CN115374629A - Method and system for predicting forest carbon sink change and spatial distribution - Google Patents
Method and system for predicting forest carbon sink change and spatial distribution Download PDFInfo
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Abstract
The invention discloses a method and a system for predicting carbon sink change and spatial distribution of forest vegetation, wherein the method comprises the following steps: step 1) aiming at a research area, establishing a basic model for calculating carbon sink in a gridding area based on a plant growth model and a land utilization change rule; step 2) establishing a function relation of the carbon sink quantity of the gridded vegetation changing along with time based on the vegetation type and the forest age according to forest cleaning forest age actual measurement data and a basic model; step 3) establishing a function relation of the carbon sink amount of the gridding soil changing along with time based on the conditions of the regional vegetation and the carbon reserve of the soil; step 4) combining the carbon sink amount of the gridding vegetation obtained in the step 2) and the carbon sink amount of the gridding soil obtained in the step 3), and calculating the carbon sink potential of the forest ecological system; and 5) setting different afforestation scenes, performing multiple self-iterations on the steps 2) -4) by adopting a spatialization algorithm to obtain forest carbon sink change and spatial distribution pattern of a designated year in the future, and selecting the optimal afforestation scene.
Description
Technical Field
The invention belongs to the technical field of forest carbon sink calculation in forestry industry, and particularly relates to a method and a system for predicting forest vegetation carbon sink change and spatial distribution.
Background
Under the background of climate change, relevant action plan laws, policies and measures are proposed by various countries in the world, and the strategic objective of striving to realize carbon neutralization before 2060 is also proposed by China. The achievement of the "carbon neutralization" goal requires overall emission reduction and sink increase. Ecosystem carbon sequestration is considered to be the most stable carbon sequestration and is also an important way to mitigate the effects of climate change and achieve carbon neutralization. The carbon sink of the terrestrial ecosystem of china has significantly offset carbon emissions from part of contemporary fossil fuel combustion and industrial activities over the past decades. Therefore, the Chinese government has proposed "carbon sink capacity consolidation and promotion action" to enhance ecological protection to consolidate the carbon sequestration of natural ecosystem and to implement ecological engineering measures to promote the carbon sink capacity of land ecosystem. In the future, artificial afforestation will be an important driving factor for carbon sink in the forest ecosystem of China. In addition, many factors influence the carbon sink of forests, such as atmospheric CO 2 Changes in concentration, nutrient limitations of the forest ecosystem, and land use all contribute to carbon sequestration, resulting in significant uncertainty in the estimation of forest carbon sequestration during "carbon peak" and "carbon neutral" periods. Therefore, accurate quantification of forest carbon sink is important for regional carbon sink management and enhancement of greenhouse gas management, and the method becomes a research hotspot in the field of earth system science.
In recent years, forest carbon sink observation technologies and evaluation methods suitable for different space-time scales are rapidly developed and perfected. The main methods for quantifying the forest carbon sink comprise vegetation investigation, a vorticity correlation method, satellite remote sensing, atmospheric inversion, model simulation and the like. There is great uncertainty in using different technical approaches to evaluate the carbon sink predictions for a particular area or world. For the simulation of future situations, a complex large-scale model is mostly used for simulation, and the precision of a simulation result and the applicability of the simulation result in each area are not ideal.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a method and a system for predicting forest vegetation carbon sink change and spatial distribution.
In order to achieve the purpose, the invention provides a method for predicting carbon sink change and spatial distribution of forest vegetation, which comprises the following steps:
step 1) aiming at a research area, establishing a basic model for calculating carbon sink in a gridding area based on a plant growth model and a land utilization change rule;
step 2) establishing a function relation of carbon sink quantity of gridding vegetation changing along with time based on different vegetation types and forest ages according to forest age actual measurement data and by combining the basic model in the step 1);
step 3) establishing a function relation of the carbon sink amount of the gridding soil changing along with time based on the conditions of the regional vegetation and the carbon reserve of the soil;
step 4) combining the carbon sink amount of the gridded vegetation obtained in the step 2) and the carbon sink amount of the gridded soil obtained in the step 3), and calculating the carbon sink potential of the forest ecological system;
and 5) setting different afforestation scenes, performing multiple self-iterations on the steps 2) to 4) by adopting a spatialization algorithm to obtain forest carbon sink change and spatial distribution pattern of a designated year in the future, and selecting the optimal afforestation scene.
As an improvement of the above method, the step 1) specifically includes:
acquiring carbon sink amount and spatial distribution in the boundary of a research area according to regional gridding carbon sink data of a reference year, wherein the carbon sink amount V corresponding to an (i, j) pixel in a grid of the reference year is obtained according to the carbon sink amount V 0(i，j) The carbon sink amount of the 9 pixel reference years around the pixel is obtained and expressed as:
V 0(i-1，j-1) V 0(i-1，j) V 0(i-1，j+1)
V 0(i，j-1) V 0(i，j) V 0(i，j+1)
V 0(i+1，j-1) V 0(i+1，j) V 0(i+1，j+1)
wherein i-1, i +1 respectively corresponds to the i-1 th row, i +1 row, j-1, j +1 respectively corresponds to the j-1 th column, j +1 column of the area grid, and the minimum positive value is selected from the rows and recorded as V min＞0 Maximum quantity is denoted as V Max ；
Judging the carbon sink V corresponding to the (i, j) pixel of the t year t(i，j) ：
When V is t(i，j) When the carbon sink is less than or equal to 0, the carbon sink amount V corresponding to the pixel in the t +1 year (t+1)(i，j) Comprises the following steps:
wherein R is the carbon sink growth rate in the research region year, R =0.014, maxP is the maximum carbon sink amount in the research region year,
is the average value of 9 pixels around the pixel;
when V is t(i，j) When the carbon sink is more than 0, the carbon sink amount V corresponding to the t +1 year pixel (t+1)(i，j) Comprises the following steps:
as an improvement of the above method, the step 2) specifically includes:
determining the vegetation type and the forest age of the area where the pixel is located according to gridding data of the vegetation type and the forest age distribution of the area, and calculating to obtain the carbon sink amount W of the pixel (i, j) in the t +1 th year in consideration of the vegetation type and the forest age (t+1)(i，j) Comprises the following steps:
when the vegetation type is deciduous coniferous forest:
wherein A is forest age, unitMaxP =462.05g Cm corresponding to conifer forest of deciduous leaf for year -2 a -1 ；
When the vegetation type is evergreen conifer forest:
wherein, corresponding to evergreen coniferous forest, maxP =620.03g Cm -2 a -1 ；
When the vegetation type is deciduous broadleaf forest:
wherein, corresponding to deciduous broad-leaved forest, maxP =625.27g Cm -2 a -1 ；
When the vegetation type is pin-wide mixed forest:
wherein, corresponding to the conidiophorous commingled forest, maxP =802.02g Cm -2 a -1 ；
When the forest type is evergreen broad-leaved forest:
wherein, corresponding to evergreen broad-leaved forest, maxP =888.467g Cm -2 a -1 。
As an improvement of the above method, the step 3) specifically includes:
determining litter data of the area where the pixel is located and litter conversion rate of soil according to gridding data of carbon reserves of vegetation and soil in the area, and calculating to obtain soil carbon sink X of the pixel (i, j) in the t +1 year (t+1)(i，j) Comprises the following steps:
X (t+1)(i，j) ＝G t(i，j) *k l *k d -S t(i，j) *k s
wherein G is t(i，j) Carbon reserves of the t-year vegetation, k l Biomass coefficient of vegetation withering; k is a radical of c The decomposition coefficient of the litters is obtained; s t(i，j) The carbon reserve of the soil in the t year, k s The decomposition rate of organic matters in soil.
As an improvement of the above method, the step 4) specifically includes:
according to the future afforestation planning scene, the afforestation scene is spatialized, and the afforestation index k of the area where the pixel is positioned is determined f And considering the carbon sink amount W of the vegetation type and the forest age in combination with the t +1 year (t+1)(i，j) And soil carbon sequestration X (t+1)(i，j) Calculating to obtain the forest vegetation and soil carbon sink potential Y under different afforestation scenes in the t +1 th year (t+1)(i，j) Comprises the following steps:
Y (t+1)(i，j) ＝W (t+1)(i，j) *k f +X (t+1)(i，j) *k f
wherein k is f The value range of the afforestation index of the area where the pixel (i, j) is located is 0-1.
A system for predicting forest vegetation carbon sink change and spatial distribution, the system comprising: a basic model establishing module, a carbon sink function establishing module based on vegetation types and forest ages, a gridding soil carbon sink function establishing module, a carbon sink potential calculating module and an iteration generating module, wherein,
the basic model establishing module is used for establishing a basic model for calculating the carbon sink in the gridding area based on a plant growth model and a land utilization change rule aiming at the research area;
the carbon sink function establishing module based on the vegetation types and the forest ages is used for establishing a function relation of carbon sink of the gridded vegetation based on different vegetation types and forest ages along with time variation according to forest cleaning forest age actual measurement data and a basic model;
the gridding soil carbon sink function establishing module is used for establishing a function relation of the gridding soil carbon sink amount changing along with time based on conditions of regional vegetation and soil carbon reserves;
the carbon sink potential calculation module is used for calculating the carbon sink potential of the forest ecological system by combining the gridded vegetation carbon sink amount obtained by the function establishment module based on the vegetation type and the forest age and the gridded soil carbon sink amount obtained by the gridded soil carbon sink amount function establishment module;
and the iteration generation module is used for setting different afforestation scenes, performing self-iteration for a plurality of times on the function establishment module of the carbon sink amount based on the vegetation type and the forest age, the gridding soil carbon sink amount function establishment module and the carbon sink potential calculation module by adopting a spatialization algorithm to obtain the forest carbon sink change and the spatial distribution pattern of the appointed year in the future, and selecting the optimal afforestation scene.
As an improvement of the above system, the processing procedure of the basic model building module specifically includes:
acquiring carbon sink amount and spatial distribution in the boundary of a research area according to regional gridding carbon sink data of a reference year, wherein the carbon sink amount V corresponding to an (i, j) pixel in a grid of the reference year is obtained according to the carbon sink amount V 0(i，j) The carbon sink amount of the 9 pixel reference years around the pixel is obtained and expressed as:
V 0(i-1，j-1) V 0(i-1，j) V 0(i-1，j+1)
V 0(i，j-1) V 0(i，j) V 0(i，j+1)
V 0(i+1，j-1) V 0(i+1，j) V 0(i+1，j+1)
wherein i-1, i +1 respectively corresponds to the i-1 th row, i +1 row, j-1, j +1 respectively corresponds to the j-1 th column, j +1 column of the area grid, and the minimum positive value is selected from the rows and recorded as V min＞0 Maximum quantity is denoted as V Max ；
Judging the carbon sink V corresponding to the (i, j) pixel of the t year t(i，j) ：
When V is t(i，j) When the carbon sink is less than or equal to 0, the carbon sink amount V corresponding to the pixel in the t +1 year (t+1)(i，j) Comprises the following steps:
wherein R is the annual carbon sink growth rate of a research region, R =0.014 and MaxP is the maximum carbon sink amount of the research region,
is the average value of the 9 pixels around the pixel;
when V is t(i，j) If the current year is more than 0, the carbon sink amount V corresponding to the t +1 year pixel (t+1)(i，j) Comprises the following steps:
as an improvement of the above system, the processing procedure of the carbon sink function establishing module based on vegetation type and forest age specifically includes:
determining the vegetation type and the forest age of the area where the pixel is located according to gridding data of the vegetation type and the forest age distribution of the area, and calculating to obtain the carbon sink amount W of the pixel (i, j) in the t +1 th year in consideration of the vegetation type and the forest age (t+1)(i，j) Comprises the following steps:
when the vegetation type is deciduous coniferous forest:
wherein A is forest age, unit is year, corresponding to deciduous coniferous forest, maxP =462.05g Cm -2 a -1 ；
When the vegetation type is evergreen conifer forest:
wherein, corresponding to evergreen coniferous forest, maxP =620.03g Cm -2 a -1 ；
When the vegetation type is deciduous broad-leaved forest:
wherein, corresponding to deciduous broad-leaved forest, maxP =625.27g Cm -2 a -1 ；
When the vegetation type is pin-wide mixed forest:
wherein, corresponding to the conidiophorous commingled forest, maxP =802.02g Cm -2 a -1 ；
When the forest type is evergreen broad-leaved forest:
wherein, corresponding to evergreen broad-leaved forest, maxP =888.467g Cm -2 a -1 。
As an improvement of the above system, the processing procedure of the gridding soil carbon sink amount function establishing module specifically includes:
determining litter data of the area where the pixel element is located and litter conversion rate of soil according to gridding data of carbon reserves of vegetation and soil carbon reserves of the area, and calculating to obtain soil carbon sink amount X of the pixel element (i, j) in the t +1 year (t+1)(i，j) Comprises the following steps:
X (t+1)(i，j) ＝G t(i，j) *k l *k d -S t(i，j) *k s
wherein, G t(i，j) Carbon reserves of the t year vegetation, k l Biomass coefficient of vegetation withering; k is a radical of formula c The decomposition coefficient of the litters is obtained; s. the t(i，j) The carbon reserve of the soil in the t year, k s The decomposition rate of soil organic matters.
As an improvement of the above system, the processing procedure of the carbon sequestration potential calculating module specifically includes:
according to the future afforestation planning scene, the afforestation scene is spatialized, and the afforestation index k of the area where the pixel is positioned is determined f And (3) considering the carbon sink amount W of the vegetation type and the forest age in combination with the t +1 year (t+1)(i，j) And soil carbon sequestration X (t+1)(i，j) Calculating to obtain the forest vegetation and soil carbon sink potential Y under different afforestation scenes in the t +1 th year (t+1)(i，j) Comprises the following steps:
Y (t+1)(i，j) ＝W (t+1)(i，j) *k f +X (t+1)(i，j) *k f
wherein k is f The value range of the afforestation index of the area where the pixel (i, j) is located is 0-1.
Compared with the prior art, the invention has the advantages that:
1. the method estimates the carbon sink amount of the forest under the future scene through high-precision standard year forest carbon sink data, vegetation types, vegetation forest ages, vegetation and soil carbon reserves and other gridding data, and the basic data are reliable;
2. the growth of carbon sink is scientifically calculated according to the physiological ecology and growth characteristics of vegetation, the carbon cycle of an ecological system and the law of land utilization change, and the scientificity is strong;
3. based on the high-resolution gridding data, the carbon sink change and the spatial distribution of the region can be obtained, the application is wide, and the method is suitable for different space-time scales;
4. by adopting a spatialization algorithm, the carbon sink quantity of the forest in the future year can be obtained by self-iterative updating, and the operation is simple and rapid;
5. setting a plurality of future afforestation scenes, balancing economic benefits, selecting the optimal afforestation scene, and providing scientific basis for policy decision.
Drawings
FIG. 1 is a technical route diagram of a method for predicting carbon sink change and spatial distribution of forest vegetation according to the invention.
Detailed Description
At present, a great amount of perfect measured data are provided for the spatial distribution of carbon sink/carbon reserve of the existing ecological system, the vegetation type and the forest age structure, and the spatial resolution also reaches 1km. And iteratively updating through a spatialization algorithm according to the physiological ecology and growth characteristics of the vegetation, the carbon cycle of an ecosystem and the law of land utilization change by using gridding data such as high-precision reference year forest carbon sink data, vegetation types, vegetation ages, vegetation and soil carbon reserves and the like to obtain the forest carbon sink amount of the future year, and selecting an optimal afforestation situation. Based on the method, the method for calculating the future forest carbon sink based on the regional boundary map, the forest carbon sink data, the regional vegetation and soil data and the afforestation plan is provided.
The method comprises the following steps: establishing a basic model for regional carbon sink calculation; establishing a function relation of carbon sink of various vegetations and the vegetations in various forest age stages along with time change; establishing a function of the carbon sink of various types of soil along with the change of time; setting a plurality of afforestation scenes, further obtaining the carbon sink amount of the forest in the future year through a plurality of times of self-iteration by using a spatialization algorithm, and selecting the optimal afforestation scene. Aiming at the problem that the forest carbon sink and the spatial layout are difficult to predict and quantitatively analyze under the future situation, the method adopts gridding data and a spatialization algorithm to predict the forest carbon sink, and can more accurately predict the forest carbon sink potential and the spatial distribution pattern under the future situation.
The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and examples.
Example 1
As shown in fig. 1, embodiment 1 of the present invention provides a method for predicting carbon sink variation and spatial distribution of forest vegetation.
Aiming at a certain area, establishing a basic model for carbon sink calculation based on a plant growth model and a land utilization change rule;
based on the physiological ecology and the growth characteristics of different vegetation, the function relation of vegetation carbon sink change along with time at each forest age stage of various types of forests is established by combining forest age actual measurement data;
establishing a function relation of regional soil carbon sink change along with time based on conditions of regional vegetation and soil carbon reserves;
and setting various afforestation scenes according to the future scene, and respectively calculating the carbon sink potential of the forest ecological system.
Setting different afforestation scenes according to the calculated vegetation carbon sequestration amount, the calculated soil carbon sequestration amount and the constraints of the vegetation type and the forest age, further obtaining the forest carbon sequestration amount in the future year through multiple self-iterations of a spatialization algorithm, and selecting the optimal afforestation scene.
As one improvement of the technical scheme, a basic model for calculating regional carbon sink is established for a certain region based on a plant growth model and a land utilization change rule; the method specifically comprises the following steps:
according to regional carbon sink data of the reference year, the carbon sink amount and the spatial distribution in the boundary of the research region are obtained, wherein the carbon sink amount corresponding to a certain pixel is V 0(i，j) Then the carbon sink amount of the peripheral 9 (3*3) pixels can be expressed as:
V 0(i-1，j-1) V 0(i-1，j) V 0(i-1，j+1)
V 0(i，j-1) V 0(i，j) V 0(i，j+1)
V 0(i+1，j-1) V 0(i+1，j) V 0(i+1，j+1)
when V is t(i，j) When the carbon sink is less than or equal to 0, the carbon sink V corresponding to the t +1 year picture element (t+1)(i，j) Comprises the following steps:
wherein, V (t+1)(i，j) The carbon sink amount g Cm of the pixel (i, j) in the t +1 year -2 a -1 (ii) a R is the annual carbon sink growth rate in the research area, g Cm -2 a -2 R =0.014 (the annual average growth rate of research results was 0.014 in recent years according to chinese forest resource verification); maxP is the annual maximum carbon sink in the study area, gm C -2 a -1 ，V min＞0 Is the smallest positive number of 9 picture elements.
When V is t(i，j) When the carbon sink is more than 0, the carbon sink amount V corresponding to the t +1 year pixel t+1(i，j) Comprises the following steps:
wherein, V (t+1)(i，j) The carbon sink amount g Cm of the pixel (i, j) in the t +1 year -2 a -1 (ii) a R is the annual carbon sink growth rate in the research area, g Cm -2 a -2 (ii) a MaxP is the annual maximum carbon sink in the study area, gm C -2 a -1 ，V Max Is the maximum of 9 picture elements.
As one improvement of the technical scheme, aiming at a certain area, based on the physiological ecology and the growth characteristics of different vegetation, the function relation of the vegetation carbon sink change along with time at each forest age stage of various types of forests is established by combining forest age actual measurement data; the method specifically comprises the following steps:
determining the vegetation type and the forest age of the area where the pixel is located according to the vegetation type and the forest age distribution data of the area, and calculating to obtain the carbon sink quantity W considering the vegetation type and the forest age 0(i，j) Comprises the following steps:
when the forest type is deciduous coniferous forest:
wherein, W (t+1)(i，j) For the pixel (i, j) in the t +1 th year, considering the carbon sink amount of vegetation type and forest age, gm -2 a -1 (ii) a A is forest age, year; maxP is the annual maximum carbon sink in the study area, g C m -2 a -1 (ii) a When the forest type is deciduous conifer Lin Shi, maxP =462.05g Cm -2 a -1 。
When the forest type is evergreen coniferous forest:
wherein, W (t+1)(i，j) For the pixel (i, j) in the t +1 th year, considering the carbon sink amount of vegetation type and forest age, gm -2 a -1 (ii) a A is forest age, year; maxP is the annual maximum carbon sink in the study area, gm C -2 a -1 Danssen (a family of medical instruments)The forest type is evergreen conifer Lin Shi, maxP =620.03g Cm -2 a -1 。
When the forest type is deciduous broad-leaved forest:
wherein, W (t+1)(i，j) For the pixel (i, j) in the t +1 th year, considering the carbon sink amount of vegetation type and forest age, gm -2 a -1 (ii) a A is forest age, year; maxP is the annual maximum carbon sink, gCm, of the study area -2 a -1 When the forest type is deciduous broad leaf Lin Shi, maxP =625.27g Cm -2 a -1 。
When the forest type is a needle-broad mixed forest:
wherein, W (t+1)(i，j) For the pixel (i, j) in the t +1 th year, considering the carbon sink amount of vegetation type and forest age, gm -2 a -1 (ii) a A is forest age, year; maxP is the annual maximum carbon sink in the study area, g C m -2 a -1 When the forest type is coniferous-broad mixed forest, maxP =802.02g Cm -2 a -1 。
When the forest type is evergreen broad-leaved forest:
wherein, W (t+1)(i，j) For the pixel (i, j) in the t +1 th year, considering the carbon sink amount of vegetation type and forest age, gm -2 a -1 (ii) a A is forest age, year; maxP is the annual maximum carbon sink in the study area, gm C -2 a -1 When the forest type is evergreen broad leaf Lin Shi, maxP =888.467g Cm -2 a -1 。
As one improvement of the technical scheme, a function relation of regional soil carbon sink change along with time is established for a certain region based on regional vegetation and soil carbon density conditions;
the method comprises the following specific steps:
determining litter data of the area where the pixel element is located, litter conversion rate of soil and the like according to the carbon reserves of the vegetation and the soil carbon reserves of the area, and calculating to obtain soil carbon sink X t(i，j) Comprises the following steps:
X (t+1)(i，j) ＝G t(i，j) *k l *k d -S t(i，j) *k s
wherein, X (t+1)(i，j) The carbon sink amount of the soil in the t +1 year of the pixel element (i, j) is gm -2 a -1 ；G t(i，j) Carbon reserve of vegetation, g cm -2 a -1 ；k l Biomass coefficient for vegetation withering (determined according to vegetation type, parameters are shown in table 1); k is a radical of c The decomposition coefficient of the litters is obtained; s t(i，j) Is the carbon reserve of soil, g cm -2 a -1 ；k s The decomposition rate of organic matters in soil.
TABLE 1 litter Biomass coefficient Table for each woodland type
| Type of forest | Estimated value (%) | Description of the invention |
| Deciduous coniferous forest | 26.997 | The main tree species include Larix Gmelini |
| Evergreen coniferous forest | 10.402 | The main species of trees including cloudsChinese fir, korean pine, chinese pine, fir and cypress |
| Mixed blending of needle and broad | 7.292 | The main tree species include Korean pine, quercus mongolica, japanese cedar, birch forest, etc |
| Deciduous broad-leaved forest | 12.218 | The main tree species include oak, birch forest and locust tree |
| Evergreen broad-leaved forest | 15.485 | The main tree species include eucalyptus, acacia, moso bamboo, shrub, etc |
As one improvement of the technical scheme, a plurality of future afforestation scenes are set for a certain area, and the carbon sink potentials of forest vegetation and soil are respectively calculated;
the method specifically comprises the following steps:
according to the future afforestation planning scene, the afforestation scene is spatialized, and the afforestation index k of the area where the pixel is positioned is determined f (0-1), calculating to obtain forest vegetation and soil carbon sink potential under different afforestation scenes;
Y (t+1)(i，j) ＝W t+1(i，j) *k f +X t+1(i，j) *k f
wherein, W (t+1)(i，j) Considering the carbon sink amount of vegetation type and forest age for the pixel (i, j) at the t +1 year, g Cm -2 a -1 ；X (t+1)(i，j) The carbon sink amount of the soil in the t +1 year of the pixel element (i, j), g Cm -2 a -1 ；k f Is the afforestation index of the area where the pixel is located, and is 0 to 1.
Example 2
The invention provides a system for predicting carbon sink change and spatial distribution of forest vegetation, which is realized by adopting the method of embodiment 1, and comprises the following steps:
a data acquisition module:
the method is used for acquiring data of region boundaries, basic year carbon sink data, vegetation types, vegetation forest ages, vegetation and soil carbon reserves aiming at a certain region;
a simulation process module:
function acquisition, namely establishing a function relation of carbon sink quantity of each component of the ecological system along with time change based on the physiological ecology and growth characteristics of vegetation, actually measured forest age and carbon cycle and turnover rate of the ecological system;
an iteration process, namely, according to the rule of land utilization change, carrying out iterative updating through a spatialization algorithm to obtain carbon sink data of the future year;
a simulation result module:
according to the regional afforestation planning, calculating to obtain vegetation carbon sequestration and soil carbon sequestration, setting different afforestation scenes, further obtaining the forest carbon sequestration in the future year through multiple self-iterations of a spatialization algorithm, and selecting the optimal afforestation scene.
Specifically, the method comprises the following steps: a basic model establishing module, a carbon sink function establishing module based on vegetation types and forest ages, a gridding soil carbon sink function establishing module, a carbon sink potential calculating module and an iteration generating module, wherein,
the basic model establishing module is used for establishing a basic model for calculating the carbon sink of the gridding area based on a plant growth model and a land utilization change rule aiming at the research area;
the carbon sink function establishing module based on the vegetation types and the forest ages is used for establishing a function relation of carbon sink of the gridded vegetation based on different vegetation types and forest ages along with time variation according to forest cleaning forest age actual measurement data and a basic model;
the gridding soil carbon sink function establishing module is used for establishing a function relation of the gridding soil carbon sink amount changing along with time based on conditions of regional vegetation and soil carbon reserves;
the carbon sink potential calculation module is used for calculating the carbon sink potential of the forest ecological system by combining the gridded vegetation carbon sink amount obtained by the function establishment module based on the vegetation type and the forest age and the gridded soil carbon sink amount obtained by the gridded soil carbon sink amount function establishment module;
and the iteration generation module is used for setting different afforestation scenes, performing self-iteration for a plurality of times on the function establishment module of the carbon sink amount based on the vegetation type and the forest age, the gridding soil carbon sink amount function establishment module and the carbon sink potential calculation module by adopting a spatialization algorithm to obtain the forest carbon sink change and the spatial distribution pattern of the appointed year in the future, and selecting the optimal afforestation scene.
Compared with the prior art, the invention has the beneficial effects that:
the method estimates the carbon sink amount of the forest in the future scene through high-precision standard annual forest carbon sink data, vegetation types, vegetation forest ages, vegetation, soil carbon reserves and other gridding data, and the basic data are reliable;
the growth of carbon sink is scientifically calculated according to the physiological ecology and growth characteristics of vegetation, the carbon cycle of an ecosystem and the law of land use change, and the scientificity is strong;
based on the high-resolution gridding data, the carbon sink change and the spatial distribution of the region can be obtained, the application is wide, and the method is suitable for different space-time scales;
by adopting a spatialization algorithm, the carbon sink quantity of the forest in the future year can be obtained by self-iterative updating, and the operation is simple and rapid;
setting a plurality of future afforestation scenes, balancing economic benefits, selecting the optimal afforestation scene, and providing scientific basis for policy decision.
Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limited. Although the present invention has been described in detail with reference to the embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (10)
1. A method of predicting forest vegetation carbon sink change and spatial distribution, the method comprising:
step 1) aiming at a research area, establishing a basic model for calculating carbon sink in a gridding area based on a plant growth model and a land utilization change rule;
step 2) establishing a function relation of carbon sink quantity of gridding vegetation changing along with time based on different vegetation types and forest ages according to forest age actual measurement data and by combining the basic model in the step 1);
step 3) establishing a function relation of the grid soil carbon sink amount changing along with time based on conditions of regional vegetation and soil carbon reserves;
step 4) combining the carbon sink amount of the gridding vegetation obtained in the step 2) and the carbon sink amount of the gridding soil obtained in the step 3), and calculating the carbon sink potential of the forest ecological system;
and 5) setting different afforestation scenes, carrying out self-iteration for multiple times in the steps 2) to 4) by adopting a spatialization algorithm to obtain the forest carbon sink change and the spatial distribution pattern of the appointed year in the future, and selecting the optimal afforestation scene.
2. The method for predicting forest vegetation carbon sink change and spatial distribution according to claim 1, wherein the step 1) specifically comprises:
acquiring carbon sink amount and spatial distribution in the boundary of a research area according to regional gridding carbon sink data of a reference year, wherein the carbon sink amount V corresponding to an (i, j) pixel in a grid of the reference year is obtained according to the carbon sink amount V 0(i,j) The carbon sink amount of the 9 pixel reference years around the pixel is obtained and expressed as:
V 0(i-1，j-1) V 0(i-1,j) V 0(i-1,j+1)
V 0(i,j-1) V 0(i,j) V 0(i,j+1)
V 0(i+1,j-1) V 0(i+1,j) V 0(i+1,j+1)
wherein i-1, i +1 corresponds to row i-1, row i +1, and j-1, j +1 of the regional grid respectivelyColumn, j +1 column, and selecting the minimum positive value from them as V min＞0 Maximum quantity is denoted as V max ；
Judging the carbon sink V corresponding to the (i, j) pixel of the t year t(i,j) ：
When V is t(i,j) When the carbon sink is less than or equal to 0, the carbon sink amount V corresponding to the pixel in the t +1 year (t+1)(i,j) Comprises the following steps:
wherein R is the carbon sink growth rate in the research region year, R =0.014, maxP is the maximum carbon sink amount in the research region year,
is the average value of 9 pixels around the pixel;
when V is t(i,j) When the carbon sink is more than 0, the carbon sink amount V corresponding to the t +1 year pixel (t+1)(i,j) Comprises the following steps:
3. the method for predicting forest vegetation carbon sink change and spatial distribution according to claim 2, wherein the step 2) specifically comprises:
determining the vegetation type and the forest age of the area where the pixel is located according to gridding data of the vegetation type and the forest age distribution of the area, and calculating to obtain the carbon sink amount W of the pixel (i, j) in the t +1 year in consideration of the vegetation type and the forest age (t+1)(i,j) Comprises the following steps:
when the vegetation type is deciduous coniferous forest:
wherein A is forest age, unit is year, and corresponds to deciduous coniferous forest、MaxP＝462.05g Cm -2 a -1 ；
When the vegetation type is evergreen conifer forest:
wherein the extract corresponds to evergreen coniferous forest, maxP =620.03g Cm -2 a -1 ；
When the vegetation type is deciduous broadleaf forest:
wherein, corresponding to deciduous forest, maxP =625.27g Cm -2 a -1 ；
When the vegetation type is pin-broad mixed forest:
wherein, corresponding to the blending forest of conidiophores and broadleaf trees, maxP =802.02g Cm -2 a -1 ；
When the forest type is evergreen broad-leaved forest:
wherein the extract corresponds to evergreen broad-leaved forest, and MaxP =888.467g Cm -2 a -1 。
4. The method for predicting forest vegetation carbon sink change and spatial distribution according to claim 3, wherein the step 3) specifically comprises:
determining litter data and soil litter conversion rate of the area where the pixel is located according to gridding data of carbon reserves of vegetation and soil in the area, and calculating to obtain the number I, j of the pixelt +1 year soil carbon sink X (t+1)(i,j) Comprises the following steps:
X (t+1)(i,j) ＝G t(i,j) *k l *k d -S t(i,j) *k s
wherein G is t(i,j) Carbon reserves of the t-year vegetation, k l Biomass coefficient of vegetation withering; k is a radical of c The decomposition coefficient of the litters is obtained; s. the t(i,j) The carbon reserve of the soil in the t year, k s The decomposition rate of organic matters in soil.
5. The method for predicting forest vegetation carbon sink change and spatial distribution according to claim 4, wherein the step 4) specifically comprises:
according to the future afforestation planning scene, the afforestation scene is spatialized, and the afforestation index k of the area where the pixel is positioned is determined f And (3) considering the carbon sink amount W of the vegetation type and the forest age in combination with the t +1 year (t+1)(i,j) And soil carbon sequestration X (t+1)(i,j) Calculating to obtain the forest vegetation and soil carbon sink potential Y under different afforestation scenes in the t +1 th year (t+1)(i,j) Comprises the following steps:
Y (t+1)(i,j) ＝W (t+1)(i,j) *k f +X (t+1)(i,j) *k f
wherein k is f The value range of the afforestation index of the area where the pixel (i, j) is located is 0-1.
6. A system for predicting forest vegetation carbon sink change and spatial distribution, the system comprising: a basic model establishing module, a carbon sink function establishing module based on vegetation types and forest ages, a gridding soil carbon sink function establishing module, a carbon sink potential calculating module and an iteration generating module, wherein,
the basic model establishing module is used for establishing a basic model for calculating the carbon sink in the gridding area based on a plant growth model and a land utilization change rule aiming at the research area;
the carbon sink function establishing module based on the vegetation types and the forest ages is used for establishing a time-varying functional relation of the carbon sink of the gridded vegetation based on different vegetation types and the forest ages according to forest cleaning forest age actual measurement data and by combining a basic model;
the gridding soil carbon sink function establishing module is used for establishing a function relation of the gridding soil carbon sink quantity changing along with time based on conditions of regional vegetation and soil carbon reserves;
the carbon sink potential calculation module is used for calculating the carbon sink potential of the forest ecological system by combining the gridded vegetation carbon sink amount obtained by the function establishment module based on the vegetation type and the forest age and the gridded soil carbon sink amount obtained by the gridded soil carbon sink amount function establishment module;
and the iteration generation module is used for setting different afforestation scenes, performing self-iteration for a plurality of times on the function establishment module of the carbon sink amount based on the vegetation type and the forest age, the gridding soil carbon sink amount function establishment module and the carbon sink potential calculation module by adopting a spatialization algorithm to obtain the forest carbon sink change and the spatial distribution pattern of the appointed year in the future, and selecting the optimal afforestation scene.
7. The system for predicting forest vegetation carbon sink change and spatial distribution of claim 6, wherein the processing of the basic model building module specifically comprises:
acquiring carbon sink amount and spatial distribution in the boundary of a research area according to regional gridding carbon sink data of a reference year, wherein the carbon sink amount V corresponding to (i, j) pixels in a grid of the reference year is 0(i,j) The carbon sink amount of the 9 pixel reference years around the pixel is obtained and expressed as:
V 0(i-1，j-1) V 0(i-1,j) V 0(i-1,j+1)
V 0(i,j-1) V 0(i,j) V 0(i,j+1)
V 0(i+1,j-1) V 0(i+1,j) V 0(i+1,j+1)
wherein i-1, i +1 respectively corresponds to the i-1 th row, i +1 row, j-1, j +1 respectively corresponds to the j-1 th column, j +1 column of the area grid, and the minimum positive value is selected from the rows and recorded as V min＞0 Maximum quantity memoryIs a V Max ；
Judging the carbon sink V corresponding to the (i, j) pixel of the t year t(i,j) ：
When V is t(i,j) When the carbon sink is less than or equal to 0, the carbon sink V corresponding to the pixel in the t +1 year (t+1)(i,j) Comprises the following steps:
wherein R is the carbon sink growth rate in the research region year, R =0.014, maxP is the maximum carbon sink amount in the research region year,
is the average value of 9 pixels around the pixel;
when V is t(i,j) When the carbon sink is more than 0, the carbon sink amount V corresponding to the t +1 year pixel (t+1)(i,j) Comprises the following steps:
8. the system for predicting forest vegetation carbon sink change and spatial distribution according to claim 7, wherein the processing of the carbon sink function establishing module based on vegetation type and forest age specifically comprises:
determining the vegetation type and the forest age of the area where the pixel is located according to gridding data of the vegetation type and the forest age distribution of the area, and calculating to obtain the carbon sink amount W of the pixel (i, j) in the t +1 year in consideration of the vegetation type and the forest age (t+1)(i,j) Comprises the following steps:
when the vegetation type is deciduous coniferous forest:
wherein A is forest age, and the unit is year, and corresponds to deciduous coniferous forest, maxP =462.05g Cm -2 d -1 ；
When the vegetation type is evergreen conifer forest:
wherein the extract corresponds to evergreen coniferous forest, maxP =620.03g Cm -2 a -1 ；
When the vegetation type is deciduous broadleaf forest:
wherein, corresponding to deciduous forest, maxP =625.27g Cm -2 a -1 ；
When the vegetation type is pin-wide mixed forest:
wherein, corresponding to the blending forest of conidiophores and broadleaf trees, maxP =802.02g Cm -2 a -1 ；
When the forest type is evergreen broad-leaved forest:
wherein the extract corresponds to evergreen broad-leaved forest, and MaxP =888.467g Cm -2 a -1 。
9. The system for predicting forest vegetation carbon sink change and spatial distribution of claim 8, wherein the processing of the gridded soil carbon sink function building module specifically comprises:
determining litter data of the area where the pixel is located and litter conversion rate of soil according to gridding data of carbon reserves of vegetation and soil in the area,the carbon sink amount X of the soil of the pixel (i, j) in the t +1 year is obtained by calculation (t+1)(i,j) Comprises the following steps:
X (t+1)(i,j) ＝G t(i,j) *k l *k d -S t(i,j) *k s
wherein G is t(i,j) Carbon reserves of the t-year vegetation, k l Biomass coefficient of vegetation withering; k is a radical of c The decomposition coefficient of the litters is obtained; s t(i,j) The carbon reserve of the soil in the t year, k s The decomposition rate of organic matters in soil.
10. The system for predicting forest vegetation carbon sink change and spatial distribution of claim 9, wherein the processing of the carbon sink potential calculating module specifically comprises:
spatializing the afforestation scene according to the future afforestation planning scene, and determining the afforestation index k of the area where the pixel is f And (3) considering the carbon sink amount W of the vegetation type and the forest age in combination with the t +1 year (t+1)(i,j) And soil carbon sequestration X (t+1)(i,j) Calculating to obtain the forest vegetation and soil carbon sink potential Y under different afforestation scenes in the t +1 th year (t+1)(i,j) Comprises the following steps:
Y (t+1)(i,j) ＝W (t+1)(i,j) *k f +X (t+1)(i,j) *k f
wherein k is f The value range of the afforestation index of the area where the pixel (i, j) is located is 0-1.
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