Open Access

Development of monitoring and assessment of forest biomass and carbon storage in China

Forest Ecosystems20141:20

DOI: 10.1186/s40663-014-0020-5

Received: 22 April 2014

Accepted: 23 September 2014

Published: 12 December 2014

Abstract

Addressing climate change has become a common issue around the world in the 21st century and equally an important mission in Chinese forestry. Understanding the development of monitoring and assessment of forest biomass and carbon storage in China is important for promoting the evaluation of forest carbon sequestration capacity of China. The author conducts a systematic analysis of domestic publications addressing “monitoring and assessment of forest biomass and carbon storage” in order to understand the development trends, describes the brief history through three stages, and gives the situation of new development. Towards the end of the 20th century, a large number of papers on biomass and productivity of the major forest types in China had been published, covering the exploration and efforts of more than 20 years, while investigations into assessment of forest carbon storage had barely begun. Based on the data of the 7th and 8th National Forest Inventories, forest biomass and carbon storage of the entire country were assessed using individual tree biomass models and carbon conversion factors of major tree species, both previously published and newly developed. Accompanying the implementation of the 8th National Forest Inventory, a program of individual tree biomass modeling for major tree species in China was carried out simultaneously. By means of thematic research on classification of modeling populations, as well as procedures for collecting samples and methodology for biomass modeling, two technical regulations on sample collection and model construction were published as ministerial standards for application. Requests for approval of individual tree biomass models and carbon accounting parameters of major tree species have been issued for approval as ministerial standards. With the improvement of biomass models and carbon accounting parameters, thematic assessment of forest biomass and carbon storage will be gradually changed into a general monitoring of forest biomass and carbon storage, in order to realize their dynamic monitoring in national forest inventories. Strengthening the analysis and assessment of spatial distribution patterns of forest biomass and carbon storage through application of remote sensing techniques and geostatistical approaches will also be one of the major directions of development in the near future.

Keywords

Biomass models Carbon accounting parameters Biomass conversion factor Root-to-shoot ratio Carbon storage

Background

Increasingly, governments worldwide attach considerable importance to monitoring and assessing forest biomass and carbon storage against a background of global climate change. To help countries implement national greenhouse gas inventories, forest biomass and carbon stock assessments, the Intergovernmental Panel on Climate Change (IPCC) has provided default values for carbon accounting parameters such as biomass expansion factors (BEF) and root-to-shoot ratios (RSR) for boreal, temperate and tropical zones in the “Good Practice Guidance for Land Use, Land-Use Change and Forestry” (IPCC [2003]). However, the application of these default parameters is fraught with great uncertainty. Development of individual tree biomass models and carbon accounting parameters suitable for national monitoring and assessment of forest biomass and carbon storage has become of fundamental importance. Over the last ten years, biomass equations for major tree species in America, Canada and some European countries have been developed or improved (Jenkins et al. [2003]; Bi et al. [2004]; Lambert et al. [2005]; Snorrason and Einarsson [2006]; Muukkonen [2007]; Návar [2009]; Blujdea et al. [2012]; Fayolle et al. [2013]). Their purpose was to assess and monitor forest biomass and carbon storage and to provide a basis for evaluating the contribution of forest ecosystems to the global carbon cycle.

The Chinese government has made great efforts in the issue of climate change and signed the “United Nations Framework Convention on Climate Change”, as well as the “Kyoto Protocol”. At the UN climate change summit opening ceremony in 2009, the Chinese President Hu Jintao proposed to raise vigorously forest carbon sequestration in China and promised to increase our forest area by 40 million hectares and our forest volume by 1.3 billion cubic meters by the year 2020 from the 2005 level (Hu [2009]). Since the start of 2009, accompanying the implementation of the 8th National Forest Inventory (NFI) in China, a program of national forest biomass modeling has begun to be implemented. This program was designed to develop individual tree biomass models and carbon accounting parameters for the major tree species in China, to provide a quantitative basis for estimating forest biomass and carbon storage and for assessing our capacity of forest carbon sequestration.

In order to help readers understand the basic situation of monitoring and assessment of forest biomass and carbon storage in China, this paper reviews the development of forest biomass studies in China, especially the status and the latest progress of monitoring and assessment of forest biomass and carbon storage. At the end, some ideas for future studies are proposed.

Review

Brief history

The earliest research on forest biomass abroad can be traced to the 1870s (Ebermeyr [1876]). Studies on forest biomass in China has only been implemented since the late 1970s when some related articles were published (e.g., Li [1978]), i.e., a century after the earliest study abroad. Due to special historical reasons, China did not participate in the International Biological Program (IBP), initiated by the International Union of Forest Research Organizations (IUFRO), during the period of 1964–1974 and thus missed the golden development stage of forest biomass research. Reviewing the development of more than 30 years in China, these preliminary studies started with estimating biomass and productivity of major forest types, gradually covering various other forest types in the country and then focused on the assessment of carbon stock of all our forest ecosystems. To monitor forest biomass and carbon storage in our national forest inventory (NFI) system, and to assure coordination between forest biomass and forest volume, compatible biomass models for individual trees of our major species were developed gradually. Monitoring and assessment of forest biomass and carbon storage based on the NFI data were subsequently conducted.

Biomass assessment studies of various forest types

Given the available literature, it appears that the earliest study on biomass assessment was conducted for two different forest types of Chinese fir, carried out by Pan et al. ([1980]), followed by studies on biomass productivity of major types of natural temperate forests on Changbai Mountain by Li et al. ([1981]), on Masson pine forests in Huitong County, Hunan Province, by Feng et al. ([1982]) and on Chinese fir forests at the Yangkou Forest Farm, Fujian Province, by Ye ([1984]). As well, Chen et al. ([1986]) studied biomass of Chinese arborvitae (Platycladus orientalis) forests, Xu ([1988]) studied biomass productivity of major forest types in Daxinganling and Ma ([1989]) developed biomass models for Chinese pine in China. Based on the results of biomass modeling of 11 tree species in northeastern China, Chen and Zhu ([1989]) compiled the first biomass handbook of the country, i.e., the “Manual of Tree Biomass for Major Species in Northeastern China”. Since then, Liu et al. ([1990]) and Liu ([1992]) respectively studied the biomass and productivity of Larix gmelini and Larix principis-rupprechtii plantations, while Liu ([1993]), Fang et al. ([1996]), Luo ([1996]) and Tian et al. ([1998]) also conducted studies on biomass and productivity of Chinese fir, Masson pine and other major forest types. Feng et al. ([1999]) systematically summarized biomass, productivity and the distribution pattern of different forest types in China. In short, through exploration and efforts over more than 20 years, we have been quite successful in assessing and monitoring biomass and productivity of major forest types in China.

Assessment of carbon storage in Chinese forest ecosystems

On the basis of biomass studies, Chinese scientists have started to assess carbon storage of forest ecosystems in China and their contribution to the global carbon balance since the beginning of the current century (Liu et al. [2000]; Zhou et al. [2000]; Wang et al. [2001]). One of the most notable achievements was that of Fang et al. ([2001]) who estimated the changes in forest biomass carbon storage in China between 1949 and 1998 using the improved method for estimating forest biomass as well as our national forest inventory data of the last 50 years, where a factor of 0.5 was used to convert biomass to carbon storage. The results show that Chinese forests released about 0.68 Pg carbon between 1949 and 1981, but increased the amount of sequestered carbon by 0.37 Pg from 1981 to 1998 (see Table 1). The main reason for this development is that since the mid-1970s, China’s massive afforestation efforts have increased forest carbon storage. From the data in Table 1, it can be seen that the amount of carbon in our plantations increased by about 0.39 Pg from 1981 to 1998, implying that carbon storage in natural forests decreased slightly during this period. Xu et al. ([2010]) investigated the relationships between forest biomass density and forest age for 36 major forest types using our national forest inventory data from 1994–1998 and 1999–2003 and estimated the potential of forest biomass carbon storage in China for the 2000–2050 period, using statistical algorithms. The results show that the carbon stock in China’s forests would increase from 5.86 Pg in 1999–2003 to 10.23 Pg in 2050, suggesting that China’s forests should become a significant carbon sink in the future.
Table 1

Periodic assessment in China of the amounts of carbon in forests and plantations, density and accumulation between 1949 and 1998

Period

Forest carbon (Pg)

Plantation carbon (Pg)

Forest carbon density (Mg · ha−1)

Annual carbon accumulation (Pg · a−1)

1949

5.06

49.45

1950–1962

4.58

46.67

−0.040

1973–1976

4.44

0.27

43.83

−0.010

1977–1981

4.38

0.33

45.75

−0.013

1984–1988

4.45

0.52

43.53

0.011

1989–1993

4.63

0.61

42.58

0.035

1994–1998

4.75

0.72

44.91

0.026

Source: Fang et al. ([2001]).

Monitoring and assessment of forest biomass and carbon storage in China

These early forest biomass studies were aimed at major or typical forest types, with developed biomass models largely based on stand or sample plot levels. Some biomass models were even based on individual tree data, most of them suited only for certain sites or local areas, not representative of larger regions. These forest carbon assessments were based on estimates of biomass per hectare for our major forest types from purposeful sampling and areas of forest types from our national forest inventory, which is not really suited for monitoring and assessing forest carbon storage.

The “International Guidelines for Forest Monitoring”, published by IUFRO ([1994]), clearly defined forest biomass as one of the important items of global, regional and national forest monitoring. In order to add forest biomass into the items of national forest inventory of China, the Ministry of Forestry conducted a key program “Research on two-variable biomass models and compatible auto-adoptive one-variable biomass models for the major tree species in China” between 1995 and 1997. The program presented compatible individual tree biomass modeling approaches, which developed tree biomass models for 11 species or species groups, i.e., Larix, Abies, Pinus koraiensis, Tilia tuan, Acer mono, Fraxinus mandshurica, Juglans mandshurica and Phellodendron amurense in northeastern China, as well as Cunninghamia lanceolata, Pinus massoniana and broadleaved species in southern China (Xu [1998]; Zhang et al. [1999]; Luo et al. [1999]; Zeng et al. [1999]; Tang et al. [2000]). Since then, Ning ([2007]) studied a biomass modeling approach for stems, branches, foliage and entire trees and developed biomass models compatible with stem volumes for Pinus sylvestris var. mongolica plantations. Cheng ([2007]) developed allometric equations for individual tree biomass from diameter and height for eight forest types of the Xiaolongshan Mountain in Gansu province.

In the statistical investigations of the 7th National Forest Inventory of China in 2009, a thematic assessment on forest biomass and carbon storage was conducted. According to the assessment results, China’s forest biomass was 15.77 Pg and forest carbon storage 7.81 Pg (Li and Lei [2010]). For that assessment, individual tree biomass models (not stand biomass models for major forest types) and carbon factors for the following 16 tree species (or groups) were collected and compiled, i.e., Picea/Abies, Tsuga/Keteleeria, Larix, Pinus koraiensis, P. sylvestris var. mongolica, P. tabulaeformis (including P. densiflora and P. thunbergii), P. armandii, P. yunnanensis, P. massoniana (including other pines), Cunninghamia lanceolata (including Taxus, Pseudotsuga, Metasequoia and Taxodium), Cupressus, Betula, Eucalyptus, other hardwood broadleaved species, other softwood broadleaved species and bamboo. Sample plot data from the 7th National Forest Inventory were used in this assessment. Forest biomass and carbon storage were estimated from plots, extended to populations and/or provinces and in the end aggregated at the national level (Li et al. [2011], [2012]). The thematic assessment results of forest biomass and carbon storage, as one of the 7th National Forest Inventory outputs, has been published by the government. As a result, the monitoring and assessment of forest biomass and carbon storage in China has managed to make a substantial contribution in this area.

New developments

Coping with climate change issues has now become a special task of forestry in China, which has resulted in considerable progress in the study and assessment of forest biomass and carbon storage. The latest progress is largely manifested in two aspects. In first instance, this is the implementation of a national program for the development of individual tree biomass models and carbon accounting parameters for major tree species. Some of them have been published as ministerial standards, providing an excellent foundation for continuous monitoring of forest biomass and carbon storage and evaluation of forest carbon sequestration capacity in China. The second aspect is the implementation of provincial and national assessment of forest biomass and carbon storage based on NFI data, which allows monitoring and assessment of forest biomass and carbon storage in China to be compatible with international standards.

Individual tree biomass modeling for major tree species

Since early 2009, the National Forest Biomass Modeling Program began to be implemented. By the end of 2013, the program had been carried out for five years. Given the thematic research on classification of modeling population and procedures for collecting samples and methodology for biomass modeling (Zeng et al. [2010]; Zeng [2010], [2011], [2013]; Zeng and Tang [2010], [2011], [2012]; Dang et al. [2011], [2012]), two technical regulations were published as ministerial standards for their application (State Forestry Administration of China, [2014a], [b]). On the basis of these two regulations, individual tree biomass models and carbon accounting parameters for the first group of five coniferous tree species, i.e., Cunninghamia lanceolata, Pinus massoniana, P. yunnanensis, P. tabulaeformis and P. elliottii, have been developed through an integrated utilization of mixed nonlinear models, dummy-variable models and simultaneous error-in-variable equations and subsequently published for application as ministerial standards (State Forestry Administration of China, [2014c], [d], [e], [f], [g]). Biomass models for the second group of eight tree species, i.e., Abies, Picea, Larix, Cryptomeria, Quercus, Betula, Schima superb and Liquidambar formosana, are in the development stage, which should be completed towards the end of 2014. Approval as ministerial standards for application will be sought.

Classification of populations for biomass modeling
According to the study results of Zeng et al. ([2010]), tree species was the basis for classification of biomass modeling populations, followed by eco-geographical regionalization and administrative division. For tree species, two aspects were considered: one, classification of populations for biomass modeling should be compatible with that for volume modeling; two, assurance that the amount of stand area and volume stock for each separate modeling population of certain tree species (or group) is sufficient. Therefore, at first the proportions of standing volumes for all tree species were calculated based on the data of the 7th NFI and tree species in the top 30 of the list were determined. Secondly, the geographical locations for modeling populations in “Tree Volume Table” (LY/T 1353-1999) were confirmed, taking into account both eco-geographical regionalization and administrative division. In the end, the scheme of classification of biomass modeling populations was presented. According to this scheme, China’s territory was classified into six regions (see Figure 1); all tree species were classified into 34 groups and 70 biomass modeling populations were specified. Specifically, 38 biomass modeling populations were classified for 20 coniferous species (or groups) and 32 populations for 14 broadleaved species (or groups). On the basis of geographical regionalization, 17 modeling populations were classified in the southwest, 16 in the south, 15 in the northeast, 9 in the northwest, 7 in Tibet and 6 in the north of China (see Table 2).
Figure 1

Population classification for biomass modeling in China. Source: Zeng ([2011]).

Table 2

Species and population classification and distribution in China for biomass modeling

No.

Tree species (groups)

Northeast

North

Northwest

South

Southwest

Tibet

Total

1

Quercus

1

0.3

0.7

1

1.8

0.2

5

2

Abies

1

 

1

 

2

1

5

3

Picea

1

 

2

 

1

1

5

4

Betula

2

0.2

0.8

 

0.8

0.2

4

5

Larix

1

1

1

 

0.8

0.2

4

6

Cunninghamia lanceolata

   

2.6

0.4

 

3

7

Pinus massoniana

   

2.7

0.3

 

3

8

Populus

2

1

1

1

0.85

0.15

6

9

Pinus yunnanensis

    

0.65

0.35

1

10

Pinus densata

    

1

1

2

11

Tilia tuan

1

     

1

12

Cupressus

 

0.25

0.75

1

0.65

0.35

3

13

F-J-P

1

     

1

14

Schima superba

   

0.7

0.3

 

1

15

Pinus tabulaeformis

0.2

0.45

0.25

 

0.1

 

1

16

Pinus koraiensis

1

     

1

17

Ulmus

0.9

0.1

    

1

18

Pinus khasya

    

1

 

1

19

Pinus armandii

  

0.25

0.1

0.5

0.15

1

20

Tsuga

  

0.05

 

0.9

0.05

1

21

Pinus sylvestris var. mongolica

1

     

1

22

Liquidambar formosana

   

1

  

1

23

Pinus elliottii

   

1

  

1

24

Salix

0.4

0.3

0.2

0.1

  

1

25

Eucalyptus

   

0.9

0.1

 

1

26

Cryptomeria

   

0.4

0.6

 

1

27

Pinus taiwanensis

   

1

  

1

28

Pinus griffithii

     

1

1

29

Robinia pseudoacacia

 

1

    

1

30

Paulownia

 

0.3

 

0.5

0.2

 

1

31

C-S-P

   

0.8

0.2

 

1

32

Other pines

0.6

0.4

    

1

33

Other coniferous trees

   

0.2

0.6

0.2

1

34

Other broadleaved trees

1

1

1

2

1

1

7

 

Total

15.1

6.3

9

15.75

17

6.85

70

Note: 1) F-J-P means Fraxinus mandshurica, Juglans mandshurica and Phellodendron amurense; C-S-P means Cinnamomum, Sassafras and Phoebe. Other pines include Pinus densiflora, P. thunbergii and others; other coniferous species include Keteleeria, Taxus, Pseudotsuga, Metasequoia, Taxodium and other conifers; other broadleaved species include all broadleaved species except those listed for the region. 2) The decimal fraction expresses the proportion of stocking volume of the species. Source: Zeng ([2011]).

Composition of individual tree biomass models
Individual tree biomass models consist of two sets: one- and two-variable models, where both sets included three parts: aboveground biomass models, component biomass models and belowground biomass models. The aboveground biomass models involve compatible stem volume models and biomass conversion factor models. The component biomass models include additive stem wood, stem bark, branches and foliage biomass models and the belowground biomass models contain root-to-shoot ratio models. Leaving component biomass models out of the discussion, individual tree biomass models mainly involve the following five equations (taking the two-variable model as an example):
M A = a 0 D a 1 H a 2 + ε 1
(1)
M B = b 0 D b 1 H b 2 + ε 2
(2)
V = c 0 D c 1 H c 2 + ε 3
(3)
B C F = d 0 D d 1 H d 2 + ε 4
(4)
R S R = e 0 D e 1 H e 2 + ε 5
(5)
where M A is aboveground biomass, M B belowground biomass, V stem volume, BCF the biomass conversion factor, RSR the root-to-shoot ratio and a i , b i , c i , d i , e i (i = 0, 1, 2) are the parameters to be estimated, and ε i the error term. If the variable H in equations 1 to 5 were removed, the equations become one-variable models. Because BCF is equal to the ratio of aboveground biomass to stem volume and RSR equal to the ratio of belowground biomass to aboveground biomass, the model parameters show the following relations:
d 0 = a 0 / c 0 , d 1 = a 1 c 1 , d 2 = a 2 c 2
(6)
e 0 = b 0 / a 0 , e 1 = b 1 a 1 , e 2 = b 2 a 2
(7)

Because equations 1 to 5 are related, estimation of the parameters requires the use of simultaneous equations (Zeng and Tang [2010], [2011], [2012]). Owing to the different sample sizes for measuring aboveground biomass and belowground biomass, it was necessary to develop separate simultaneous equations for above- and belowground biomass.

A height-diameter regression model can be used as a “bridge” to link two-variable biomass or volume models with one-variable models. If such a height-diameter regression model were introduced into the system of simultaneous equations, an integrated biomass equation system including both one- and two-variable models could be developed (Zeng [2013]).

Parameters of individual tree biomass models
Based on the biomass measurement data for 13 tree species (or groups), for which collection had been completed by the end of 2012, the individual tree biomass models were estimated using nonlinear simultaneous error-in-variable equations (State Forestry Administration 2014b). The parameter estimates are presented in Table 3.
Table 3

Parameter estimates of tree biomass model systems for 13 tree species or groups in China

Tree species (groups)

Number of samples

Model

Aboveground biomass equation (1)

Belowground biomass equation (2)

Stem volume equation (3)

Biomass conversion factor model (4)

Root-to-shoot ratio model (5)

a 0

a 1

a 2

b 0

b 1

b 2

c 0

c 1

c 2

d 0

d 1

d 2

e 0

e 1

e 2

Abies

150

One-variable

0.15777

2.2674

 

0.01092

2.6672

 

0.13438

2.5198

 

1.1740

−0.2524

 

0.0692

0.3998

 

Two-variable

0.15777

2.2674

0.0000

0.01484

2.9248

−0.4191

0.08366

1.8997

0.8657

1.8859

0.3677

−0.8657

0.0941

0.6574

−0.4191

Picea

451

One-variable

0.24686

2.1451

 

0.04371

2.2254

 

0.15021

2.4523

 

1.6435

−0.3072

 

0.1770

0.0803

 

Two-variable

0.22121

2.0120

0.1925

0.05006

2.3902

−0.2382

0.08042

1.6940

1.0960

2.7506

0.3180

−0.9035

0.2263

0.3783

−0.4306

Cunninghamia lanceolata

303

One-variable

0.07629

2.3909

 

0.02533

2.2666

 

0.11013

2.5580

 

0.6927

−0.1671

 

0.3320

−0.1244

 

Two-variable

0.06326

2.0339

0.4745

0.02196

2.0339

0.3171

0.07416

1.8040

1.0022

0.8530

0.2300

−0.5277

0.3471

0.0000

−0.1574

Cupressus

136

One-variable

0.22284

2.1986

 

0.08499

1.9766

 

0.15048

2.3080

 

1.4808

−0.1094

 

0.3814

−0.2219

 

Two-variable

0.19087

2.0064

0.3218

0.10163

2.1985

−0.3715

0.10175

1.8225

0.8130

1.8759

0.1839

−0.4913

0.5325

0.1920

−0.6933

Larix

300

One-variable

0.10555

2.3895

 

0.02215

2.5209

 

0.12464

2.5162

 

0.8468

−0.1266

 

0.2098

0.1314

 

Two-variable

0.08243

2.0780

0.4412

0.01781

2.2464

0.3888

0.07098

1.8067

1.0050

1.1614

0.2714

−0.5638

0.2161

0.1684

−0.0524

Pinus massoniana

302

One-variable

0.14221

2.3141

 

0.02268

2.3692

 

0.17234

2.4225

 

0.8252

−0.1083

 

0.1595

0.0551

 

Two-variable

0.08982

1.9746

0.5470

0.02678

2.4916

−0.1974

0.08215

1.8749

0.8821

1.0934

0.0997

−0.3351

0.2982

0.5171

−0.7444

Pinus yunnanensis

150

One-variable

0.09492

2.3567

 

0.01654

2.3449

 

0.17251

2.3759

 

0.5502

−0.0193

 

0.1743

−0.0118

 

Two-variable

0.07023

2.1039

0.4112

0.01436

2.2264

0.1928

0.09653

1.8889

0.7924

0.7275

0.2151

−0.3812

0.2045

0.1225

−0.2184

Pinus tabulaeformis

150

One-variable

0.12338

2.3460

 

0.02630

2.3666

 

0.15585

2.3885

 

0.7916

−0.0425

 

0.2132

0.0206

 

Two-variable

0.10081

2.0712

0.4142

0.02627

2.3650

0.0024

0.11016

1.9165

0.7115

0.9151

0.1547

−0.2972

0.2606

0.2938

−0.4118

Pinus elliottii

154

One-variable

0.08893

2.4234

 

0.06849

2.0813

 

0.14039

2.4275

 

0.6334

−0.0041

 

0.7702

−0.3421

 

Two-variable

0.06148

2.0342

0.6162

0.07425

2.1666

−0.1350

0.08277

1.8701

0.8822

0.7428

0.1640

−0.2661

1.2077

0.1324

−0.7511

Quercus

460

One-variable

0.14233

2.3798

 

0.07334

2.1410

 

0.11681

2.5073

 

1.2184

−0.1275

 

0.5153

−0.2388

 

Two-variable

0.09160

1.8578

0.7811

0.07328

2.1399

0.0017

0.06751

1.8578

0.9719

1.3569

0.0000

−0.1908

0.8000

0.2821

−0.7794

Betula

450

One-variable

0.10130

2.4191

 

0.05549

2.1976

 

0.17682

2.3643

 

0.5729

0.0548

 

0.5478

−0.2215

 

Two-variable

0.05992

2.0638

0.6032

0.05536

2.1961

0.0026

0.07971

1.8252

0.9153

0.7517

0.2386

−0.3121

0.9240

0.1322

−0.6005

Populus

602

One-variable

0.08653

2.4685

 

0.02702

2.3420

 

0.15280

2.4447

 

0.5663

0.0239

 

0.3123

−0.1265

 

Two-variable

0.07055

2.3333

0.2215

0.04079

2.6147

−0.4465

0.07139

1.9406

0.8255

0.9882

0.3926

−0.6040

0.5782

0.2814

−0.6680

Schima superba

150

One-variable

0.17922

2.2574

 

0.07816

2.1354

 

0.18518

2.3413

 

0.9678

−0.0839

 

0.4361

−0.1220

 

Two-variable

0.10504

1.9906

0.5209

0.06898

2.0730

0.1218

0.08043

1.9249

0.8132

1.3061

0.0658

−0.2923

0.6567

0.0824

−0.3991

This table shows the equations for one- and two-variable individual tree aboveground biomass, belowground biomass and compatible stem volume models, biomass conversion factor models and root-to-shoot ratio models. These models provided the quantitative basis for estimating forest biomass and carbon storage in the 8th NFI of China. The reason for the development of biomass models for tree species (or groups), instead of modeling populations of Table 2, was to maintain consistency with those in the 7th NFI.

New assessment of forest biomass and carbon storage in China

Carrying out provincial or national assessments of forest biomass and carbon storage based on continuous forest inventory data has recently become the major direction of development. As a result, considerable progress has been made.

Provincial assessment of forest biomass and carbon storage

Based on the data of our continuous forest inventory (CFI) between 1994 and 2008 in the Da-Xing mountains of Inner Mongolia, Fu et al. ([2013]) used regression equations correlating biomass and stocking volume for different forest types to estimate forest biomass and carbon storage in this mountainous forest region and to analyze the dynamic changes of forest carbon storage and carbon density. Lu et al. ([2013]) used continuous forest inventory data in Qinghai Province and employed biomass expansion factors (BEF) to estimate carbon storage and carbon density of forest vegetation in the province and to analyze development trends over the past 20 years. Based on the continuous forest inventory data from 1998 to 2008 in Fujian Province, Zheng et al. ([2013]) used regression analysis to relate biomass and stocking volume for different forest types to estimate carbon storage and carbon density of forest vegetation and to analyze dynamic changes during this period. Wen et al. ([2014]) used the continuous forest inventory data of Jiangsu Province in 2000 and 2005 in a continuous function method for biomass expansion to estimate forest biomass and net primary productivity (NPP) of the province and to conduct comparative analyses from age groups, dominant species and geographic regions.

National assessment of forest biomass and carbon storage

In the statistical analysis of the 8th national forest inventory of China in 2013, a thematic assessment on forest biomass and carbon storage was carried out. According to the assessment results, China’s forest biomass was 17.00 Pg and forest carbon storage 8.43 Pg (Xu [2014]). The method applied for this assessment was almost the same as that used in the 7th NFI, but only seven old biomass models among 16 tree species groups were still utilized; for the other 9 tree species groups, 13 new biomass models were developed (see Table 3). Biomass models for Abies and Picea were separated from the Abies/Picea group, the biomass model for Pinus elliottii was separated from that of other pines, and the biomass models for Schima superb and Liquidambar formosana were separated from that of other broadleaved species. The assessment of forest biomass and carbon storage, the result of the 8th national forest inventory, had also been published by the government, which means that the monitoring and assessment of forest biomass and carbon storage in China have become the routine work.

Conclusions

After developing for nearly forty years, the national forest inventory system of China has been in line with international standards in both methodology and techniques, with organization management and system operation effective and standardized. At about 415,000 the number of permanent sample plots is especially large with frequent remeasurement, seven times in total. The statistical results are quite informative, a rare global phenomenon. In general, the national forest inventory system of China ranks among the top in the world (Lei et al. [2009]; Lin et al. [2013]; Tang [2014]). Viewed from the current direction of development, future monitoring and assessment of forest biomass and carbon storage could be improved in three ways.

Improvement of biomass models and carbon accounting parameters

During the five year period of the 8th NFI, biomass sample collection for 15 tree species (groups) and 38 modeling populations had been completed (save for 2 of the 15 species, i.e., Cupressus and Populus, involving 5 modeling populations). Therefore only biomass models for 13 tree species had been established. When all the models for the 15 species or groups are developed, the biomass of about 70% of standing tree volume can be estimated. The other 30% of standing volume involves nearly 20 tree species (groups) and requires the establishment of more than 30 biomass models. In addition, there are large numbers of economic forests, sparsely forested areas, bamboos and shrubs in China. According to Fang et al. ([1996]), the biomass of these resources accounts for about 13% of the total forest vegetation. In order to monitor and assess the entire forest biomass and carbon storage, it is necessary to develop biomass models and carbon accounting parameters for bamboo species and shrubs (including economic shrubs). Besides live biomass and carbon storage (including above- and belowground parts), the carbon stock of all forest ecosystems should also include the carbon stored in dead trees, litter layers and organic soils (IPCC [2003]). In future, all models and parameters related to monitoring and assessment of biomass and carbon of forest ecosystems will need to be developed and improved step by step.

Conversion from thematic assessment to general monitoring of forest biomass and carbon storage

During the last two national forest inventories, thematic assessments were carried out for presenting the results of forest biomass and carbon storage (Li and Lei [2010]; Li et al. [2011], [2012]). With the publication of individual tree biomass models and carbon accounting parameters for other species as ministerial standards in successive future national forest inventories, thematic assessment on forest biomass and carbon storage will gradually become a general monitoring routine, similar to estimating forest volumes (Zeng et al. [2010]; Zeng [2011]; Zeng et al. [2013]). What is required is to consolidate individual tree biomass models and carbon accounting parameters with stem volume equations for all tree species (or groups) into our statistical software. In such a scenario we can present the results of forest biomass and carbon storage in related statistical tables and realize the dynamic monitoring of forest biomass and carbon storage in the national forest inventory of China.

Analysis of spatial distribution of forest biomass and carbon storage based on remote sensing

At a regional scale, on either a provincial or national level, the main method for assessing forest biomass and carbon storage is based on forest resource inventory data (Deng and Shangguan [2011]; Li et al. [2011]; Wang and Deng [2014]). More recently, studies on forest carbon estimation combining forest inventory data with remote sensing data have gradually proliferated (Xue et al. [2009]; Pang and Li [2012]; Liu et al. [2012]; Tu and Peng [2012]; Huang and Chen [2013]; Huang et al. [2013]; Wang et al. [2014]). Application of remote sensing from various sources, especially high resolution data, could greatly improve the timeliness of assessment of forest biomass and carbon storage. In addition, geostatistics, differential geometry and other technical means, have also been gradually applied in the analysis of spatial patterns of forest biomass and carbon storage (He et al. [2013]; Liu and Ruan [2013]; Zhao et al. [2013]). Strengthening the analysis and assessment of spatial distribution patterns of forest biomass and carbon storage through application of remote sensing techniques and geostatistical approaches will be one of the major directions of development in the near future.

Declarations

Acknowledgments

I acknowledge the Forest Biomass Modeling Program in the National Forest Inventory (FBMP-NFI), funded by the State Forestry Administration of China, for providing biomass measurement data. I also thank the Forestry Departments of the various provinces for their efforts in sample collection.

Authors’ Affiliations

(1)
Academy of Forest Inventory and Planning, State Forestry Administration

References

  1. Bi H, Turner J, Lambert MJ: Additive biomass equations for native eucalypt forest trees of temperate Australia. Trees 2004, 18: 467–479. 10.1007/s00468-004-0333-zView ArticleGoogle Scholar
  2. Blujdea VNB, Pilli R, Dutca I, Ciuvat L, Abrudan IV: Allometric biomass equations for young broadleaved trees in plantations in Romania. For Ecol Manage 2012, 264: 172–184. 10.1016/j.foreco.2011.09.042View ArticleGoogle Scholar
  3. Chen CG, Zhu JF: Manual of Tree Biomass for Main Species in Northeastern China. Chinese Forestry Press, Beijing; 1989.Google Scholar
  4. Chen LZ, Chen QL, Bao XC, Ren JK, Miu YG, Hu YH: Studies on Chinese arborvitae ( Platycladus orientalis ) forest and its biomass in Beijing. J Plant Ecol 1986, 10(1):17–25.Google Scholar
  5. Cheng TR: Research on the Forest Biomass and Carbon Storage in Xiaolong Mountains, Gansu Province. Dissertation, Beijing Forestry University, Beijing; 2007.Google Scholar
  6. Dang YF, Zeng WS, Wang XJ: Analysis on carbon content factors of different organs of larch in northeastern China. For Res Manage 2011, 4: 30–34.Google Scholar
  7. Dang YF, Wang XJ, Zeng WS: Using segmented modeling approach to construct tree volume and biomass equations for larch in northeastern China. For Res 2012, 25(5):558–563.Google Scholar
  8. Deng L, Shangguan ZP: Methods for forest carbon storage estimation based on forest inventory data. Bull Soil Water Conserv 2011, 31(6):143–147.Google Scholar
  9. Ebermeyr E: Die gesamte Lehre der Waldstreu mit Rucksicht auf die chemische statik des Waldbaues. Springer, Berlin; 1876.Google Scholar
  10. Fang JY, Liu GH, Xu SL: Biomass and net production of forest vegetation in China. Acta Ecol Sin 1996, 16(4):497–508.Google Scholar
  11. Fang JY, Chen AP, Peng CH, Zhao SQ, Ci LJ: Changes in forest biomass carbon storage in China between 1949 and 1998. Science 2001, 292: 2320–2322. 10.1126/science.1058629View ArticlePubMedGoogle Scholar
  12. Fayolle A, Doucet JL, Gillet JF, Bourland N, Lejeune P: Tree allometry in central Africa: testing the validity of pantropical multi-species allometric equations for estimating biomass and carbon stocks. For Ecol Manage 2013, 305: 29–37. 10.1016/j.foreco.2013.05.036View ArticleGoogle Scholar
  13. Feng ZW, Chen CY, Zhang JW, Wang KP, Zhao JL, Gao H: Determination of biomass of Pinus massoniana stand in Huitong county, Hunan province. Sci Silv Sin 1982, 18(2):127–134.Google Scholar
  14. Feng ZW, Wang XK, Wu G: The Biomass and Productivity of Forest Ecosystem in China. Science Press, Beijing; 1999.Google Scholar
  15. Fu HF, Yan W, Chen JJ: Forest carbon storage and its dynamics in Da-Xing mountains of inner Mongolia. J Arid Land Res Environ 2013, 27(9):166–170.Google Scholar
  16. He P, Zhang HR, Lei XD, Xu G, Gao X: Estimation of forest aboveground biomass based on geostatistics. Sci Silv Sin 2013, 49(5):101–109. doi:10.11707/j.1001–7488.20130514 doi:10.11707/j.1001-7488.20130514Google Scholar
  17. Hu JT: Working together to meet challenges of climate change—the speech at the UN climate change summit opening ceremony. Res Environ Inhabitant 2009, 20: 14–15.Google Scholar
  18. Huang YP, Chen JS: Advances in the estimation of forest biomass based on SAR data. Rem Sens Land Resour 2013, 25(3):7–13.Google Scholar
  19. Huang JL, Ju WM, Zheng G, Kang TT: Estimation of forest aboveground biomass using high spatial resolution remote sensing imagery. Acta Ecol Sin 2013, 33(20):6497–6508. 10.5846/stxb201212211841View ArticleGoogle Scholar
  20. IPCC (2003) Good Practice Guidance for Land Use, Land Use Change and Forestry. The Institute for Global Environmental Strategies for the IPCC, JapanGoogle Scholar
  21. IUFRO:International Guidelines for Forest Monitoring. IUFRO World Series Vol 5. IUFRO Secretariat, Vienna; 1994.Google Scholar
  22. Jenkins JC, Chojnacky DC, Heath LS, Birdsey RA: National-scale biomass estimators for United States tree species. For Sci 2003, 49(1):12–35.Google Scholar
  23. Lambert MC, Ung CH, Raulier F: Canadian national tree aboveground biomass equations. Can J For Res 2005, 35: 1996–2018. 10.1139/x05-112View ArticleGoogle Scholar
  24. Lei XD, Tang MP, Lu YC, Hong LX, Tian DL: Forest inventory in China: status and challenges. Int For Rev 2009, 11: 52–63.Google Scholar
  25. Li WH: Concept of forest biomass productivity and its basic studying approach. Nat Res 1978, 1: 71–92.Google Scholar
  26. Li HK, Lei YC: Estimation and Evaluation of Forest Biomass Carbon Storage in China. Chinese Forestry Press, Beijing; 2010.Google Scholar
  27. Li WH, Deng KM, Li F (1981) Research on biomass productivity of major ecosystems in the Changbai mountain. For Ecosyst Res (1):34–50 Li WH, Deng KM, Li F (1981) Research on biomass productivity of major ecosystems in the Changbai mountain. For Ecosyst Res (1):34–50Google Scholar
  28. Li HK, Lei YC, Zeng WS: Forest carbon storage in China estimated using forest inventory data. Sci Silv Sin 2011, 47(7):7–12.Google Scholar
  29. Li HK, Zhao PX, Lei YC, Zeng WS: Comparison on estimation of wood biomass using forest inventory data. Sci Silv Sin 2012, 48(5):44–52.Google Scholar
  30. Lin GZ, Wen XR, Zhou CG, She GH: Review and progress of China’s continuous forest inventory system. Open J For 2013, 3(1):17–22.Google Scholar
  31. Liu ZG: Research on biomass and productivity of Larix principis-rupprechtii plantations. J Beijing For Univ 1992, 14(Sp.1):114–123.Google Scholar
  32. Liu XZ: Study on biomass of Masson pine forests at different ages. For Res Manage 1993, 2: 77–80.Google Scholar
  33. Liu S, Ruan HH: Spatial pattern analysis of forest biomass and NPP in Guangdong and Guangxi provinces of south China based on geostatistics. Chin J Ecol 2013, 32(9):2502–2509.Google Scholar
  34. Liu SR, Chai YX, Cai TJ, Peng CH: Research on biomass and net primary productivity of Larix gmelini plantations. J Northeast For Univ 1990, 18(2):40–46.Google Scholar
  35. Liu GH, Fu BJ, Fang JY: Carbon dynamics of Chinese forests and its contribution to global carbon balance. Acta Ecol Sin 2000, 20(5):733–740.Google Scholar
  36. Liu SN, Zhou T, Shu Y, Dai M, Wei LY, Zhang X: The estimating of the spatial distribution of forest biomass in China based on remote sensing and downscaling techniques. Acta Ecol Sin 2012, 32(8):2320–2330. 10.5846/stxb201009301390View ArticleGoogle Scholar
  37. Lu H, Liu K, Wu JH: Change of carbon storage in forest vegetation and current situation analysis of Qinghai province in recent 20 years. Res Environ Yangtze Basin 2013, 22(10):1333–1338.Google Scholar
  38. Luo TX: Patterns of Net Primary Productivity for Chinese Major Forest Types and Their Mathematical Models. Dissertation, Commission for Integrated Survey of Natural Resources, the Chinese Academy of Sciences and State Planning Commission, Beijing; 1996.Google Scholar
  39. Luo QB, Zeng WS, He DB, Bao TH, Lin WR: Establishment and application of compatible tree aboveground biomass models. J Nat Res 1999, 14(3):271–277.Google Scholar
  40. Ma QY: A study on the biomass of Chinese pine forests. J Beijing For Univ 1989, 11(4):1–10.Google Scholar
  41. Muukkonen P: Generalized allometric volume and biomass equations for some tree species in Europe. Eur J For Res 2007, 126: 157–166. 10.1007/s10342-007-0168-4View ArticleGoogle Scholar
  42. Návar J: Allometric equations for tree species and carbon stocks for forests of northwestern Mexico. For Ecol Manag 2009, 257: 427–434. 10.1016/j.foreco.2008.09.028View ArticleGoogle Scholar
  43. Ning B: Study on Stand Structure Dynamic and Biomass for Mongolian Scots Pine in Plantation. Dissertation, Northeast Forestry University, Harbin; 2007.Google Scholar
  44. Pan WC, Li LC, Gao ZH: Biomass and nutrient elements distribution of two different forest types of Chinese fir. Hunan For Sci Technol 1980, 4: 1–14.Google Scholar
  45. Pang Y, Li ZY: Inversion of biomass components of the temperate forest using airborne Lidar technology in Xiaoxing’an mountains, northeastern of China. Chin J Plant Ecol 2012, 36(10):1095–1105. 10.3724/SP.J.1258.2012.01095View ArticleGoogle Scholar
  46. Snorrason A, Einarsson SF: Single-tree biomass and stem volume functions for eleven tree species used in Icelandic forestry. Icelandic Agric Sci 2006, 19: 15–24.Google Scholar
  47. State Forestry Administration of China:Technical Regulation on Sample Collections for Tree Biomass Modeling. China Standard Press, Beijing; 2014.Google Scholar
  48. State Forestry Administration of China:Technical Regulation on Methodology for Tree Biomass Modeling. China Standard Press, Beijing; 2014.Google Scholar
  49. State Forestry Administration of China (2014c) Tree Biomass Models and Related Parameters to Carbon Accounting for Cunninghamia Lanceolata. China Standard Press, Beijing Google Scholar
  50. State Forestry Administration of China (2014d) Tree Biomass Models and Related Parameters to Carbon Accounting for Pinus Massoniana. China Standard Press, Beijing Google Scholar
  51. State Forestry Administration of China (2014e) Tree Biomass Models and Related Parameters to Carbon Accounting for Pinus Yunnanensis. China Standard Press, BeijingGoogle Scholar
  52. State Forestry Administration of China (2014f) Tree Biomass Models and Related Parameters to Carbon Accounting for Pinus Tabulaeformis. China Standard Press, BeijingGoogle Scholar
  53. State Forestry Administration of China (2014g) Tree Biomass Models and Related Parameters to Carbon Accounting for Pinus Elliottii. China Standard Press, BeijingGoogle Scholar
  54. Tang SZ: Scientific System and Reliable Results. China Green Times, 26 Feb. China Green Times Press, Beijing; 2014.Google Scholar
  55. Tang SZ, Zhang HR, Xu H: Study on establishing and estimating method of compatible biomass model. Sci Silv Sin 2000, 36(Sp.1):19–27.Google Scholar
  56. Tian DL, Pan HH, Kang WX, Fang HB: Study on biomass of second generation of Chinese fir plantations. J Cent S Univ For Technol 1998, 18(3):ll-16.Google Scholar
  57. Tu YY, Peng DL: Forest carbon storage estimation based on PCA and SPOT-5. J Cent S Univ For Technol 2012, 32(6):101–103.Google Scholar
  58. Wang KD, Deng LY: Dynamics of forest vegetation carbon stock in Fujian province based on national forest inventories. J Fujian Coll For 2014, 34(2):145–151.Google Scholar
  59. Wang XK, Feng ZW, Ouyang ZY: Vegetation carbon storage and density of forest ecosystems in China. Chin J Appl Ecol 2001, 12(1):13–16.Google Scholar
  60. Wang CW, Hu YM, Shen DC, Huang SL, Zhu JY, Wang L: Assessing the capability of CBERS-02B CCD for estimating subtropical forest aboveground carbon storage. Sci Silv Sin 2014, 50(1):88–96. doi:10.11707/j.1001–7488.20140114 doi:10.11707/j.1001-7488.20140114Google Scholar
  61. Wen XR, Jiang LX, Liu L, Lin GZ, Zheng Y, Xie XJ, She GH: Estimation of forest biomass, net primary production and analysis on spatial distribution pattern for Jiangsu province. J Northwest For Univ 2014, 29(1):36–40.Google Scholar
  62. Xu ZB: Biomass productivity of major forest types in Daxinganling. Chin J Ecol 1988, 7(Sp):49–54.Google Scholar
  63. Xu H: Studies on Standing Tree Biomass Models and the Corresponding Parameter Estimation. Dissertation, Beijing Forestry University, Beijing; 1998.Google Scholar
  64. Xu JD: Results and analysis of eighth national forest inventory of China. J For Econ 2014, 36(3):6–8.Google Scholar
  65. Xu B, Guo ZD, Piao SL, Fang JY: Biomass carbon stocks in China’s forests between 2000 and 2050: a prediction based on forest biomass–age relationships. Sci China Life Sci 2010, 53(7):776–783. 10.1007/s11427-010-4030-4View ArticlePubMedGoogle Scholar
  66. Xue W, Zhang QL, Zhao PX, Li WZ: Review of the estimation of forest biomass by remote sensing. Res Soil Water Conserv 2009, 16(2):209–211.Google Scholar
  67. Ye JZ: Annual dynamic of the biomass of Chinese fir forests on Yangkou forestry farm, Fujian province. J Nanjing For Univ 1984, 4: 1–9.Google Scholar
  68. Zeng WS: Sample collection approach for modeling of single-tree biomass equations. Central South For Inv Plan 2010, 29(2):1–6.Google Scholar
  69. Zeng WS: Methodology on Modeling of Single-Tree Biomass Equations for National Biomass Estimation in China. Dissertation, Chinese Academy of Forestry, Beijing; 2011.Google Scholar
  70. Zeng WS: Generalized tree biomass equations of Chinese fir in China. Central South For Inv Plan 2013, 32(4):4–11.Google Scholar
  71. Zeng WS, Tang SZ: Using measurement error modeling method to establish compatible single-tree biomass equations system. For Res 2010, 23(6):797–803.Google Scholar
  72. Zeng WS, Tang SZ: Establishment of belowground biomass equations for larch in northeastern and Masson pine in southern China. J Beijing For Univ 2011, 33(2):1–6.Google Scholar
  73. Zeng WS, Tang SZ: Modeling compatible single-tree biomass equations of Masson pine ( Pinus massoniana ) in southern China. J For Res 2012, 23(4):593–598. 10.1007/s11676-012-0299-4View ArticleGoogle Scholar
  74. Zeng WS, Luo QB, He DB: Study on compatible nonlinear tree biomass models. Chin J Ecol 1999, 18(4):19–24.Google Scholar
  75. Zeng WS, Tang SZ, Huang GS, Zhang M: Population classification and sample structure on modeling of single-tree biomass equations for national biomass estimation in China. For Res Manage 2010, 3: 16–23.Google Scholar
  76. Zeng M, Nie XY, Zeng WS: Compatible tree volume and aboveground biomass equations of Chinese fir in China. Sci Silv Sin 2013, 49(10):74–79. doi:10.11707/j.1001–7488.20131012 doi:10.11707/j.1001-7488.20131012Google Scholar
  77. Zhang HR, Tang SZ, Wang FY: Study on establishing and estimating method of biomass model compatible with volume. For Res 1999, 12(1):53–59.Google Scholar
  78. Zhao MW, Yue TX, Zhao N, Sun XF: Spatial distribution of forest vegetation carbon stock in China based on HASM. Acta Geogr Sin 2013, 68(9):1212–1224. doi:10.11821/dlxb201309005 doi:10.11821/dlxb201309005Google Scholar
  79. Zheng DX, Liao XL, Li CW, Ye QL, Chen PL: Estimation and dynamic change analysis of forest carbon storage in Fujian province. Acta Agric Univ Jiangxiensis 2013, 35(1):112–116.Google Scholar
  80. Zhou YR, Yu ZL, Zhao SD: Carbon storage and budget of major Chinese forest types. Acta Phytoecologica Sinica 2000, 24(5):518–522.Google Scholar

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