PALSAR backscatter to estimate above-ground forest biomass: A case study in Western Siberia

Remote Sensing of Environment 137 (2013) 139–146

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The use of ALOS/PALSAR backscatter to estimate above-ground forest biomass: A case study in Western Siberia Anna Peregon ⁎, Yoshiki Yamagata 1 Center for Global Environmental Research, National Institute for Environmental Studies, 16-2, Onogawa, Tsukuba, Ibaraki 305-0051, Japan

a r t i c l e

i n f o

Article history: Received 7 October 2010 Received in revised form 5 June 2013 Accepted 9 June 2013 Available online 16 July 2013 Keywords: Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar SAR backscatter Boreal forest Above-ground forest biomass (AGB) Western Siberia

a b s t r a c t The estimates of above-ground forest biomass (AGB) in Northern Eurasia are highly uncertain, despite the global importance of AGB for ecosystem services and its role as carbon stores. In this paper, we demonstrate the potential of ALOS/PALSAR (Advanced Land Observing Satellite/Phased Array L-band Synthetic Aperture Radar), for the estimation of AGB in the range of 10–190 tons (dry matter)/ha in mixed and deciduous forests at the southern edge of the boreal region in Western Siberia. Various regression models were tested to determine the relationship between forest biomass derived from field measurements and radar backscatter. The best results were obtained using HV-polarized backscatter with the Water Cloud model, giving estimation errors in terms of root mean square errors (RMSE) between 25% and 32% of the mean biomass, and coefficient of determination (R2) between 0.35 and 0.49 for the whole range of SAR backscatter used in the analysis. The method displayed a higher prediction accuracy with RMSE of 15%, and the R2 between 0.55 and 0.72 when restricted to SAR backscatter (σ0) b − 12.6 dB where the model was clearly defined. The SAR-based estimates offer a potential of rapid, high resolution and low cost mapping of the lower biomass woody vegetation (sparse or young forests on shallow peat) in Siberia, the area where more accurate national or large scale forest inventories hardly exist. © 2013 Elsevier Inc. All rights reserved.

1. Introduction Forests play an important role among different terrestrial ecosystems in the global carbon cycle by sequestering a large quantity of carbon in their wooden biomass, which mitigates the increase of atmospheric carbon concentrations (IPCC, 2007). In the current context of global deforestation and climate change, a wide range of organizations need estimates of forest biomass to assess the state of the World's forests and their rate of change, as well as to reinforce sustainable forestry and to monitor international treaties like the Kyoto protocol. Recently, much effort has gone into estimation of the carbon content in tropical forests as they had been recognized critical to future climate stabilization (Gurney et al., 2002; Stephens et al., 2007; Tans et al., 1990), while there is a degree of consensus on the essential role of boreal forests in global climate regulation (Liski et al., 2003). Given the vastness and remoteness of the boreal forests in Northern Eurasia, the quantitive estimations of forest biomass would be impossible without space-based Earth observations, which allow the rapid generation of extensive data sets describing land surface properties (Gaveau, Balzter, & Plummer, 2003).

⁎ Corresponding author at: Institute of Soil Science and Agrochemistry SB RAS, Prospect Ak. Lavrentyeva, 8/2, 630090, Novosibirsk, Russia. Tel.: +7 383 363 90 25. E-mail address: [email protected] (A. Peregon). 1 Tel.: +81 2 98 50 25 45; fax: +81 2 98 50 29 60. 0034-4257/$ – see front matter © 2013 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.rse.2013.06.012

Optical satellite images have a long history of being used for estimation of forest parameters and assessment of wooden biomass with different quality results. However, the weather conditions and other atmospheric disturbances make the optical images prone to errors (Lillesand, Kiefer, & Chipman, 2004). 1.1. SAR data for biomass estimations In recent years, many researchers have demonstrated the potential of microwave remote sensing for forest stem volume (or biomass) estimations in the boreal forests. It has been proven at L- and P-bands with backscatter analysis (Le Toan, Beaudoin, Riom, & Guyon, 1992; Ranson et al., 1997; Rauste, 2005; Santoro, Eriksson, Askne, & Schmullius, 2006), and using interferometric coherence technique (Askne, Santoro, Smith, & Fransson, 2003; Eriksson, Santoro, Wiesmann, & Schmullius, 2003; Gaveau et al., 2003); at C-band using ERS-1/2 repeat-pass interferometry (Santoro, Askne, Smith, & Fransson, 2002, Santoro, Shvidenko, McCallum, Askne, & Schmullius, 2007), and when combining ERS tandem interferometric coherence and JERS backscatter (Balzter et al., 2002; Tansey et al., 2004; Wagner et al., 2003). Some studies considered SAR (Synthetic Aperture Radar) application for forest biomass estimations in Central Siberia (Schmullius et al., 2001; Tansey et al., 2004; Wagner et al., 2003). A general experience is that the long wavelength (L-band) SAR is most adequate for biomass estimations (Rosenqvist, Shimada, Ito, & Watanabe, 2007; Thiel, Drezet, Weise, Quegan, & Schmullius, 2006).

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In that case, the detected radiation is mostly due to backscattering from the branching elements and stems of the trees, and thus radar should respond in a characteristic way to forest volume and biomass (Saatchi & Moghaddam, 2000). Even though L-band backscatter is strongly correlated with forest biomass, a limiting factor is the saturation of the backscatter–biomass relationship at a certain biomass level (Imhoff, 1995). The saturation level may depend on the tree species and forest type as well as on the ground surface type (Rauste, 2005). It makes the SAR data useful for accurate estimates of carbon stocks in relatively homogeneous, young or sparse boreal forests, but less useful in complex canopies of mature, higher biomass forests (Le Toan et al., 2004). ALOS/PALSAR was operated in-orbit from January 2006 until April 2011. It shows a great potential for forestry applications in the boreal regions due to high signal/noise ratio, high resolution (~ 20 m), provision of cross-polarized data, and because data are being systematically collected across the Northern hemisphere (Rosenqvist et al., 2007). ALOS/PALSAR data have been shown to respond to the aboveground biomass (AGB) of boreal forests in Scandinavia: in Sweden (Eriksson, Magnusson, Fransson, Sandberg, & Ulander, 2007; Magnusson et al., 2007) and Finland (Rauste, Lönniqvist, Molinier, Ahola, & Häme, 2007, Rauste, Lonnqvist, & Ahola, 2008); Northern America — Alaska (Suzuki, Kim, Ishii, & Nicoll, 2009), and Central Siberia (Thiel, Thiel, & Schmullius, 2009). Although achieving promising results, the estimation of forest biomass remains problematic and goes much behind the operational stage, not only because of the effect of saturation of the signal at high biomass levels, but also because of the spatial heterogeneity of natural ecosystems leading to large uncertainties. Moreover, a gap still exists in large areas of Western Siberia, where ground measurements are limited and no studies have been conducted based on remote sensing data. There is no universally accepted methodology for assessing the AGB of woody boreal landscapes. Thus, there is a continued need of both

new experimental data and a further improvement of the existing models for biomass estimation from SAR data. A key component of this work was checking the capability of summer-time ALOS/PALSAR Fine Beam Double (FBD) polarization observations for estimation of AGB in complex canopies along a wide gradient of ground water content, i.e., forested peatlands, peat swamp forests and dry upland forests at the southern edge of the boreal region in Western Siberia, where detailed ground reference data were available. In this way, the effect of environmental conditions and spatial heterogeneity of natural ecosystems have been considered.

2. Materials and methods 2.1. Test site Western Siberia refers to the area bordered by the Ural Mountains in the west, the Yenisey River in the east, the Kara Sea in the north and the Altai Mountains and Kazakh Hills in the south (Fig. 1). The test site was located in Western Siberia, Russia near 56° 51′N, 78° 53′ E on the border between the southern boreal and boreonemoral vegetation zones (Tuhkanen, 1984). The area is fairly flat, with ground elevations between 25 and 95 m above sea level. The annual mean temperature is — 1.1 °C. Annual mean precipitation ranges from 450 to 560 mm yr−1, with two-thirds of that comes in summer months; and total evaporation is 500 mm (Sergeev, Dobrovolsky, & Gerasimova, 1977). The area is dominated by hemi-boreal mixed forests, where the common tree species are birch (Betula pubescens Ehrh.) and aspen (Populus tremula L.) in nearly equal proportions, with less contribution of spruce (Picea obovata L.), fir (Abies sibirica L.) and Siberian pine (Pinus sibirica) — up to 15–20% of the total. Deciduous trees are sparsely distributed. Pine trees (Pinus sylvestris L.) predominate in extremely wet conditions and compose homogeneous forest stands on peatlands.

Fig. 1. Location of the test site and distribution of ground observation plots as shown in ALOS/PALSAR scene 2007-07-16: R-HH, G-HV, B-HH/HV.

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2.2. Ground reference data Ground reference data for the test site consisted of a digital stand boundary map as well as in-situ data collected by local experts during field campaigns in 2006, which corresponds rather well with the dates of ALOS/PALSAR acquisitions (see Table 1). The test site was in a size of ~ 3500 km2 and contained a whole diversity of ecosystems ranging from dry upland forests to sparse peat swamp forests, forested peatlands, and treeless peatlands. The stand boundary map with a set of inventory units has been originally derived in the Russian Forest Survey from aerial photographs in terms of displaying homogeneous forest cover and its properties. In our study, the forest parameters were measured on the ground by local experts at 430 (100 m2 each) observation plots. The density of field measurements was higher at low to moderate wooden biomass due to the large diversity of low-productive ecosystems. This point-wise inventory was made based on standardized forestry methodologies in designated forest stands with an area of N10 ha. The spatial distribution of observation plots is shown in Fig. 1. Under given definition of the stand as a homogeneous unit, the set of forest parameters was attributed to the whole area of the stand with a certain level of confidence. 2.3. Above-ground biomass estimates We have not operated by direct forest biomass measurements, but used a set of in-situ forest parameters, i.e.. tree height, average age, diameter of tree stems, and number of trees per hectare converted to estimates of forest stem volume (V), using the growth (yield) tables elaborated for major tree species in the region by Shvidenko, Schepaschenko, Nilsson, and Boului (2007a). The forest biomass was calculated with a certain stem volume by using a semi-empirical phytomass model developed by the International Institute for Applied Systems Analyses (IIASA) (Shvidenko, Schepaschenko, Nilsson, & Bouloui, 2007b). The model is parameterized for dominant tree species, site indexes (bonitat) and ecological regions based on an extensive set of sample plots collected in the forests of Northern Eurasia. In this study, we used the model coefficients elaborated for the southern taiga region, the Asian part of Russia. The IIASA model provides estimates for not only commercial wooden biomass, but the total AGB, including the fractions of stem wood (F1), bark (F2), branches (F3), and foliage (F4), but excluding shrubs, grasses and necromass. The model is based on calculating the ratio (Ri) of biomass fractions (Fi, tons/ha) to stem volume (V, m3/ha) as a function of the biometric characteristics of forests Tj, which are defined for each forest stand (j), i.e.:
 i i R ¼ F =V ¼ f T j :

ð1Þ

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To estimate biomass, Eq. (2) was used for pine-dominated stands, and Eq. (3) for a wide range of forest species such as spruce, fir, cedar, birch, aspen, and mixed forest stands.

i

c1

R ¼ c0  SI  A i

c1

ðc2þc3·RSþc4sqrðRSÞÞ

c2

c3

;

R ¼ c0  A  SI  RS expðc4  A þ c5  RSÞ;

ð2Þ ð3Þ

where A is the age (years), SI the site index (coded as 3, 4,…, 13 for Ic, Ib,…, Vb site indexes, respectively), RS is the relative stocking, and c1 − c5 are regression coefficients taken from Shvidenko et al. (2007b) or http://www.iiasa.ac.at/Research/FOR/russia_ghg.html? sb=7. The biomass fractions, calculated with Eq. (1) were further aggregated into the total biomass value. The average forest biomass was 73 tons/ha, with no forest biomass found in treeless peat-accumulating wetlands, and the highest biomass of 190 tons/ha detected in upland deciduous forests (standard deviation 44 tons/ha), although the biomass rarely exceeded 150 tons/ha. Fig. 2 illustrates the distribution of forest biomass over forest stands used in the analysis. These values are dry AGB, not carbon content. All estimates of forest biomass will refer to dry organic matter (DOM) hereafter. 2.4. ALOS/PALSAR data Since boreal region is prone to large seasonal variability in temperature and snow cover, this could in turn contribute to seasonal variability in the SAR data (Pulliainen, Kurvonen, & Hallikainen, 1999; Santoro et al., 2011). We used only summer and early-autumn SAR acquisitions as the relationship between radar data and biomass values was found to be more pronounced in the summer months (Rauste, 2005; Rauste et al., 2008; Santoro et al., 2006, 2009). The satellite data consisted of five ALOS/PALSAR scenes acquired during 2007 and 2008 (Table 1). Table 1 gives an overview of environmental conditions at the time of image acquisition measured at the station “Severnoe” located within the test site (http://rp5.ru, in Russian). The environmental conditions were similar for all dates of SAR acquisitions. The ALOS/PALSAR data were pre-processed in Japan Aerospace Exploration Agency (JAXA). The scenes were geometrically calibrated using SRTM Digital Elevation Model (DEM) with 90-meter spatial resolution and radiometrically normalized with the algorithms described in Shimada, Isoguchi, Tadano, and Isono (2009). Since the errors in image registration and location of sample plots may produce high estimation errors at the pixel level because the field plots are typically small in upland forests (Mäkelä & Pekkarinen, 2004), we applied edge erosion to reduce the risk of discrepancy in

Table 1 ALOS/PALSAR data over the test site.a ID

Acquisition date

Environmental conditions

1

2007-07-16

2

2008-07-01

3

2008-07-18

4

2008-08-16

5

2008-09-02

T ~ 18 before T ~ 19 before T ~ 23 before T ~ 15 before T ~ 18 before

°C, dry (7 mm precipitation data acquisition)b °C, dry (3 mm precipitation data acquisition) °C, dry (4 mm precipitation data acquisition) °C, dry (6 mm precipitation acquisition) °C, dry (8 mm precipitation data acquisition)

5 days 2 days 6 days 1 day 3 days

a All scenes: orbit frame/path: 509/1130; flight direction: ascending (acquisition at 10:30 a.m.); mode: FBD; dual polarization: HH/HV; off-nadir angle: 34.3°, 12.5 m pixel spacing. b Air temperature in time of image acquisition (~10:00 a.m.).

Fig. 2. Distribution of above-ground forest biomass (aggregated for each 10 tons/ha) for all observation plots.

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geolocation. A buffer zone corresponding to two pixel sizes of the SAR imagery was removed from the perimeter of each forest stand. Before regression analyses, the radar data were averaged by a 5-by-5 pix window to reduce the effect of speckle and spatial heterogeneity of the forest stands. The digital numbers of the SAR signal amplitude have been initially extracted from the imagery, and they were later converted to backscattering coefficients in dB based on Eq. (4):

0

σ ¼ 20  logðDNPALSAR Þ−83 dB;

ð4Þ

where σ0 is the backscattering coefficient in dB, and DNPALSAR is the ALOS/PALSAR signal amplitude. 3. Analysis

The slope of regression lines is similar from scene to scene along the different dates of SAR acquisitions, suggesting that one equation with unified regression coefficients can be used for biomass estimations. The backscatter values tend to increase rapidly in both the HH and HV polarization modes in the low range of forest biomass, from 0 to 60–70 tons/ha, showing good perspectives for biomass retrieval in forested peatlands and large parts of peat swamp forests, whereas the SAR backscatter seems to lose sensitivity to AGB in dense upland forests. The saturation limit identified at the biomass values of ~ 70 tons/ha, where root mean square error (RMSE) decreases with increasing biomass values. The whole set of field observation plots was stratified by ecosystem type proportions and split into two equal subsets: one for determining parameters of the backscatter model (i.e., 215 observation plots reserved for training), and the other 208 plots intended for testing the model and validation.

3.1. Analysis of SAR backscatter

3.2. Forward modeling

The dynamic range of the backscatter in the HH polarization mode was 17.5 dB (σ0 varied between − 20 and − 2.5 dB), similar to the SAR backscatter (18 dB) in the HV polarization mode (σ0 between − 28 and − 10 dB). Nevertheless, the average backscatter was found to be about 5–8 dB lower in the HV polarization mode compared with HH (two examples of the backscatter curves are shown in Fig. 3). Fig. 4 shows the relationship between the average mean SAR backscatter (σ0 averaged within each 10-ton/ha interval of AGB) and the corresponding forest biomass for the whole range of natural ecosystems. Whereas the forested peatland and upland forest classes are clearly defined by corresponding SAR-backscatter, this is in particular problematic concerning the peat swamp forest which is entirely overlapped with adjacent classes. Completely different scattering mechanisms (i.e., double-bounce effect) can occur due to the seasonal flooding conditions in peat swamp forests, when the SAR signal often increased if standing water is present on the land surface (Dwivedi, Rao, & Bhattacharya, 1999; Kasischke & Bourgeau-Chavez, 1997). In this case, a very high backscatter returns to the SAR sensor and the regression model eventually produces high biomass value, which will be incorrectly assigned to dry upland forest. The crowd of points with high SAR backscatter corresponding to low biomass values that are potentially contributing to this sort of uncertainty is obvious in Fig. 3 (left panel).

Various single-variable and multivariate regression models were tested to find the relationship between forest biomass and radar backscatter (Table 2). In the equations, β0, β1, and β2 are regression coefficients estimated by means of the least squares criterion, and AGB is the above-ground biomass (tons/ha) calculated with Eq. (1). The only ALOS/PALSAR scenes in the HV polarization mode were further used for model development due to the stable relationship revealed between the SAR backscatter and above-ground forest biomass. The curves of b, d and e-models (bold in Table 2) do not differ much from one another as it is shown in Fig. 3. The Water Cloud model, developed by Attema and Ulaby (1978) (Table 2, Eq. e) was found most reliable in the capacity of forward regression model as it produces the highest coefficients of determination (0.65–0.81). The Water Cloud model also showed stability in all estimated parameters (coefficients of regression model, Table 3). Hereafter, we consider the Water Cloud model as being most promising for biomass estimations. 3.3. Inverse modeling (biomass retrieval) and model validation Based on the equation and regression coefficients in the forward model, an inverse relationship could be established, with forest biomass as the dependent variable, and the backscattering coefficient

Fig. 3. ALOS/PALSAR backscatter (an example of two scenes) with regression models tested in the study, see Table 2: solid line: e, Water Cloud model; fine dashed line: b model; thick dashed line: d model.

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Fig. 4. The relationship between average mean SAR backscatter (σ0 averaged within each 10-ton/ha interval of AGB) in HH and HV polarization modes and corresponding forest biomass for whole range of natural ecosystems and environmental conditions. The ecosystem type specified from field measurements.

as the independent variable. The Water Cloud model for the combined summer data set is written in Eq. (5).

AGB ¼

1 σ 0 þ 12:6 ln −0:03 −10:19

! ð5Þ

where AGB is the above-ground forest biomass (tons/ha), and σ0 is the backscattering coefficient in dB calculated for the SAR signal amplitude with Eq. (4). The main difficulty of biomass retrieval with Water Cloud model (Eq. (5)) is that the model in fact produces uncertain results when inverted (Askne et al., 2003). Since the model is essentially exponential, it approaches an asymptotic limit, and in some cases the observation values fall outside the range covered by the model curve. In these cases, a strict inversion of the model would result in either infinite or negative estimates, and both do not represent the real biomass. Following Askne et al. (2003), these estimates were arbitrarily set equal to either the highest biomass observed in the training data set (190 tons/ha) or zero biomass. The model performance for biomass retrieval was finally tested against two validation subsets to consider the uncertain results in the Water Cloud model (Table 4): (1) whole range of SAR backscatter, and (2) SAR backscatter lower than − 12.6 dB, which is similar to the value of β0 parameter in the model equation. Both field and predicted values were limited at 190 tons/ha, as all possible sensitivity of SAR backscatter to AGB will be lost by this point, and thus making predictions at such high AGB values has no validity.

Fig. 5 shows an agreement between observed forest biomass and biomass retrieved from SAR backscatter with Eq. (5) in three ALOS/ PALSAR scenes at 208 validation plots. For the validation stands the errors (RMSE) were estimated to be 46–55 tons/ha (25–32% of mean biomass, R2 between 0.35 and 0.49) for the whole set of SAR backscatter used in the analysis (Table 4). The method would give more accurate biomass estimates with RMSEs varied between 21 and 26 tons/ha (~ 15% of mean biomass, R2 in the range of 0.55–0.72) if restricted to the SAR backscatter (σ0) b − 12.6 dB, where the Water Cloud model (Eq. (5)) is clearly defined. 4. Discussion In this study, the ALOS/PALSAR signal tended to increase with increasing forest biomass in both the HH and HV polarization modes, thus being in agreement with other studies (e.g., Rauste, 2005; Santoro et al., 2006). However, the backscatter in Siberian forests seems more variable compared with boreal and hemiboreal forests in Sweden (Santoro et al., 2009) and Finland (Rauste et al., 2007). Apparently, this is caused by the wide range of natural ecosystems considered in the analyses — sparse forest on deep peatlands (forested peatlands), wet forests in transit areas between peatlands and upland forests, and dry upland forests. A saturation of SAR signal was observed by analogy with all previous estimates of the forest biomass based on L-band data (e.g., Rauste, 2005; Rauste et al., 2008; Santoro et al., 2009). While the saturation level differed from one acquisition date to another, the signal saturated at a fairly low biomass of 60–70 tons/ha. Based on the data set combined from three test sites (Hawaii/USA: broad-leaved, North

Table 2 Coefficient of determination (R2) from the five ALOS/PALSAR scenes in HH/HV polarization modes. Expected value of the backscatter (σ0) estimated with different functions of above-ground biomass (AGB). Backscattering models 0

(a) σ = β0 + β1AGB (b) σ0 = β0 + β1ln(AGB) (c) σ0 = β0 + β1ln(AGB) + β2(ln(AGB))2 (d) σ0 = β0 + β1sqrt(AGB) (e) σ0 = β0 + β1exp(β2AGB)a a

16 July 2007

01 July 2008

18 July 2008

16 Aug. 2008

02 Sept. 2008

0.31/0.44 0.63/0.76 0.64/− 0.45/0.62 0.68/0.81

0.18/0.36 0.34/0.55 −/− 0.26/0.49 0.45/0.68

0.38/0.47 0.47/0.65 0.49/0.66 0.46/0.60 0.54/0.71

0.24/0.38 0.39/0.55 −/− 0.32/0.50 0.49/0.67

0.37/0.44 0.45/0.59 0.45/0.59 0.43/0.53 0.49/0.65

The Water Cloud model; (−) ln squared term was not statistically significant.

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Table 3 Parameters of the Water Cloud model for all the ALOS/PALSAR scenes (where n is the number of field observations; β0, β1, β2 are regression coefficients in model equation, and R2 is the coefficient of determination). Scene

n

β0

β1

β2

R2

16 July 2007 01 July 2008 18 July 2008 16 Aug. 2008 02 Sept. 2008 All combineda

215 175 215 175 215 175

−12.41 −12.18 −12.66 −12.58 −12.79 −12.60

−11.55 −9.72 −9.14 −9.75 −8.97 −10.19

−0.04 −0.03 −0.03 −0.03 −0.03 −0.03

0.81 0.68 0.71 0.67 0.65 0.72

This multi-temporal combination assumes the optimal parameters calculated in the Water Cloud model and the model curve fits a stack of backscatter measurements with corresponding biomass values. a Regression model created with whole set of SAR backscatter, i.e., the SAR data from 5 ALOS/PALSAR scenes corresponding to 175 in situ biomass values.

Table 4 Capacity of the Water Cloud model for forest biomass estimations. Scene

16 July 2007 01 July 2008 18 July 2008 16 August 2008 02 September 2008 All combined

Whole range of (σ 0) backscatter (AGB ≤ 190 tons/ha)

σ 0 b −12.6 dB (AGB ≤ 190 tons/ha)

R2

RMSE, tons/ha

R2

RMSE, tons/ha

0.47 0.40 0.49 0.43 0.35 0.42

55 50 46 53 52 51

0.60 0.70 0.55 0.72 0.59 0.60

25 24 26 21 26 25

Carolina/USA: pine forests, and France: pine forests), Imhoff (1995) reports the L-band saturation level at 40 tons/ha of dry biomass. In boreal forests, the 40 tons/ha limit corresponds to a forest stem volume of approximately 70 m3/ha (Rauste, 2005). The saturation level in our study is generally in agreement with Israelsson, Askne, Fransson, and Sylvander (1995) who found the saturation level at 100 to 150 m3/ha of stem volume (~ 60 to 85 tons/ha of biomass) for JERS-1 SAR data in a boreal test site in Sweden. It is also similar to the saturation level reported by Rauste, Häme, Pulliainen, Heiska, and Hallikainen (1994) at about 120 m3/ha of the stem volume corresponding to ~ 70 tons/ha of dry biomass in a coniferous forest site in Germany. Nevertheless, this value seems high enough for biomass estimates in the majority of boreal forests in Western Siberia, as these forest stands typically have less than 170 m3/ha of the stem volume (Gaveau et al., 2003; Santoro et al., 2006). We have used the signal in the HV polarization mode for creating a regression model. Previous studies also revealed a better relationship between AGB and SAR data in the HV rather than the HH polarization

mode, as HV is much less influenced by soil and vegetation moisture (Collins et al., 2009; Mitchard et al., 2009; Sandberg, Ulander, Fransson, Holmgres, & Le Toan, 2011). HV is also less influenced by topography (Van Zyl, 1993). The Water Cloud model was found most reliable for developing a radar-based model of forest biomass by Attema and Ulaby (1978) and is being steadily used for stem volume and biomass retrieval from SAR data (Askne et al., 2003; Fransson & Israelsson, 1999; Kurvonen, Pulliainen, & Hallikainen, 1999; Santoro et al., 2006). The major confusion in the results of Water Cloud model is caused by biomass estimates in mature forests with high biomass values, as the SAR signal often appeared above the saturation level. Fig. 5 shows in situ biomass measurements versus biomass retrieved from PALSAR data, which was rather uniform at low to moderate biomass values. The discrepancy increased at high biomass values leading to an underestimation of AGB. These uncertainties could be related to the combined effect of the following factors: (1) spatial heterogeneity of forest stands (Rauste et al., 1994; Saatchi et al., 2012); (2) radar calibration and orthorectification (Van Zyl, 1993); and (3) field estimation errors propagating through the analysis of forest biomass (Balzter et al., 2002; Santoro et al., 2006; Shvidenko et al., 2007b). In addition, backscatter responds differently with respect to soil and vegetation moisture conditions and surface topography, adding to observed prediction errors (Mitchard et al., 2009; Sandberg et al., 2011). This effect could be minimized by using the SAR data acquired during the dry summer season under similar meteorological conditions. The results of this study, i.e., estimation error (RMSE) between 25% and 32% of the mean biomass (coefficient of determination R2 in the range of 0.35–0.49) are in agreement with Sandberg et al. (2011) showing an error (RMSE) between 31% and 46% of the mean biomass value and (adjusted) R2 between 0.4 and 0.6 achieved with L-band SAR in forest stands with mixed species and complex landscapes in southern Sweden. 5. Conclusions The analysis shows that AGB could be predicted using radar data for large areas in certain geographical regions and in mixed forest stands with species composition similar to our test site. While the SARbased biomass retrieval was found fairly uncertain in mature forests, it seems especially challenging for the estimation of forest biomass in sparse, low-productive, or young forests on shallow peat in near wetland areas. These natural ecosystems are often omitted in official forest inventories as improper for commercial use, although they have a large potential as sources/sinks of atmospheric carbon. Overall, the SARapplications would give more details to the total C-accounting in Western Siberia where more accurate national or large scale forest inventories hardly exist, and where the ground-based inventory is costly and time-consuming. In this context, our results are in line with major

Fig. 5. Ground-measured AGB vs. AGB retrieved from ALOS/PALSAR backscatter.

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manuals and recommendations for SAR-based biomass monitoring and assessment, elaborated by International organizations (IGCOS, 2004; FAO, 2001; European Space Agency's BIOMASS mission: LeToan et al., 2011). Acknowledg-ements The study was conducted within the framework of the Forest Carbon Monitoring System (FCMS) Research Project (A-801, MOE, GERF), under the auspices of the National Institute for Environmental Studies (NIES) and Japan Aerospace Exploration Agency (JAXA). The work was undertaken (in part) within the framework of the JAXA Kyoto & Carbon Initiative. ALOS PALSAR data were provided by the JAXA Earth Observation Research Center. The study is also relevant to NASA LCLUC project “Changes of Land Cover and Land Use and Greenhouse Gas Emissions in Northern Eurasia: Impacts on Human Adaptation and Quality of Life at Regional and Global Scales”. 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