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Aboveground biomass estimation at different scales for subtropical forests in China

Botanical StudiesAn International Journal201758:45

https://doi.org/10.1186/s40529-017-0199-1

Received: 3 September 2016

Accepted: 26 October 2017

Published: 9 November 2017

Abstract

Background

The accurate estimation of forest biomass at different scales is the critical step in the assessment of forest carbon stocks. We used three models at increasing scales: allometric model at ecoregional scale (model 1), dummy variable allometric model at both ecoregion and regional scales (model 2), and allometric model at regional scale (model 3) to estimate the aboveground biomass of six subtropical forests in China. Furthermore, we also tested whether wood density can improve the accuracy of the allometric model at regional scale.

Results

Aboveground biomass estimates for six subtropical forests were significantly affected by the ecoregions (p < 0.05). Model 1 and model 2 had good fitness with higher values of R 2, lower RSE (residual standard error) and MPSE (mean percent standard error) than model 3. The values of MPSE for model 1, model 2, and model 3 ranged from 2.79 to 30.40%, 5.15 to 40.94%, and 13.25 to 80.81% at ecoregion scale, respectively. At regional scale, MPSE of model 2 was very similar to that of model 1, and was less than model 3. New allometric models with wood density had greater R 2, lower RSE and MPSE than the traditional allometric models without wood density variable for six subtropical forests at regional scale.

Conclusion

The dummy variable allometric models have better performances to estimate aboveground biomass for six subtropical forests in China, which provided an effective approach to improve the compatibility of forest biomass estimations from different scales. New allometric models with wood density substantially improved accuracies of aboveground biomass estimation for subtropical forests at regional scale.

Keywords

Aboveground biomassDummy variable modelWood densityScaleAllometric equation

Background

Carbon (C) sequestration and accumulation in forests as aboveground biomass (AGB) is important for mitigating climate change. The estimation of forest biomass at a range of scales has been recognized as one of the most critical steps in the assessment of forest C stocks (Montagu et al. 2005; Tomppo et al. 2010). Tropical and subtropical forests have been reported to account for more than 40% of the global gross primary production (GPP) and net primary production (NPP) (Zhou et al. 2006; Beer et al. 2010; Pan et al. 2011). Long-term eddy covariance observations demonstrate that average net ecosystem production (NEP) of East Asian subtropical forests is 362 g C m−2 year−1, greater than that of Asian tropical and temperate forests, and also higher than that of forests at the same latitude in North America, Europe and Africa (Yu et al. 2014). Subtropical forest biome in China covers 2.5 × 106 km2, occupies about 25% of the total forest area in China (Wu 1995), and plays critical role in C sink and climate change regulating (Zhou et al. 2006; Tan et al. 2011; Yu et al. 2014). However, C budgets of these forests remain uncertain resulted in limited number of inventory plot biomass data and accuracies of allometric equations for estimating AGB of forests in subtropical region (Zhang et al. 2005; Xu et al. 2015; Xiang et al. 2016). Thus, developing allometric equations for subtropical forests is essential for accurate estimating C sequestration in subtropical region (Zaehle et al. 2006; Hudiburg et al. 2009).

Field inventory methods (e.g., harvest method, allomatric modeling, and biomass expansion factor methods) are often used to estimate forest biomass at local and regional scales (Brown et al. 1989; Fang et al. 2001; Wang 2006; Pajtik et al. 2008; Williams et al. 2012). Remote sensing methods can provide spatial information on AGB at large scales, but this method still linked with the relationship between remote sensing dataset and field inventory AGB dataset (Drake et al. 2003; Su et al. 2016). Thus, many scientists gave efforts to improve the tree allometric models at single tree, plot, regional, national, or even worldwide scales, using easily measured dimensional variables, such as diameter at breast high (DBH) and tree height (H) (Brown et al. 1989; Ter-Mikaelian and Korzukhin 1997; Chave et al. 2005; Návar 2009; Genet et al. 2011). However, different models may lead to greatly variation of biomass estimation because of difference in climatic conditions, site quality, and forest types (Muukkonen 2007; Fu et al. 2017). Therefore, sampling at different scales and creating general biomass model were very important to reduce the uncertainty of applying different models (Chave et al. 2014). Some studies have advanced the possibility of generalizing allometric equations across regional boundaries (Návar et al. 2013; Paul et al. 2013; Chave et al. 2014). Zeng et al. (2011) used dummy variable model to develop generalized biomass model of Pinus massoniana at regional scale in south China, and indicated dummy model had good performance. However, few study has compared the accuracy of these allometric equations for forest biomass estimations from site to regional scales (Návar et al. 2013). Moreover, allometric models at different scales for other main subtropical forests in china, such as evergreen broadleaf forest, deciduous broadleaf forest, and mixed forests were very lack (Xu et al. 2015; Xiang et al. 2016). Some studies indicate that wood density variable can greatly improve accuracies of biomass model for AGB estimates in tropical forests and subtropical evergreen broadleaved forest (Baker et al. 2004; Chave et al. 2005, 2014; Goodman et al. 2014; Xu et al. 2015). Chave et al. (2014) successfully developed the universal allometric model for tropical forests with wood density based on global database, which were widely used for AGB estimation in tropical forests. However, the performance of the allometric model with wood density was worth further testing in subtropical forests. Therefore, the development of generalized biomass allometric model at different scales was urgent to quantify the regional biomass and C storage of subtropical forests.

Subtropical region in China has varied ecological zones and forest types (Chinese Academy of Sciences 2001). It was very necessary to develop general biomass models based on ecological region to improve accuracies of AGB estimation. The main objectives of this study were (1) to develop the allometric models at different scales for aboveground biomass estimation of subtropical forests in China; (2) to assessment the accuracy of the allometric models at different scales for AGB estimates, and (3) to test the performance of the allometric model when wood density variable is available.

Materials and methods

The experimental site

The study region covered most of the subtropical regions of China (22°–34°N, 98°–123°E) extending across eight ecoregions (Fig. 1). The total forested area is approximately 2.5 × 106 km2 in China (Wu 1995). The region was classified eight ecological zones (Table 1; Fig. 1) (Fu et al. 2013). The mean annual precipitation (MAP) ranges from 831 to 1342 mm and the mean annual temperature (MAT) varies from 12.5 to 19.1 °C (Table 1). The primary forests in this region were evergreen broadleaf forests (EBF). Due to long-term anthropogenic disturbances, the current forests were not primitive and were classified into six categories: Cunninghamia lanceolata (CL), coniferous mixed broadleaf forest (CMBF), subtropical deciduous broadleaf forest (DBF), evergreen broadleaf forest (EBF), Eucalyptus tree species forest (ETS), and Pinus massoniana (PM) based on China’s vegetation classification system (Chinese Academy of Sciences 2001).
Fig. 1

The sampling plot locations for the six forest types in the subtropical region of China. See Table 1 for the abbreviations of the forest types and ecoregions

Table 1

Summary characteristics of six forests and eight ecoregions in the subtropical region of China

Forest types

N

n

D2H

AGB (kg)

Min

Max

Mean

Min

Max

Mean

Cunninghamia lanceolata forest (CL)

6

219

16.97

23,469.40

2878.59

1.25

358.95

49.86

Coniferous mixed broadleaf forest (CMBF)

6

77

20.39

9973.85

1587.48

1.35

156.32

42.84

Deciduous broadleaf forest (DBF)

5

40

50.69

9481.50

2157.55

2.76

240.26

59.28

Evergreen broadleaf forest (EBF)

6

277

80.69

42,360.52

4492.07

3.85

719.51

121.74

Eucalyptus tree species forest (ETS)

5

76

84.24

6149.86

1882.65

2.25

201.05

46.72

Pinus massoniana forest (PM)

7

283

16.25

19,822.5

2297.85

1.01

388.74

53.75

Ecoregions

M

n

MAT (°C)

MAP (mm)

Min

Max

Mean

Min

Max

Mean

Yangtze river delta ecological zone (ER1)

4

55

5.3

17.2

14.0

491

1381

907

Evergreen broadleaf forest ecological zone in the mountains of Zhejiang and Fujian provinces (ER2)

6

254

11.6

20.4

16.5

1022

1653

1342

Ecological zone in Jiangnan and Nanling mountains and hill (ER3)

6

185

13.0

20.7

16.9

1069

1626

1385

Evergreen broadleaf forest ecological zone in the mountains of the west Hunan, Guizhou and Hubei provinces (ER4)

6

161

7.4

17.7

14.8

798

1356

1073

Karst evergreen broadleaf forest and agricultural ecological zone in Guizhou and Guangxi provinces (ER5)

3

121

10.1

21.1

16.9

887

1493

1254

Ecological zone of Sichuan Basin (ER6)

2

39

7.3

18.1

15.4

677

1232

911

Ecological zone on Yunnan Plateau (ER7)

2

36

− 8.3

19.6

12.5

618

1129

831

South humid subtropical ecological zone (ER8)

6

121

6.9

22.9

19.1

833

1770

1252

Abbreviations in the brackets indicated each forest type and individual ecoregion, respectively in the subtropical region of China. Climate data, including mean annual temperature (MAT) and mean annual precipitation (MAP) was obtained from the National Climate Center (http://ncc.cma.gov.cn/cn/)

N number of forest distributed ecoregions, n number of sampling plots, M number of forest types

The dataset

The AGB data of the six subtropical forests (CL, CMBF, DBF, EBF, ETS, and PM) was collected from the large forest biomass dataset in China, which was compiled from published biomass studies and pre-existing datasets published between 1978 and 2008 (Luo et al. 2013). Moreover, we reviewed almost all related publications in China from 2008 to 2013 and recorded information of sampling plots on locations, forest types, stand age, stand density, DBH, tree height, wood density and AGB for the six subtropical forest types. The biomass components of sample trees (stems, branches, leaves, etc.) was measured using destructive harvesting and oven weighing method. Then, AGB of average tree (kg) for each plot was calculated from stand AGB (Mg ha−1) and stand density. Our criteria for selected sampling tress was that the sample trees for each forest types were distributed as evenly as possible in the diameter classes in our dataset. Together, we collected 972 records of plot measured AGB data for the average trees of six subtropical forest types (Table 1; Additional file 1: Figure S1), 316 records of which had wood density information. The locations and forest types of the dataset were shown in Fig. 1.

Model description

Allometric model at different scales

We used general allometric equations to estimate AGB of six subtropical forests at individual ecoregional scale (model 1) and all subtropical regional scale (model 3), respectively. These allometric equations based on D 2 H (D, diameter of the tree at breast height, cm, and H, tree height, m), which have been widely used to estimate AGB for forests (Jenkins et al. 2003; Muukkonen 2007; Návar 2009).

$${ \ln }\,(AGB) = a + b\ln\, (D^{2} H) + \varepsilon$$
(1)
where AGB is the aboveground biomass, a and b are parameters, and ɛ is the additive error. Then, the estimate of aboveground biomass is as follows:
$$AGB_{\text{est}} = \exp \left( {{{a + RSE^{2} } \mathord{\left/ {\vphantom {{a + RSE^{2} } 2}} \right. \kern-0pt} 2}} \right) \times \left( {D^{2} H} \right)^{b}$$
(2)
where RSE is the residual standard errors of the regressions.
We considered ecoregion as dummy variable and used dummy variable allometric model to estimate AGB for six subtropical forests at both ecoregion scale and regional scale (model 2). The general form of the dummy variable allometric model was as follows (Wang et al. 2008; Zeng et al. 2011).
$${ \ln }\,(AGB) = a_{0} + \sum {a_{i} z_{i} + } b\ln\, (D^{2} H) + \varepsilon$$
(3)
$$AGB_{\text{est}} = \exp \left( {{{a_{0} + \sum {a_{i} z_{i} } + RSE^{2} } \mathord{\left/ {\vphantom {{a_{0} + \sum {a_{i} z_{i} } + RSE^{2} } 2}} \right. \kern-0pt} 2}} \right) \times \left( {D^{2} H} \right)^{b}$$
(4)
where z i is the dummy variable, a i is the ecoregion-specific parameter, and other symbols are the same as Eqs. (1) and (2). The dummy variables are 0, 1. In model 2, we take each ecoregion as dummy variable, if forest has six distributed ecoregions, we used six dummy variables, z1, z2, z3, z4, z5 and z6, when z1 = 1, the others = 0, when z2 = 1, the others = 0, etc. (Zeng et al. 2011).

Testing the importance of wood density for accurate estimated AGB

At regional scale, we used 316 plot AGB data with wood density (WD) records to test whether WD improved accuracy of allometric model. D 2 H × WD as variable was used to fit allometric model compared with the model without WD variable (Chave et al. 2014; Xu et al. 2015).
$${ \ln }\,(AGB) = a + b\ln \,(D^{2} H \times WD) + \varepsilon$$
(5)
then, the estimate of biomass is as follows:
$$AGB_{\text{est}} = \exp \left( {{{a + RSE^{2} } \mathord{\left/ {\vphantom {{a + RSE^{2} } 2}} \right. \kern-0pt} 2}} \right) \times \left( {D^{2} H \times WD} \right)^{b}$$
(6)

Accuracy assessment of models

The coefficient of determination (R 2), residual standard error of the regression (RSE), and mean percent standard error (MPSE) were used to assess the accuracies of the models (Zeng et al. 2011). MPSE were defined as follows:

$$MPSE = \frac{1}{n}\sum {\left| {{{({\text{y}}_{i} - \hat{y})} \mathord{\left/ {\vphantom {{({\text{y}}_{i} - \hat{y})} {\hat{y}}}} \right. \kern-0pt} {\hat{y}}}} \right|} \times 1 0 0$$
(7)
where n is number of plots; y i and \(\hat{y}\) are the observed and estimated values of AGB respectively.

Results

Allometric models for aboveground biomass estimation at different scales

The values of AGB and D2H varied obviously in each forest type and each ecoregion, especially in DBF, ETS, CMBF and EBF (Table 1; Additional file 1: Figure S1). There was clear distinction of climate (e.g., MAT and MAP) among eight ecoregions (Table 1).

Model 1 had better performance for CL in ER4 (R 2 = 0.987) and ER5 (R 2 = 0.986), for CMBF in ER4 (R 2 = 0.978) and ER6 (R 2 = 0.961), for DBF in ER1 (R 2 = 0.975) and ER4 (R 2 = 0.961), for EBF in ER7 (R 2 = 0.978) and ER4 (R 2 = 0.968), for ETS in ER2 (R 2 = 0.999) and ER4 (R 2 = 0.979), and for PM in ER5 (R 2 = 0.986), ER6 (R 2 = 0.979), ER3 (R 2 = 0.970) and ER2 (R 2 = 0.960), and showed lower performance for CMBF in ER2 (R 2 = 0.889), and for EBF in ER8 (R 2 = 0.885) (Table 2; Additional file 1: Figure S2).
Table 2

Parameters of allometric models for estimating aboveground biomass of six forests at individual ecoregion scale in the subtropical region of China

Forests

Ecoregions

Allometric models at individual ecoregion scale (model 1)

n

R 2

RSE

F value

CL

ER1

exp(− 3.006 + 0.008) × (D 2 H)0.857

14

0.918

0.123

134.9***

ER2

exp(− 2.065 + 0.029) × (D2H)0.762

92

0.946

0.241

1574.6***

ER3

exp(− 1.431 + 0.034) × (D2H)0.669

57

0.925

0.260

679.1***

ER4

exp(− 1.659 + 0.009) × (D2H)0.688

18

0.987

0.138

1203.8***

ER5

exp(− 3.611 + 0.017) × (D2H)0.936

12

0.986

0.184

690.6***

ER8

exp(− 2.067 + 0.033) × (D2H)0.749

26

0.954

0.256

499.8***

CMBF

ER1

exp(− 4.280 + 0.011) × (D2H)1.169

7

0.955

0.148

104.8***

ER2

exp(− 1.746 + 0.057) × (D2H)0.744

34

0.889

0.338

255.6***

ER3

exp(− 0.527 +0.044) × (D2H)0.552

7

0.912

0.295

51.5***

ER4

exp(− 0.615 + 0.001) × (D2H)0.590

7

0.978

0.053

222.3***

ER6

exp(− 1.953 + 0.043) × (D2H)0.777

14

0.961

0.294

293.6***

ER8

exp(− 1.391 + 0.048) × (D2H)0.72

8

0.932

0.102

82.2***

DBF

ER1

exp(− 2.054 + 0.019) × (D2H)0.803

11

0.975

0.195

305.3***

ER2

exp(− 2.202 + 0.036) × (D2H)0.774

11

0.955

0.270

191.6***

ER3

exp(− 14.935 + 0.106) × (D2H)2.623

5

0.904

0.460

28.2*

ER4

exp(− 4.803 + 0.020) × (D2H)1.120

7

0.961

0.198

122.9***

ER8

exp(− 0.841 + 0.034) × (D2H)0.722

6

0.908

0.262

39.3**

EBF

ER2

exp(− 2.909 + 0.054) × (D2H)0.920

55

0.932

0.328

729.7***

ER3

exp(− 0.482 + 0.022) × (D2H)0.592

29

0.901

0.209

245.2***

ER4

exp(− 1.699 + 0.011) × (D2H)0.820

48

0.968

0.149

1372.4***

ER5

exp(− 0.610 + 0.023) × (D2H)0.656

89

0.913

0.216

909.0***

ER7

exp(− 2.067 + 0.004) × (D2H)0.817

28

0.978

0.086

1168.7***

ER8

exp(− 2.761 + 0.068) × (D2H)0.911

28

0.885

0.368

200.8***

ETS

ER2

exp(− 1.305 + 0.000) × (D2H)0.687

9

0.999

0.017

5074.8***

ER3

exp(− 5.646 + 0.029) × (D2H)1.245

12

0.900

0.240

90.0***

ER4

exp(− 3.615 + 0.001) × (D2H)0.859

6

0.979

0.044

184.0***

ER7

exp(− 1.352 + 0.021) × (D2H)0.631

8

0.924

0.204

73.3***

ER8

exp(− 3.062 + 0.026) × (D2H)0.905

41

0.904

0.230

368.8***

PM

ER1

exp(− 2.515 + 0.020) × (D2H)0.843

23

0.948

0.201

379.7***

ER2

exp(− 2.071 + 0.040) × (D2H)0.804

53

0.960

0.283

1214.7***

ER3

exp(− 2.176 + 0.036) × (D2H)0.798

75

0.970

0.217

2374.4***

ER4

exp(− 2.589 + 0.037) × (D2H)0.839

75

0.942

0.271

1181.8***

ER5

exp(− 3.448 + 0.011) × (D2H)0.973

20

0.986

0.146

1301.2***

ER6

exp(− 2.366 + 0.018) × (D2H)0.831

25

0.979

0.187

1058.0***

ER8

exp(− 2.030 + 0.081) × (D2H)0.797

12

0.935

0.403

143.1***

*** Indicates significant at p < 0.001 level; ** indicates significant at p < 0.01 level, * indicates significant at p < 0.05 level. See Table 1 for the abbreviations of the forest types and ecoregions

Model 2 considered the effects of the ecoregions on AGB estimation. The statistical results showed ER2 significantly influenced AGB estimations for CL (p < 0.05), and for PM (p < 0.01), respectively. ER8 significantly affected on AGB estimations for DBF (p < 0.001), EBF (p < 0.001), CMBF (p < 0.05) and ETS (p < 0.05). ER4, ER5, ER3, and ER8 significantly affected on AGB estimation for EBF, especially ER4 and ER5 (p < 0.0001) (Table 3; Additional file 1: Figure S3).
Table 3

Parameters of dummy variable allometric model for estimating aboveground biomass of six forest types at both regional scale and ecoregion scale in the subtropical region of China

Forests

Ecoregions

Dummy variable allometric model (model 2)

R 2

RSE

F value

CL

General

exp(− 2.064 + 0.166 + 0.142 + 0.083 − 0.009 + 0.071 + 0.031) × (D2H)0.739

0.947

0.249

631.5***

ER1

exp(− 2.064 + 0.031) × (D2H)0.739

   

ER2*

exp(− 2.064 + 0.166 + 0.031) × (D2H)0.739

   

ER3

exp(− 2.064 + 0.142 + 0.031) × (D2H)0.739

   

ER4

exp(− 2.064 + 0.083 + 0.031) × (D2H)0.739

   

ER5

exp(− 2.064 − 0.009 + 0.031) × (D2H)0.739

   

ER8

exp(− 2.064 + 0.071 + 0.031) × (D2H)0.739

   

CMBF

General

exp(− 1.391 − 0.18 − 0.143 −0.134 − 0.239 + 0.398 + 0.048) × (D2H)0.720

0.920

0.311

134.5***

ER1

exp(− 1.391 + 0.048) × (D2H)0.720

   

ER2

exp(− 1.391 − 0.180 + 0.048) × (D2H)0.720

   

ER3

exp(− 1.391 − 0.143 + 0.048) × (D2H)0.720

   

ER4

exp(− 1.391 − 0.134 + 0.048) × (D2H)0.720

   

ER6

exp(− 1.391 − 0.239 + 0.048) × (D2H)0.720

   

ER8*

exp(− 1.391 + 0.398 + 0.048) × (D2H)0.720

   

DBF

General

exp(− 2.191 − 0.338 + 0.328 − 0.177 + 0.803 + 0.045) × (D2H)0.823

0.940

0.301

106.6***

ER1*

exp(− 2.191 + 0.045) × (D2H)0.823

   

ER2

exp(− 2.191 − 0.338 + 0.045) × (D2H)0.823

   

ER3

exp(− 2.191 + 0.328 + 0.045) × (D2H)0.823

   

ER4

exp(− 2.191 − 0.177 + 0.045) × (D2H)0.823

   

ER8***

exp(− 2.191 + 0.803 + 0.045) × (D2H)0.823

   

EBF

General

exp(− 1.896 − 0.141 + 0.451 + 0.321 + 0.103 + 0.184 + 0.038) × (D2H)0.785

0.915

0.277

481.8***

ER2

exp(− 1.896 + 0.038) × (D2H)0.785

   

ER3*

exp(− 1.896 −0.141 + 0.038) × (D2H)0.785

   

ER4***

exp(− 1.896 + 0.451 + 0.038) × (D2H)0.785

   

ER5***

exp(− 1.896 + 0.321 + 0.038) × (D2H)0.785

   

ER7

exp(− 1.896 + 0.103 + 0.038) × (D2H)0.785

   

ER8**

exp(− 1.896 + 0.184 + 0.038) × (D2H)0.785

   

ETS

General

exp(− 2.495 − 0.015 − 1.918 − 0.221 − 0.232 + 0.028) × (D2H)0.859

0.954

0.237

288.5***

ER2

exp(− 2.495 + 0.028) × (D2H)0.859

   

ER3

exp(− 2.495 − 0.015 +0.028) × (D2H)0.859

   

ER4***

exp(− 2.495 − 1.918 + 0.028) × (D2H)0.859

   

ER7*

exp(− 2.248 − 0.221 + 0.028) × (D2H)0.859

   

ER8*

exp(− 2.248 − 0.232 + 0.028) × (D2H)0.859

   

PM

General

exp(− 2.369 + 0.168 + 0.053 − 0.102 + 0.048 + 0.072 + 0.156 + 0.032) × (D2H)0.821

0.964

0.252

1055.0***

ER1

exp(− 2.369 + 0.032) × (D2H)0.821

   

ER2**

exp(− 2.369 + 0.168 + 0.032) × (D2H)0.821

   

ER3

exp(− 2.369 + 0.053 + 0.032) × (D2H)0.821

   

ER4

exp(− 2.369 − 0.102 + 0.032) × (D2H)0.821

   

ER5

exp(− 2.369 + 0.048 + 0.032) × (D2H)0.821

   

ER6

exp(− 2.369 + 0.072 + 0.032) × (D2H)0.821

   

ER8

exp(− 2.369 + 0.156 + 0.032) × (D2H)0.821

   

*** Indicates significant at p < 0.001 level; ** indicates significant at p < 0.01 level; * indicates significant at p < 0.05 level. See Table 1 for the abbreviations of the forest types and ecoregions

Model 2 had better performance to estimate AGB for PM (R 2 = 0.964), ETS (R 2 = 0.954), and CL (R 2 = 0.947), and showed lower performance to estimate AGB for EBF (R2 = 0.915) and CMBF (R 2 = 0.920) (Table 3; Additional file 1: Figure S3).

Model 3 showed better performance to estimate AGB for PM (R 2 = 0.959) and CL (R 2 = 0.944), and had lower performance to estimate AGB for the other four forest types, especially for ETS (R 2 = 0.759) (Table 4; Additional file 1: Figure S4).
Table 4

Parameters of allometric models for estimating aboveground biomass of six forests at regional ecoregion scale in the subtropical region of China

Forests

Allometric model at regional scale (model 3)

n

R 2

RSE

F value

CL

exp(− 1.920 + 0.032) × (D2H)0.736

219

0.944

0.252

3668.5***

CMBF

exp(− 1.485 + 0.062) × (D2H)0.718

77

0.890

0.353

607.4***

DBF

exp(− 1.536 + 0.110) × (D2H)0.733

40

0.839

0.466

198.3***

EBF

exp(− 1.481 + 0.056) × (D2H)0.757

277

0.874

0.334

1905.5***

ETS

exp(− 3.776 + 0.138) × (D2H)0.995

76

0.759

0.526

233.4***

PM

exp(− 2.394 + 0.036) × (D2H)0.830

283

0.959

0.267

6547.2***

*** Indicates significant at p < 0.001 level. See Table 1 for the abbreviations of the forest types

Assessment the accuracies of the allometric models at different scales

We compared measured AGB and predicted AGB estimated from three allometric models at different scales, and found that model 1 and model 2 had better accuracies for AGB estimations than model 3 (Fig. 2, Table 5). The MPSE values of three models varied obviously for AGB estimations in the same forest in different ecoregions, and showed increasing trend with increasing scales. At ecoregional scale, the values of MPSE from model 2 were similar to model 1, less than model 3 for CL, PM and DBF in the distributed ecoregions except for CL and PM in ER5, and for DBF in ER3 and ER4. For EBF and ETS, MPSE showed the same trend in the ecoregions except for EBF in ER1 and ETS in ER2. For all the forests, MPSE of Model 1, model 2, and model 3 ranged from 2.79 to 30.40%, 5.15 to 40.94%, and 13.25 to 80.81% at ecoregional scale, respectively. At regional scale, MPSE of model 2 was very similar to model 1, and was clearly less than model 3 in six subtropical forests (Table 5).
Fig. 2

Comparison measured AGB (aboveground tree biomass) and predicted AGB for six subtropical forests from three allometric models at different scales (model 1, model 2 and model 3) in China. Model 1: allometric model at ecoregion scale, model 2: dummy variable allometric model at both ecoregion scale and regional scale, and model 3: allometric model at regional scale. See Table 1 for the abbreviations of the six forest types

Table 5

MPSE for three allometric models at different scales developed in the subtropical region of China

Forests

Models

MPSE (%)

ER1

ER2

ER3

ER4

ER5

ER6

ER7

ER8

Region

CL

Model 1

9.51

19.38

20.26

10.06

11.72

  

20.41

17.92

Model 2

10.83

19.27

21.49

11.71

29.08

  

21.07

19.44

Model 3

26.04

21.66

23.99

21.99

27.36

  

24.02

23.01

CMBF

Model 1

9.66

26.40

17.89

3.64

 

22.08

 

7.75

19.31

Model 2

20.97

26.16

30.69

7.45

 

25.55

 

23.19

23.98

Model 3

20.68

28.28

28.79

13.25

 

31.29

 

53.38

29.42

DBF

Model 1

12.95

21.70

14.00

12.89

   

14.61

15.73

Model 2

12.53

23.90

40.94

25.35

   

17.67

22.22

Model 3

18.46

37.84

60.54

29.13

   

72.78

39.07

EBF

Model 1

 

25.24

13.26

9.82

16.41

 

5.61

30.40

17.02

Model 2

 

30.74

17.25

9.87

18.32

 

5.29

30.65

19.14

Model 3

 

32.27

29.85

24.61

21.70

 

11.01

31.03

25.02

ETS

Model 1

 

0.97

18.77

2.79

  

16.48

17.65

14.38

Model 2

 

8.20

26.37

5.15

  

20.46

17.54

16.96

Model 3

 

29.45

23.77

80.81

  

39.21

20.15

28.28

PM

Model 1

14.45

18.94

15.85

20.86

11.92

15.32

 

27.19

17.80

Model 2

14.63

19.73

16.05

20.75

19.16

15.39

 

28.35

18.55

Model 3

15.18

24.97

16.59

23.05

18.40

15.38

 

33.35

20.49

See Table 1 for the abbreviations of six forests and eight ecoregions

Testing importance of wood density for Aboveground biomass estimation

Figure 3 showed that WD variable used in allometric model greatly improved the estimate accuracies with higher R 2, lower RSE and MPSE than traditional allometric model without WD variable for six forests (Figs. 3, 4). The allometric model with WD variable developed by Chave et al. (2014) showed lower MPSE than traditional model for CL, DBF, EBF and PM, especially in EBF and DBF, and showed greater MPSE for AGB estimations in CL, CMBF, DBF, ETS and PM than allometric model with WD variable from our dataset (Fig. 4). In EBF, model created by Chave et al. (2014) showed similar lower MPSE to our model with WD variable (Fig. 4).
Fig. 3

Fitted curves for each forest type and all forest types at regional scale in subtropical region of China applied the allometric model with wood density variable, and the allometric model without wood density variable. See Table 1 for the abbreviations of the six forest types

Fig. 4

Compared the measured AGB (aboveground tree biomass) and the estimated AGB in subtropical forests by the allometric model with wood density, the allometric model without wood density, and the model widely used in tropical trees created by Chave et al. (2014) \(\left( {{\text{AGB}}_{\text{est}} \; = \;0.0 6 7 3\; \times \; \left( {\rho D^{ 2} H} \right)^{0. 9 7 6} } \right)\), respectively (a), and compared MPSE among these three allometric models (b)

Discussion

Allometric models for aboveground biomass estimation at different scales

Many scientists gave efforts to improve the tree allometric models at single tree, plot, regional, national, or even worldwide scales (Brown et al. 1989; Chave et al. 2005; Návar 2009; Genet et al. 2011). In this study, we developed three allometric models from ecoregion to regional scales. Three allometric models using D 2 H as the predictive variable offered good fitness of AGB allometric models at different scales (R 2 ranged from 0.759 to 0.999) (Tables 2, 3, 4). This indicates D 2 H as variable could improve accuracy of models (Muukkonen 2007; Návar 2009; Xu et al. 2015). Muukkonen (2007) found that allometric equations with only DBH as an independent variable provided lower overall estimations of tree biomass. Models at different scales may lead to variation of biomass estimation because of difference of climatic conditions, site quality, and forest structures (Muukkonen 2007; Fu et al. 2017). In this study, we found that model 1 and model 2 had better accuracies for AGB estimations than model 3 (Fig. 2; Table 3). The MPSE values of three models varied obviously for AGB estimations in the same forest in different ecoregions, and showed increasing trend with increasing scales. Case and Hall (2008) found that prediction error of generalized tree biomass equations for ten species in the boreal forest region of west-central Canada increased from regional to national scale. However, MPSE of model 2 was very similar to model 1, and obviously less than model 3 in six forests (Table 5), which indicated that dummy variable allometric model considered ecoregion factors could be proposed as general model to estimate AGB for subtropical forests, and provide a more effective new approach to improve the compatibility of forest biomass estimates at the ecoregional, and regional scales.

Assessment the accuracies of the allometric models at different scales

Regional climate data affected the precision of the regional model (Drake et al. 2003; Dewalt and Chave 2004; Chave et al. 2005; Wang 2006; Fu et al. 2017). In this study, MAT and MAP were clearly distinct among eight ecoregions (Table 1). Ecoregions including ER2, ER3, ER4, ER5, and ER8 significantly affected AGB estimations (Table 3). The aboveground biomass of PM, CL, and ETS was greater in the southern central regions with higher temperature and greater rainfall, than in the west regions with lower temperatures and less rainfall. The influences of climate were even more significant in EBF (Table 3). Therefore, forest regional climate data should be considered when the regional models were employed (Muukkonen 2007; Fu et al. 2017).

The number of plots applied to develop the allometric equations in ETS, DBF, and CBMF forest types (N < 100) may not be enough to represent the full range of species present at the study areas (Table 1). Návar (2009) reported that several hundred sampling plots were needed for fitting regional allometric equations. Moreover, three model at different scales that have been developed were more robust when there were not enough trees with diameters between 25 and 40 cm (Additional file 1: Figure S1), and the majority of samples had insufficient trees with a diameter of more than 25 cm, which would lead larger estimated error of AGB (Wang 2006; Zaehle et al. 2006; Hudiburg et al. 2009; Xiang et al. 2016).

Testing importance of wood density for aboveground biomass estimation

Wood density strongly varies among different geographical regions, climate gradients, and correlated to forest structure, tree architecture (Baker et al. 2004; Chave et al. 2005, 2014). Thus, wood density can improve the performance of allometric model. In this study, we compared the performance of the allometric model with wood density variable with traditional model without wood density variable at regional scale, and the model created by Chave et al. (2014), and found the model with wood density variable had better performance than other two models (Figs. 3, 4). It indicated that taking wood density as variables in the allometric model could greatly improve accuracy of biomass model (Chave et al. 2014; Xu et al. 2015). The reason was that wood density could reflect site climate, forest structure, and trees architecture, and reduce the effects of site climate and forest structure on AGB estimations. The model created by Chave et al. (2014) showed better performance for AGB estimation of EBF, similar to our model with wood density variable. This result suggests that the model created by Chave et al. (2014) can be used AGB estimation of EBF in subtropical region of China.

Conclusions

This study showed that ecoregions significantly affected AGB estimation for six subtropical forests in China. Dummy variable allometric model considered ecoregion as dummy variable had better performance similar to allometric model at both individual ecoregional scale and regional scale. Furthermore, we tested the performance of allometric model with wood density at regional scale and found wood density as an important variable in the allometric models greatly improved the accuracies of AGB estimations in six subtropical forests. Our findings showed that dummy variable allometric model considered ecoregion factors could be proposed as general model to estimate AGB for subtropical forests, and provide a more effective new approach to improve the compatibility of forest biomass estimates at the ecoregional, and regional scales. Moreover, the new allometric models with wood density, diameter, and tree height were more accurate than the traditional models without wood density in AGB estimations for subtropical forests at regional scales.

Declarations

Authors’ contributions

All authors contributed substantially to the work reported here. The four authors participated in the design of the study. SP and NH analyzed the data and wrote the manuscript. GY wrote and reviewed the manuscript. QW performed the data collection and reviewed the manuscript. All authors read and approved the final manuscript.

Acknowledgements

We thank Professor Zeng Weisheng for the assistance with information on the dummy variable model, and thank Professor Martin Kent (Plymouth University, UK) for the improvement of English usage. We also greatly thank two anonymous reviewers for their insightful comments.

Competing interests

The authors declare that they have no competing interests.

Availability of data and materials

Please see Table 1.

Consent for publication

Authors agree to the terms of the Springer Open Copyright and License Agreement.

Ethics approval and consent to participate

Not applicable, the study involves no human parparticipants.

Funding

This study is partially supported by the National Natural Science Foundation of China (No. 31770655, 31570471, 31290221).

Publisher’s Note

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Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors’ Affiliations

(1)
Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, CAS
(2)
Key Laboratory of Ecological Restoration in the Hilly Area, Pingdingshan University
(3)
College of Resources and Environment, University of Chinese Academy of Sciences

References

  1. Baker TR, Phillips OL, Malhi Y et al (2004) Variation in wood density determines spatial patterns in Amazonian forest biomass. Glob Change Biol 10:545–562View ArticleGoogle Scholar
  2. Beer C, Reichstein M, Tomelleri E et al (2010) Terrestrial gross carbon dioxide uptake: global distribution and covariation with climate. Science 329:834–838View ArticlePubMedGoogle Scholar
  3. Brown S, Gillespie AJ, Lugo AE (1989) Biomass estimation methods for tropical forests with applications to forest inventory data. For Sci 35:881–902Google Scholar
  4. Case B, Hall RJ (2008) Assessing prediction errors of generalized tree biomass and volume equations for the boreal forest region of west-central Canada. Canada J For Res 38:878–889View ArticleGoogle Scholar
  5. Chave J, Andalo C, Brown S et al (2005) Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia 145:87–99View ArticlePubMedGoogle Scholar
  6. Chave J, Rejou-Mechain M, Burquez A et al (2014) Improved allometric models to estimate the aboveground biomass of tropical trees. Glob Change Biol 20:3177–3190View ArticleGoogle Scholar
  7. Chinese Academy of Sciences (2001) Vegetation of China and geographic pattern-illustrition for vegetation map of the People’s Republic of China (1:1000000). Geological Publishing House, BeijingGoogle Scholar
  8. Dewalt SJ, Chave J (2004) Structure and biomass of four lowland neotropical forests. Biotropica 36:7–19Google Scholar
  9. Drake JB, Knox RG, Dubayah RO et al (2003) Above-ground biomass estimation in closed canopy Neotropical forest using lidar remote sensing: factors affecting the generality of relationships. Glob Ecol Biogeogr 12:147–159View ArticleGoogle Scholar
  10. Fang JY, Chen AP, Peng CH et al (2001) Changes in forest biomass carbon storage in China between 1949 and 1998. Science 292:2320–2322View ArticlePubMedGoogle Scholar
  11. Fu BJ, Liu GH, Ouyang ZY et al (2013) Study on ecological regionalization in China. Science Press, BeijingGoogle Scholar
  12. Fu LY, Lei XD, Hu HD et al (2017) Integrating regional climate change into allometric equations for estimating tree aboveground biomass of Masson pine in China. Ann For Sci 74:42–57View ArticleGoogle Scholar
  13. Genet A, Wernsdörfer H, Jonard M et al (2011) Ontogeny partly explains the apparent heterogeneity of published biomass equations for Fagus sylvatica in central Europe. For Eco Manag 261:1188–1202View ArticleGoogle Scholar
  14. Goodman RC, Phillips OL, Baker TR (2014) The importance of crown dimensions to improve tropical tree biomass estimates. Ecol Appl 24:680–698View ArticlePubMedGoogle Scholar
  15. Hudiburg T, Law B, Turner DP et al (2009) Carbon dynamics of Oregon and Northern California forests and potential land-based carbon storage. Ecol Appl 19:163–180View ArticlePubMedGoogle Scholar
  16. Jenkins JC, Chojnacky DC, Heath LS et al (2003) National- scale biomass estimators for United States tree species. For Sci 49:12–35Google Scholar
  17. Luo YJ, Wang XK, Zhang XQ et al (2013) Biomass and its allocation of forest ecosystem in China. Chinese Forestry Press, BeijingGoogle Scholar
  18. Montagu KD, Duttmer K, Barton CVM et al (2005) Developing general allometric relationship for regional estimates of carbon sequestration-an example using Eucalyptus pilularis from seven contrasting sites. For Ecol Manag 204:113–127View ArticleGoogle Scholar
  19. Muukkonen P (2007) Generalized allometric volume and biomass equations for some tree species in Europe. Eur J For Res 126:157–166View ArticleGoogle Scholar
  20. Návar J (2009) Allometric equations for tree species and carbon stocks for forests of northwestern Mexico. For Ecol Manag 257:427–434View ArticleGoogle Scholar
  21. Návar J, Ríos-Saucedo J, Pérez-Verdín G et al (2013) Regional aboveground biomass equations for North American arid and semi-arid forests. J Arid Environ 97:127–135View ArticleGoogle Scholar
  22. Pajtik J, Konopka B, Lukac M (2008) Biomass functions and expansion factors in young Norway spruce (Picea abies) trees. For Ecol Manag 256:1096–1103View ArticleGoogle Scholar
  23. Pan YD, Birdsey RA, Fang JY et al (2011) A large and persistent carbon sink in the world’s forests. Science 333:988–993View ArticlePubMedGoogle Scholar
  24. Paul KI, Roxburgh SH, Jacquel RE et al (2013) Development and testing of allometric equations for estimating above-ground biomass of mixed-species environmental plantings. For Ecol Manag 10:483–494View ArticleGoogle Scholar
  25. Su YJ, Guo QH, Xue BL et al (2016) Spatial distribution of forest aboveground biomass in China: estimation through combination of spaceborne lidar, optical imagery, and forest inventory data. Remote Sens Environ 173:187–199View ArticleGoogle Scholar
  26. Tan ZH, Zhang YP, Douglas S et al (2011) An old-growth subtropical Asian evergreen forest as a large carbon sink. Atmos Environ 45:1548–1554View ArticleGoogle Scholar
  27. Ter-Mikaelian MT, Korzukhin MD (1997) Biomass equations for sixty-five North American tree species. For Ecol Manag 97:1–24View ArticleGoogle Scholar
  28. Tomppo E, Gschwantner T, Lawrence M et al (2010) National forest inventories: pathways for common reporting. Springer Press, New YorkView ArticleGoogle Scholar
  29. Wang CK (2006) Biomass allometric equations for 10 co-occurring tree species in Chinese temperate forests. For Ecol Manag 222:9–16View ArticleGoogle Scholar
  30. Wang ML, Bruce EB, Zhao DH (2008) An empirical comparison of two subject-specific approaches to dominant heights modeling: the dummy variable method and the mixed model method. For Ecol Manag 255:2659–2669View ArticleGoogle Scholar
  31. Williams RJ, Zerihum A, Montagu KD et al (2012) Allometry for estimating aboveground tree biomass in tropical and subtropical eucalypt woodlands: towards general predictive equations. Aust J Bot 53:607–619View ArticleGoogle Scholar
  32. Wu ZY (1995) Vegetation of China. Science Press, BeijingGoogle Scholar
  33. Xiang WH, Zhou J, Ouyang S et al (2016) Species-specific and general allometric equations for estimating tree biomass components of subtropical forests in southern China. Eur J For Res 135:1–17View ArticleGoogle Scholar
  34. Xu YZ, Zhang JX, Franklin SB et al (2015) Improving allometry models to estimate the above and belowground biomass of subtropical forest, China. Ecosphere 6:1–15View ArticleGoogle Scholar
  35. Yu GR, Chen Z, Piao SL et al (2014) High carbon dioxide uptake by subtropical forest ecosystems in the East Asian monsoon region. Pro Natl Acad Sci 111:4910–4915View ArticleGoogle Scholar
  36. Zaehle S, Sitch S, Prentice C et al (2006) The importance of age-related decline in forest NPP for modeling regional carbon balances. Ecol Appl 16:1555–1574View ArticlePubMedGoogle Scholar
  37. Zeng WS, Zhang HR, Tang SZ (2011) Using the dummy variable model approach to construct compatible single-tree biomass equations at different scales- a case study for Masson pine (Pinus massoniana) in southern China. Can J For Res 41:1547–1554View ArticleGoogle Scholar
  38. Zhang L, Huang Y, Luo TX et al (2005) Age effects on stand biomass allocation to different components: a case study in forests of Cunninghamia lanceolata and Pinus massoniana. J Grad Sch Chin Acad Sci 22:170–178Google Scholar
  39. Zhou GY, Liu SG, Li ZA et al (2006) Old-growth forests can accumulate carbon in soils. Science 314:1417View ArticlePubMedGoogle Scholar

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