# US 12,118,572 B2 · Dynamic carbon sink measurement method for afforestation carbon sink and forest management carbon sink projects

> Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt

- Number: US 12,118,572 B2
- Document ID: US12118572B2
- Status: Granted
- Kind: B2
- Application number: 18/593,966
- Filed: 2024-03-03
- Published: 2024-09-12
- Granted: 2024-10-15
- Earliest claimed: 2023-03-06
- Assignees: Sichuan Provincial Institute of Forestry and Grassland Inventory and Planning; Sichuan Forestry Survey, Design and Research Institute Co., Ltd; Chengdu Academy of Agriculture and Forestry Sciences
- Applicants: Sichuan Provincial Institute of Forestry and Grassland Inventory and Planning; Sichuan Forestry Survey, Design and Research Institute Co., Ltd; Chengdu Academy of Agriculture and Forestry Sciences
- Inventors: Fei Gao; Yongjun Wang; Nana Li; Yingze Tian; Changhong Lai; Fang Song
- Web page: https://patentgrep.com/patent/US12118572B2

## CPC

- G06Q 30/018: Certifying business or products
- B01D 53/1475: {Removing carbon dioxide}
- G06Q 10/103: {Workflow collaboration or project management}
- G06Q 50/02: Agriculture; Fishing; Forestry; Mining
- Y02P 90/84: Greenhouse gas [GHG] management systems
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- B01D: SEPARATION (separating solids from solids by wet methods B03B, B03D; by pneumatic jigs or tables B03B; by other dry methods B07; magnetic or electrostatic separation of solid materials from solid materials or fluids, separation by high-voltage electric fields B03C; centrifuges, vortex apparatus B04; presses per se for squeezing-out liquid from liquid-containing material B30B9/02; treatment of water C02F, e.g. softening by ion-exchange C02F1/42; {arrangements of air intake cleaners in gas turbine plants F02C7/05} ; arrangements or mounting of filters in air-conditioning, air-humidification or ventilation F24F13/28)
- Y02P: CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS

## Abstract

The present invention relates to a dynamic carbon sink measurement method for afforestation carbon sink and forest management carbon sink projects, comprising: (1) monitoring the project boundary; (2) monitoring the baseline IoT; (3) monitoring the project IoT; (4) monitoring the real-time total carbon stock and carbon change; (5) determining the monitoring end and monitoring the automatic period. The invention dynamically obtains the baseline and project carbon density by deploying carbon layer carbon density IoT monitoring sample plots. Combining the division and area computation of baseline carbon layers, remote sensing monitoring of project carbon layer changes, the invention dynamically updates the project carbon layer area, dynamically and periodically obtains the baseline and project carbon layer total carbon stock, the total carbon stock and the carbon change within the project boundary, which significantly improves monitoring timeliness and precision for projects, while reducing monitoring costs, making it suitable for large-scale promotion and application.

## Claims

### Claim 1

A dynamic carbon sink measurement method for afforestation carbon sink and forest management carbon sink projects, comprising following steps:
monitoring a project boundary, including: determining the project boundary;
monitoring a baseline IoT, including:
dividing baseline carbon layers and computing an area;
baseline carbon layer carbon density IoT monitoring, including: designing and deploying baseline IoT sample plots, dynamically monitoring an arbor per unit area, a deadwood per unit area, and a litter per unit area in the baseline carbon layers, and computing the baseline carbon layer carbon density:
(a) after measuring a perimeter of the sample plots, fixing a tree diameter measurement sensor at a breast height position of each sample tree in a zigzag route, recording tree species and tree type information of the sample tree, and fixing a communication gateway in a center of the sample plots;
(b) after the communication gateway obtains the data collected by each tree diameter measurement sensor and synchronizes a clock with each tree diameter measurement sensor, the communication gateway transmits stored information back to a server through a mobile communication network or Beidou short message, and disconnects a connection with the tree diameter measurement sensor after successful transmission, and then enters a sleep state;
(c) the tree diameter measurement sensor automatically enters the sleep state when there is no connection with the communication gateway;
(d) the communication gateway and the tree diameter measurement sensor automatically wake up at a set wake-up time, wake-up duration, and wake-up frequency, completing steps (b) and (c) until an end of a monitoring cycle;
computing a baseline carbon layer total carbon stock, including: by multiplying the baseline carbon layer carbon density by the area of each carbon layer patch, obtaining the baseline carbon layer total carbon stock;
monitoring the project IoT, including:
dividing the project carbon layers, including: within the project boundary, dividing the project carbon layers according to project activity types;
monitoring remote sensing change of the project carbon layers, including: conducting a change detection of remote sensing images for two periods, updating the project carbon layer patches and generating the changes of the patches through an interpretation, verification, and validation of the changed patches, obtaining a current status database and change database of the project carbon sinks, and a latest carbon layer patch area for total processing measurement of the project carbon layers, wherein a layout method and process of the project IoT sample plot monitoring are consistent with the baseline IoT sample plot monitoring;
project carbon layer carbon density IoT monitoring, including: designing and deploying the IoT sample plots for the project according to the project carbon layers adjusted by the remote sensing change monitoring, dynamically monitoring the arbor of the forest per unit area, the deadwood per unit area, the litter per unit area in the project carbon layers, and greenhouse gas emissions of the project, and computing the baseline carbon layer carbon density;
computing the project carbon layer total carbon stock, including: by multiplying the project carbon layer carbon density by the area of each carbon layer patch, obtaining the project carbon layer total carbon stock;
monitoring total carbon stock and carbon change in real time, including: computing the total carbon stock for the current monitoring cycle within the project boundary, as well as a carbon change rate per unit time;
determining the end of monitoring and monitoring the automatic period including: running and monitoring periodically according to the set time of the IoT monitoring sample plots; ending the monitoring if the monitoring of the project boundary, baseline, and project activities all ceases and results in withdrawal of the project.

### Claim 2 (depends on 1)

The dynamic carbon sink measurement method for afforestation carbon sink and forest management carbon sink projects according to claim 1, characterized in that specific steps in the baseline carbon layer carbon density IoT monitoring are as follows:
designing the baseline sample plots, including: based on a project scope and precision requirements, selecting a sample plot layout scheme, determining a number and size of the baseline sample plots;
monitoring the baseline IoT sample plots, including: according to the designated sample plots, fixing tree diameter measurement sensors at the breast height position of sample trees in baseline sample plots, connecting the tree diameter measurement sensors to the communication gateway so that the sensors can monitor and collect data on the diameter at breast height and a growth status of the sample trees, and transmitting the data back to the server through communication to realize the monitoring of various carbon pools in the plots;
computing the baseline arbor carbon density, including: computing the baseline arbor carbon density by using a biomass expansion factor method;
computing the baseline deadwood carbon density, including: based on the computation results in the computing the baseline arbor carbon density, a “carbon stock change method” combined with a “default value method” is used to compute the baseline deadwood carbon density;
computing the baseline litter carbon density, including: based on the computation results in the computing the baseline arbor carbon density, the “carbon stock change method” combined with the “default value method” is used to compute the baseline litter carbon density;
computing the baseline carbon layer carbon density, including: summing up the carbon stock of biomass, dead wood, and litter of all tree species.

### Claim 3 (depends on 2)

The dynamic carbon sink measurement method for afforestation carbon sink and forest management carbon sink projects according to claim 2, characterized in that specific steps in the computing the baseline arbor carbon density are as follows:
according to the data of the sample tree species, the diameter at breast height, and a standing tree type from the sample plots collected in the IoT sample plots in the monitoring the baseline IoT sample plots, a volume of standing trees for each tree species per unit area is computed by using a unified single entry volume table in this area, with diameter as an independent variable and volume as a dependent variable, where a computation formula is as follows:
v
base
,
i
,
j
,
t
=
∑
j
=
1
∑
k
=
1
f
j
(
d
k
,
i
,
j
,
t
)
s
where vbase,i,j,t is the volume of standing trees per unit area in the baseline for tree species j in the i-th carbon layer at the t-th monitoring; dk,i,j,t is a DBH of k-th sample tree for tree species j in the i-th carbon layer at the t-th monitoring; s is the area of the sample plot; fj is an empirical equation for the single entry volume of this tree species j; j is the tree species j; k is the k-th sample tree; t is the t-th monitoring; i is the i-th carbon layer;
computing biomass by using the biomass expansion factor method, with volume as the independent variable and biomass as the dependent variable; obtaining three default values for wood density, root-to-crown ratio, and biomass expansion factor from tables, where a computation formula is as follows:
wbase,i,j,t=vbase,i,j,t×WDj×BEFj×(1+Rj)
where vbase,i,j,t is the baseline biomass per unit area for tree species j in the i-th carbon layer at the t-th monitoring; WDj is the density of standing trees of tree species j; BEFj is a dimensionless biomass expansion factor that converts a trunk biomass of standing trees of tree species j into above-ground biomass; Rj is the root-to-crown ratio of standing trees of tree species j;
computing carbon stock by using a carbon content of biomass to convert biomass into carbon stock; converting carbon stock to carbon dioxide equivalent by using the ratio of carbon dioxide analysis to a molecular weight of carbon, where a computation formula is as follows:
c
tree
-
⁢
base
,
i
,
j
,
t
=
44
12
×
w
base
,
i
,
j
,
t
×
CF
j
where ctree_base,i,j,t is the baseline arbor carbon density for tree species j in the i-th carbon layer at the t-th monitoring; CF; is the carbon content of standing trees of tree species j;
where a computation formula in the computing the baseline deadwood carbon density is as follows:
cdw_base,i,j,t=ctree_base,i,j,t×DFj
where cdw_base,i,j,t is the baseline dead wood carbon density for tree species j in the i-th carbon layer at the t-th monitoring; DF; is the ratio of the baseline dead wood carbon content to the baseline arbor carbon stock for tree species j, which is the default value;
where a computation formula in the computing the baseline litter carbon density is as follows:
cli_base,i,j,t=fj(wj)×ctree_base,i,j,t
where cli_base,i,j,t is the baseline litter carbon density for tree species j in the i-th carbon layer at the t-th monitoring; fj(wj) is a correlational relationship between the baseline litter biomass and the baseline arbor biomass for tree species j, which is the default value;
where a computation formular in the computing the baseline carbon layer carbon density is as follows:
c
base
,
i
,
t
=
∑
j
=
1
(
c
tree
-
⁢
base
,
i
,
j
,
t
+
c
dw
-
⁢
base
,
i
,
j
,
t
+
c
li
-
⁢
base
,
i
,
j
,
t
)
where cbase,i,t is the baseline carbon layer carbon density in the i-th carbon layer at the t-th monitoring.

### Claim 4 (depends on 3)

The dynamic carbon sink measurement method for afforestation carbon sink and forest management carbon sink projects according to claim 3, characterized in that a computation formula in the computing a baseline carbon layer total carbon stock is as follows:
c
base
,
t
=
∑
i
=
1
(
c
base
,
i
,
t
×
s
base
,
i
)
where cbase,t is the baseline carbon layer total carbon stock at the t-th monitoring; sbase,i,t is the area of the baseline carbon layer patches in the i-th carbon layer.

### Claim 5 (depends on 4)

The dynamic carbon sink measurement method for afforestation carbon sink and forest management carbon sink projects according to claim 4, characterized in that the specific steps in the monitoring remote sensing change of the project carbon layers are as follows:
after pre-processing the updated remote sensing images, obtaining the patches with changes in land type within the monitoring area; determining the patch numbers, boundary, and area of the changed patches, initially judging a cause of the changes; performing quality check to ensure that there are no overlapping or fragmented patches;
collecting archive materials, using data verification methods to confirm a current land class, reasons for changes, current forest stand factors, management factors, and project activity factors indoors for the changed patches supported by archive materials; for the changed patches without archive materials support, conducting field investigation methods to confirm the current land class, the reasons for changes, current forest stand factors, management factors, and the project activity factors, refining and filling in the investigation factors for patches with inconsistencies in important factors such as land class and the reasons for changes, forming investigation factors and filling in the remote sensing change patch database with correct attribute logic based on the results of indoor and field investigations;
using the remote sensing change patch database to update a graphics and attributes in a previous carbon layer pattern database, performing attribute logic and spatial topology quality check on the updated results to ensure logical correctness and no overlapping, gaps, or multi-component topology errors, using GIS software to recompute the area of each carbon layer patch to create a project carbon layer database of the current state, and obtaining the latest carbon layer patch area used for an overall processing and quantification of project carbon layers;
performing spatial union by using the previous carbon layer patch database and the project carbon layer database of the current state, adding attribute factors such as the reasons for changes, change basis, and change time, improving and filling in attribute factors according to the attribute quality check logic of the change database, creating the project carbon layer change database.

### Claim 6 (depends on 4)

The dynamic carbon sink measurement method for afforestation carbon sink and forest management carbon sink projects according to claim 4, characterized in that specific steps in the project carbon layer carbon density IoT monitoring are as follows:
designing the project sample plots: based on the project scope and precision requirements, selecting a project sample plot layout scheme, determining the number and size of the project sample plots, wherein the size of the project sample plots is consistent with that of the baseline sample plots;
monitoring the project IoT sample plots;
computing the project arbor carbon density, including: computing the project arbor carbon density by using the biomass expansion factor method;
computing the project dead wood carbon density, including: based on the computation results in- the computing the project arbor carbon density, using the “carbon stock change method” combined with the “default value method” to compute the project dead wood carbon density;
computing the project litter carbon density, including: based on the computation results in the computing the project arbor carbon density, using the “carbon stock change method” combined with the “default value method” to compute the project litter carbon density;
computing the project greenhouse gas emissions per unit area: the greenhouse gas emissions caused by arbor burning resulting from forest fires within the boundary of comprehensive projects include two parts: arbor and dead organic matter, where a computation formula is as follows:
GHGcurrent,i,t=GHGtree_current,i,t+GHGdom_current,i,t
where GHGcurrent,i,t represents an increase in greenhouse gas emissions in the i-th carbon layer at the t-th monitoring; GHGtree_current,i,t represents the increase in non-CO2 greenhouse gas emissions caused by arbor burning due to forest fires in the i-th carbon layer at the t-th monitoring; GHGdom_current,i,t represents the increase in non-CO2 greenhouse gas emissions caused by dead organic matter burning due to forest fires in the i-th carbon layer at the t-th monitoring;
the increase in non-CO2 greenhouse gas emissions caused by arbor burning due to forest fires is computed by using biomass and combustion factors from the previous monitoring, where a computation formula is as follows:
GHGtree_current,i,t=wtree_curent,i,t-1×COMFi×(EFCH4,i×GWPCH4+EFN2O,i×GWPN2O)
where wtree_curent,i,t-1 represents the arbor per unit area in the i-th carbon layer at the t−1st monitoring; COMFi is a combustion index for each vegetation type in the i-th carbon layer, which is a default value; EFCH4,i is a CH4 emission index in the i-th carbon layer; GWPCH4 is the equivalent that converts CH4 to CO2, defaulting to 25; EFN2O,i is a N2O emission index in the i-th carbon layer; GWPN2O is the equivalent that converts N2O to CO2, defaulting to 298;
GHGdom_current,i,t=0.07×(cdw_curent,i,t-1+cli_curent,i,t-1)
where cdw_curent,i,t-1 is the dead wood biomass per unit area in the i-th carbon layer at the t−1st monitoring; cli_curent,i,t-1 is the litter biomass per unit area in the i-th carbon layer at the t−1st monitoring;
computing the project carbon layer carbon density: the project carbon layer carbon density is the sum of the arbor carbon stock, the dead wood carbon stock, and the litter carbon stock for all tree species minus greenhouse gas emissions, where a computation formula is as follows:
c
current
,
i
,
t
=
∑
j
=
1
(
c
tree
-
⁢
current
,
i
,
j
,
t
+
c
dw
-
⁢
current
,
i
,
j
,
t
+
c
li
-
⁢
current
,
i
,
j
,
t
)
-
GHG
current
,
i
,
t
where ccurrent,i,t is the project carbon layer carbon density in the i-th carbon layer at the t-th monitoring; cbase,i,t is the baseline carbon layer carbon density in the i-th carbon layer at the t-th monitoring.

### Claim 7 (depends on 6)

The dynamic carbon sink measurement method for afforestation carbon sink and forest management carbon sink projects according to claim 6, characterized in that a computation formula in the computing the project carbon layer total carbon stock is as follows:
c
current
,
t
=
∑
i
=
1
(
c
current
,
i
,
t
×
s
current
,
i
)
where ccurrent,t represents the project carbon layer total carbon stock at the t-th monitoring; scurrent,i,t represents the area of the project carbon layer patches in the i-th carbon layer.

### Claim 8 (depends on 7)

The dynamic carbon sink measurement method for afforestation carbon sink and forest management carbon sink projects according to claim 7, characterized in that the total carbon stock in the monitoring total carbon stock and carbon change in real time is a difference between the project carbon layer total carbon stock and the baseline carbon layer carbon stock, where a computation formula is as follows:
ct=ccurrent,t−cbase,t
where ct represents the total carbon stock at the t-th monitoring;
carbon change is the change in carbon stock over a certain monitoring period, computed as follows:
Δ
⁢
c
t
2
,
t
1
=
c
t
2
-
c
t
1
t
2
-
t
1
where Δct2,t1 is the change in carbon stock from the t1-th monitoring to the t2-th monitoring; ct2 is the total carbon stock at the t2-th monitoring; ct1 is the total carbon stock at the t-th monitoring; t2 and t1 represent the monitoring period, which is set based on the IoT monitoring sample plots.