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J. For. Res.
DOI 10.1007/s11676-017-0504-6
ORIGINAL PAPER
Estimating and mapping forest biomass in northeast China using
joint forest resources inventory and remote sensing data
Xinchuang Wang1,2 • Shidong Wang1 • Limin Dai2
Received: 8 March 2017 / Accepted: 22 April 2017
Northeast Forestry University and Springer-Verlag GmbH Germany 2017
Abstract Being able to accurately estimate and map forest
biomass at large scales is important for a better understanding of the terrestrial carbon cycle and for improving
the effectiveness of forest management. In this study, forest
plot sample data, forest resources inventory (FRI) data, and
SPOT Vegetation (SPOT-VGT) normalized difference
vegetation index (NDVI) data were used to estimate total
forest biomass and spatial distribution of forest biomass in
northeast China (with 1 km resolution). Total forest biomass at both county and provincial scales was estimated
Project funding: This study was supported by the National Natural
Science Foundation of China (No. 41401500), the National Key
Technologies R&D Program of China (2012BAD22B04), the China
Postdoctoral Science Foundation (2015M580629, 2016M590679), the
Key Scientific Research Projects of Higher Education of Henan
Province, China (16A420003, 17A420001), Scientific and
Technological Innovation Team of Universities in Henan Province,
China (18IRTSTHN008 ), Funds for Fundamental Scientific Research
in Colleges in Henan Province, China (NSFRF1630) and Innovation
Research Team of Henan Polytechnic University, China (B2017-16)
and the China Coal Industry Association Guidance Program
(MTKJ-2015-285).
using FRI data of 11 different forest types obtained by
sampling 1156 forest plots, and newly-created volume to
biomass conversion models. The biomass density at the
county scale and SPOT-VGT NDVI data were used to
estimate the spatial distribution of forest biomass. The
results suggest that the total forest biomass was 2.4 Pg
(1 Pg = 1015 g), with an average of 77.2 Mg ha-1, during
the study period. Forests having greater biomass density
were located in the middle mountain ranges in the study
area. Human activities affected forest biomass at different
elevations, slopes and aspects. The results suggest that the
volume to biomass conversion models that could be
developed using more plot samples and more detailed
forest type classifications would be better suited for the
study area and would provide more accurate biomass
estimates. Use of both FRI and remote sensing data
allowed the down-scaling of regional forest biomass
statistics to forest cover pixels to produce a relatively fineresolution biomass map.
Keywords Forest biomass Biomass density Spatial
distribution Human disturbance Remote sensing
The online version is available at http://www.springerlink.com
Corresponding editor: Tao Xu.
& Xinchuang Wang
wangxc_382@163.com
& Limin Dai
lmdai@iae.ac.cn
1
Henan Polytechnic University, Jiaozuo 454000,
People’s Republic of China
2
State Key Laboratory of Forest and Soil Ecology, Institute of
Applied Ecology, Chinese Academy of Sciences,
Shenyang 110164, People’s Republic of China
Introduction
Forest ecosystems form the largest reservoir of organic
carbon on the Earth (Dixon et al. 1994; Melillo et al. 2002;
Phillips et al. 1998). They balance regional and global
carbon budgets and mitigate global warming induced by
the increase in atmospheric CO2 (Woodwell et al. 1978).
Forest biomass is an important indicator of stored carbon
and of potential carbon sequestration in terrestrial systems
(Le Toan et al. 2011). Accurate mapping of forests and
accurate estimates of biomass were important for a better
123
X. Wang et al.
understanding of the activity of the terrestrial carbon cycle
and better understanding will lead to more effective forest
management (Liu et al. 2006; Sun et al. 2016; Liu et al.
2014).
There are different methods of estimating forest biomass
(West 2004). Field measurements are considered the most
accurate but they are time consuming and labour intensive
and cannot provide large-scale, continuous spatial distribution of biomass (Brown 2002). Other methods include
the average biomass method (Woodwell et al. 1978; Dixon
et al. 1994), the volume to biomass conversion method
(Brown et al. 1989; Fang et al. 2001; Pan et al. 2004; Smith
et al. 2003), and the remote sensing method (Boyd et al.
1999; Lu et al. 2004; Tan et al. 2007; Yemshanov et al.
2012). These methods have been used to estimate biomass
at larger scales. The volume to biomass conversion method
is widely used to estimate total regional biomass because it
can utilize forest inventory (FRI) data (Fang et al. 2001;
Pan et al. 2004; Wang et al. 2017). The remote sensing
approach can provide full coverage of a geographic area
and show the properties and spatial variability of ecosystems at many scales (Prince and Goward 1995; Running
et al. 1994; Zheng et al. 2004).
Research estimates of forest biomass, either at global or
regional scales, vary greatly (Houghton et al. 2001;
Blackard et al. 2008) for several reasons. First, different
methods of estimation, coupled with uncertainty due to
natural or human factors, lead to different results (Pan et al.
2004; Zhang et al. 2014; Ren et al. 2016). Second,
parameter values of biomass estimation models change
with forest type, age, density, and site. Even for the same
forest type, other parameters cannot be accurately set
(Kramer 1982; Brown and Schroeder 1999). Forest biomass estimates are scale-dependent (Li and Lei 2010). A
larger scale biomass estimate may be not suitable for use at
smaller scale regions. The extent of any such discrepancies
is difficult to estimate.
Maps of large-scale forest biomass may be generated
from remote sensing data. Low-resolution spatial imagery
and high-resolution temporal imagery, such as provided by
MODIS and NOAA-AVHRR, have been used to estimate
forest biomass for relatively large geographic areas (Tan
et al. 2007; Anaya et al. 2009; Zhang and Kondragunta
2006; González-Alonso et al. 2006). Accurate biomass
maps, based on remote sensing data, may be created by
analyzing the correlation between spectral information
detected by remote sensing and directly observed forest
biomass (Du et al. 2014). It is difficult to establish this
correlation because of the complexity of forest canopy
characteristics and the uncertainties of remote sensing
information such as data quality and refinement, the spatial
resolution of data, and the saturation effects of data (Lu
2007). The normalized difference vegetation index (NDVI)
123
is one of the most frequently used indices derived from
remote sensing data. It is particularly useful for the estimation of biomass but has some drawbacks such as NDVI
saturation which means that continued increases in forest
biomass will not be reflected in the information derived
from NDVI when forest canopy density is greater than 80%
(González-Alonso et al. 2006; Tan et al. 2007). The
accuracy and spatial resolution of forest biomass maps at
large scales based on remote sensing can be improved.
This study used sampled biomass data and FRI and
remote sensing data to estimate and map the forest biomass
of three provinces in northeast China. The primary objectives were: to get a more accurate estimate of the total
forest biomass; to produce more spatially detailed biomass
maps of the study area; and, to analyze the spatial variations in regional forest biomass to provide a reference
baseline for forest policy-making and management.
To achieve these objectives, total forest biomass of each
county and province was estimated using FRI data and
forest sample data. The flow chart for the estimation and
mapping using FRI and remote sensing data is shown in
Fig. 1. The sample data was obtained from 1156 plots in 11
different types of forest. New models of forest volume to
biomass conversion were created. The FRI data were fitted
to the models and used to calculate total biomass. The
estimated biomass density for each county, together with
SPOT-Vegetation (SPOT-VGT) NDVI data, were then
used to develop a remote sensing-based model which
provided the spatial distribution of biomass. Finally,
regional spatial variations in biomass were analyzed in
terms of the model-produced spatial distribution together
with digital elevation model (DEM) data in order to provide baseline reference data for forest policy-making and
management.
Materials and methods
Study area
The study area (38430 –53230 N and 118500 –135050 E) is
in northeast China within Heilongjiang, Jilin and Liaoning
provinces and covers an area of 78.7 9 104 km2. The
region is influenced by the high latitude East Asia monsoon
and from south to north has warm temperate, temperate,
and cold temperate climates. Average annual temperatures
range from 11.5 to 4.8 C. Different temperature zones are
defined by average annual temperatures or average temperatures of the hottest mouth. Average annual temperatures range from 13 to 20 C and 2 to 8 C in warm
temperate and temperate zone, respectively. Average
annual temperatures are less than 0 C while Average
temperatures of the hottest month are higher than 10 C in
Estimating and mapping forest biomass in northeast China using joint forest resources…
Fig. 1 The flow chart for estimating and mapping forest biomass using joint FRI and remote sensing data
cold temperate zone. The Changbai, Xiaoxing’an, and
Daxing’an mountains, as well as the Sanjiang, Liaohe, and
Songhua river plains, are within the region (Fig. 2).
Northeast China is one of the most densely-forested
regions in China. Its land area is 8.2% of the country’s total
but its forest area and reserves account for 20.9 and 19.6%,
respectively, of China’s totals. The region is an important
area of commercial forests and a source for various forest
products. It is also the most important natural forest area
and forms 80.3% of the region’s forested area and accounts
for 89.5% of its forest volume. Tree species include:
Dahurian larch (Larix gmelinii Rupr.) and Asian white
birch (Betula platyphylla L.) in the northern Daxingan
Mountains; Korean pine (Pinus koraiensis Siebold and
Zuccarini), amur linden (Tilia amurensis Rupr.), and Korean birch (Betula costata Trautv.) in the Xiaoxingan
Mountains area; Korean pine (P. koraiensis Siebold and
Zuccarini), Manchurian fir (Abies holophylla Maxim.), and
Siberian elm (Ulmus pumila L.) in the Changbai Mountains
area; and Mongolian oak (Quercus mongolica Fisch. ex
Ledeb.), and other deciduous species in the southern region
(Fig. 2).
123
X. Wang et al.
Fig. 2 Location of the study area
Data
Biomass plot data and tree allometric equations
Forest resource inventory data
In total, 1156 plots which consisted of 609 plots sampled
by Xu et al. (2007) and Chen (2003) and 547 new plots
sampled in this study were used to stratify biomass data by
field sampling. Data for each of the 609 plots included plot
number, location, geographical position (latitude, longitude
and elevation), forest of origin, dominant species, number
of species, average specimen age, average specimen density, and biomass and volume per unit area. In order to
estimate the biomass of each tree, 333 tree allometric
equations from Luo (1996) together with tree allometric
equations from the Northeast Forest Biomass Handbook
(Chen and Guo 1986) were used. In order to estimate
volumes in a sample area, we used models provided by the
relevant forestry management departments.
Forest resource inventory (FRI) data were obtained from
two sources: the 2006 forest management planning and
design inventory (FMPDI) for Liaoning and Jilin provinces
published by the Forestry Department of Liaoning Province, and the national continuous forest resource inventory (NCFRI) for Heilongjiang province (2004–2008),
published by the Chinese Ministry of Forestry. With the
FMPDI data, areas and volumes of various age groups of
different forest types were recorded by county. In the
NCFRI data, forest area and timber volumes were provided
for each major forest type by province.
FRI data usually include estimates of timber volume
(wood typically harvested for commercial products), and
lack information about non-commercial components (roots,
branches and foliage) that should be included to calculate
total forest biomass.
123
Vegetation types
Data for the distribution of different forest types were
obtained from the Vegetation Atlas of China (EBVMC
2001) at a scale of 1:1,000,000. The Atlas contains data for
573 vegetation types and 75 forest types mainly obtained
from ground observations. The China Earth Science Data
Estimating and mapping forest biomass in northeast China using joint forest resources…
Sharing Network Center provided digital data sets relevant
to the study area. In order to facilitate the quantification of
the biomass contributions of various forest types, all forest
types were reduced to two: deciduous needle-leaf forests
dominated by L. gmelinii; and, broadleaf forests dominated
by Q. mongolica and B. platyphylla.
NDVI data
The normalized difference vegetation index (NDVI) is the
ratio of the difference between visible red reflectance and
near infrared reflectance to the sum of the two quantities. It
is used as an index to predict the amount and the state of
vegetation cover and the changes of the vegetation (Myneni et al. 1995). The NDVI data in this study are from the
2006 SPOT Vegetation NDVI data set pre-processed and
released by the Flemish Institute for Technological
Research (VITO) VEGETATION Image Processing Center
(CTIV). The VGT-S10 product provides a 10-day maximally-corrected, synthesized 1-km resolution NDVI data
set. The set is created by processing the raw data through a
series of geometric, radiometric and atmospheric corrections and then compositing the corrected data using multiband synthesis and maximized synthesis techniques. A
maximum value composite procedure (MVC) was used to
further reduce the residual atmospheric and bidirectional
effects to produce the annual maximum NDVI data set
(Holben 1986).
province; and, Caohekou Forest Field and Daqinggou
National Nature Reserve in Liaoning province. These areas
were selected because they are typical of the region in
terms of climatic and geographic factors (Fig. 2).
Field surveys were carried out from July to September
from 2007 to 2010. Sample plots were identified as well as
tree layers of the various forest types. Sample plot quadrants 20 9 20 m were randomly selected and height and
diameter at breast height (DBH) [ 2.0 cm were measured.
In total, 547 new sample plots were created.
DBH values were substituted into the corresponding
allometric equations for the species as shown in Eq. 1. Tree
biomass of the sample plot was estimated which was used
to calculate biomass per unit area (Eq. 2).
TB ¼ a Db H c
ð1Þ
where TB (kg) is the total biomass per tree, D (cm) the
DBH, H (m) the tree height, and a, b and c are three
parameters.
PN
TBi
W ¼ i¼1
ð2Þ
A
where W (Mg ha-1) is the biomass per unit plot area, TBi
(Mg)the biomass per tree, N the number of trees with DBH
[ 2 cm, and A (ha) the area of the plot.
The DBHs and heights were substituted into the volume
equations (Eq. 3) of the corresponding species to estimate
the volume of each tree in the plot and then used to calculate the tree volume per unit area.
DEM data
TV ¼ a Db H c
The digital elevation model (DEM) data used in this study
were from the Shuttle Radar Topography Mission (SRTM)
digital elevation database, jointly produced by NASA and
the USGS National Imagery and Mapping Agency
(NIMA). This database was first publicly released in 2003
and has been revised several times. The current database is
version 4.1. The DEM data in this study area were downloaded from the International Scientific & Technical Data
Mirror Site, Computer Network Information Center, Chinese Academy of Sciences.
where TV (m3) is the total tree volume, D (cm) the DBH,
H (m) the height, and a, b and c are three parameters of
volume equations.
Methods
Sample plot survey and data pre-processing
Lack of sufficient, high quality sample plots is a major
barrier to the development of robust biomass estimates and
their validation (Wulder et al. 2008). We selected the following areas in which to locate sample plots: Huzhong
Forestry Bureau, Maoershan Forest Field, and Lianshui
National Nature Reserve in the Dailing district of Heilongjiang province; Loushuihe Forestry Bureau in Jilin
ð3Þ
Establishment of volume to biomass conversion models
and estimation of biomass based on the models
The volume to biomass conversion method, also called the
biomass conversion factor method, is the most efficient and
reliable method for the estimation of the biomass of a forest
ecosystem (Fang et al. 1998; Li and Lei 2010). The biomass is calculated by multiplying the average biomass per
unit volume by the total volume of the forest type (Li and
Lei 2010). Volume to biomass conversion methods include
those identified in the IPCC guidelines as well as the biomass conversion factor continuous function method (which
combines biomass data with national FRI data) and
empirical models for biomass estimation. The method we
used to estimate regional biomass combines data from an
empirical survey of tree volume and biomass with data
from the National Forest Resources Survey. This provides
us with reliable observed volume and biomass data for
123
X. Wang et al.
biomass to volume regression model for each of the different forest types was established through regression
analysis (Eq. 4).
Wi ¼ aVi þ b
ð4Þ
where Wi (Mg ha-1) is the biomass density of the forest
type i, Vi (m3 ha-1) the volume per unit area of the forest
type i, and a and b are two parameters. The root mean
square error (RMSE) was calculated in order to measure
the difference between observed and predicted biomass
density. Table 1 shows the volume (V) to biomass (W) regression models and the RMSE for the 11 forest types in
the 1156 sample plots.
Finally, the total forest biomass of each plot was calculated using Wi combined with the FRI data (for Heilongjiang province) and FPDI data (for every city and
county in Liaoning and Jilin provinces), as shown in Eq. 5.
n
X
Ct ¼
A i Wi
ð5Þ
i¼1
where Ct (Mg) is the total biomass of forest type i for the
county or province, Wi (Mg ha-1) the biomass per unit area
of forest type i for the same region, and Ai (ha) the area of
forest type i in the same region.
Fig. 3 Distribution of typical survey areas and forest sampling plots
in northeast China
different forest types as well as comprehensive forest
resource data on a regional or national scale. Thus, we can
build an accurate volume to biomass conversion model.
We used data for 1156 plots (609 plots from the work of
other researchers and 547 plots surveyed as part of this
study). We categorized the data into 11 forest types. The
spatial distribution of the plots is shown in Fig. 3. The
Combination of forest types
The forests in Liaoning, Jilin and Heilongjiang provinces
were classified into 23, 24, and 23 types respectively in the
FRI data. We aggregated the data into 11 forest types based
on their similarity to the dominant species (Table 2) to
estimate biomass using the volume to biomass conversion
model described above.
Table 1 Parameters to calculate forest biomass density (W, Mg ha-1) and their root mean square errors (RMSE), Biomass density is expressed
as a function of stand growing volume(V, m3 ha-1), W = aV ? b, where a (Mg m-3) and b (Mg ha-1) are parameters for a forest type
Forest type
a (Mg m-3)
b (Mg ha-1)
Plot number
R2
RMSE (Mg ha-1)
Abies and Picea forests
0.444
26.291
18
0.977
koraiensis forests
0.634
7.134
88
0.990
8.527
8.877
Larix gmelinii forests
0.590
21.802
141
0.915
12.748
Pinus sylvestris var. mongolica forests
0.405
51.804
82
0.606
20.373
Pinus tabulaeformis forests
0.822
8.056
217
0.993
4.269
Robinia forests
0.575
38.699
11
0.999
1.323
mixed broadleaf-conifer forests
0.683
13.442
226
0.917
Betula forests
0.523
35.715
45
0.912
5.089
Populus and Salix forests
0.635
20.573
99
0.899
10.032
Quercus mongolica forests
1.207
- 2.989
26
0.997
2.164
mixed broad-leaved forests
0.705
16.365
203
0.888
12.997
123
13.95
Estimating and mapping forest biomass in northeast China using joint forest resources…
Table 2 Eleven recombined forest types in the northeast China’s provinces
Recombined forest type
Primary type
Abies and Picea forests
Picea forests, Abies forests, Pinus densiflora, and mixed coniferous forests
Pinus koraiensis forests
Pinus koraiensis forests
Larix gmelinii forests
Larix gmelinii forests
Pinus sylvestris var. mongolica
forests
Pinus sylvestris var. mongolica forests
Pinus tabulaeformis and Cupressus
cypress forests
Pinus tabuleformis and cypress forests
Robinia forests
Robinia pseudoacacia L., Maackia amurensis Rupr et Maxim forests
mixed broadleaf-conifer forests
Coniferous and deciduous mixed forests
Betula forests
Betula platyphylla Suk, Betula costata, and Betula forests
Populus and Salix forests
Populus, Salix, Populus tomentosa Carr, and other soft deciduous forests
Quercus forests
Quercus forests
Hard broad or mixed broad-leaved
forests
Fraxinus manchurica, Juglans mandshurica Maxim, Phellodendron amurense, Tilia tuan Szyszyl, Ulmus
pumila L., Acer L., and others hard deciduous, deciduous mixed and mixed forests
Mapping forest biomass using remote sensing
According to Tan et al. (2007), there is a significant correlation between the annual maximum NDVI (NDVImax)
and biomass estimated from FRI data. Accordingly, we
used the vegetation distribution map and SPOT VGT
NDVI data to map the higher-resolution spatial distribution
of forest biomass.
The following procedure to map forest biomass was
used. First, based on the estimates of biomass, the forest
types of the various counties in Jilin and Liaoning provinces were aggregated into either coniferous forests or
deciduous broadleaf forests (any mixed forests were classified as coniferous). Their areas, biomass and biomass
densities were recalculated. Second, using ArcGIS 9.3, we
obtained forest distribution data at the county level by
overlaying the forest vegetation distribution maps and the
county-level maps of Jilin and Liaoning provinces. The
average areas of the forests and the values of NDVImax were
calculated in each region. Third, the calculated forest areas
and forest areas derived from FRI data were compared. We
selected those counties that showed differences in forest
areas between the two data sets. The vegetation map was
not created at the same time as the FRI data were collected
hence there were inconsistencies between the map and the
FRI data. Fourth, we extracted the areas of coniferous and
deciduous forests from the selected counties and interpolated the average NDVImax for each type. Fifth, the relationship between average NDVImax and biomass density for
each forest type was calculated. We selected the data of 28
counties and cities where there was a greater area of
deciduous broadleaf forests and the data of 18 counties and
cities where there was a greater area of coniferous forests.
The data was used to create remote sensing models of
deciduous broadleaf and coniferous forests. Sixth, we used
SPSS to create regression models by selecting the best
model according to the coefficient of determination,
residual value, and P value. Finally, the selected model
simulated the biomass density of each forest type and
generated a map of forest density in the study area. The
models for biomass density estimates are Eqs. 6 and 7.
For the deciduous broadleaf forests:
Bk ¼ 2 expð0:0355 DN 4:7425Þ
ð6Þ
with
R2 = 0.818,
RMSE = 6.678 Mg ha-1,
-11
P = 3.96 9 10 .
For the coniferous forests:
and
Bz ¼ 2 expð7:6865 965:4802
Þ
DN
ð7Þ
with
R2 = 0.717,
RMSE = 7.178 Mg ha-1,
and
-6
P = 9.44 9 10 where Bk and Bz (Mg ha-1) are the pixel
biomass densities of deciduous broadleaf and coniferous
forests, respectively, and DN is the gray pixel value of
SPOT VGT NDVImax.
Analysis of DEM-based forest biomass spatial
characteristics
ArcGIS 9.3 was used to generate slope and aspect maps of
the study area using the DEM data. According to the
geomorphological characteristics of northeast China, the
elevation was divided into four levels: \ 300, 300–500,
500–1000 and[ 1000 m. There were four classes of slope:
steep C 26; moderate 16–26; gentle 6–16; and flat 0–
6 according to national standards and the characteristics of
123
123
22.79
139.44
11.17
31.69
17.07
44.66
100.99
110.19
196.52
252.20
949.40
3
4
5
6
7
8
9
10
11
Total
384.16
113.62
65.88
36.42
46.58
21.34
7.13
10.33
7.00
54.13
11.35
10.39
Biomass
(Tg)
1261.72
492.06
170.84
40.89
139.01
114.64
8.91
32.16
20.05
195.95
5.46
41.74
Area
(104 ha)
1024.57
389.99
191.16
32.41
96.75
99.56
4.93
16.28
17.10
140.67
5.68
30.04
Biomass
(Tg)
Middle-aged forest
543.51
239.69
64.39
29.54
65.33
34.72
2.52
6.75
9.50
76.71
0.05
14.33
Area
(104 ha)
569.04
254.23
93.08
27.46
56.00
42.22
1.60
5.70
8.62
66.57
0.06
13.50
Biomass
(Tg)
Near-mature forest
297.43
103.71
23.23
43.83
32.95
23.35
2.60
2.48
3.21
52.27
1.50
8.31
Area
(104 ha)
338.21
124.25
34.86
43.41
29.51
35.50
1.81
2.40
3.32
49.03
3.19
10.93
Biomass
(Tg)
Mature forest
110.17
25.69
18.09
21.68
8.40
4.46
0.85
0.18
0.69
27.87
0.61
1.65
Area
(104 ha)
124.79
32.60
24.03
21.36
7.46
7.97
0.67
0.18
0.91
25.91
1.06
2.62
Biomass
(Tg)
Over-mature forest
3162.23
1113.35
473.06
246.12
346.68
221.83
31.95
73.25
44.63
492.23
30.41
88.71
Area
(104 ha)
Total
2440.76 ± 313.87
914.69
409.01
161.05
236.30
206.59
16.13
34.89
36.94
336.31
21.35
67.49
Biomass (Tg)
77.19 ± 9.93
82.16
86.46
65.44
68.16
93.13
50.49
47.63
82.79
68.32
70.20
76.08
Biomass density
(Mg ha-1)
(1) Abies and Picea forests; (2) Pinus koraiensis forests; (3) Larix forests; (4) Pinus sylvestris var. Mongolica forests; (5) Pinus tabulaeformis and Cupressus orientalis forests; (6) Robinia
forests; (7) mixed broadleaf-conifer forests; (8) Betula forests; (9) Populus and Salix forests; (10) Quercus mongolica forests; (11) mixed broad-leaved forests
22.68
2
Area
(104 ha)
Young forest
1
Forest
type
Table 3 Biomass of different forest types and age groups in the study area
X. Wang et al.
Estimating and mapping forest biomass in northeast China using joint forest resources…
slopes in northeast China. There were five aspects: shady
slope (N, NE), semi-shady slope (E, NW), sunny slope (S,
SW), semi-sunny slope (W, SE), and flat—using the
magnetic azimuth. ArcGIS 9.3 was used to analyse the
characteristics of forest biomass at different elevations,
slopes, and aspects.
Results
Biomass and density characteristics of different
forest types and age groups
Table 3 shows the estimated biomass for different forest
types and ages in the study area using the parameters of the
forest volume to biomass conversion model (Table 1) and
the FRI data. Table 3 further illustrates that the total forest
biomass in the study area was 2440.8 ± 313.9 Tg
(1 Tg = 1012 g). The biomass of different forest types
were between 16.1 and 914.7 Tg. The biomass of each
forest type is directly related to its area. Of all forest types
in the study area, the mixed broadleaf forests, Q. Mongolica forests, L. gmelinii forests, and B. platyphylla forests
accounted for 37.5, 16.8, 13.8 and 9.7% of the total biomass, respectively. The same forest types accounted for
35.2, 15.6, 15.0, and 11.0% of the total forest area,
respectively. Middle-aged and young forests accounted for
39.9% and 30.0% of the total forest area and 42.0 and
15.7% of the total forest biomass, respectively.
Biomass densities of the forest types varied between
47.6 and 93.1 Mg ha-1, with an average of
77.2 ± 9.9 Mg ha-1. Biomass density was related to the
forest type and age and was the greatest in mixed broadleaf-conifer forests. The total areas of near-mature, mature,
and over-mature mixed broadleaf-conifer forests accounted
for 32.0% of the total area. Biomass density of P. tabulaeformis forest was the least. The total area of near-mature, mature and over-mature P. tabulaeformis forest
accounted for 27.0% of the total area. Biomass densities of
all forests varied with age. Ranges of the biomass densities
for young, middle-aged, near-mature, mature and overmature forests were 32.6–62.6, 50.6–111.9, 63.5–144.6,
69.6–213.2, and 78.4–178.6 Mg ha-1, respectively. Overmature forest showed the greatest biomass density
(113.3 Mg ha-1), which was 2.8 times the least biomass
density of young forest (40.5 Mg ha-1).
Comparison of estimated biomass based on remote
sensing and volume to biomass conversion models
Fig. 4 Frequency distribution (a) and spatial distribution (b) of forest
biomass density in the study area
the three provinces were calculated from the map of biomass density (Table 4) and shows that the estimated total
biomass densities of Jilin and Liaoning provinces were
almost equal but that the estimated total biomass density of
Heilongjiang province was very different. To verify the
accuracy of the results from the remote sensing method on
a smaller scale, 35 counties with greater forest areas were
selected to estimate their biomass densities using the two
schemes. Figure 5 shows the correlations obtained by the
analysis.
The estimations obtained using the remote sensing
method show the spatial distribution of biomass density
and can be used to further analyse the spatial distribution of
forest biomass.
Figure 4b shows the spatial distribution of biomass density
as estimated by Eqs. 6 and 7 using vegetation type and
SPOT VGT NDVI data. Biomass and biomass density of
123
123
436.21
3211.31
87.27
1775.15
741.10
63.17
436.21
Remote sensing
275.55
700.13
94.47
2034.00
2750.84
442.90
2440.76
3162.23
74.77
1429.11
1911.35
807.98
58.09
442.90
Biomass conversion factor
continuous function
257.27
754.38
93.37
Area
(104 ha)
Biomass
density
(Mg ha-1)
Biomass
(Tg)
Area
(104 ha)
Area
(104 ha)
Biomass
density
(Mg ha-1)
Area
(104 ha)
Biomass
(Tg)
Biomass
(Tg)
Biomass
density
(Mg ha-1)
Helongjiang Province
Jilin Province
Liaoning Province
Method
Table 4 Estimated results of forest biomass and density in the study area based on volume to biomass conversion model and remote sensing
All
Biomass
(Tg)
Biomass
density
(Mg ha-1)
X. Wang et al.
Fig. 5 Correlation relationship of estimated forest biomass of 35
counties/cities in the study area based on remote sensing and volume
to biomass models
Spatial differentiation of biomass and biomass
density
Horizontal distribution and statistical characteristics
Biomass density in the study area increased from north to
south, and then decreased from north to south. Regions
with the greatest biomass density ([ 120 Mg ha-1) were
mainly in the centre of the study area while regions with
the least biomass density (\ 40 Mg ha-1) were mainly in
the south as shown in Fig. 4b.
The histogram of biomass densities in northeast China
showed that most forests were in the range
80–120 Mg ha-1. Biomass densities of 28.6% of the forests were 40–80 Mg ha-1 (Fig. 4a). Areas with biomass
densities less than 10 Mg ha-1 and those greater than
120 Mg ha-1 were very small, accounting for 4.4 and
9.8% of the total forest area, respectively.
Vertical distribution characteristics
Table 5 shows biomass and biomass densities at different
elevations, slopes and aspects. Biomass was mainly distributed in areas of 300–1000 m (74.3% of the total biomass); the remaining was on the plains below 300 m
(22.0%), and in the mountains above 1000 m (3.7%).
Biomass at different elevations is related to forested area at
different elevations. Forests at 300–1000 m have the largest distributional area (70.9% of the total area). Forests at
elevations\ 300 and[ 1000 m account for 25.8 and 3.3%
of the total forest area, respectively.
Biomass increased with forest area and density varied
significantly with elevation, increasing as elevation
increased. Biomass densities at 500–1000 and [ 1000 m
Estimating and mapping forest biomass in northeast China using joint forest resources…
Table 5 Biomass and biomass
density characteristics of forests
at different elevations, slopes
and aspects in the study area
Statistic index
Statistic interval
Elevation (m)
B 300
Area (104 ha)
828.13
605.44
73.11
1153.92
982.10
85.11
500–1000
1122.52
1062.87
94.69
106.74
100.42
94.08
3211.31
2750.84
85.66
Total
Level slope
1601.54
1294.20
80.81
Slow slope
1245.67
1119.13
89.84
328.34
305.21
92.96
35.76
32.29
90.30
3211.31
2750.84
85.66
Slope
Steep slope and over
Total
Aspect
Biomass density (Mg ha-1)
300–500
[ 1000
Slope
Biomass (Tg)
Flat
Shady slope
6.26
3.40
54.39
819.80
698.54
85.21
Semi-shady Slope
774.91
670.02
86.46
Semi-sunny slope
814.94
695.86
85.39
795.41
3211.31
683.01
2750.84
85.87
85.66
Sunny slope
Total
were 11.3 and 10.5% more than biomass densities at
300–500 m. Biomass density at 300–500 m was 16.4%
greater than biomass density at \ 300 m elevation.
Discussion
Effect of volume-to-biomass conversion models
on estimated biomass at different scales
Slope distribution characteristics
Biomass was mainly distributed on flat and gentle slopes
(Table 5). Biomass on different slopes were in the order of
flat [ gentle [ moderate [ steep, accounting for 47.0,
40.7, 11.1 and 1.2% of forest biomass, respectively. This
result is related to the distribution of forests on different
slopes. Forest areas by slope were flat [ gentle [ moderate [ steep, accounting for 50.0, 38.8, 10.2, and 1.1% of
the total area, respectively.
Changes in average forest biomass density first
increased and then decreased with slopes increasing
(Table 5). In other words, the average biomass of forests on
the flats (0% slope) was significantly less than the average
biomass on other slopes.
Aspect distribution characteristics
There was little difference in forest biomass between semishady, sunny, semi-sunny and shady slopes. However, the
biomass and biomass densities of forests on these aspects
were significantly greater than for forests on flat areas
(Table 5).
The number of sample plots and the classification of forest
types used to establish the forest biomass conversion model
affected the accuracy of the estimates of regional forest
biomass. At the national scale, Fang et al. (2001) created a
volume-to-biomass conversion model that used the conversion factor continuous function method with data from
published literature to estimate China’s forest biomass and
carbon storage. However, their model did not fully consider the effect of forest age on the relationship between
forest volume and biomass, and the number of plots used
for analysis was inadequate (Zhao and Zhou 2004). Pan
et al. (2004) used the data obtained from 5415 plots to
estimate the biomass of different types of forests of different age groups. Pan et al. (2004) compared their results
with those of Fang et al. (2001) and found that the model
may have overestimated forest biomass by 21%. Fang et al.
(2001)’s study had been considered accurate in its estimation of China’s forest biomass. We used Pan et al.’s
(2004) model and the FRI data to estimate biomass of the
different forest types in the three provinces in the study
area. Table 6 shows that the biomass estimates of the two
models differed greatly. For Liaoning province, the results
were close, differing by only 0.5 Tg, while for Jilin and
Heilongjiang provinces, the biomass varied by up to 123.9
and 166.3 Tg, respectively. In general, the estimates given
by Pan et al.’s (2004) model were 16.4% greater than those
of this study.
123
X. Wang et al.
Table 6 Estimated results of forest biomass and density in the three province of the study area by the volume to biomass models of this study
and Pan et al. (2004)
Method
Liaoning Province
Jilin Province
Heilongjiang
Total
Biomass
(Tg)
Biomass density
(Mg ha-1)
Biomass
(Tg)
Biomass
(Tg)
Biomass density
(Mg ha-1)
Biomass
(Tg)
Biomass density
(Mg ha-1)
Models of
this study
257.27
58.09
754.38
93.37
1429.11
74.77
2440.76
77.19
Pan’s models
(2004)
256.81
57.98
878.30
108.70
1595.39
82.87
2730.50
86.35
Biomass density
(Mg ha-1)
China is a vast territory. The climate ranges from tropical to cool temperate in which many types of forest grow.
A volume-to-biomass conversion model that works well at
the national scale may not work well on a regional scale.
Previous studies have shown that limited numbers of
sample plots and restrictions in the numbers of forest types
can reduce the efficacy of a model that may be accurate on
a small scale if it is used on a larger scale to estimate
biomass and carbon reserves (Botkin and Simpson 1990;
Wulder et al. 2008). To counter this limitation, we surveyed 547 plots in six selected typical regions and collected published data from 609 plots in the study area and
its surrounds. We partitioned the data into 11 forest types
and created the volume-to-biomass conversion model for
the study area. The locations, vegetation types, and characteristics of the surveyed plots were all typical for the
vegetation of northeast China, and the forest type classification used matches the main forest types of northeast
China. In contrast, in the model built by Pan et al. (2004),
only seven types of forests matched those in northeast
China and the number of plots used was much lower than
the number of plots used in this study. Thus the model
developed in this study may be considered to be a better fit
for northeast China.
The preceding analysis shows that the scale of the model
has a great effect on the estimation of biomass. A model on
a national scale may not be suitable for regional or local
applications, and thus the creation of a regional volume-to–
biomass conversion model is necessary for accurate estimation of regional forest biomass. In order to accurately
estimate carbon storage and carbon cycle activity in a
regional or global forest ecosystem, a scale-specific model
is required.
Comparison with previous biomass estimations
Li and Lei (2010) used data from the National Forest
Resources Statistics (2004–2008) 7th NFI to produce a set
of biomass estimates for forests in Jilin province using
various models. The models included the continuous
function of biomass conversion factor proposed by Fang
123
et al. (2001), the empirical model for biomass estimation,
the IPCC variable biomass expansion factor, and the IPCC
fixed biomass conversion factor. Jiao and Hu (2005) used
FRI data from the 1st NFI (1973–1976) though the 6th NFI
(1999–2003), and assumed a linear relationship between
the biomass of a tree and its volume in order to estimate
and analyze the carbon storage of various species in Heilongjiang Province over a recent 30-year period. Wang
et al. (2008) used FRI data from the 3rd NFI (1984–1988)
through the 6th NFI (1999–2003) to create a biomass–
volume model based on dominant tree species in Liaoning
province.
Table 7 shows forest biomass and densities given by
different models in three provinces of northeast China. Our
results, using the empirical model, are roughly consistent
with the biomass and density estimated by Li and Lei
(2010). Their biomass estimate is 2.3% lower than ours and
their density estimate 11.7% higher. The results obtained
by Wang et al. (2008), using the mixed method to estimate
biomass and biomass density in Liaoning province, differed from our results, being lower by 29.9 and 45.4%,
respectively. These differences could be caused by different estimation methods and their use of FRI data from a
different period. Excluding the results from Liaoning province, the results of our study are close to the estimates
obtained by Li and Lei (2010) using the IPCC variable
biomass expansion factor method. However, biomass and
density estimates for Jilin province obtained by Li and Lei
(2010) using the IPCC fixed expansion factor method, the
empirical model for biomass estimation, and the conversion factor continuous function method were 50.0, 47.2 and
39.2% higher than ours, respectively. Our estimates for
Helongjiang province were close to those of Jiao and Hu
(2005) but differed greatly from those of Li and Lei (2010)
which were 10.6% lower than ours. Overall, the total biomass and average biomass density given by Li and Lei
(2010) using the empirical model were 29.1 and 27.4%
higher than ours, respectively.
The preceding analysis shows that there are large differences between the estimates of forest biomass provided
by different models. Large-scale biomass estimation
Estimating and mapping forest biomass in northeast China using joint forest resources…
Table 7 Comparison of different forest biomass and density estimation results with previous biomass estimations
Study area
Time
Area
(104 ha)
Biomass
(Tg)
Liaoning
Province
2006
442.90
257.27
2004–2008
387.62
1999–2003
Jilin Province
Heilongjiang
Province
The study area
Biomass density
(Mg ha-1)
Methodology
Data source
References
58.09
Biomass conversion factor
continuous function method
The FPDI data
in 2006
This study
251.52
64.90
Empirical model for biomass
estimation
The seventh
FRI data
Li and Lei
(2010)
322.57
140.60
43.60
Empirical model for biomass
estimation
The sixth FRI
Data
Wang et al.
(2008)
2006
807.98
754.38
93.37
Biomass conversion factor
continuous function method
The FPDI data
in 2006
This study
2004–2008
761.15
688.42
90.44
IPCC variable biomass expansion
factor
The seventh
FRI Data
Li and Lei
(2010)
2004–2008
761.15
1066.28
140.08
IPCC fixed biomass conversion
factor
The seventh
FRI Data
Li and Lei
(2010)
2004–2008
761.15
1046.12
137.44
empirical model for biomass
estimation
The seventh
FRI Data
Li and Lei
(2010)
2004–2008
761.15
988.94
129.92
Biomass conversion factor
continuous function method
The seventh
FRI Data
Li and Lei
(2010)
2004–2008
1911.35
1429.11
74.77
Biomass conversion factor
continuous function method
The seventh
FRI Data
This study
2004–2008
2057.63
1854.34
90.12
Empirical model for biomass
estimation
The seventh
FRI Data
Li and Lei
(2010)
1999–2003
1798.00
1202.00
66.88
Biomass conversion factor
continuous function method
The seventh
FRI data
Jiao and Hu
(2005)
2004–2008
3206.40
3151.98
98.30
Empirical model for biomass
estimation
The seventh
FRI data
Li and Lei
(2010)
models may not be suitable for smaller scale biomass
estimates. The effects of differences in scale on the accuracy of forest biomass estimates are obvious. Thus, it is
important to develop and establish an accurate small-scale
biomass estimation model and to move incrementally from
small-scale to larger scale estimation in order to accurately
estimate forest biomass at national and global scales.
Impact of human activities on biomass density
In general, the spatial features of forest biomass density in
the study area show that overall biomass density increases
as the elevation increases, first increasing and then
decreasing as the elevation increases. Biomass density on
flat areas is significantly lower than on slopes. The reasons
for this are not clear.
The significant differences in forest biomass density at
different elevations, slopes and aspect are related to human
disturbance. Forests with low biomass are mostly secondary forests, young in age, and disturbed by human
activity. On the plain areas (elevation \ 300 m), forest
plantations are mainly trees with low biomass and low
biomass density. In contrast, forests growing at [ 500 m
are mostly mixed deciduous and mixed coniferous–deciduous forests with greater biomass and higher biomass
densities and have been less affected by human
interference. Farming is common on the plains where forest
vegetation is easily disturbed by human activity and leads
to low carbon density. As slopes increase, farming is
reduced and forest vegetation is less affected and results in
higher carbon densities. On steep slopes, soil erosion
seriously limits forest growth, which results in a decrease
in biomass densities. With respect to biomass density differences in slope aspect, flat areas (i.e. no slope aspect) are
often affected by human activities which results in lower
forest biomass density.
Human activity is extremely influential in the decrease
in regional forest biomass density in northeast China and
leads to spatial differences in forest biomass. In other
words, only by reducing human activity and by strengthening forest protection and reconstructing degraded forest
ecosystems, in conjunction with the current Natural Forest
Resources Protection Project and the Returning Farmland
to Forest and Grassland Project in northeast China, can
carbon sequestration in regional forests be significantly
increased.
Uncertainties in mapping forest biomass based
on remote sensing
We recognize that some uncertainties can lead to inaccuracies in forest biomass maps created in this study. Many
123
X. Wang et al.
factors can influence accuracy, such as the quality of
remote sensing data, correction and compositing of the
empirical data, and the spatial resolution and the saturation
effect of remote sensing data (Lu 2007; Tan et al. 2007).
The SPOT-VGT NDVI dataset was used to produce the
annual maximum NDVI data for forest biomass estimation
by MVC. The effects of atmosphere and cloud may be
compensated for to some degree but will still have some
influence on the SPOT-VGT data set.
In this study, the models of deciduous broadleaf and
coniferous forests were established by regression analysis of
the average biomass density and NDVImax of forested areas in
counties having similar or greater forested areas in the vegetation map than found in the FRI data. The R2 and RMSE of
our models were better than those of Tan et al. (2007), which
were R2 = 0.56, RSME = 13.98 Mg C ha-1. The better
results of our model may be due to the relatively high spatial
resolution of data we used (1 km compared to 8 km in Tan
et al. 2007), as well as the data optimized in creating the
model, and the separation of models for different forest
types. Although the spatial resolution of SPOT-VGT NDVI
(1 km) was finer than that of NOAA/AVHRR NDVI (8 km),
it is still coarse for forest biomass mapping. Mixed pixels
abound in the SPOT-VGT NDVI dataset, which leads to
difficulties in the identification of the precise range and
spectral information for a forested area and subsequent
uncertainty in mapping the biomass.
Remote sensing is more sensitive to crown surfaces than
below-canopy factors. When canopy density is greater than
80%, increases in forest biomass will not be shown in the
information derived from remote sensing (Tucker and
Sellers 1986). Tan et al. (2007) suggested that NDVI saturation may cause the biomass density to be underestimated in a region with dense canopy cover, such as in the
Changbai Mountains. In this study, we used the average
biomass density and NDVImax of the forested area at a
county level to create models to estimate forest biomass.
The comparisons of biomass based on remote sensing and
estimates from volume to biomass conversion models
showed that the maps could reflect the spatial distribution
of biomass density at the county level. It is still very hard to
test the accuracy of the biomass estimate at the pixel level
(1 km). If plot level biomass estimates at this scale are
available in the future, validation will be possible (Du et al.
2014).
Conclusion
We estimated regional forest biomass using both FRI data
and plot survey data. Vegetation distribution data, remote
sensing data, and DEM data were used to map the spatial
distribution and forest characteristics. The main
123
conclusions are: a large-scale biomass estimation model
may not be suitable for smaller scale forest biomass estimation; the regional volume to biomass conversion model
provides an accurate estimate of the regional forest biomass; and, the forest biomass in northeast China at different elevations, slopes, and aspects is greatly affected by
human activity. Overall, the variations in both regional
biomass and biomass density with respect to elevation and
slope indicate that forest vegetation in northeast China is
very vulnerable to interference from human activities. The
only way to protect and stabilize the regional forest biomass is to reduce such activities.
Acknowledgements The authors thank all staff members in the
Natural Forest Conservation Group (NFCG) of the Institute of
Applied Ecology (IAE) for their help. We are grateful to the authors
of Xu et al. (2007) and Chen (2003) who allowed us to use their data
for the 609 plots.
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