, firstly, the method needs a large amount of regional survey data, which not only requires the reliability of data, but also limits the popularization range of the model. In addition, the single-tree biomass equation does not strictly distinguish tree types, and the difference is only in the selection of model parameters.
For the remote sensing method, the ground information is still relied on, a professional is required to actually measure a plurality of selected sample sites, and the measurement result is required to be as accurate as possible. In addition, when a forest with multiple trees and a complex mixed mode exists, the accuracy of the inversion method can be greatly reduced, and the complex forest is the real landform situation in the ecological system.
In addition, neither of the above-mentioned methods takes into account factors strongly related to photosynthesis, such as weather, and only one estimation method is given in units of years, and the results are clearly not fine enough. Therefore, how to quickly and accurately consider a plurality of influencing factors influencing photosynthesis becomes a big difficulty in estimating the carbon sink amount.
Disclosure of Invention
In order to solve the problems, the invention provides a forest carbon sink amount detection method based on a tree growth contour, which is based on remote sensing image data and knowledge base data, obviously reduces the calculation complexity and is used for solving the dilemma that the existing method has complicated steps and large time overhead for estimating the carbon sink amount; and the detection fineness can be optimized, and the accuracy of carbon sink amount estimation is improved.
In order to achieve the purpose, the invention adopts the technical scheme that: a forest carbon sink amount detection method based on a tree growth contour comprises the following steps:
s10, establishing a knowledge base for calculating the single-wood carbon sink rate based on the tree growth contour: establishing growth models corresponding to various tree forms; then according to the respective corresponding growth forms, establishing a template coding tree of the tree form and a calculation formula of the single-tree carbon sink rate to form an illumination carbon sink model of the single tree;
s20, acquiring a remote sensing image and segmenting the veneer; then calculating the code tree of the single tree, comparing the code tree with the template code tree in a knowledge base to obtain the type of the single tree, and acquiring an illumination carbon sink model of the type; finally, acquiring the estimated quantity of the single-wood carbon sink through an illumination carbon sink model by utilizing the image characteristics of the single wood in combination with environmental factors;
and S30, calculating the sum of the carbon sequestration estimated quantity of each single tree to obtain the estimated carbon sequestration estimated quantity of the whole forest remote sensing image.
Furthermore, after the growth models are respectively established according to the growth forms of each tree shape, template coding trees of the tree shapes and a calculation formula of the carbon sink rate of the single wood are established to form the illumination carbon sink model of the single wood.
Further, in the illumination carbon sink model of the single tree, estimating each layer of leaves of the tree, and iteratively calculating the average illumination intensity of each layer; and combining the quantitative relation between the carbon sink rate and the illumination intensity as well as the plant species in the knowledge base to obtain a specific numerical value of the carbon sink rate.
Further, the crown carbon sink rate is calculated as:
wherein n represents the number of leaf layers of a single tree, ph (·) represents the functional relationship between the illumination intensity and the photosynthetic rate, S i Denotes the area of the ith leaf, L i Represents the average light intensity of the ith layer, L represents the light intensity of the current day, gamma represents the leaf absorptivity, L j Represents the average light intensity of the j-th layer, S 1 Indicating the area of the leaf of layer 1.
Further, the cylindrical carbon sequestration rate is calculated as:
wherein n represents the number of leaf layers of a single tree, ph (·) represents the functional relationship between the illumination intensity and the photosynthetic rate, S i Denotes the area of the ith leaf, L i Represents the average light intensity of the ith layer, L represents the light intensity of the current day, gamma represents the leaf absorptivity, L j Represents the average light intensity of the j-th layer, S 1 Indicating the area of the leaf of layer 1.
Further, the cone carbon sequestration rate is calculated as:
wherein n represents the number of leaf layers of a single tree, ph (·) represents the functional relationship between the illumination intensity and the photosynthetic rate, S i Denotes the area of the ith leaf, L i Represents the average light intensity of the ith layer, L represents the light intensity of the current day, gamma represents the leaf absorptivity, L j Represents the average light intensity of the j-th layer, S 1 Indicating the area of the leaf of layer 1.
Further, the calculation formula of the conical lamellar carbon sequestration rate is:
wherein n represents the number of leaf layers of a single tree, ph (·) represents the functional relationship between the illumination intensity and the photosynthetic rate, and S i Denotes the area of the ith leaf, L i Represents the average light intensity of the ith layer, L represents the light intensity of the current day, gamma represents the leaf absorptivity, L j Represents the average light intensity of the j-th layer, S 1 Indicating the area of the leaf of layer 1.
Further, the multi-crown carbon sink rate is the accumulation of a plurality of crowns, and the crown calculation formula is as follows:
wherein n represents the number of leaf layers of a single tree, ph (·) represents the functional relationship between the illumination intensity and the photosynthetic rate, and S i Denotes the area of the ith leaf, L i Represents the average light intensity of the i-th layer, L represents the light intensity of the day, gamma represents the leaf absorption coefficient, and L j Represents the average light intensity of the j-th layer, S 1 Represents the area of the layer 1 leaf;
after a plurality of crowns are calculated by the formula, the carbon sink rates of the plurality of crowns are obtained through accumulation.
Further, separating the images in a single-tree form by using a structural information theory to obtain a plurality of individual single trees;
analyzing the extracted segmentation result of the single wood on the image: firstly, connecting edges of each pixel point to eight adjacent pixel points, and obtaining an optimal coding tree based on a greedy thought of minimized structural entropy after a graph is built; and then comparing the obtained coding trees with those in the knowledge base in sequence, calculating the similarity between the two coding trees through a recursive optimal matching submodule, selecting the coding tree with the maximum similarity in the knowledge base, and taking the corresponding category as the category of the individual.
Further, the carbon sink rate is calculated by using the image characteristics of the single wood and combining with the environmental factors through a calculation formula of the carbon sink rate of the single wood in the illumination carbon sink model, and the estimated carbon sink amount is estimated through integration.
The beneficial effects of the technical scheme are as follows:
the method mainly aims at tree growth model establishment and illumination model carbon sink amount estimation. Firstly, establishing methods of several tree growth models are provided, and then, respectively corresponding illumination model carbon sink amount estimation methods under assumed conditions are provided. According to the invention, the daily carbon sink of a single tree can be calculated only by a few parameters. Which growth model each tree corresponds to depends on its type, and the inference of the type is based on the structural information theory proposed by Li, and the similarity between the coding tree corresponding to the target and the coding tree in the knowledge base is used for classification. The invention has simple operation: after the knowledge base is established, only characteristic calculation needs to be extracted from a given remote sensing image every time, and various forest data do not need to be consulted or extra software is not used; in addition, the technology does not involve a complex calculation process, and the daily carbon sink amount of an area can be estimated in a short time. The invention has wide application range: the remote sensing image reflects the real forest land condition, which is embodied as follows: the mixed mode is complex, the tree species are various, and the inversion method is difficult to process; compared with the traditional research method, the method has better mobility, and can process and estimate the current day carbon sink amount of forests in different areas. The invention has good accuracy: because the invention focuses on estimating the daily carbon sink amount and considers the factors which are not considered by a general model but are quite important to the photosynthesis, including the weather condition, the tree species category, the leaf absorbance and the like, the accuracy of the estimation result of the carbon sink amount is better than that of the traditional model.
Most of the prior art strongly depends on various statistical data, such as relevant parameters of a forest land data fitting model required by a model simulation method, the distribution condition of tree species can be inferred only by counting a plurality of actual measuring points for vegetation, and the like, so that the accuracy of data directly influences the accuracy of a calculation result. Therefore, the method can quickly and accurately acquire the model parameters by establishing the model of the growth form of the real tree and combining the knowledge base data without using a large amount of extra data and software.
The carbon sink amount estimation strategy independent of the geographic position is provided, is used for rapidly estimating the carbon dioxide absorption amount of any area, and is widely applied compared with other schemes. In addition, the remote sensing image data is analyzed by using the structural information theory, so that a certain theoretical basis is provided and the interpretability is provided.
On the basis of the existing research, a plurality of factors which are strongly related to photosynthesis, such as illumination intensity, leaf light absorption rate and the like, are considered. Compared with other estimation models, the result is more accurate, and therefore scientific research personnel can judge the whole forest condition more conveniently.
Drawings
FIG. 1 is a schematic flow chart of a forest carbon sink amount detection method based on a tree growth contour according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described with reference to the accompanying drawings.
In this embodiment, referring to fig. 1, the present invention provides a method for detecting a forest carbon sink amount based on a tree growth profile, including:
s10, establishing a knowledge base for calculating the single-wood carbon sink rate based on the tree growth contour: establishing growth models corresponding to various tree forms; then according to the respective corresponding growth forms, establishing a template coding tree of the tree form and a calculation formula of the single-tree carbon sink rate to form an illumination carbon sink model of the single tree;
s20, acquiring a remote sensing image and segmenting the veneer; then calculating the coding tree of the single tree, comparing the coding tree with template coding trees in a knowledge base to obtain the type of the single tree, and acquiring an illumination carbon sink model of the type; finally, obtaining the estimated amount of the single-wood carbon sink through an illumination carbon sink model by utilizing the image characteristics of the single wood and combining environmental factors;
and S30, calculating the sum of the carbon sequestration estimated quantity of each single tree to obtain the estimated carbon sequestration estimated quantity of the whole forest remote sensing image.
As an optimization scheme of the above embodiment, the plurality of tree forms include a crown shape, a cylindrical shape, a conical layer shape and a multi-crown shape, and after the growth models are respectively established according to the growth forms of each tree form, the template coding tree of the tree form and the calculation formula of the carbon sink rate of the single tree are established to form the illumination carbon sink model of the single tree.
Through observation of the growth form of trees, the outline can be roughly divided into the following five categories:
crown shape: mainly comprises a phoebe naeberry tree, an osmanthus tree, a camphor tree, a ginkgo tree, a phoenix tree, a maple tree and the like.
Cylindrical shape: mainly comprises poplar, white poplar, populus euphratica and the like.
Conical shape: mainly comprises cypress, arborvitae, sabina vulgaris and the like.
Conical layer shape: mainly comprises fir, metasequoia, pine, masson pine, chinese pine, etc.
Multiple crown shapes: mainly comprising willow and the like.
After mathematical models are respectively established according to each growth form, the carbon absorption rate under the irradiation of certain light intensity is analyzed.
Estimating leaves of each layer of the tree in an illumination carbon sink model of a single tree, and iteratively calculating the average illumination intensity of each layer; and combining the quantitative relation between the carbon sink rate and the illumination intensity as well as the plant species in the knowledge base to obtain a specific numerical value of the carbon sink rate.
The calculation of the crown carbon sequestration rate is:
wherein n represents the number of leaf layers of a single tree, ph (·) represents the functional relationship between the illumination intensity and the photosynthetic rate, and S i Denotes the area of the ith leaf, L i Represents the average light intensity of the ith layer, L represents the light intensity of the current day, gamma represents the leaf absorptivity, L j Represents the average light intensity of the j-th layer, S 1 Showing the area of the leaves of layer 1.
The cylindrical carbon sequestration rate was calculated as:
wherein n represents the number of leaf layers of a single tree, and Ph (·) represents the functional relationship between the illumination intensity and the photosynthetic rate，S i Denotes the area of the ith leaf, L i Represents the average light intensity of the i-th layer, L represents the light intensity of the day, gamma represents the leaf absorption coefficient, and L j Represents the average light intensity of the j-th layer, S 1 Indicating the area of the leaf of layer 1.
The cone carbon sink rate is calculated as:
wherein n represents the number of leaf layers of a single tree, ph (·) represents the functional relationship between the illumination intensity and the photosynthetic rate, S i Denotes the area of the ith leaf, L i Represents the average light intensity of the ith layer, L represents the light intensity of the current day, gamma represents the leaf absorptivity, L j Represents the average light intensity of the j-th layer, S 1 Showing the area of the leaves of layer 1.
The calculation of the conical laminar carbon sequestration rate is:
wherein n represents the number of leaf layers of a single tree, ph (·) represents the functional relationship between the illumination intensity and the photosynthetic rate, and S i Denotes the area of the ith leaf, L i Represents the average light intensity of the ith layer, L represents the light intensity of the current day, gamma represents the leaf absorptivity, L j Represents the average light intensity of the j-th layer, S 1 Indicating the area of the leaf of layer 1.
The multi-crown carbon sink rate is the accumulation of a plurality of crowns, and the crown calculation formula is as follows:
wherein n represents the number of leaf layers of a single tree, ph (·) represents the functional relationship between the illumination intensity and the photosynthetic rate, S i Denotes the area of the ith leaf, L i Represents the average light intensity of the i-th layer, L represents the light intensity of the day, gamma represents the leaf absorption coefficient, and L j Represents the average light intensity of the j-th layer, S 1 Represents the area of the layer 1 leaf;
after a plurality of crowns are calculated by the formula, the carbon sink rates of the crowns are obtained through accumulation.
As an optimization scheme of the embodiment, images are separated in a single-tree form by using a structural information theory to obtain a plurality of individual single trees;
and analyzing the extracted segmentation result of the single wood on the image: firstly, connecting edges of each pixel point to eight adjacent pixel points, and obtaining an optimal coding tree based on a greedy thought of minimized structural entropy after a graph is built; and then comparing the obtained coding tree with the coding tree in the knowledge base in sequence, calculating the similarity between the two coding trees through a recursive optimal matching sub-module, selecting the coding tree with the maximum similarity in the knowledge base, and taking the corresponding category as the category of the individual.
And calculating the carbon sink rate by using the image characteristics of the single wood and combining with environmental factors through a calculation formula of the carbon sink rate of the single wood in the illumination carbon sink model, and estimating the estimated carbon sink amount through integration.
The foregoing shows and describes the general principles and broad features of the present invention and advantages thereof. 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 forest carbon sink amount detection method based on a tree growth profile is characterized by comprising the following steps:
s10, establishing a knowledge base for calculating the single-wood carbon sink rate based on the tree growth contour: establishing growth models corresponding to various tree forms; then according to the respective corresponding growth forms, establishing a template coding tree of the tree form and a calculation formula of the single-tree carbon sink rate to form an illumination carbon sink model of the single tree;
s20, acquiring a remote sensing image and segmenting the veneer; then calculating the coding tree of the single tree, comparing the coding tree with template coding trees in a knowledge base to obtain the type of the single tree, and acquiring an illumination carbon sink model of the type; finally, acquiring the estimated quantity of the single-wood carbon sink through an illumination carbon sink model by utilizing the image characteristics of the single wood in combination with environmental factors;
and S30, calculating the sum of the carbon sequestration estimated quantity of each single tree to obtain the estimated carbon sequestration estimated quantity of the whole forest remote sensing image.
2. The method as claimed in claim 1, wherein the plurality of tree forms includes a crown shape, a cylindrical shape, a conical layer shape and a multi-crown shape, and after a growth model is established according to each tree form growth form, a template coding tree of the tree form and a calculation formula of a carbon sink rate of the single tree are established to form an illumination carbon sink model of the single tree.
3. The method as claimed in claim 2, wherein the method comprises estimating the leaves of each layer of the tree in the illumination carbon sink model of a single tree, and iteratively calculating the average illumination intensity of each layer; and combining the quantitative relation between the carbon sink rate and the illumination intensity as well as the plant species in the knowledge base to obtain a specific numerical value of the carbon sink rate.
4. A forest carbon sink amount detection method based on a tree growth profile as claimed in claim 3, wherein the crown carbon sink rate is calculated by:
wherein n represents the number of leaf layers of a single tree, ph (·) represents the functional relationship between the illumination intensity and the photosynthetic rate, and S i Denotes the area of the ith leaf, L i Represents the average light intensity of the ith layer, L represents the light intensity of the current day, gamma represents the leaf absorptivity, L j Represents the average light intensity of the j-th layer, S 1 Indicating the area of the leaf of layer 1.
5. A forest carbon sequestration detection method based on a tree growth profile as claimed in claim 3, wherein the cylindrical carbon sequestration rate is calculated as:
wherein n represents the number of leaf layers of a single tree, ph (·) represents the functional relationship between the illumination intensity and the photosynthetic rate, S i Denotes the area of the ith leaf, L i Represents the average light intensity of the ith layer, L represents the light intensity of the current day, gamma represents the leaf absorptivity, L j Represents the average light intensity of the j-th layer, S 1 Representing leaves of layer 1Area.
6. The forest carbon sink amount detection method based on the tree growth profile as claimed in claim 3, wherein the calculation formula of the conical carbon sink rate is as follows:
wherein n represents the number of leaf layers of a single tree, ph (·) represents the functional relationship between the illumination intensity and the photosynthetic rate, S i Denotes the area of the ith leaf, L i Represents the average light intensity of the ith layer, L represents the light intensity of the current day, gamma represents the leaf absorptivity, L j Represents the average light intensity of the j-th layer, S 1 Indicating the area of the leaf of layer 1.
7. The forest carbon sink amount detection method based on the tree growth profile as claimed in claim 3, wherein the calculation formula of the conical layer-shaped carbon sink rate is as follows:
wherein n represents the number of leaf layers of a single tree, ph (·) represents the functional relationship between the illumination intensity and the photosynthetic rate, S i Denotes the area of the ith leaf, L i Represents the average light intensity of the i-th layer, L represents the light intensity of the day, gamma represents the leaf absorption coefficient, and L j Represents the average light intensity of the j-th layer, S 1 Denotes the 1 st layerArea of the leaves.
8. A forest carbon sink amount detection method based on a tree growth profile as claimed in claim 3, wherein a multi-crown carbon sink rate is an accumulation of a plurality of crowns, and a crown calculation formula is firstly carried out as follows:
wherein n represents the number of leaf layers of a single tree, ph (·) represents the functional relationship between the illumination intensity and the photosynthetic rate, S i Denotes the area of the ith leaf, L i Represents the average light intensity of the i-th layer, L represents the light intensity of the day, gamma represents the leaf absorption coefficient, and L j Represents the average light intensity of the j-th layer, S 1 Represents the area of the layer 1 leaf;
after a plurality of crowns are calculated by the formula, the carbon sink rates of the plurality of crowns are obtained through accumulation.
9. The forest carbon sink amount detection method based on the tree growth contour is characterized in that the images are separated in a single tree form by using a structural information theory to obtain a plurality of individual single trees;
and analyzing the extracted segmentation result of the single wood on the image: firstly, connecting edges of each pixel point to eight adjacent pixel points, and obtaining an optimal coding tree based on a greedy thought with minimized structural entropy after a graph is built; and then comparing the obtained coding tree with the coding tree in the knowledge base in sequence, calculating the similarity between the two coding trees through a recursive optimal matching sub-module, selecting the coding tree with the maximum similarity in the knowledge base, and taking the corresponding category as the category of the individual.
10. The method as claimed in claim 9, wherein the carbon sink rate is calculated by using the image characteristics of the single wood in combination with environmental factors through a calculation formula of the carbon sink rate of the single wood in the illumination carbon sink model, and the estimated amount of carbon sink is estimated by integration.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116992191A (en) * | 2023-09-27 | 2023-11-03 | 西安中碳环境科技有限公司 | Forest carbon sink dynamic monitoring and evaluating system driven by multi-source remote sensing data |
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| CN116992191A (en) * | 2023-09-27 | 2023-11-03 | 西安中碳环境科技有限公司 | Forest carbon sink dynamic monitoring and evaluating system driven by multi-source remote sensing data |
| CN116992191B (en) * | 2023-09-27 | 2023-12-08 | 西安中碳环境科技有限公司 | Forest carbon sink dynamic monitoring and evaluating system driven by multi-source remote sensing data |
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