CN-121980133-A - Forestry carbon sink dynamic baseline investigation method based on historical sample plot

CN121980133ACN 121980133 ACN121980133 ACN 121980133ACN-121980133-A

Abstract

The invention belongs to the technical field of forest carbon sink metering and monitoring, and particularly discloses a forest carbon sink dynamic baseline investigation method based on a historical sample plot. The method comprises the steps of collecting historical sample data of different periods, integrating the historical sample data into a data platform for storage management, arranging project samples in a carbon sink project area, investigating the project sample data, utilizing a geographic information system to carry out spatial analysis, selecting a historical sample with tree species composition and forest stand density relative errors not exceeding 15% from the data platform as a reference sample, fitting a growth curve of carbon reserves along with forest ages or time as a dynamic base line by adopting a statistical model based on the historical data of the reference sample, and calculating the difference between the project carbon reserves and the dynamic base line carbon reserves to be used as carbon sink. The invention can fully utilize the historical pattern data, reduce the cost and improve the accuracy, flexibility and adaptability of the carbon sink calculation. The method is suitable for forest carbon sink measurement and carbon sink project monitoring.

Inventors

HUANG ZEYUAN
BI SHEN
YANG SILIAN
JIA WANQING
LIAO XUEYI
ZHANG HANWEN
SHI CANBIAO
PAN HAO
ZHANG LUN

Assignees

云南林海森林资源资产评估有限公司

Dates

Publication Date: 20260505
Application Date: 20260408

Claims (7)

1. The forestry carbon sink dynamic baseline investigation method based on the historical plot is characterized by comprising the following steps of: s1, collecting historical pattern data distributed through forestry technical service in different periods, and integrating the historical pattern data into a data platform for unified storage and management; S2, laying out project sample areas in the carbon sink project area, and conducting investigation to obtain project sample area data; S3, in the same geographic position and ecological environment as the project sample plot, carrying out space analysis by utilizing a geographic information system, and selecting a historical sample plot with the relative error of tree species composition and forest stand density not exceeding 15% from a data platform as a reference sample plot, wherein the tree species composition is calculated according to the plant number of a tree species group; S4, based on historical data of the selected control sample, fitting a growth curve of the carbon reserves along with forest age or time to serve as a dynamic baseline by adopting a statistical model, and forming a growth model to predict the dynamic baseline carbon reserves; S5, calculating a difference value between the project carbon reserves and the dynamic baseline carbon reserves to serve as a carbon sink.
2. A method for dynamically investigating a forest carbon sink based on a historic sample as claimed in claim 1, wherein the historic sample data is derived from samples laid out in forest resource planning design surveys, job design surveys, forest resource asset assessment surveys and various forest resource specialty surveys developed over the years.
3. A method for dynamically investigating a forest carbon sink based on a history pattern as recited in claim 1, wherein in the selection of the reference pattern in step S3, the site conditions and climate factors of the history pattern are required to be consistent with those of the project pattern.
4. The historical pattern-based forestry carbon sink dynamic baseline investigation method according to claim 1, wherein the statistical model adopted in the step S4 is selected from one or more of a linear regression model, a quadratic polynomial regression model, an exponential regression model, a nonlinear mixed effect model, a generalized linear model, a biomass and volume differential growth model, a spatial statistical model, a growth and harvest model, a machine learning model and a bayesian statistical model, and an optimal model is selected through model fitting goodness-of-fit indexes.
5. A method for dynamic baseline investigation of forestry carbon sinks based on historical plots as defined in claim 1 or 4, wherein the dynamic baseline carbon reserves in step S4 are obtained by statistical model fitting after updating the ages of the forest of the control plots to project plot investigation years based on matched historical plot data.
6. A historical pattern-based forestry carbon sink dynamic baseline investigation method as defined in claim 1, further comprising a dynamic baseline updating step of incorporating newly acquired forest resource planning design investigation, job design investigation, forest resource asset assessment investigation and various forest resource special investigation data in a regional scope as new historical data into a data platform for baseline updating and model optimization of a subsequent monitoring period.
7. A method for dynamic baseline investigation of forestry carbon sinks based on historical patterns as defined in claim 1, wherein the calculation and adjustment of the dynamic baseline carbon reserves is performed again in step S4 based on the matching results of the historical pattern data and the reference pattern to update the dynamic baseline carbon reserves.

Description

Forestry carbon sink dynamic baseline investigation method based on historical sample plot Technical Field The invention belongs to the technical field of forest carbon sink metering and monitoring, and particularly relates to a forest carbon sink dynamic baseline investigation method based on a historical sample plot. Background Carbon sequestration forestation and forest management projects are important means for coping with climate change, and the core of the carbon sequestration forestation and forest management projects is to accurately measure carbon sequestration generated by the projects. Calculation of carbon sink generally involves the difference between the project carbon reserves and the baseline carbon reserves. The existing carbon sink project is mostly investigated and monitored by adopting a fixed sample land, but the fixed sample land has the defects that the network construction of ① fixed sample land requires a large amount of manpower and material resources to perform early investigation, accurate positioning and embedding of permanent marks, the long-term maintenance cost is huge, the resetting is difficult, the fixed sample land ② loses representativeness due to the peripheral land utilization change and cannot reflect the newly-appearing forest type or land utilization pattern change, the coordinates of ③ fixed sample land need to be strictly kept secret, but the data cannot be disclosed and the requirement ‌ of the carbon sink project on transparency is difficult to meet. The existing dynamic baseline method has the limitations that Verra VM is taken as an example, dynamic adjustment of carbon sequestration is carried out based on continuous clear data historical data, but the method relies on continuous clear data, the sample distribution is based on large grids, the local geographical features and the influence of microclimate differences on forest growth are ignored in a mountain area, a complicated terrain or an area with large altitude change, so that data can be deviated, and meanwhile, the method cannot fully integrate the influence of climate change on forest growth, so that long-term carbon sequestration prediction is deviated. In recent years, a dynamic carbon sink metering method for carbon sink forestation projects, disclosed by an authorized bulletin number CN116881604B, has been developed, which realizes automatic data acquisition and carbon reserve calculation by laying out an internet of things monitoring sample. The method reduces the manual investigation cost to a certain extent, improves the monitoring timeliness, but has the inherent limitations that ① equipment cost and maintenance burden are that a large number of hardware equipment such as sensors, communication relay devices and the like are required to be arranged on an Internet of things monitoring sample area, initial investment cost is high, maintenance and replacement cost are high, ② data quality and equipment reliability are high, the sensor can cause data loss or errors to influence continuity and accuracy of carbon reserve calculation after long-term field operation, ③ still depends on project area sample area arrangement, the method still needs to arrange new monitoring sample areas in a project area, a large amount of historical sample area data which is arranged for other forestry technical services cannot be fully utilized, repeated investigation and resource waste exist, ④ is lack of a dynamic baseline adjustment mechanism, the method mainly focuses on real-time monitoring of the project area carbon reserve, does not establish a dynamic baseline comparison mechanism based on the historical data, and extra carbon reserves generated by project activities are difficult to accurately strip. In view of the foregoing, there is a need for a dynamic baseline survey method for carbon sink that can fully utilize historical pattern data, does not need to lay new patterns or internet of things equipment on a large scale, flexibly adapts to geographic environment and climate change, and is lower in cost and higher in efficiency. ‌ ‌ A Disclosure of Invention The invention aims to provide a forestry carbon sink dynamic baseline investigation method based on a historical pattern, which can dynamically adjust a baseline of a carbon sink project through calculation according to historical pattern data, a growth model of a non-carbon sink project and pattern data monitored by the carbon sink project, so that accuracy, flexibility and adaptability of carbon sink calculation are improved. The technical scheme adopted by the invention for realizing the purposes is as follows: A method for dynamically investigating a baseline of a forestry carbon sink based on a historical pattern, the method comprising the following steps performed in sequence: s1, collecting historical pattern data distributed through forestry technical service in different periods, and integrating the historical pattern data into a data platform f