Forest Structure Dependency of the Relation Between L-Band

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Forest Structure Dependency of the Relation Between L-Band σ 0 and Biophysical Parameters Manabu Watanabe, Associate Member, IEEE, Masanobu Shimada, Senior Member, IEEE, Ake Rosenqvist, Takeo Tadono, Member, IEEE, Masayuki Matsuoka, Shakil Ahmad Romshoo, Kazuo Ohta, Ryoichi Furuta, Kazuki Nakamura, and Toshifumi Moriyama, Member, IEEE

Abstract—Biophysical parameters and L-band polarimetry synthetic aperture radar observation data were taken for 59 test sites in Tomakomai national forest, which is located in the northern 0 0 part of Japan. Correlations between the derived σHH , σHV , and 0 σVV and the biophysical parameters are investigated and yield the following results. 1) The above-ground biomass–σ 0 curves 0 0 , 100 tons/ha for σHH , and saturate above 50 tons/ha for σVV 0 when all forest species are included over 100 tons/ha for σHV 0 in the curves. 2) The σHH –above-ground biomass curve for one forest species indicates a higher saturation level than that for the other forest species. Dependence on the forest species was absent for VV polarization and low for HV polarization. 3) A simple three-component scattering model indicates that volume scattering accounts for 80%–90% when the above-ground biomass exceeds 50 tons/ha. The surface-scattering components are up to ∼20% for young stands, and the volume-scattering components are down to 70%. The origin of the dependency 0 among the forest species was examined for the σHH –above-ground biomass. It is concluded that a possible cause of the dependency is the different characteristics of the stands rather than forest species. Index Terms—Forestry, synthetic aperture radar (SAR).

I. I NTRODUCTION

T

HE ADVANCED Land Observing Satellite (ALOS), which was launched on January 24, 2006, carries the Phased-Array L-band Synthetic Aperture Radar (PALSAR). It also has a unique observation strategy [1], which includes multiseasonal global coverage of spatial and temporal consistency on a continental scale. The plan will provide the parameters necessary to extract meaningful regional-scale data. We are currently developing algorithmic routines to process and interpret these data as they become available.

Manuscript received November 6, 2005; revised May 24, 2006. M. Watanabe, M. Shimada, T. Tadono, K. Ohta, R. Furuta, and T. Moriyama are with the Japan Aerospace Exploration Agency, Earth Observation Research and Application Center, Tokyo 104-6023, Japan (e-mail: [email protected]). A. Rosenqvist is with Space Program Development, Space Systems Division, Swedish Space Corporation, 171 04 Solna, Sweden (e-mail: ake.rosenqvist@ ssc.se). M. Matsuoka is with the Research Institute for Humanity and Nature, Kyoto 602-0878, Japan (e-mail: [email protected]). S. A. Romshoo is with the University of Kashmir Hazratbal, Srinagar 190006, Jammu and Kashmir, India (e-mail: [email protected]). K. Nakamura is with the National Institute of Polar Research, Tokyo 173-8515, Japan (e-mail: [email protected]). Digital Object Identifier 10.1109/TGRS.2006.880632

One possible use of L-band SAR data is to estimate a forest’s above-ground dry biomass (hereafter AG biomass) by using a backscattering coefficient. The saturation of radar signals in areas with a high biomass prevents us from retrieving complete biomass information for the ground; however, it is still useful for monitoring young stands. While mature forest stands develop from a net carbon sink to a state of equilibrium (carbon released through respiration is approximately equal to the carbon acquired by photosynthesis), young forest stands bind relatively more carbon [2] and play an important role in the carbon cycle. Many studies have examined the relation between backscattering coefficients and the AG biomass [3], [4]. Kasischke et al. [5] reviewed the applicability of σ 0 to forests and concluded that the upper levels of sensitivity for L-band systems (hereafter the saturation level) range between < 100 tons/ha for a complex tropical forest canopy and 250 tons/ha for a simpler forest dominated by a single species. They also revealed that HV polarization is most sensitive to the biomass, and that VV is least sensitive. Dobson et al. [6] examined the dependency of σ 0 on a tree’s geometric properties, such as tree size, shape, and orientation of the scattering elements. They classified stands by the vegetation attributes and developed inversion formulas, which led to the biophysical parameters from multiple frequencies and multiple polarizations. Watanabe et al. [7] described the high correlation between 0 and the AG biomass for a single conifer species and L−σHH confirmed that the saturation level was about 200 tons/ha, while other conifer species revealed low correlations. We add more test sites in this paper and examine the origin of the dispersion observed in the relation between L−σ 0 and the biophysical parameters. Representative scattering models are introduced in Section II. We describe our test site in Section III and explain our field measurements and data analyses. The derived σ 0 are plotted against the biophysical parameters for each forest species in Section IV. We then discuss the possible cause of different σ 0 –biophysical parameter curves among different forest species in Section V. II. I NTERACTION OF L-B AND R ADAR W ITH F ORESTS Several physical models have been developed to interpret backscattering data collected over forests (e.g., [8]). However, detailed models require substantial field data, such as the leaf/branch density and orientation distribution, and it is often very difficult to apply the data.

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In contrast, Freeman and Durden [9] suggested a simple three-component scattering model (hereafter TS model). The model provides information about the contribution of each scattering mechanism as


 |Shh |2 = fs |β|2 + fd |α|2 + fv


 |Svv |2 = fs + fd + fv ∗ Shh Svv  = fs β + fd α + fv /3
 |Shv |2 = fv /3 ∗ ∗ Shh Shv  = Shv Svv  = 0.

Fig. 1. Soil moisture data were measured for 50 sites.

(1)

Here, fv , fd , and fs are the contributions to the power in VV polarization from volume, dihedral, and surface scattering, respectively. α and β are the parameters that can be estimated from some forest parameters, such as trunk radius and tree number density. Determining whether double bounce or surface scattering is the dominant contribution by using the sign of ∗ ) [10] enables us to identify the contribution of Re(Shh Svv each scattering mechanism only from SAR polarization data, without any field data. We apply this simple model to our data in this paper. III. E XPERIMENT A. Site Description The study area, Tomakomai, is located on Hokkaido in northern Japan; it is predominantly a cool-temperate forest. There is a well-managed national forest with almost flat topography. Minimal litter and understory vegetation appear in this site because of the frequent cutting of the undergrowth and thinning. Many 500 m × 500 m stands are distributed and often subdivided into several substands. One or more even-aged forest species are planted in a stand or a substand. There are four types of conifers; two are spruce, Akaezo-matsu (Japanese, Nomenclature: Picea glehnii Masters, hereafter A-spruce) and Ezo-matsu (Nomenclature: Picea jezoensis Carr., hereafter E-spruce); another is Todomatsu, (Japanese, Nomenclature: Abies sachalinensis, hereafter T-fir); and the last is larch, Kara-matsu (Japanese, Nomenclature: Larix leptolepis Gord, hereafter larch). There are also some broadleaf stands. B. Field Measurements of the Biophysical Parameters Field measurements were performed for three to four days before each PiSAR observation conducted in November 2002, August 2003, and August 2004. We collected the biophysical parameters for the AG biomass, the basal area (total tree cross section per unit area, hereafter B-area), and weighted tree height (tree height weighted for a tree cross section divided by the B-area, hereafter W-height) for several sites. One site consisted of a 20 m × 20 m area in a stand; we measured the tree heights and diameters at breast height (hereafter DBH) for all trees in the site. A smaller 10 m × 10 m subsite was designated

within the site if there were many small trees. Only small trees with a diameter less than a specific criterion (typically 5–10 cm) were measured in this subsite, while the rest of the trees were measured in the full (20 m × 20 m) site. The number of trees measured totaled more than 5000, with 50–100 trees per site on average. Several field measurements were often performed in a stand to verify the field measurements obtained with two different groups. We measured a total of 59 sites or 56 stands. Five sites were omitted because of ambiguous measurement locations or for other reasons. The data from 51 sites were aggregated and used in the analysis. Nineteen of those were T-fir, 15 were A-spruce, 11 were larch, and three were E-spruce and broadleaf sites. We measured soil moisture simultaneously with other field measurements for 50 sites. Data from some sites were lost due to a malfunction of the measurement device. The results are summarized in Fig. 1. The values ranged from 10% to 34%, and the average was 21.9%. Soil roughness data were also collected for some sites where field measurements were performed in 2004. We estimate soil standard height deviation at 0.9 and 1.2 cm from two sites’ data, and the maximum difference is 3.2 and 4.2 cm. C. Biomass Estimation From the Field Data We first calculated the trunk volume V (in cubic meters) for each tree from the tree height H (in meters) and the DBH (in centimeter). The volume formulas derived in the Tomakomai area [11], which includes our test sites, were used in this process. The formulas for each forest species are expressed below. A-spruce and E-spruce: log V = − 4.0744 + 1.824080 log DBH + 0.934568 log H(DBH < 50 cm)

(2)

= − 4.5137 + 1.568947 log DBH + 0.842787 log H(50 cm ≤ DBH < 60 cm).

(3)

T-fir: log V = −4.0971 + 1.681121 log DBH + 1.131348 log H(DBH < 90 cm).

(4)

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Fig. 2. Slant range image over Tomakomai was selected from the PiSAR data derived in August 2004 (HH, red; HV, green; VV, blue). The incident angle is between 43◦ and 48◦ from the lower to upper sections. Square structures seen in the image represent stands. Red, white, green, yellow, and blue polygons represent the sites at which σ 0 was obtained; the colors indicate the forest species that dominate the site and represent A-spruce, E-spruce, T-fir, larch, and broadleaf. Notations in the image designate the angles between the radar range direction and tree alignment defined in Fig. 12.

Larch and broadleaf: log V = − 4.068644 + 1.756152 log DBH + 0.906210 log H(DBH < 10 cm)

(5)

= − 4.335395 + 1.903051 log DBH + 1.025410 log H(10 cm ≤ DBH < 20 cm)

(6)

= − 4.441199 + 1.853014 log DBH + 1.166956 log H(20 cm ≤ DBH < 30 cm)

(7)

= − 4.332596 + 1.848675 log DBH + 1.088954 log H(30 cm ≤ DBH).

(8)

We converted the derived trunk volumes to above-ground volumes by using a constant-expansion factor of 1.36, which is defined by the ratio of the AG biomass to the trunk biomass and represents the quantity of crown biomass to the trunk biomass. This value depends on the forest species and tree ages. We estimate its effect in Section V. Finally, we converted the aboveground volumes to AG biomass using the dry-matter density, i.e., 0.391 g/cm3 for A-spruce, 0.314 g/cm3 for E-spruce, 0.329 g/cm3 for T-fir, 0.444 g/cm3 for larch, and 0.453 g/cm3 for broadleaf [12]. D. PiSAR Data Analysis PiSAR is an airborne SAR and consists of L-band SAR from the Japan Aerospace Exploration Agency (JAXA) and X-band SAR from the National Institute of Information and Communication Technology (NICT). Fully polarimetric data are available. The spatial resolution is about 2 m in four-look images at a 12-km cruising altitude. Many PiSAR observations were performed in this area from 2002 to 2004; however, we used only the data obtained on August 3, 2004 to reduce the calibration uncertainty since 0.2–0.3 dB systematic errors were noted among the data taken in summer and autumn. Five separate observations were performed in the afternoon within

2 h on August 3, 2004, along the same flight course over the Tomakomai test site. The weather conditions on the flight day were cloudy with occasional rain, and the average temperature was 21.7 ◦ C. The meteorological observatory near the test site recorded precipitation of less than 0.5 mm, and thus, we did not consider the effect of rain. We analyzed three flight data units since only three of the five data units could be processed by SIGMA-SAR, which was developed by Shimada [13] and has been used to process not only PiSAR data but also JERS-1 and ALOS PALSAR data. The data from each flight were calibrated using corner reflectors deployed within the area [14]. The incidence angle of the PiSAR changed from 10◦ to 60◦ within the area; however, the angle of our test sites ranged from 43◦ to 48◦ , and the incidence angle dependency of σ 0 was ignored. An image obtained over the Tomakomai site is presented in Fig. 2. RGB colors were assigned for HH, HV, and VV. The dark areas in the middle of the image signify very young tree stands. σ 0 for the 51 sites were obtained from the three flights and were then averaged. Although a field measurement was performed in the 20 m × 20 m site, we estimated the σ 0 value for the site from a larger area within the stand to reduce the statistical uncertainty and speckle noise uncertainty. The sites are illustrated in Fig. 2, and the line colors represent the forest species (red is A-spruce, white is E-spruce, green is T-fir, yellow is larch, and blue is broadleaf). The sites at which σ 0 was obtained included the field measurement site. The mean site size was 1100 pixels, which corresponds to 0.6 ha. Tree growth of about 25 cm/year was reported in the Tomakomai site, which is comparable to a 1.5-tons/ha annual increase in biomass. Thus, we ignored the change of biophysical parameters for the two years in which the field measurements were performed. IV. R ESULTS A. σ 0 Versus Biophysical Parameters The derived biophysical parameters are plotted against σ 0 in Fig. 3. The upper, middle, and lower panels of Fig. 3 indicate 0 0 0 , σHV , and σVV derived for each site and plotted against the σHH

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0 , (middle) σ 0 , and (lower) σ 0 . (Left) Weighted height (in meters). (Center) Basal area Fig. 3. Tree biophysical parameters are plotted against (upper) σHH HV VV (in square meters per hectare). (Right) AG biomass (in tons per hectare). The colors of the marks denote the forest species specified in Fig. 2.

TABLE I SLOPES OF THE σ 0 –AG BIOMASS PLOTS (IN DECIBEL HECTARES PER T ON ] W ERE C ALCULATED W ITHIN 0–100, 50–150, AND 100–150 tons/ha FOR E ACH P OLARIZATION

W-height (left section of Fig. 3), B-area (center section), and AG biomass (right section), respectively. The forest species are represented by the different colors assigned in Fig. 2. The standard deviations of σ 0 were estimated from the three flightdata units and are provided in the plot as error bars. The standard deviations were typically 0.4 dB. B. Saturation Levels of the σ 0 –AG Biomass We estimated the saturation levels of the AG biomass by examining the slope within several ranges. The slopes of the σ 0 –AG biomass plots were calculated within 0–100, 50–150, and 100–150 tons/ha for each polarization. The results are summarized in Table I. The slopes in the 0–100 tons/ha had high values of 0.03–0.05 dB ha/ton. In contrast, the slopes in the 100–200 tons/ha had lower values of around 0.01 dB ha/ton. Saturation appeared at more than 50 tons/ha for VV polarization and at more than 100 tons/ha for HH polarization when we set the saturation criteria at 0.01 dB ha/tons. While saturation appeared in the range of 50–150 tons/ha, the

HV polarization data indicated a significant slope, even for 100–200 tons/ha. Some plots exhibited forest species dependency of the σ 0 –biophysical relations. A discrepancy was apparent in the 0 W-height plot between A-spruce and the others for the σHH plot, while no dependency of the forest species was noted in the 0 plot. We averaged the data within the ranges B-area for the σHV of 0–50, 50–100, 100–150, and > 150 tons/ha and replotted it in Fig. 4 to highlight the dependency in the AG biomass–σ 0 plots. We excluded the E-spruce data here since the biomass of the E-spruce sites ranged from 0.1 to 114 tons/ha, for which there were only three sites. The discrepancy in the AG biomass 0 plots among different forest species was apparent for σHH 0 and marginally visible for σHV . We verified these differences by a statistical test. The biophysical parameters were almost saturated at around 100 tons/ha in the AG biomass. Therefore, we extracted the data for an AG biomass of more than 100 tons/ha and compared the averaged σ 0 value of the A-spruce with those of the other species. Mann–Whitney’s U test was used to determine the difference between the two groups, and a difference of P < 0.05 was considered significant. The test results indicate that A-spruce and the others are in different groups for the HH and HV plots and in the same group for the VV plot. C. Adjusted Coefficients of Determination and Model Fitting We applied the logarithmic model and a third-order polynomial function model used in [15] to determine which models are

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0 Fig. 4. σHH values and AG biomass in Fig. 3 are averaged within the ranges of 0–50, 50–100, 100–150, and > 150 tons/ha. The colors indicate the forest species specified in Fig. 2.

TABLE II ADJUSTED COEFFICIENTS OF DETERMINATION R∗2 BETWEEN σ 0 AND THE BIOPHYSICAL PARAMETERS WERE EXAMINED USING TWO REGRESSION MODELS, LOGARITHMIC AND THIRD-ORDER POLYNOMIAL FUNCTIONS, AND THE CORRELATION COEFFICIENTS AND FITTING PARAMETERS ARE SUMMARIZED. BOLD FIGURES INDICATE COEFFICIENTS HIGHER THAN 0.8

appropriate to express the σ 0 –biophysical relations. Adjusted coefficients of determination (R∗2 ) were used to clarify which models with different numbers of parameters are preferable. We first examined R∗2 and fitting parameters for the data set that included all forest species. The results are summarized in Table II. The two models did not clearly indicate a difference. The best 0 and the B-area in both regression R∗2 appeared between σHV models and was 0.80 for the logarithm model and 0.85 for the 0 polynomial model. The R∗2 between the biomass and the σHV also correlated well at 0.80 and 0.74. Mann–Whitney’s U test revealed a difference among the forest species in this relation; this occurred because the test is applied for a biomass range of more than 100 tons/ha. Moderate correlations were obtained 0 plots. The good for the B-area and AG biomass in the σVV correlations seen here also indicate that these relations weakly depend on the forest species. In contrast, the W-height–σ 0 curves revealed a clear difference. Fig. 5 presents the fitting results for the curves with A-spruce and the other species. We used the logarithmic function; the formulas are inserted in the figures. The curves for the W-height–σ 0 revealed explicit differences. An error analysis for two fitting parameters for these plots indicated that the A-spruce curve differs from the other species’ curves.

V. D ISCUSSION A. Error Estimation of the Measured Biomass Parameters We could not examine the tree densities or the expansion factors defined in Section III-C for all forest species and all

generations in the test site since the test site was restricted to one with cut trees. However, we had an opportunity to collect a few tree samples. We selected three trees (A-spruce, T-fir, and larch) from the test sites and investigated the densities and expansion factors to verify whether the tree densities used in this paper correspond to the representative values of trees in our test site, and how much uncertainty results from the assumption a constant expansion factor of 1.36. We also referred to the expansion factors studied by Fukuda et al. [16], who examined some major Japanese forest species. We measured the tree diameters every 2 m at 1 m, 3 m, 5 m, . . ., from the tree bottom to the top. The length of the last section was obtained at more than 2 m and less than 4 m from the top. The tree volume was then calculated as [11] Volume = (g1 + g3 + · · · + gn−1 )x 2 + gn /3 x l1

(9)

where gα represents the cross section at α (in meters) from the bottom, and l1 represents the length of the last section. The last section was regarded as a cone and was multiplied by 1/3. The tree densities were measured directly from the three trees. Small samples were taken and kept in a constant-temperature chamber for five days at a temperature of 85 ◦ C. The samples’ weights and volumes were subsequently measured by an electric balance and measuring cylinder. The expansion factors were directly obtained by measuring the weight of the tree crown and trunk. The derived parameters are summarized in Table III and compared with the value used in our analysis

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Fig. 5.

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Logarithmic functions are fitted to the A-spruce (red) and other (blue) data sets. The plots are on the same order as in Fig. 3.

TABLE III COMPARISON OF THE EXPANSION FACTORS AND TREE DENSITIES USED IN THIS ANALYSIS WITH THOSE MEASURED FROM A-SPRUCE, T-FIR, AND LARCH OBTAINED AT THE TOMAKOMAI SITE. EXPANSION FACTORS DERIVED BY FUKUDA ET AL. [16] ARE ALSO LISTED

and with the one derived by Fukuda et al. [16]. Only one tree in three species was measured at this time. However, the obtained values were consistent with those documented in the literature [16]. Both our measurements and the value derived by Fukuda et al. [16] indicated that the expansion factors for A-spruce and T-fir were 1.3 and that for larch was 1.1. The difference in the expansion factor from a constant value of 1.36 was −6.6% for A-spruce, −2.2% for T-fir, and −16.9% for larch. We recalculated the AG biomass using the expansion factors derived by Fukuda et al. [16], although the values are not continuous between tree ages above and below the age of 0 are 21 years. The results are illustrated in Fig. 6, where σHH plotted against the recalculated AG biomass. The differences among the forest species are also apparent, and the assumption that the expansion factor is constant did not seriously affect the results. Tree density measurement was also compared with that used in this paper and is listed in Table III. The uncertainties

Fig. 6. Forest species dependency of the expansion factors was taken into account, and the AG biomass was recalculated. The derived AG biomass is 0 . The colors indicate the forest species specified in Fig. 2. plotted against σHH

were smaller than those for the expansion factors, and the values were consistent with the ones used here. We also evaluated the AG biomass error associated with the field experiment by using the data collected in the same stand

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Fig. 7. Correlations between tree height and DBH are presented (left) for evergreen trees and (right) for deciduous trees. The colors indicate the forest species specified in Fig. 2. TABLE IV SUMMARY OF TREE CHARACTERISTICS AND STAND CHARACTERISTICS. EXPANSION FACTORS ARE FROM FUKUDA ET AL. [16]. TREE DISTRIBUTION IS DEFINED IN FIG. 12

with a different group or timing. Two field measurements were performed in one stand, and three measurements were taken in another stand. The biomass values were estimated from each measurement and compared. The maximum differences among the measurements were 4.5 and 8.4 tons/ha for each stand; thus, we concluded that the errors that emerged from our field measurements were less than 10 tons/ha and did not seriously affect the results. B. Allometric Relation Correlations between tree height and DBH are plotted in Fig. 7 for each forest species. T-fir and broadleaf plots exhibited slightly steeper slopes than E-spruce and larch plots. This indicates that the former groups have characteristics of relatively greater tree heights with thin diameters. However, this difference does not seem to affect the backscattering process since the A-spruce allometric relation does not exhibit specific characteristics, as seen in the σ 0 –biophysical parameters. 0 C. Stand Characteristics and the σHH –AG Biomass Curve

The clearest example of the forest species’ dependencies for the σ 0 –AG biomass plots appears in the HH polarization. 0 –AG biomass Fig. 8 contains a magnified image of the σHH 0 plot. This figure reveals that some A-spruce sites exhibit σHH values of 1.5–2 dB greater than the other species, while the AG biomass values are almost the same within the ranges of 50–100 and 100–150 tons/ha. We then classified the data into four groups to address the possible cause of dependency. Groups 0 values of 50–100 tons/ha (group 1) 1 and 2 had greater σHH and 100–150 tons/ha (group 2). Only the A-spruce data were 0 included in these groups. Groups 3 and 4 displayed low σHH values of 50–100 tons/ha (group 3) and 100–150 tons/ha (group 4). Group 3 included T-fir, larch, A-spruce, and

0 Fig. 8. Four groups with similar AG biomass values and similar σHH were 0 –AG biomass plot to compare the characteristics. The selected from the σHH blue squares in the plot represent the sites which have high alignment angles, as indicated in Fig. 2. The colors indicate the forest species specified in Fig. 2.

broadleaf, and group 4 included T-fir and larch. A few data located between the four groups were ignored to clarify the difference among the groups. The characteristics of forest species for each group are 0 values reveal a clear summarized in Table IV. While the σHH discrepancy between groups 1 and 2 and groups 3 and 4, no explicit difference was observed for the characteristics of the forest species. For example, the expansion factors, which represent the crown biomass, are of almost the same value for the A-spruce and the T-fir, while a significant discrepancy was 0 between the two species. We must also observed in the σHH note that two A-spruce sites were included in group 3. These facts signify the difficulty in explaining the different curves 0 and the AG biomass using the differences in the between σHH characteristics of the forest species. We next investigated the stand characteristics using histograms of the heights and the DBH for each group and species. The results are provided in Fig. 9 and Table IV. Each plot in

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Fig. 9. Histograms of the tree heights (above) and DBH (below) are plotted for each group and forest species. The tree numbers were normalized in the 20 m × 20 m area to compare each group directly.

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Fig. 10. Contribution of three scattering components (surface: plus, double-bounce: cross, volume: circle) was calculated for each site and plotted against biophysical parameters. (Left) Weighted height (in meters). (Center) Basal area (in square meters per hectare). (Right) Above-ground biomass (in tons per hectare).

Fig. 11. Phase differences and correlations between HH and VV polarization, φHHVV , and ρHHVV were calculated for each site and plotted against the AG biomass. The blue square in the left image represents the site that belongs to group 3. The colors indicate the forest species specified in Fig. 2.

Fig. 9 includes a few sites’ data in the same group; therefore, the tree numbers in the plots were normalized within the 20 m × 20 m area, which enabled us to directly compare the tree numbers among the plots. Groups 1 and 2 exhibited symmetric distributions with a greater average value in the height plot. In contrast, groups 3 and 4 displayed nonsymmetric distributions. The peaks of the distributions for groups 3 and 4 were lower than those of groups 1 and 2, while the tails extended higher than those of groups 1 and 2. Histograms of the DBH revealed the same results, which may be due to an allometric correlation between the height and the DBH, as shown in Fig. 7, and may indicate the same phenomenon. The tree numbers within the 20 m × 20 m plot and the skew are presented in Fig. 9. Obvious differences in the distributions appeared in the skew. The skew was 0.1 and −0.2 for groups 1 and 2 and was greater (at 1.5 and 0.9) for groups 3 and 4. There were many small trees in groups 3 and 4, which increased the total biomass in these groups. It is important that the two A-spruce sites were classified into group 3 and revealed the same distribution characteristics of dense small trees and lower average tree height as those of the other three species in group 3. This strongly suggests that the cause of the different curves between groups 1 and 2 and groups 3 and 4 is a difference in stand structure. D. Applying a Simple Scattering Model We applied the TS model [9] introduced in Section II to our data. The contribution of three scattering components (surface,

double bounce, and volume scattering) was calculated for each site and plotted against the biophysical parameters in Fig. 10. The model indicated that volume scattering is a dominant component and accounts for 80%–90% of the total scattering, except for young stands. The surface-scattering components for young stands with an AG biomass of less than 50 tons/ha or a W-height of 5 m were increased to 20%, and the volumescattering components were reduced to 70%. These values are in good agreement with the ones derived in [17]. The phase difference and correlation between HH and VV polarizations φHHVV and ρHHVV were also calculated and plotted against the AG biomass in Fig. 11. The phase difference for A-spruce exhibited weak correlation with a coefficient of determination of 0.5 and reached 60◦ for a higher biomass. In contrast, the phase differences for the other forest species were around 20◦ . No differences among the forest species were noted for 0 plot except for the young A-spruce sites, the ρHHVV −σHH and ρHHVV was about 0.3. Decomposition analyses for each group presented in Fig. 8 were also performed; the results are summarized in Table V. An explicit difference between groups 0 value (groups 1 and 2) and groups with with a higher σHH 0 a lower σHH value (groups 3 and 4) can be noted for the 0 groups reveal a φHHVV phase differences. While the lower σHH ◦ ◦ 0 of −14.5 and −17.5 , the higher σHH groups reveal greater φHHVV and are −30.9◦ and −27.2◦ . The other parameters do not exhibit clear differences between the two groups. The phase difference between HH and VV polarizations is primarily caused by double bounce and radar transmission in the trunk

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TABLE V FRACTION CONTRIBUTION FROM THE SURFACE, DOUBLE BOUNCE, AND VOLUME SCATTERING FOR FOUR GROUPS

0 layer. The higher σHH groups had a smaller tree number density, as indicated in Fig. 9; thus, the large φHHVV values were likely caused by double bounce, such as ground trunk or branch trunk in the crown layer.

E. Characteristics of the σ 0 –AG Biomass Curves 0 The σHV –AG biomass plot in Fig. 3 correlates well without any species classifications and has a wide dynamic range of 6 dB. This result supports previous work, such as [5]. 0 –AG biomass plot also reveals small disperWhile the σVV sion without any classifications, it has a narrow dynamic range of 4 dB. These results are explained well by the fact that VV polarization is more sensitive to the crown layer and tends to saturate at lower levels of the biomass [6]. 0 –AG biomass plot reveals the forest species deThe σHH pendency to some degree, which is pronounced at more than 100 tons/ha. There are two possible explanations for this.

1) The double-bounce contribution may be greater for groups 1 and 2 if scatterers in the canopy are homogeneously distributed, as assumed in the TS model. If that is the case, volume scattering would be the dominant process in that biomass. However, inhomogeneous distribution, which creates large gaps in the tree crown, increases the contribution from ground trunk double bounce 0 intensity. and could therefore increase the σHH 2) There could be different values of volume scattering due to differences in branch density, which is related to DBH density. The stand characteristics are the key features in either case. Three different plantation alignments were predominant in our test site. One was two lines, another was one line, as defined in Fig. 12, and the third was random (see also Table IV). Many A-spruce stands are planted with two lines. The A-spruce stands in groups 3 and 4 were also planted with two lines. The type of tree distribution apparently does not affect the increase in 0 value. The tree alignment directions relevant to the the σHH radar directions defined in Fig. 12 were also examined for some A-spruce stands. The measured alignment angles are presented in Fig. 2 and summarized in Table IV. Many data indicated that the direction was nearly parallel to the radar range direction, and two data revealed large angles of 60◦ and 110◦ . However, 0 value, as indicated by the blue the latter group had a high σHH

Fig. 12. Several types of tree distribution seen in the test site are depicted. Interval of the trees was measured for 12 A-spruce sites and averaged as shown in the figure. θ is defined as the angles between radar range direction and tree alignment.

square in Fig. 8, and no clear difference was noted between groups 1 and 2 and groups 3 and 4. The difference in tree number density is the most likely 0 value. Tree stands with factor to explain the variety of σHH sparse trees enable the radar signal to penetrate deeply into the canopy layer. This increases the double bounce for case (1) or the total amount of interaction in the crown for case (2). As a 0 may be increased. Note that the discussion related result, σHH to inhomogeneous tree crowns was based on a small sample, and that it is necessary obtain additional data to confirm it. VI. S UMMARY AND C ONCLUSION Biophysical parameters were taken in 2002, 2003, and 2004 from 59 test sites in the Tomakomai national forest, which is located in the northern part of Japan. Airborne multipolarization SAR (PiSAR) observations were also carried out over Tomakomai in summer 2004. The L-band σ 0 values for three polarization modes (HH, HV, and VV) were derived in the sites where the field measurements were performed. The correlations between the derived σ 0 and the biophysical parameters were investigated and yielded the following results and conclusions. 1) The AG biomass appears to be saturated above 50 tons/ha 0 0 , 100 tons/ha in σHH , and saturated over in σVV 0 100 tons/ha in σHV when all forest species are included. 0 –AG biomass plot for A-spruce revealed greater 2) The σHH saturation levels than those for the other forest species. No dependency of the forest species was observed for 0 0 –AG biomass curve. The σHV –AG biomass plot the σVV yielded marginal results.

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3) Both logarithmic and third-order polynomial functions described well the relations between the σ 0 and the bio0 0 –basal area, the σHV –AG physical parameters. The σHV 0 0 biomass, the σVV –basal area, and σVV –AG biomass plots were well correlated without forest species classification. These facts indicate that the relations weakly depend on the forest species. 4) A simple TS model indicated that volume scattering accounts for 80%–90% when the AG biomass exceeds 50 tons/ha or the weighted tree height exceeds 5 m. The surface-scattering components for young stands were increased to 20%, and the volume-scattering components were reduced to 70%. 5) The origin of the dependency was discussed for the 0 –AG biomass. We concluded that one possible cause σHH of the dependency is the different characteristics of the stands, such as a different tree number density, rather than the different characteristics of the forest species.

[9] A. Freeman and S. L. Durden, “A three-component scattering model for polarimetric SAR data,” IEEE Trans. Geosci, Remote Sens., vol. 36, no. 3, pp. 963–973, May 1998. [10] J. J. van Zyl, “Unsupervised classification of scattering behavior using radar polarimetry data,” IEEE Trans. Geosci. Remote Sens., vol. 27, no. 1, pp. 36–45, Jan. 1989. [11] Project department, Forestry Agency, Stand Volume Table -East Japan-, Japan Forestry Investigation Committee, Oct. 1998. [12] Forestry and Forest Products Research Institute, Wood Industrial Handbook, 1982, Tokyo, Japan: Maruzen. [13] M. Shimada, “Verification processor for SAR calibration and interferometry,” Adv. Space Res., vol. 23, no. 8, pp. 1477–1486, 1999. [14] M. Shimada, T. Tadono, and M. Watanabe, “Determination of polarimetric calibration parameters of L band SAR using uniform forest data,” in Proc. IGARSS, Anchorage, AK, Sep. 2004. [15] J. Santos, C. Freitas, L. Araujo, L. Dutra, J. Mura, F. Gama, L. Soler, and S. Sant’Anna, “Airborne P-band SAR applied to the aboveground biomass studies in the Brazilian tropical rainforest,” Remote Sens. Environ., vol. 86, no. 4, pp. 482–493, 2003. [16] M. Fukuda, T. Iehara, and M. Matsumoto, “Biomass expansion factor by main forest type of Japan,” in Proc. Jpn. Forest Kantou Branch, 2002. No. 54. [17] J. R. Baker and A. J. Luckman, “Microwave observations of boreal forests in the NOPEX area of Sweden and a comparison with observations of a temperate plantation in the United Kingdom,” Agricult. For. Meteorol., vol. 98/99, pp. 389–416, Dec. 1999.

ACKNOWLEDGMENT The authors would like to thank S. Uratsuka (NICT), H. Oguma and K. Inukai (National Institute for Environmental Studies), M. Fukuda (JAXA ISAS), and S. Nakamura (Musashi Institute of Technology) for supporting the experiments in Tomakomai. The authors are also grateful to K. Matsumora, M. Sutou, Y. Komatsu, H. Yasuda, and T. Hayasaka (Tokyo University), who assisted with the field activities. Field measurements are collaborative research with the National Institute for Environmental Studies. The authors would also like to thank the PiSAR flight team in JAXA NICT for providing the data analyzed in this study. R EFERENCES [1] A. Rosenqvist, M. Shimada, M. Watanabe, T. Tadono, and K. Yamauchi, “Implementation of systematic data observation strategies for ALOS PALSAR, PRISM and AVNIR-2,” in Proc. IGARSS, Anchorage, AK, Sep. 2004, pp. 4527–4530. [2] M. L. Imhoff, “Radar backscatter and biomass saturation: Ramifications for global biomass inventory,” IEEE Trans. Geosci. Remote Sens., vol. 33, no. 2, pp. 511–518, Mar. 1995. [3] M. C. Dobson, F. T. Ulaby, T. Le Toan, A. Beaudoin, E. S. Kasischke, and N. Christensen, “Dependence of radar backscatter on coniferous forest biomass,” IEEE Trans. Geosci. Remote Sens., vol. 30, no. 2, pp. 412–415, Mar. 1992. [4] A. Beaudoin, T. Le Toan, S. Goze, E. Nezry, A. Lopes, E. Mougin, C. C. Hsu, H. C. Han, J. A. Long, and R. T. Shin, “Retrieval of forest biomass from SAR data,” Int. J. Remote Sens., vol. 15, no. 14, pp. 2777–2796, Sep. 1994. [5] E. S. Kasischke, J. M. Melack, and M. Dobson, “The use of imaging radars for ecological applications—A review,” Remote Sens. Environ., vol. 59, no. 2, pp. 141–156, Feb. 1997. [6] M. C. Dobson, F. T. Ulaby, L. E. Pierce, T. L. Sharik, K. M. Bergen, J. Kellndorfer, J. R. Kendra, E. Li, Y. C. Lin, A. Nashashibi, K. Sarabandi, and P. Siqueria, “Estimation of forest biophysical characteristics in northern Michigan with SIR-C/X-SAR,” IEEE Trans. Geosci. Remote Sens., vol. 33, no. 4, pp. 877–895, Jul. 1995. [7] M. Watanabe, M. Shimada, R. Furuta, A. Rosenqvist, and T. Tadono, “Tight correlation between forest parameters and backscattering coefficient derived by the L-band airborne SAR (PiSAR),” in Proc. IGARSS, Anchorage, AK, Sep. 2004, pp. 2340–2343. [8] F. T. Ulaby, L. Sarabandi, K. Mcdonald, M. White, and M. C. Dobson, “Michigan microwave canopy scattering model,” Int. J. Remote Sens., vol. 11, no. 7, pp. 1223–1253, 1990.

Manabu Watanabe (A’03) received the B.Sc. degree in physics from Shinsyu University, Nagano, Japan, in 1991 and the Ph.D. degree in astrophysics from Nagoya University, Nagoya, Japan, in 2000. He is currently a Researcher with the Japan Aerospace Exploration Agency, Earth Observation Research and Application Center, Tokyo, Japan, where he is a part of the Daichi (ALOS) data analysis group. His primary duty is analyzing SAR data for understanding radar scattering from forests. He has authored or coauthored several conference papers. Dr. Watanabe is a member of the Remote Sensing Society of Japan.

Masanobu Shimada (M’98–SM’03) received the B.S. and M.S. degrees in aeronautical engineering from Kyoto University, Kyoto, Japan, in 1977 and 1979, respectively, and the Ph.D. degree in electrical engineering from the University of Tokyo, Tokyo, Japan, in 1999. In 1979, he joined the National Space Development Agency of Japan (now Japan Aerospace Exploration Agency), where he designed a scatterometer that helped to reduce ambiguous wind vectors. Before launch of JERS-1 in 1992, he focused mainly on the JERS-1 SAR calibration, successfully calibrating it by combining in-flight SAR characterization and SAR responses from man-made and natural targets. Since 1995, he has been with the Earth Observation Research Center, Tokyo, where he is in charge of the JERS-1/ALOS science project (rainforest mapping projects and SAR interferometry projects). Since 1999, he has been a Senior Scientist and a leader of the land group. His current research interests are SAR calibration and SAR interferometric applications including polarimetric SAR interferometry (crustal deformation detection, tree height detection, and continental land movement).

WATANABE et al.: FOREST STRUCTURE DEPENDENCY BETWEEN L-BAND σ 0 AND BIOPHYSICAL PARAMETERS

Ake Rosenqvist received the M.S. degree in surveying from the Royal Institute of Technology, Stockholm, Sweden, in 1988 and the Dr.Eng. degree in microwave remote sensing from the University of Tokyo, Tokyo, Japan, in 1997. In 1990, he joined the Swedish Space Corporation, Solna, where he was engaged in the national SPOT program and in the execution of ODA projects in Southeast Asia. As the first foreign national, he was invited to the National Space Development Agency of Japan (NASDA) in 1993, where he became involved in the development of the JERS-1 applications program and the initiation and management of the JERS-1 SAR Global Forest Mapping project. Between 1997 and 2000, he was with the EU Joint Research Centre, Ispra, Italy, where his work focused on the development of regional-scale SAR applications to forestry and CH4 emission modeling from tropical wetlands. In 2000, he returned to NASDA/Japan Aerospace Exploration Agency, where he initiated the ALOS Kyoto and Carbon Initiative, an international science project set out to support terrestrial carbon cycle science and environmental convention information needs with ALOS data. The work involved, among others, the design and implementation of global systematic data observation strategies for ALOS PALSAR. He is currently with the Swedish Space Corporation, working with the development of small satellite Earth observing missions. Dr. Rosenqvist was awarded the ISPRS President’s Honorary Citation in 2000 for promotion of Earth observing applications to the UNFCCC.

Takeo Tadono (A’04–M’06) received the B.A. and M.A. degrees in civil engineering and the Ph.D. degree in energy and environment engineering from the Nagaoka University of Technology, Niigata, Japan, in 1993, 1995, and 1998, respectively. He is currently a Researcher with the Earth Observation Research Center, Japan Aerospace Exploration Agency, Tokyo, Japan, where he is in charge of the calibration and validation of optical sensors onboard the Advanced Land Observing Satellite, i.e., PRISM and AVNIR-2. His research interests include developing inversion algorithms for retrieving geophysical parameters, particularly for estimating soil moisture and snow parameters from microwave remotesensing data.

Masayuki Matsuoka received the Doctorate degree in environmental science from Chiba University, Chiba, Japan, in 1998. He is currently a Researcher with the Research Institute for Humanity and Nature, Kyoto, Japan. His recent research interest is in vegetation and land cover changes over the Asian region.

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Shakil Ahmad Romshoo was born in India in 1964. He received the B.S. degree in forestry and the M.S. degree in remote sensing and GIS from the Asian Institute of Technology, Pathumthani, Thailand, in 1988 and 1997, respectively, and the Ph.D. degree in civil engineering, with a major in radar remote sensing, from the University of Tokyo, Tokyo, Japan, in 2000. From 2000 to 2003, he was a Scientist with the Japanese Aerospace Exploration Agency. He is currently an Associate Professor heading the Department of Geology and Geophysics at the University of Kashmir, Srinagar, India. His research interests include modeling land surface processes and retrieving geo- and biophysical parameters using SAR data.

Kazuo Ohta received the B.Sc. degree in geophysics and the M.S. degree in solar planetary electromagnetism using satellites’ and ground stations’ data from the University of Tokyo, Tokyo, Japan, in 1983 and 1985, respectively. He is currently an Associate Senior Engineer at the Earth Observation Research Center, Japan Aerospace Exploration Agency, Tokyo, Japan.

Ryoichi Furuta received the B.Sc. degree and the Ph.D. degree in geotechnical and its numerical analysis from Gifu University, Gifu, Japan, in 2000 and 2003, respectively. He is currently a Research Scientist with the Japan Aerospace Exploration Agency, Tokyo, where he is involved primarily in the research and development of disaster monitoring by the Advanced Land Observing Satellite. He has authored or coauthored over 40 journal articles, book chapters, and conference papers. His research has and continues to focus on natural disasters and their mitigation.

Kazuki Nakamura was born in Tokyo, Japan, in January 1974. He received the B.S. degree in integrated arts and sciences and the M.S. degree in education from the Hokkaido University of Education, Kushiro, Japan, in 1998 and 2000, respectively, and the Ph.D. degree in science from Chiba University, Chiba, Japan, in 2003. He was with the Communications Research Laboratory (now the National Institute of Information and Communications Technology) where he was involved in monitoring sea ice, wetland vegetation, and forests using SAR data. He is currently with the National Institute of Polar Research, Tokyo, Japan, where he studies changes of ice sheets in Antarctica using SAR data. Dr. Nakamura is a member of the Remote Sensing Society of Japan and the Japanese Society of Snow and Ice.

Toshifumi Moriyama (M’98) was born in Fukui, Japan, on January 1, 1972. He received the B.E., M.E., and Ph.D. degrees in information engineering from Niigata University, Niigata, Japan, in 1994, 1995, and 1998, respectively. He was with Fujitsu System Integration Laboratories, Ltd., from 1998 to 2003, and the National Institute of Information and Communication Technology from 2003 to 2005. He is currently with the Earth Observation Research Center, Japan Aerospace and Exploration Agency, Tokyo, Japan. His interests are in radar polarimetry and microwave remote sensing. Dr. Moriyama is a member of the Institute of Electronics, Information and Communication Engineers of Japan and the Remote Sensing Society of Japan.