Forest change-based aboveground carbon reserve evaluation and prediction method and system

Abstract

The invention relates to an above-ground carbon reserve evaluation and prediction method and system based on forest change, and the method comprises the steps: constructing a corresponding carbon absorption model after forest interference according to a forest state data set and an above-ground biomass data set; tracking pixels with corresponding forest change states on a spatial scale by adopting a pixel-level discrimination technology; dividing a forest change state on a time scale, and setting a corresponding forest aboveground carbon reserve estimation mathematical model; acquiring an evaluation value of forest aboveground carbon reserves, and predicting future carbon reserve changes by using the prediction model; and obtaining an evaluation value and a prediction value of the forest aboveground carbon reserve corresponding to the target forest region according to the query request. The beneficial effects of the invention are that the method achieves the finer and more accurate estimation of the forest aboveground carbon reserve for different forest change activities, and carries out the prediction based on the prediction model.

Description

Technical Field

[0001] The present invention relates to the technical field of forestry remote sensing, and more specifically, it relates to a method and system for evaluating and predicting aboveground carbon storage based on forest changes. Background Art

[0002] Forests are one of the largest terrestrial ecosystems on Earth, with important ecological and climate regulation functions, and are an important natural solution to address climate and ecological emergencies. Forests absorb carbon dioxide from the atmosphere through photosynthesis and store it in the form of biomass, thus forming a huge carbon reservoir. However, forest resources face global threats such as land use change, fire, and climate change, which have led to significant changes in forest area and carbon storage. Accurately evaluating and predicting the impact of forest changes on aboveground carbon storage is crucial for formulating scientific carbon emission management policies and protecting the global ecosystem. However, there has not yet been a comprehensive pan-tropical assessment of the aboveground carbon storage in secondary and degraded forests, which has led to large errors in assessing their role in the carbon cycle and high uncertainty in previous estimates of forest aboveground carbon storage.

[0003] Traditional carbon storage assessment methods mainly rely on ground observations, remote sensing image inversion, and biomass models. However, these methods have some limitations. For example, ground observations require a large amount of manpower and time and have a limited coverage area; although remote sensing technology provides the possibility of wide-area monitoring, during the process of inverting aboveground biomass, it is limited by data resolution, observation accuracy, and algorithm models, resulting in large uncertainties in the inversion results. In addition, existing methods mainly focus on static analysis and lack a comprehensive assessment and prediction of the annual forest dynamic change process. Summary of the Invention

[0004] The object of the present invention is to address the deficiencies of the prior art and propose a method and system for evaluating and predicting aboveground carbon storage based on forest changes.

[0005] In the first aspect, a method for evaluating and predicting aboveground carbon storage based on forest changes is provided, including:

[0006] Step 1: Construct a corresponding carbon absorption model after forest disturbance according to the forest state dataset and the aboveground biomass dataset; the carbon absorption model after forest disturbance includes a carbon absorption model for regeneration after forest loss and a carbon absorption model for recovery after forest degradation;

[0007] Step 2: Use pixel-level discrimination technology to track pixels with corresponding forest change states at the spatial scale;

[0008] Step 3: Divide the forest change states on the time scale and set corresponding mathematical models for estimating the aboveground carbon storage of the forest;

[0009] Step 4: Obtain forest change activity events within a preset range, calculate the carbon budget corresponding to the forest change activity events based on the mathematical model for estimating the aboveground carbon storage of the forest, obtain the evaluation value of the aboveground carbon storage of the forest, and use a prediction model to predict the future change in carbon storage, and import the estimation and prediction results into the database;

[0010] Step 5: Obtain a query request including the target forest area, and obtain the evaluation value and prediction value of the aboveground carbon storage of the forest corresponding to the target forest area according to the query request.

[0011] Preferably, in Step 2, the forest change states at the spatial scale include: forest loss, forest degradation, forest regeneration, and undisturbed forest.

[0012] Preferably, in Step 3, the forest change states at the time scale include: forest loss, forest degradation, regeneration after forest loss, recovery after forest degradation, and stable forest growth.

[0013] Preferably, in Step 1, the carbon absorption model after forest disturbance is used to simulate the change in aboveground carbon accumulation of the forest with age, and the model parameters include the asymptote of aboveground carbon of the forest, the growth rate coefficient, the growth curve shape coefficient, and the error term.

[0014] Preferably, in Step 3, in the mathematical model for estimating the aboveground carbon storage corresponding to forest loss, calculate the loss of aboveground carbon storage according to the initial carbon storage of the forest, the carbon loss caused by forest degradation, the carbon absorbed during recovery after forest degradation, and the carbon absorbed during regeneration after forest loss; in the mathematical model for estimating the aboveground carbon storage corresponding to forest degradation, calculate the loss of aboveground carbon storage according to the forest degradation carbon loss ratio and the initial carbon storage of the forest.

[0015] Preferably, in Step 3, in the mathematical model for estimating the aboveground carbon storage corresponding to regeneration after forest loss, calculate the absorbed aboveground carbon according to the time of forest loss occurrence and the change in carbon storage; in the mathematical model for estimating the aboveground carbon storage corresponding to recovery after forest degradation, calculate the absorbed aboveground carbon according to the time of forest degradation occurrence and the change in carbon storage; in the mathematical model for estimating the aboveground carbon storage corresponding to stable forest growth, calculate the absorbed aboveground carbon according to the forest age and the change in carbon storage.

[0016] Preferably, in Step 4, the mathematical model for estimating the aboveground carbon storage of the forest obtains the annual aboveground carbon storage of the forest by calculating the sum of the baseline carbon storage and the carbon increase amount minus the carbon loss amount.

[0017] In a second aspect, there is provided an aboveground carbon storage evaluation and prediction system based on forest change for executing any of the methods in the first aspect, including:

[0018] A building module for constructing a corresponding carbon absorption model after forest disturbance according to a forest status data set and an above-ground biomass data set; the carbon absorption model after forest disturbance includes a carbon absorption model for regeneration after forest loss and a carbon absorption model for recovery after forest degradation.

[0019] A tracking module for tracking pixels with corresponding forest change states at a spatial scale by using pixel-level discrimination technology.

[0020] A partitioning module for partitioning forest change states on a time scale and setting a corresponding mathematical model for estimating the above-ground carbon storage of forests.

[0021] A first acquisition module for acquiring forest change activity events within a preset range, calculating the carbon budget corresponding to the forest change activity events based on the mathematical model for estimating the above-ground carbon storage of forests, obtaining an evaluation value of the above-ground carbon storage of forests, and predicting future carbon storage changes by using a prediction model, and importing the estimation and prediction results into a database.

[0022] A second acquisition module for acquiring a query request including a target forest area and obtaining an evaluation value and a predicted value of the above-ground carbon storage of forests corresponding to the target forest area according to the query request.

[0023] In a third aspect, a computer storage medium is provided, in which a computer program is stored; when the computer program runs on a computer, the computer is enabled to execute the method according to any one of the first aspects.

[0024] In a fourth aspect, an electronic device is provided, including:

[0025] A memory for storing a computer program;

[0026] A processor for executing the computer program to implement the method according to any one of the first aspects.

[0027] The beneficial effects of the present invention are as follows: The present invention realizes a more refined and accurate estimation of the above-ground carbon storage of forests (including loss and absorption) for different forest change activities, and makes predictions based on a prediction model. It solves the technical problems of high human and material costs, poor reliability, low spatio-temporal resolution, no consideration of the impact of forest degradation, inability to accurately estimate the carbon budget caused by annual forest dynamic changes, and inability to accurately predict the future above-ground carbon storage of forests in the existing methods for estimating the above-ground carbon storage of forests, and provides a quantitative basis for the distribution of the above-ground carbon storage of forests. Description of the Drawings

[0028] Figure 1Flowchart of a method for evaluating and predicting aboveground carbon storage based on forest change provided by an embodiment of the present invention;

[0029] Figure 2 Schematic diagram of the annual forest loss and degradation area and the loss and increase of forest carbon storage calculated by the method provided by an embodiment of the present invention;

[0030] Figure 3 Schematic structural diagram of a system for evaluating and predicting aboveground carbon storage based on forest change provided by an embodiment of the present invention. Detailed implementation manners

[0031] The present invention will be further described below in conjunction with embodiments. The description of the following embodiments is only for helping to understand the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several modifications can still be made to the present invention, and these improvements and modifications also fall within the protection scope of the claims of the present invention.

[0032] Embodiment 1:

[0033] Before introducing the technical solution of the present invention, it is necessary to explain relevant terms as follows.

[0034] Forest change: Forest change refers to the changes caused by the mutual conversion and evolution between various states of the forest, including forest loss, forest degradation, forest regeneration, etc. Forest change is usually caused by human activities (such as agricultural expansion, urbanization, forestry, etc.) and natural factors (such as fires and climate change, etc.).

[0035] Aboveground forest carbon storage: Aboveground forest carbon storage refers to the amount of carbon elements stored in the forest ecosystem, including the carbon storage of vegetation parts such as trees, branches, and leaves. These carbon storages are crucial for climate regulation, can affect the concentration of greenhouse gases in the atmosphere, and thus affect the earth's energy balance, thereby causing the greenhouse effect, resulting in an increase in the earth's surface temperature and global climate change.

[0036] Chapman-Richards model: The Chapman-Richards model is a semi-empirical model that uses the space-time substitution method to simulate the aboveground carbon accumulation of forests in different regions with the increase of forest age, including the carbon absorption model for forest regeneration after forest loss and the carbon absorption model for forest restoration after forest degradation, to calculate the aboveground carbon absorption after forest disturbance.

[0037] Embodiment 1 of the present application provides a method for assessing and predicting aboveground carbon storage based on forest changes, which is used to solve the technical problems of high human and material costs, poor reliability, low spatio-temporal resolution, lack of consideration of the impact of forest degradation, inability to accurately estimate the carbon budget caused by annual forest dynamic changes, and inability to accurately predict future aboveground carbon storage of forests in existing methods for estimating aboveground carbon storage of forests.

[0038] Specifically, as Figure 1 shown, the method includes:

[0039] Step 1: Construct a corresponding carbon absorption model after forest disturbance according to the forest status dataset and the aboveground biomass dataset; the carbon absorption model after forest disturbance includes a carbon absorption model for regeneration after forest loss and a carbon absorption model for recovery after forest degradation.

[0040] In Step 1, using the TMF forest status dataset and the CCI AGB dataset, the spatio-temporal substitution method is used to simulate the increase in aboveground carbon accumulation in forests in different regions with the increase of forest age, and a Chapman-Richards model applicable to this region is constructed.

[0041] It should be noted that the first key input data is the TMF forest status dataset, which provides six classification data of undisturbed forests, degraded forests, lost forests, regenerated forests, permanent and seasonal waters, etc. The time span is from 1990 to 2023, and the spatial resolution is 30m. The TMF forest status dataset can be used to estimate the time (in years) since the last disturbance event for any restored forest, which is considered a good way to quantify the age of secondary forests and degraded forests. The second key input data is the aboveground biomass dataset (CCI AGB) of the ESA Climate Change Initiative in 2020, with a spatial resolution of 500m. It is resampled to 30m to correspond to the TMF forest status data, and multiplied by a conversion factor (0.456) to be converted into aboveground carbon storage data. Combining the TMF and AGC datasets, using the spatial substitution for time substitution method, the increase in aboveground carbon accumulation in forests in different regions with the increase of forest age is simulated, and a Chapman-Richards model applicable to this region is constructed.

[0042] The main body of the Chapman-Richards model is to use the spatio-temporal substitution method to simulate the increase in aboveground carbon accumulation in forests in different regions with the increase of forest age, including a carbon absorption model for regeneration after forest loss and a carbon absorption model for recovery after forest degradation. Its formula is:

[0043] Y t = A(1 - e -kt ) c ±ε; A, k, c > 0

[0044] where, Yt It is an estimation model for the increase in aboveground carbon accumulation in forests in this region with the increase of forest age t. A is the asymptote of aboveground carbon in forests in this region, k is the growth rate coefficient of Y, which is a function of age, c is the coefficient determining the shape of the growth curve; ε is an error term. Assume that after a given year, the aboveground carbon storage in forests can recover to an amount equivalent to that before the disturbance and reach the pre-calculated asymptote.

[0045] Step 2: Use pixel-level discrimination technology to track pixels with corresponding forest change states at the spatial scale.

[0046] In Step 2, the forest change states at the spatial scale include: forest loss, forest degradation, forest regeneration, and undisturbed forest.

[0047] It should be noted that in this application, the forest changes occurring in a given year are determined by comparing the forest states of the pixel in the previous year and that in this year. The transition from an undisturbed forest to a degraded forest in this year is defined as forest degradation, the transition from an undisturbed forest and a degraded forest to a lost forest is recorded as forest loss, the transition from a lost forest to a regenerated forest is recorded as forest regeneration, and the undisturbed forest. At a resolution of 30m, pixel-level discrimination technology is used to track all pixels with forest changes and draw a forest change map with a spatial resolution of 30m and a time span from 1991 to 2023.

[0048] Step 3: Divide the forest change states on the time scale and set corresponding mathematical models for estimating aboveground carbon storage in forests.

[0049] In Step 3, the forest change states on the time scale include: forest loss, forest degradation, regeneration after forest loss, recovery after forest degradation, and stable forest growth. Different forest change states will produce different aboveground carbon benefits in forests, corresponding to different mathematical models for estimating aboveground carbon storage in forests.

[0050] In the mathematical model for estimating aboveground carbon storage corresponding to forest loss, calculate the loss of aboveground carbon storage according to the initial carbon storage in the forest, the carbon loss caused by forest degradation, the carbon absorbed during the recovery after forest degradation, and the carbon absorbed during the regeneration after forest loss; in the mathematical model for estimating aboveground carbon storage corresponding to forest degradation, calculate the loss of aboveground carbon storage according to the forest degradation carbon loss ratio and the initial carbon storage in the forest.

[0051] It should be noted that the forest loss and forest degradation in Step 3 have different meanings from those in Step 2. Step 2 refers to the pixels with forest loss and forest degradation in the forest state map of a certain year, while Step 3 refers to the time period during which forest loss and forest degradation occur in the entire research time series of a certain pixel, but this time period only lasts for one year. In the time series from 1991 to 2023 of a 30m pixel, if forest regeneration occurs after forest loss, the intermediate process is defined as regeneration after forest loss; if forest degradation occurs, the subsequent process is defined as recovery after forest degradation; if the entire time series shows that the forest is undisturbed, this process is defined as stable forest growth.

[0052] In the mathematical model for estimating the aboveground carbon storage of the forest corresponding to the regeneration after forest loss, the aboveground carbon absorbed is calculated based on the time of forest loss and the change in carbon storage; in the mathematical model for estimating the aboveground carbon storage of the forest corresponding to the recovery after forest degradation, the aboveground carbon absorbed is calculated based on the time of forest degradation and the change in carbon storage; in the mathematical model for estimating the aboveground carbon storage of the forest corresponding to the stable forest growth, the aboveground carbon absorbed is calculated based on the forest age and the change in carbon storage.

[0053] Step 4: Obtain forest change activity events within a preset range, calculate the carbon budget corresponding to the forest change activity events based on the above mathematical model for estimating the aboveground carbon storage of the forest, obtain the evaluation value of the aboveground carbon storage of the forest, and use a prediction model to predict the future change in carbon storage, and import the estimation and prediction results into the database.

[0054] In Step 4, the above mathematical model for estimating the aboveground carbon storage of the forest obtains the annual aboveground carbon storage of the forest by calculating the sum of the baseline carbon storage and the carbon increase minus the carbon loss.

[0055] Step 5: Obtain a query request including the target forest area, and obtain the evaluation value and prediction value of the aboveground carbon storage of the forest corresponding to the target forest area according to the query request.

[0056] Example 2:

[0057] Based on Example 1, Example 2 of the present application provides a more specific method for evaluating and predicting the aboveground carbon storage based on forest changes, including:

[0058] Step 1: Construct a corresponding carbon absorption model for the forest after being disturbed according to the forest state dataset and the aboveground biomass dataset; the carbon absorption model for the forest after being disturbed includes a carbon absorption model for regeneration after forest loss and a carbon absorption model for recovery after forest degradation.

[0059] Step 2: Use pixel-level discrimination technology to track the pixels with corresponding forest change states at the spatial scale.

[0060] Step 3: Divide the forest change states on the time scale and set the corresponding mathematical models for estimating the aboveground carbon storage of forests.

[0061] In Step 3, the forest change states on the time scale include: forest loss, forest degradation, regeneration after forest loss, recovery after forest degradation, and stable forest growth.

[0062] Specifically, the dedicated estimation model for the aboveground carbon storage loss caused by forest loss events is:

[0063]

[0064] Among them, in the 33 years (1991 - 2023) covered by the TMF forest state dataset, there are at most 3 recorded forest losses, and it is only possible to identify whether degradation has occurred before the first loss. AGC loss1 ,AGC loss2 ,AGC loss3 are respectively the maximum three aboveground carbon storage losses of a pixel with forest loss, C is the initial carbon storage of the forest, AGC degraded is the aboveground carbon loss caused by forest degradation, AGC recover is the aboveground carbon absorbed during the recovery after forest degradation, AGC regorwth1 ,AGC regrowth2 are respectively the aboveground carbon absorbed during the regeneration after two forest losses.

[0065] The dedicated estimation model for the aboveground carbon storage loss caused by forest degradation events is:

[0066] AGC degraded = C * α

[0067] Among them, AGC degraded is the aboveground carbon loss caused by forest degradation, C is the initial carbon storage of the forest, and α is the forest degradation carbon loss ratio calculated using the 2020 TMF forest state data and CCI AGB data.

[0068] The dedicated estimation model for the aboveground carbon absorbed during the stable forest growth process is:

[0069]

[0070] Among them, AGC uptake (t) is the aboveground carbon absorbed each year during the stable forest growth process, C 2020The carbon storage of the pixel forest in 2020. If it is greater than the pre-calculated asymptote, the forest of the pixel is considered mature, and the above-ground carbon absorption approaches 0. T is the forest age calculated using the Chapman-Richards model. If T is less than 33, it is excluded as an error. ΔY t is the difference in above-ground carbon storage between the t-th year and the (t - 1)-th year simulated by the Chapman-Richards model.

[0071] The dedicated estimation model for the above-ground carbon absorption during the restoration process after forest degradation is:

[0072]

[0073] Among them, AGC recover (t) is the above-ground carbon absorption per year during the restoration process after forest degradation. year degradation is the time when the forest degrades. year deforestation is the time when losses occur after forest degradation. ΔY t is the difference in above-ground carbon storage between the t-th year and the (t - 1)-th year simulated by the Chapman-Richards model.

[0074] The dedicated estimation model for the above-ground carbon absorption during the regeneration process after forest loss is:

[0075]

[0076] Among them, AGC regrowth (t) is the above-ground carbon absorption per year during the restoration process after forest loss. year deforestation1 and year deforestation2 are the times when forest losses occur before and after respectively. ΔY t is the difference in above-ground carbon storage between the t-th year and the (t - 1)-th year simulated by the Chapman-Richards model.

[0077] Step 4: Obtain forest change activity events within a preset range, calculate the carbon budget corresponding to the forest change activity events based on the mathematical model for estimating the above-ground carbon storage of the forest, obtain the evaluation value of the above-ground carbon storage of the forest, and use the prediction model to predict the future carbon storage change, and import the estimation and prediction results into the database.

[0078] Exemplarily, the preset range is the tropical range. Through Step 4 of this application, a more refined and accurate estimation of the above-ground carbon storage of the forest (including losses and absorption) is achieved, and the CMIP6 model is used to predict the future carbon storage change, and the estimation and prediction results are imported into the database.

[0079] The calculation model for the above-ground carbon storage of forest change is:

[0080] AGC 总 = AGC 基 + ∑AGC 净变化 ;

[0081] AGC 净变化 = AGC 损失 + AGC 增加

[0082] where, AGC 总 is the estimated annual aboveground carbon stock in forests, AGC 基 is the aboveground carbon stock in forests in the reference year, AGC 净变化 is the aboveground carbon budget in forests resulting from forest change, AGC 损失 is the aboveground carbon loss in forests due to forest loss and forest degradation, AGC 增加 is the aboveground carbon sequestered during the regeneration after forest loss, recovery after forest degradation, and stable growth of forests.

[0083] The dedicated prediction model for the future net change in aboveground carbon in forests is the CMIP6 model, which simulates the net change in forests and aboveground carbon under scenarios of different future greenhouse gas emission concentrations (SSP126, SSP245, SSP370, SSP585).

[0084] Step 5: Obtain a query request containing the target forest area, and obtain the evaluation value and prediction value of the aboveground carbon stock in forests corresponding to the target forest area according to the query request.

[0085] In Step 5, the target forest area can be any area within a preset range, such as relevant latitudes and longitudes, regions, countries within the tropics, or the entire tropics.

[0086] To visually demonstrate the technical effects achieved by the method for evaluating and predicting aboveground carbon stock based on forest change provided in the present invention, please refer to Figure 2 , in the present invention, the method is used to calculate the annual forest loss and degradation area and the loss and increase in forest carbon storage in tropical protected areas; please refer to Table 1, in the present invention, the method is used to estimate the aboveground carbon stock in tropical protected areas from 2016 to 2021, and the estimation results are compared with the existing aboveground carbon stock map. It can be seen from the results in Table 1 that the estimation results of the proposed method are close to the existing aboveground carbon stock map, indicating that the proposed method can effectively estimate the annual aboveground carbon stock in forests and fills part of the gap in the aboveground carbon stock on the time scale.

[0087] Table 1 Comparison results of aboveground carbon stock in tropical protected areas and existing map

[0088]

[0089] It should be noted that for the same or similar parts in this embodiment and Embodiment 1, reference can be made to each other and will not be elaborated in this application.

[0090] Embodiment 3:

[0091] Based on Embodiments 1 and 2, Embodiment 3 of this application provides an assessment and prediction system for aboveground carbon storage based on forest changes, including:

[0092] A construction module, configured to construct a corresponding carbon absorption model after forest disturbance according to the forest state data set and the aboveground biomass data set; the carbon absorption model after forest disturbance includes a carbon absorption model for regeneration after forest loss and a carbon absorption model for recovery after forest degradation;

[0093] A tracking module, configured to track pixels with corresponding forest change states at the spatial scale by using pixel-level discrimination technology;

[0094] A division module, configured to divide the forest change states on the time scale and set a corresponding mathematical model for estimating the aboveground carbon storage of the forest;

[0095] A first acquisition module, configured to acquire forest change activity events within a preset range, calculate the carbon budget corresponding to the forest change activity events based on the mathematical model for estimating the aboveground carbon storage of the forest, obtain an evaluation value of the aboveground carbon storage of the forest, and use a prediction model to predict the future carbon storage change, and import the estimation and prediction results into the database;

[0096] A second acquisition module, configured to acquire a query request including a target forest area, and obtain an evaluation value and a prediction value of the aboveground carbon storage of the forest corresponding to the target forest area according to the query request.

[0097] Specifically, the system provided in this embodiment is the system corresponding to the methods provided in Embodiments 1 and 2. Therefore, for the same or similar parts in this embodiment and Embodiments 1 and 2, reference can be made to each other and will not be elaborated in this application.

Claims

1. A method for assessing and predicting aboveground carbon storage based on forest change, characterized in that: include: Step 1: construct a corresponding carbon absorption model for forests after disturbance based on the forest state dataset and the aboveground biomass dataset; the carbon absorption model for forests after disturbance includes a carbon absorption model for regeneration after forest loss and a carbon absorption model for restoration after forest degradation; Step 2: Use pixel-level discrimination technology to track pixels with corresponding forest change status at a spatial scale; Step 3: Divide the forest change status on the time scale and set up the corresponding mathematical model for estimating forest aboveground carbon storage; Step 4: obtaining forest change activity events within a preset range, calculating the carbon budget corresponding to the forest change activity events based on the mathematical model for estimating forest ground carbon reserves, obtaining an assessment value of forest ground carbon reserves, and using the prediction model to predict future carbon reserve changes, and importing the estimation and prediction results into a database; Step 5: Obtain a query request containing a target forest area, and obtain an assessment value and a prediction value of forest aboveground carbon storage corresponding to the target forest area according to the query request.

2. The aboveground carbon storage assessment and prediction method based on forest change according to claim 1 is characterized in that: In step 2, the forest change states at different spatial scales include: forest loss, forest degradation, forest regeneration, and undisturbed forest.

3. The aboveground carbon storage assessment and prediction method based on forest change according to claim 2 is characterized in that: In step 3, the forest change states on the time scale include: forest loss, forest degradation, regeneration after forest loss, recovery after forest degradation and stable forest growth.

4. The aboveground carbon storage assessment and prediction method based on forest change according to claim 3 is characterized in that: In step 1, the carbon absorption model after forest disturbance is used to simulate the change of aboveground carbon accumulation in forest with age, and the model parameters include the asymptote of aboveground carbon in forest, the growth rate coefficient, the growth curve shape coefficient and the error term.

5. The aboveground carbon storage assessment and prediction method based on forest change according to claim 4 is characterized in that: In step 3, in the mathematical model for estimating aboveground carbon stocks in forests corresponding to the forest loss, the aboveground carbon stock loss is calculated based on the initial carbon stock of the forest, the carbon loss caused by forest degradation, the carbon recovered and absorbed after forest degradation, and the carbon regenerated and absorbed after forest loss; in the mathematical model for estimating aboveground carbon stocks in forests corresponding to forest degradation, the aboveground carbon stock loss is calculated based on the forest degradation carbon loss ratio and the initial carbon stock of the forest.

6. The aboveground carbon storage assessment and prediction method based on forest change according to claim 5 is characterized in that: In step 3, in the mathematical model for estimating aboveground carbon stocks in the forest corresponding to the regeneration after forest loss, the absorbed aboveground carbon is calculated based on the time of forest loss and the change in carbon stocks; in the mathematical model for estimating aboveground carbon stocks in the forest corresponding to the recovery after forest degradation, the absorbed aboveground carbon is calculated based on the time of forest degradation and the change in carbon stocks; in the mathematical model for estimating aboveground carbon stocks in the forest corresponding to the stable growth of the forest, the absorbed aboveground carbon is calculated based on the forest age and the change in carbon stocks.

7. The method for evaluating and predicting aboveground carbon storage based on forest changes according to claim 6, characterized in that: In step 4, the mathematical model for estimating aboveground carbon storage in forests calculates the sum of the baseline carbon storage and the carbon increase minus the carbon loss to obtain the annual aboveground carbon storage in forests.

8. A system for assessing and predicting aboveground carbon storage based on forest changes, characterized in that: Used to perform the method according to any one of claims 1 to 7, comprising: A construction module is used to construct a corresponding carbon absorption model for a forest after being disturbed according to a forest state data set and an above-ground biomass data set; the carbon absorption model for a forest after being disturbed includes a carbon absorption model for regeneration after forest loss and a carbon absorption model for restoration after forest degradation; A tracking module is used to track the pixels with corresponding forest change status at a spatial scale using pixel-level discrimination technology; The division module is used to divide the forest change status on the time scale and set the corresponding mathematical model for estimating forest aboveground carbon storage; The first acquisition module is used to acquire forest change activity events within a preset range, calculate the carbon budget corresponding to the forest change activity events based on the mathematical model for estimating forest ground carbon reserves, acquire the assessed value of forest ground carbon reserves, and use the prediction model to predict future carbon reserve changes, and import the estimation and prediction results into a database; The second acquisition module is used to obtain a query request containing a target forest area, and obtain an evaluation value and a predicted value of forest aboveground carbon storage corresponding to the target forest area according to the query request.

9. A computer storage medium, characterized in that The computer storage medium stores a computer program; when the computer program is executed on a computer, the computer executes any one of the methods described in claims 1 to 7.

10. An electronic device, characterized in that: include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the method according to any one of claims 1 to 7.

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