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Biophysical estimation in tropical forests using JERS-1 SAR and VNIR imagery. II. Aboveground woody biomass C. Wang a; J. Qi b a Department of Geography, University of Missouri, Columbia, MO, USA b Center for Global Change and Earth Observations and Department of Geography, Michigan State University, East Lansing, MI, USA Online Publication Date: 01 December 2008

To cite this Article Wang, C. and Qi, J.(2008)'Biophysical estimation in tropical forests using JERS-1 SAR and VNIR imagery. II.

Aboveground woody biomass',International Journal of Remote Sensing,29:23,6827 — 6849 To link to this Article: DOI: 10.1080/01431160802270123 URL: http://dx.doi.org/10.1080/01431160802270123

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International Journal of Remote Sensing Vol. 29, No. 23, 10 December 2008, 6827–6849

Biophysical estimation in tropical forests using JERS-1 SAR and VNIR imagery. II. Aboveground woody biomass C. WANG*{ and J. QI{ {Department of Geography, University of Missouri, Columbia, MO, USA {Center for Global Change and Earth Observations and Department of Geography, Michigan State University, East Lansing, MI, USA (Received 19 December 2006; in final form 22 May 2008 )

Downloaded By: [Wang, C.] At: 21:21 5 November 2008

Accurate estimates of aboveground biomass in tropical forests are important in carbon sequestration and global change studies. Tropical forest biomass estimation with microwave remote sensing is limited because of the strong scattering and attenuation properties of the green canopy. In this study a microwave/optical synergistic model was developed to quantify these effects to Synthetic Aperture Radar (SAR) signals and to better estimate woody structures, which are closely related to aboveground biomass. With a Leaf Area Index (LAI) retrieved from Japan Earth Resources Satellite (JERS)-1 Very Near Infrared Radiometer (VNIR) imagery, leaf scattering and attenuation to woody scattering were quantified and removed from the total backscatter in a modified canopy scattering model. Woody scattering showed high sensitivity to biomass .100 tonnes/ha in tropical forests. Tree height and stand density were derived from the JERS-1 SAR image with a root mean square error (RMSE) of 4 m and 161 trees/ha, respectively. Aboveground biomass was calculated using a general allometric equation. Biomass in secondary dry dipterocarps (Dipterocarpaceae family of tropical lowland deciduous trees) was overestimated. The modelled biomass in mixed deciduous and dry evergreen forests fit better with ground measurements. In mountainous areas with steep slopes, the topographic effects in the SAR image could not be properly corrected and therefore the results are unreliable.

1.

Introduction

Tropical forests account for approximately 40% of the terrestrial biomass and provide a repository for about 17% of the total land-based carbon stocks (Lucas et al. 2004). Tropical forests have a high potential for carbon exchange because of the ongoing deforestation, reforestation and afforestation as a result of both human activities and natural processes. To date, most studies on tropical forest change have been based on optical remote sensing images (Skole and Tucker 1993, Tucker and Townshend 2000) in which reflected signals are sensitive to green canopies in the visible–near-infrared spectral region. However, the sensitivity is highly variable depending on spectral bands, seasonal change, cloud cover, local climate and the growing stage of forests (Lucas et al. 2004). A large discrepancy in the forest biomass estimation and carbon budget occurs when comparing satellite-based

*Corresponding author. Email: [email protected] International Journal of Remote Sensing ISSN 0143-1161 print/ISSN 1366-5901 online # 2008 Taylor & Francis http://www.tandf.co.uk/journals DOI: 10.1080/01431160802270123


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C. Wang and J. Qi

results to direct field biomass measures and industrial logging records, as seen in earlier studies (Whittaker et al. 1974, Turner et al. 1995). Quantification of woody biomass is thus needed to better understand the changes in tropical forests and their effects on biodiversity, the global carbon cycle and climate change. In the current study, the aboveground biomass is represented by the total woody biomass of trees of diameter >10 cm (Brown and Lugo 1992). Saplings and seedlings are not taken into account. The green canopy biomass is represented by the Leaf Area Index (LAI), the total single-sided leaf area per unit ground area (Turner et al. 1999). The LAI estimation from optical images was discussed in Part I of this study (Wang and Qi 2008, this issue). Since the late 1980s, Synthetic Aperture Radar (SAR) imagery has been used to estimate the aboveground forest biomass because radar signals can penetrate green canopies and reach woody structures. One of the most common approaches was to empirically relate ground-measured biomass to SAR backscatter intensity. Le Toan et al. (1992) found that there was a good correlation between forest biomass and SAR backscatter coefficients (s0) at lower frequencies (P- and L-bands). C-band data primarily interacted with the crown layer (Saatchi and McDonald 1997) and a rapid saturation of the C-band s0 occurred in forests at higher biomass (Dobson et al. 1992). Luckman et al. (1997) found that SAR data at L-band could discriminate between different levels of forest biomass up to 110 tonnes/ha. Biomass estimation could be improved by using images in multiple frequencies and polarizations in the regression (Harrell et al. 1997, Kellndorfer et al. 1998, Wagner et al. 2003). However, the empirical relationships were highly affected by forest structures such as tree species, age, height, leaf and branch orientation distribution, and other factors including topography, soil moisture and local disturbances (Luckman et al. 1998). Biomass retrieval by empirical regression was shown to introduce large deviations from the regression line (Dobson et al. 1995, Harrell et al. 1997). Aboveground biomass can also be simulated by using canopy scattering models, taking these structural parameters into account (Ulaby et al. 1990, Sun et al. 1991, Karam et al. 1995, Sun and Ranson 1995). These models are a theoretical approximation of forest scattering based on first- or second-order radiative transfer functions (RTFs). These RTF models were mostly applied in boreal and temperate broadleaf forests in sparse to medium density (Dobson et al. 1995, Ranson and Sun 1997, Saatchi and Moghaddam 2000). When biomass reaches about 100 tonnes/ha, which is common in tropical forests, radar backscatter intensities saturate rapidly because of strong attenuation from green canopies (Dobson et al. 1992). The deficiencies of applying SAR images in tropical forests can be compensated for when optical data are also analysed. For example, Rignot et al. (1996) combined the classifications from Thematic Mapper (TM) and L-band Spaceborne Imaging Radar (SIR)-C images to achieve a higher accuracy of land cover and deforestation/ regrowth mapping. Salas et al. (2002) used both TM and Japan Earth Resources Satellite (JERS)-1 data to map land use and land cover change, and to assess deforestation and secondary vegetation in tropical forests. It was also reported that a synergistic use of optical and SAR images over vegetated areas could reduce green canopy effects on SAR signals and thus extend its validity in biomass estimation (Moghaddam and Dungan 2000, Wang et al. 2004). The effects of green vegetation on SAR backscatter are controlled by its biophysical parameters, which can be derived by optical remote sensing. For example, forest canopy cover can be estimated in a range of 0–100% (Wang et al. 2005) and green LAI up to 4.0

Biophysical estimation in tropical forests II

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depending on the different spectral bands, vegetation indices and forest types (Chen and Cihlar 1996, Shabanov et al. 2005). This information could be used to quantify canopy attenuation to radar signals in the RTF models, and therefore extend the threshold of signal saturation. Beyond this threshold, both SAR and optical signals are no longer sensitive to forest biomass change. The aim of the current study was to build a microwave/optical synergistic model and to apply it to aboveground woody biomass estimation. The study area was the tropical seasonal forests in northern Thailand. A modified canopy scattering model was developed and leaf contribution was quantified based on the LAI data from the JERS-1 Very Near Infrared Radiometer (VNIR) image in Part I of this study (Wang and Qi 2008, this issue). After removing leaf backscatter and compensating leaf attenuation to woody structures, tree height and stand density at each pixel was estimated in the JERS-1 SAR image by model inversion. With these woody structures the aboveground biomass was calculated in a general allometric equation.


2.

Study area and data collection

As described in Part I of this study, the study area was the Greater Mae Chaem Watershed in northern Thailand. Tropical seasonal forests cover 80% of the watershed and are categorized into five types distributed along an elevation gradient: dry dipterocarps, mixed deciduous, pine transition, tropical dry (lower) evergreen, and tropical moist (upper) evergreen forests. Dry dipterocarps belong to the Dipterocarpaceae family of fruiting tropical lowland rainforest trees. These forests are mostly young regeneration at low elevation close to human residences, and suffered from heavy human disturbance such as burning and shifting to agriculture. Mixed deciduous forests are located at higher elevations further away from residential villages. Tropical dry evergreen forests are on the upper mountains and are even less disturbed because of the difficulty of access. Moist evergreen forests are located at the very top of mountains and are often covered by clouds throughout the year. Part I of this study provides detailed information about the study area, forest type map and LAI distribution (Wang and Qi 2008, this issue). 2.1

Field data collection

The experimental design of the field trip was described in Part I of this study. A total of 32 study sites were visited on 20–27 January 2002: dry dipterocarps (nine), mixed deciduous (nine), pine transition (five), dry evergreen (six), and moist evergreen (three). At each study site, 10 sample points were evenly selected along a 300-m transect. A point-quadrant method was applied to collect the ground biophysical data. Centred at each sample point, four quadrants were divided parallel and perpendicular to the transect direction. The nearest tree in each quadrant was selected and its tree height, stem height, diameter at breast height (DBH) and the distance to the sample point were measured. The stand density at each study site (S) was calculated as (number of trees/m2): Si ~

4 d12 zd22 zd32 zd42 n P

S~

Si

i

n

ð1Þ

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C. Wang and J. Qi

where Si is the stand density at the ith sample point, n is the number of points in each transect (n510), and d1, d2, d3 and d4 are the distances of the sample point to the nearest tree in each quadrant. The forest aboveground woody biomass (B) at each study site was calculated with the allometric equation (Le Toan et al. 1992):


B~rTp

DBH 2 HS 2

ð2Þ

where DBH and H are statistical means of DBH and tree height at the 10 sample points at each study site. The constant T is the shape factor converting bole biomass to total woody biomass: T50.45 is applied in tropical forests. The constant r is the mean dry wood density (including bark): in tropical forests the dry wood density is 610 kg m23 (Luckman et al. 1997). Woody biomass in this study is expressed in units of tonnes/ha. Due to a lack of detailed forest inventory data, the same shape factor and dry woody density were used to calculate woody biomass in all forest types. It should be noted that errors were introduced by this assumption. 2.2

Remotely sensed SAR images

Two scenes of the JERS-1 SAR images were acquired on 27 February 1998, eight days earlier than the JERS-1 VNIR images (described in Part I). The JERS-1 SAR image was in L-band HH (in transmitting and receiving directions) polarization at a pixel size of 18 m. The two scenes were in the same orbit and were easily mosaiced into a large scene. Only the mosaic image in the study area was used (figure 1(a)). The seasonal change between image acquisition and fieldwork was assumed to be limited because both were in the dry season. The land cover change was also ignored because the study area was a protected national forest and no logging activities were allowed. 2.3

Geometric and topographic correction of the SAR image

The JERS-1 SAR image was processed with a 363 Lee filter to reduce speckle noise and to increase the signal-to-noise ratio with an increased number of looks. Due to the high topographic variation, the study area was divided into four quadrants overlaid with a narrow zone. Each part of the data was geometrically corrected by the ground control points collected during the field trip. In a third-order polynomial geometric model, the total errors are 51.68, 25.23, 42.13 and 48.78 m in the lower right, upper left, upper right, and lower left quadrants, respectively. After geocorrection the four quadrants were mosaiced again. To constrain the total error in one pixel, the geometrically corrected SAR image was resampled to a 60 m pixel size using the nearest-neighbour method. SAR is a side-looking sensor and its image is severely distorted by forms of radar shadow, layover and foreshortening in mountainous areas. The foreshortening effect in the SAR image can be corrected with 30-m Digital Elevation Model (DEM) data (figure 1(b)). Using the method described in Van Zyl et al. (1993), the backscatter amplitude (A) of the SAR image was corrected to a reference surface on which the local incidence angle (g) of each pixel is 0:

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Figure 1. (a) The JERS-1 SAR image, (b) DEM data, (c) local incidence angle image and (d) topographically corrected SAR image. Radar shadows and layovers are shown in white in (c).

Atopocor ~A|

sin g cos ha sin g0

ð3Þ

where Atopocor represents the topographically corrected backscatter amplitude of each pixel. g0 is the SAR incidence angle recorded in meta data, g is the local incidence angle, and ha is the azimuth slope. Setting h as the local slope angle and DQ as the relative azimuth angle between the local aspect and SAR azimuth, the local incidence angle g was calculated as (Pairman et al. 1997): cos g~cos h cos g0 zsin h sin g0 cos Dr

ð4Þ

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C. Wang and J. Qi

The azimuth slope ha can be determined from the relationship: tan ha ~tan h sin Dr

ð5Þ

Radar shadows and layovers occur in areas with high relief and steep slopes. By comparing the local incidence angle (g) and local slope (h), the areas with layover and radar shadow can be determined:


Layover : gv900 and hwg0 Radar shadow : gw900 Figure 1(c) shows the local incidence angle image in the study area. In areas with layovers and shadows, the topographic effects were irreversible. These areas are shown in white in figure 1(c) and were masked out in the following processes. In some high mountainous areas with large local slopes, the foreshortening effect was so severe that the correction was poorly performed, despite the non-occurrence of layovers and shadows. These areas show strong distortions in the topographically corrected image (figure 1(d)). In other areas, the topographic effects on the SAR image were reduced, resulting in higher variation of scattering from tropical seasonal forests. 3. 3.1

Approach A modified radiative transfer model

The forest area was simplified as a three-layer vegetation composition: leaf canopy, branch + leaf canopy, and trunk layers atop a rough soil surface (figure 2). For different forest types, the components (leaves, branches, trunks) in each layer and their biophysical parameters may vary. Saplings, seedlings and grass/shrub under the tree canopy were not considered in the simulation. The total backscatter intensity of a pixel was an additive contribution from components in all layers as well as double bounces in between. To simplify the process, triple interaction among leaves, branches and trunks was neglected. In a first-order RTF solution, the total backscatter (stotal) was composed of surface scattering from soil ground (ssoil), volume scattering from leaves (sleaf), branches

Figure 2. Geometry of a forest canopy and scattering components: 1, soil surface scattering; 2, leaf volume scattering; 3, branch volume scattering; 4, trunk volume scattering; 5, leaf–soil interaction; 6, branch–soil interaction; 7, trunk–soil interaction.


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(sbranch), trunks (strunk), interactions between soil ground and leaves (sleaf–soil), branches (sbranch–soil) and trunks (strunk–soil) (in power units): stotal ~ssoil zsleaf zsbranch zstrunk


zsleaf{soil zsbranch{soil zstrunk{soil

ð6Þ

Karam et al. (1995) developed a forest canopy scattering model to simulate each component in equation (6). However, in their model the soil backscatter, ssoil, was a very simple first-order RTF solution that assumed soil as a continuous and slightly rough dielectric surface. It was thus insufficient to simulate radar scattering under various conditions of soil roughness and moisture, especially in young sparse dry dipterocarps, where soil surface played an important role in total backscatter. In the current study, a more successful integral equation model (IEM; Fung et al. 1992) was introduced into the model of Karam et al. to better simulate soil surface scattering. Application of the IEM can be extended to a wide range of soil surface conditions. It reduces to a small perturbation model when the surface is slightly rough and to a Kirchhoff model when the surface is very rough. Taking into account the attenuation from canopies above the ground surface, soil backscatter was expressed as (in power units): ssoil ~t1p t2p t3p sspq t3q t2q t1q

ð7Þ

where both p and q can represent H or V polarizations, ti (i51, 2, 3) is the polarized attenuation factor from the three layers of forest components [leaf (1), branch (2), or trunk (3)], and sspq is the pqth element of the surface scattering matrix, described in detail in Fung et al. (1992). In equation (6), the leaf volume scattering coefficient, sleaf, was an additive contribution from all leaves in the first (s1leaf ) and second (s2leaf ) layers. It was calculated in a first-order RTF solution (Karam et al. 1995): sleaf ~s1leaf zs2leaf ~4pQleaf1 pq

1{t11 p t11 q 1{t12 p t12 q z4pQleaf2 t1 p t1 q pq k1mp zk1q sec g k2p zk2q sec g 1 1

ð8Þ

where kit (i51, 2; t5p, q) is the extinction coefficient in the ith layer and tth polarization. It is the total extinction cross-section of all leaves. t11 t and t12 t are the attenuation factors at polarization t (H or V) for leaves in layers 1 and 2, respectively. The total leaf attenuation factor in two layers t1t ~t11 t |t12 t . g is the local incidence angle as described in equation (4). For each layer (1 or 2), leaf 2 leaf ~n S F Qleaf i pq pq T, where Fpq is the element of the scattering amplitude matrix of one leaf, and ni is the number of leaves in the ith layer. The ensemble average ,. is used in the equation to represent the statistical average over all leaves in this layer. The leaf distribution in tropical forests is assumed to be approximately spherical. Similarly, branch volume scattering, sbranch, is a first-order solution of radiative transfer equations for branches in the second layer (layer 2): | sbranch ~4pQbranch pq

1{t2p t2q t1p t1q k2p zk2q sec g

ð9Þ

branch 2 branch where Qbranch ~Nb SFpq is the element of the scatter amplitude matrix T, Fpq pq

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C. Wang and J. Qi

of a single branch, and Nb is the number of branches in the second layer. The ensemble average ,. is used to represent the statistical average over all branches in this layer. We adopted a plagiophile as the probability distribution function of branches (Goel and Strebel 1984). The trunk volume scattering, strunk, is derived in a similar way to the branch volume scattering except that branch attenuation is also considered: strunk ~4pQtrunk pq

1{t3p t3q t1p t1q t2p t2q k3p zk3q sec g

ð10Þ


trunk 2 trunk where Qtrunk ~N S F is the element of the scatter amplitude matrix of t pq pq T, Fpq a single trunk in the third layer, and Nt is the number of trunks in the forest. The ensemble average ,. is used to represent the statistical average over all trunks in this layer. We adopted an erectophile as the PDF of trunks (Goel and Strebel 1984). The backscatter amplitude of leaf–soil double bounce, sleaf–soil, was a statistically additive contribution from all leaves in the first (leaf canopy) and second (leaf + branch) layers. It can be described as (in power units): sleaf{soil ~sleaf1{soil zsleaf2{soil

ð11Þ

where t1p {t1q 2 2 2 t3q t2q sleaf1soil ~4p cos gt2p t3p Rpp zRqq e{4k s cos g Qleaf1 pq k1q {k1p and t12 p {t12 q 2 2 2 sleaf2soil ~4p cos gt11 p t2p t3p Rpp zRqq e{4k s cos g Qleaf2 t3q t2q t11 q pq k2q {k2p Here Rpp and Rqq are the Fresnel reflectivity of soil at p and q polarizations, k is the wave number, and s is the root-mean-square height of the soil roughness. Similarly, the double bounce of branch–soil (sbranch–soil) and trunk–soil (strunk– soil) can be described as (in power units): t2p {t2q t3q t1q k2q {k2p t3p {t3q 2 2 2 strunksoil ~4p cos gt1p t2p Rpp e{4k s cos g Qtrunk t2q t1q pq k3q {k3p

sbranchsoil ~4p cos gt1p t3p Rpp e{4k

3.2

2 2

s cos2 g

Qbranch pq

ð12Þ

Quantification of leaf contribution with LAI data

Except for leaf scattering, all scattering components in equations (9)–(12) are attenuated by leaf canopies. To be consistent with the JERS-1 SAR system factors, only one polarization (H) was considered in forward and backward scattering. Equation (6) can then be rewritten as: stotal ~sleaf zsleaf{soil zt21 ðsbranch zstrunk zssoil zsbranch{soil zstrunk{soil Þ

ð13Þ

To simplify the expression, the attenuation factors from the branch and trunk layers are covered by their scattering components and are not listed in this equation. The leaf scattering (sleaf and sleaf–soil) and leaf attenuation factor (t1) are merely from the


6835

green canopies in layers 1 and 2. With green biomass (represented by the LAI) extracted from optical imagery, these components can be quantified in the modified canopy scattering model. Defining the height of the leaf canopy (layer 1 + layer 2) as Hleaf, the leaf attenuation factor in equation (13) (forward or backward) is exponentially related to the extinction coefficient (kt), which is statistically averaged over the spherical orientation probability distribution (Karam et al. 1995): t1 ~e{kt Hleaf =cos g


kt ~

4pN ImSFtt T k

ð14Þ

where Ftt is the co-polarization component of the scattering matrix of a single leaf, Im is the imaginary part of Ftt, and k is the wave number. In equation (14), N is the leaf density (number/m3) in each layer. It is closely related to LAI, the total single-sided leaf area per unit ground area (projected from layers 1 and 2). All tree species are broadleaves in the study area. In the canopy scattering model, a single leaf is simulated as an ellipse with radii of a (long axis) and b (short axis). Leaf density N in equation (14) can then be related to the LAI (LAI in the equation) by: N~

LAI pabHleaf

ð15Þ

Leaf densities in layers 1 and 2 were assumed to be the same. As described in Part I of the study, LAI was retrieved from the JERS-1 VNIR data with a modified Gaussian regression technique. With this information, leaf attenuation in each layer could be quantified. The leaf scattering (sleaf and sleaf–soil) was also calculated in equations (8) and (11), at a given height of leaf canopies. As a result, linking LAI to the RTF model quantified the leaf scattering and its attenuation to woody components and we could therefore improve the accuracy of the biomass estimation with SAR imagery. 3.3

Model simulation

By replacing the simple soil scattering model with the IEM and linking several leaf biophysical parameters to the optical imagery, a microwave/optical synergistic canopy scattering model was developed to simulate scattering from tropical forests. Consistent with the JERS-1 SAR configuration, only scattering in the L-band and HH polarization at an incidence angle of 35u was considered. The model was simulated with the variables of biophysical attributes listed in table 1. To examine the contribution of the biophysical parameters in table 1 to the total backscatter, only one parameter in the table was changed during each simulation while others were represented by their mean values. It was found that branch volume scattering, trunk–soil double bounce and leaf volume scattering were the primary components in canopy scattering in L-band HH polarization. As a result, leaf contribution to total scattering in tropical forests cannot be neglected as in past studies in coniferous forests. The contribution of leaves, branches and trunks was demonstrated by examining the variation in simulated backscatter coefficients with LAI (figure 3(a)), branch density (figure 3(b)), tree height (figure 3(c)) and stand density (figure 3(d)).

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C. Wang and J. Qi Table 1. Input biophysical parameters for model simulation.

Parameter Leaf size (m)


Branch size (m) Trunk size (m) LAI Branch density (number/m2) Tree density (number/m2) Tree height (m)

K long axis Elliptic ratio Radius Radius

Range

Mean

0.02–0.07 1.2–1.5 0.016–0.047 0.06–0.22 0.2–8.0 0.5–3.0 0.03–0.18 6–24

0.05 1.5 0.031 0.1 1.0 1.5 0.1 10

3.3.1 Contribution of leaves. When LAI values increase, both leaf volume scattering and leaf–soil interactions increase rapidly and then slow down to the saturation point (figure 3(a)). The leaf–soil interaction intensity is far less than the leaf volume scattering. The contributions of other components decrease due to leaf attenuation. As described in equation (9), the attenuation loss in dB is primarily linearly related to the amount of leaves. When the LAI changes from 0 (no green leaves) to 8, the increased backscatter from the leaves is compensated by the backscatter loss from the other components, and therefore the total backscatter increase is only 1 dB. This confirms that biomass estimation with SAR images is difficult in tropical forests because of leaf attenuation in dense canopies. 3.3.2 Contribution of branches. With the increase in branch density (number/m2), both branch volume scattering and branch–soil interaction are higher (figure 2(b)). When branch density increases from 0.5 to 3 branches per m2, the backscatter

Figure 3. Simulated backscatter with four major biophysical parameters: (a) LAI; (b) branch density; (c) tree height; (d) and stand density.


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increment is 5 and 3 dB, respectively. The branch–soil interaction increases more quickly to reach saturation. The leaf volume scattering does not change. The scattering of other components decreases almost linearly because of attenuation in the branch layer. 3.3.3 Contribution of trunks. Trunk-related backscatter varies with both tree height (figure 3(c)) and stand density (figure 3(d)). In contrast to the leaf and branch layers, the trunk–soil double bounce is much stronger than trunk volume scattering. When the tree height increases from 6 to 24 m, trunk volume scattering increases slightly and then decreases to 7 dB. Trunk–soil double bounce falls by more than 40 dB. The soil surface backscatter, leaf–soil and branch–soil interaction reach infinity (–80 dB in the model) when trees are higher than 18 m. The total backscatter only increases by 2 dB when the tree height ranges from 6 to 24 m. When stand density (number of trees/m2) increases, the trunk–soil interaction first increases and then decreases after stand density .0.08 (figure 3(d)). The possible reason is that in denser forests there is less space for trunk–soil double bounce. The decrease is approximately 3 dB when stand density ranges from 0.03 to 0.18 trees/m2. However, trunk volume backscatter increases up to 5 dB. Evidently, there is a greater chance of trunk volume backscatter at a higher stand density. The leaf and branch volume scattering is not affected by the variation in stand density. The scattering in other components decreases almost linearly. The total backscatter decreases by only 1 dB. 3.4

Model inversion and biomass estimation

Three major forest types were considered in the model inversion: dry dipterocarps, mixed deciduous, and tropical evergreen. Tropical evergreen includes pine transition zone, tropical dry evergreen and tropical moist evergreen forests. For each forest type, leaf size and dielectric constants were predefined in table 2. As described in section 3.2, once the LAI at each pixel was calculated from the JERS-1 VNIR image, the leaf density could be calculated at a given green canopy height. As a result, leaf scattering (sleaf and sleaf–soil) and leaf attenuation (t21 ) could be quantified and removed from the modelled total backscatter (smodel) Table 2. Forest structural parameters for model inversion.*

Leaf radius in (long, short) axis (cm) eleaf esoil ebranch etrunk Leaf density (number/m3) Height of layer 1 (m) Height of layer 2 (m) Height of layer 3 (Hstem) (m) Branch radius (cm) (Branch density)2 (number/m3)

Dry dipterocarps

Mixed deciduous Tropical evergreen

(7, 5)

(5, 3.3)

(4, 3)

(16.94, 25.7) (5.3, 20.65) (19.7, 26.53) (13.57, 24.66) 90.956LAI/ (H2Hstem) (H2Hstem)62/3 (H2Hstem)/3 0.5596H21.328 0.3336H21.082 S68

(22.29, 27.26) (5.3, 20.65) (19.7, 26.53) (13.57, 24.66) 192.926LAI/ (H2Hstem) (H2Hstem)62/3 (H2Hstem)/3 0.5596H21.328 0.3336H21.082 S616

(28.23, 28.95) (10.23, 21.95) (26.53, 28.41) (23.26, 27.53) 265.266LAI/ (H2Hstem) (H2Hstem)/3 (H2Hstem)62/3 0.5596H21.328 0.3336H21.082 S632

e, dielectric constant; H, tree height; S, forest stand density. *LAI is calculated from the JERS-1 VNIR image.

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C. Wang and J. Qi

(in power units): smodel{noleaf ~

smodel {sleaf {sleaf{soil t21

ð16Þ

The smodel–noleaf in equation (16) was primarily composed of scattering contributions from branches and trunks. From equations (7)–(12), these scattering components were functions of a set of woody structural parameters: smodelnoleaf ~f ðHbranch , Htrunk , Rbranch , Sbranch , DBH , S Þ

ð17Þ

where Rbranch is the radius of a branch and Sbranch is the density of branches in layer 2. Hbranch and Htrunk are heights of layers 2 and 3, respectively. As shown in figure 4, ground-measured stem height (Hstem, in m) and DBH (in cm) were highly correlated to tree height (H, in m): Hstem ~0:559|H{1:328 Downloaded By: [Wang, C.] At: 21:21 5 November 2008

DBH ~1:332|Hz4:29

ð18Þ

The relationships between other biophysical parameters were also assumed as: H~Hlayer1 zHlayer2 zHlayer3 Hlayer1 ~c1 |ðH{Hstem Þ Hlayer2 ~ð1{c1 Þ|ðH{Hstem Þ Hlayer3 ~Hstem

ð19Þ

Rbranch ~DBH =4 Sbranch ~c2 |S where c1 and c2 are coefficients that vary in different forest types (table 2).

Figure 4. Scatterplot of stem height and DBH to tree height measured at the study sites. R2 is the coefficient of determination for each regression.


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Based on these assumptions, ground measurements, and LAI from the VNIR image, there were only two unknown parameters in simulation of the total backscatter coefficient: tree height (H) and forest stand density (S):


smodelnoleaf ~f ðH, SÞ

ð20Þ

A least-square-error optimization technique was applied to estimate these two biophysical parameters. Defining the modelled total backscatter coefficient (in dB) as s1model and the JERS-1 SAR observed backscatter coefficient as s0SAR , the 2 standard criteria in optimization was to minimize s0model {s0SAR . For each forest type map, given the initial values of tree height and stand density at each pixel at the first iteration, s0model was generated at the next iteration and compared with s0SAR . The iteration continued until the derivative of s0model {s0SAR between the two adjacent iterations was less than 1026, which assumed that s0model agreed with s0SAR . The model also stopped when the iteration numbers exceeded 104, which indicated that the model inversion did not converge. To reduce computing time in the optimization process, the JERS-1 SAR image was resampled to 300 m pixel size, comparable to the size of the study sites. Once the tree height and stand density at each pixel were modelled, the DBH was calculated with equation (18). The aboveground woody biomass was then calculated with equation (2). 3.5

Uncertainty analysis

The error (e) of model simulation is defined as the absolute difference between the modelled and the JERS-1 SAR observed backscatter coefficients (dB): e~s0model {s0SAR ð21Þ The subtraction in dB units was used because only relative errors were needed to evaluate model simulation and inversion. For validation purposes, the modelled tree height and stand density were compared with ground measurements at the study sites. Due to the relatively small number of degrees of freedom (df) of the ground measurements, the measured tree height and stand density were prone to high uncertainty, especially in heterogeneous forests. To examine these uncertainties, the coefficient of variation (CV) at each study site was calculated for both tree height and stand density: CV~

s x

ð22Þ

where s is the standard deviation of the measurements, and x¯ is the mean value. At each study site, the upper and lower endpoints at a 95% confidence interval (CI) of each biophysical attribute were also calculated (x+1:96s=pffiffinffi), where n is the total number of sample points, n510 for stand density and n540 for tree height. 4. 4.1

Results Relationship of backscatter and biomass at study sites

At each study site, the SAR backscatter coefficient was extracted in a 565 window in the topographically corrected JERS-1 SAR image (at 60 m pixel size). The

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backscatter coefficient was also simulated with ground-measured biophysical attributes. Figure 5(a) shows the points scattered along a 1 : 1 line. The root mean square error (RMSE) of the model simulation on these study sites was 0.94 dB. Most residuals were between ¡1 dB (figure 5(b)). The average residual was 0.44 dB. The only site with a residual higher than 2 dB was the Teak plantation site in the dry dipterocarps forests. The regular pattern in planted trees results in a very high backscatter in the SAR image. This site was not used in the following analysis. Figure 5 shows that, with ground-measured biophysical attributes, the synergistic model in this study could be used to predict the backscatter at an approximate accuracy of 1 dB in the study area.

Figure 5. Comparison of JERS-1 SAR observed and modelled backscatter coefficients: (a) scatterplot and (b) residuals. The 1 : 1 line is also drawn in (a).



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In agreement with most studies of radar remote sensing in forests, the observed SAR backscatter coefficients were logarithmically related to the ground biomass at the 31 ground sites (without the Teak site) (figure 6(a)). In the fitted s0–biomass curve, the JERS-1 SAR backscatter increased rapidly and then slowed down to saturate at a biomass of around 100 tonnes/ha, consistent with the results of past studies (Dobson et al. 1992, Le Toan et al. 1992, Luckman et al. 1997). In figure 6(b), the modelled total backscatter showed a similar logarithmic relationship with biomass at the study sites. However, the increase slowed down rapidly and turned to saturation at an even lower biomass (around 50 tonnes/ha), possibly because of the attenuation effects from the dense green canopies in the tropical forests. Using the ground-measured LAI and green canopy height at the study sites, the scattering coefficient from woody forests (s0model–noleaf) was quantified by subtracting leaf scattering (sleaf and sleaf–soil) and compensating for leaf attenuation (t21 ) from JERS-1 backscatter (equation (16)). The s0model–noleaf was a combined contribution of volume scattering of branches and trunks and their interaction with the soil surface. As shown in figure 6(c), this also had a logarithmic relationship with biomass at the study sites. Unlike figure 6(b), the fitted curve in figure 6(c) had a more sensitive relationship between s0model–noleaf and biomass. The increase in woody backscatter did not slow down until the biomass reached 100 tonnes/ha.

Figure 6. Logarithmic curves of backscatter coefficients with biomass: (a) JERS-1 observed; (b) modelled; and (c) modelled (no leaf) backscatter. The variable x in the equations represented ground measured biomass.

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After this point, the increase in s0model–noleaf with biomass slowed down but still increased at a higher slope than the JERS-1 backscatter in figure 6(a). In dense moist evergreen forests (biomass reaching 500 tonnes/ha), both s0model–noleaf and JERS-1 SAR observed backscatter saturated at about 28 dB. Figure 6(c) shows that, after the leaf contribution to total backscatter was quantified and removed, the threshold of biomass estimation with the SAR image was extended. The coefficients of the three curve fittings and their correlation coefficients (R) are also listed in each plot (figures 6(a)–6(c)). The s0model–noleaf–biomass curve fitting showed the highest correlation coefficient (R50.788). At a df of 28 (31 study sites – 3 coefficients in regression), the critical x20:05,28 is 41.34, larger than all of the three curve fittings in figure 6. As a result, all curve fittings were valid in describing the relationships between backscatter and biomass.


4.2

The biomass map

Figure 7 shows the estimated biomass map obtained by model inversion in the study area. The non-forest areas are masked out in the process. The dry dipterocarps had the lowest biomass (50–100 tonnes/ha). There are also some areas with biomass ,50 tonnes/ha. The biomass in forests closer to human settlements is lower than in those far from habitation. The mixed deciduous forests have a higher biomass, ranging from 50 to 200 tonnes/ha. The biomass in tropical evergreen forests is highly heterogeneous. The estimated biomass ranges from 50 to .300 tonnes/ha. Tropical evergreen forests are located atop mountains with steep topography. As shown in

Figure 7. Forest aboveground woody biomass map in the study area (300 m pixel size). Only the areas that cover both SAR and VNIR data are mapped. The non-forest areas were masked out in the study.


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figures 1(c) and 1(d), the topographic effects in these areas were often high and could be properly corrected, which resulted in large model inversion errors in biomass estimation in these areas.


4.3

Validation and uncertainty analysis

In the error map (figure 8), model errors in most dry dipterocarps were lower than 1 dB, indicating that the model simulation worked well in these forests. The errors were also low in most of the mixed deciduous forests, although in some areas the error reached 2–3 dB. Errors in the tropical evergreen forests were generally high. In the moist evergreen forests in the middle of the study area, the errors even reached 4 dB and higher. In these areas, biophysical estimation by model inversion was not reliable. Ground-measured tree heights at the study sites are compared with the modelled results in figure 9(a). The average CV among all of the study sites (except the Teak plantation) is 0.352. The 95% CI of tree height measurements at each study site is also shown in the figure. In younger forests that had lower tree heights, the 95% CI was also smaller. Tree height in more matured forests was relatively heterogeneous, resulting in a larger variation in the ground measurements. As shown in figure 9(a), the modelled tree height was overestimated in the young forests, mostly dry dipterocarps, in which the measured tree heights were between 5 and 10 m whereas the modelled ones were 10–15 m. For forests with higher trees, the modelled tree height fit better with the ground measurements. The total RMSE of the tree height estimation was about 4 m at a relative error of 30.3%.

Figure 8.

The absolute error map (300 m unit size) of model simulation.


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Figure 9. Scatterplots of modelled and measured (a) tree height and (b) stand density at the study sites (except the teak plantation). The 1 : 1 lines are drawn in the plots.

The ground-measured stand density had a much higher variation than the tree height measurements. In figure 9(b), the 95% CI was smaller for forests at low density and was much larger for those at high density. The average CV at all study sites (except the Teak plantation) was 0.524. Aside from the high variation, the measured and modelled stand density at the study sites scattered along the 1 : 1 line. The modelled values at study sites with low densities fit better with the ground measurements than those sites with high densities, possibly because of lower saturation effects. As shown in figure 9(b), there were four study sites at which the modelled stand density was found to be saturated at 500 trees/ha. These four sites were in evergreen forests: two in pine transition, one in dry evergreen and one in moist evergreen. Model errors at these sites were higher than 2 dB and therefore the modelled stand densities were questionable. The total RMSE of stand density estimation was 235 trees/ha when all sites were considered. This reduced to 161 trees/ ha when these four sites were removed. The relative error of the stand density estimation was 25.5–36.1% with or without these four sites. Both ground-measured and modelled biomass were calculated from the same allometric equation (equation (2)). As DBH was calculated from its linear relationship with tree height (equation (18)), the uncertainties in biomass estimation


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were primarily attributed to tree height and stand density. As a result, there was no need to make a 1 : 1 comparison at individual study sites. Instead, considering the apparent variation of biomass in different forest types, the modelled and measured biomass were averaged and compared in each forest type (figure 10). The modelled biomass was obviously overestimated in dry dipterocarps, primarily because of the overestimation in the modelled tree height as shown in figure 9(a). The difference between the modelled and measured biomass was smaller in the mixed deciduous forests. In the pine transition and dry evergreen forests, the average values of the modelled biomass fit the measured values better. A standard error bar is displayed on the top of each column in figure 10. In general, the modelled biomass had a lower standard error than the ground measurements. The standard error of both modelled and measured biomass in the pine transition was very high, indicating high heterogeneity in the zone. Both modelled and measured biomass values at moist evergreen forest sites were .400 tonnes/ha. The RTF model tended to saturate in these dense mature forests and therefore the results are questionable, although the standard error was very low. 5.

Discussion

The modelled results reveal a heterogeneous biophysical distribution in tropical seasonal forests. They agree with the ground measurements that dry dipterocarps had the lowest biomass, followed by mixed deciduous forests. Tropical evergreen forests had much a higher biomass in the study area. However, the modelled biomass in the tropical evergreen forests was highly questionable. Model errors in these forests could be higher than 4 dB in areas with high relief and steep slopes on mountain tops. The errors in biophysical estimation were not merely from model simulation and inversion. They could also be partially from data preprocessing. The JERS-1 SAR image was topographically corrected with the 30-m DEM data that was digitized from 20-m contour lines in topographic maps. Errors may be introduced during digitization. It was also difficult to georeference satellite images to the DEM data with the limited ground control points that can be observed in tropical forests. The

Figure 10. Comparison of modelled and measured biomass at the study sites. A standard error bar is added in each column. Different forest types include: dipt, dry dipterocarps; mixed, mixed deciduous; pine trans, pine transition; dry ever, dry evergreen; moist ever, moist evergreen.


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study area was highly mountainous and therefore the topographic distortions in the side-looking SAR image cannot be easily corrected. In areas with radar shadows, layovers and strong foreshortening, the topographic distortion is irreversible. Errors were also introduced during the field data collection, especially in measuring stand density with a point-quadrant method, which was time efficient but less accurate. The biophysical attributes (DBH, height, stand density, biomass) at each study site were an average of 10 sample points that were evenly distributed along a 300-m transect. These ground data only approximately represent the biophysical properties of a long and narrow transect (300 m long and 5–15 m wide depending on the site). They could not be matched accurately when comparing with the modelled biophysical attributes in the image. A general allometric equation from Le Toan et al. (1992) was adopted to calculate the aboveground biomass with woody structures of DBH, tree height and stand density. This equation assumed a tree as a cylinder compensated by a constant shape factor. Many other allometric equations have been built in past studies to empirically relate aboveground biomass from DBH and other attributes (Monteith 1979, Pastor et al. 1984). Some researches also found that, for certain specific tree species, the best empirical relationship between biomass and DBH was observed when using log(DBH) (Luckman et al. 1997) or a non-integral power of DBH (Uhl et al. 1988, Brown et al. 1989) in a linear regression model. Although these more complicated models gave the minimum variance in estimating aboveground biomass, they cannot be easily applied in this study because intensive ground measurements were needed to build the best fit of the regression. Further examination will be conducted if ground truthing data for the aboveground biomass in Southeast Asian tropical seasonal forests are available in the future. This study took advantage of both optical and SAR remote sensing imagery. Optical images extracted green canopy properties such as the LAI. When the extracted LAI was input in the radiative transfer models, leaf scattering and its attenuation to woody structures was quantified. In this way the accuracy of the woody biophysical estimation could be improved. However, it is often not possible to acquire optical and SAR imagery simultaneously. As the green leaf properties in the peak growing season do not change rapidly, optical data acquired on similar dates can approximate the leaf properties when SAR data are acquired. Optical and SAR data in different years are also valid if there is no significant land cover change. In the past few years rich optical data have been and are still being acquired from Landsat, SPOT and ASTER, as well as high-resolution IKONOS and Quickbird, and low-resolution AVHRR, and MODIS. With these images, it is also possible to create an annual LAI product for the optical/SAR synergistic model developed in this study. SAR image acquisition, however, is limited. Currently, only a few satellites with SAR sensors such as ERS-2, Radarsat-1 and Envisat are still operative. These sensors are in C-band and their application in tropical forests is thus very limited. In 2006 the Advanced Observing Satellite (ALOS) designed by the Japan Aerospace Exploration Agency (JAXA) was launched successfully. It has four sensors onboard: one radar sensor (the Phased Array type L-band Synthetic Aperture Radar (PALSAR), a follow-up of JERS-1 SAR), two optical sensors (AVNIR-2, a follow-up of JERS-1 VNIR), and the Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM). While PALSAR and AVNIR-2 acquire SAR and optical data almost simultaneously, the PRISM collects high-resolution elevation data that


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improve the topographic correction on both SAR and optical images. ALOS images provide great potentials to continue the optical/SAR synergistic model development and application in mountainous forests. The synergistic model in this study was applied to tropical forests. It could also be modified to fit coniferous and deciduous forests as well as other ecosystems. In the near future this model will be tested in other forests with less topographic variation when ALOS SAR and optical imagery are available. 6.

Conclusions


A microwave/optical synergistic canopy scattering model was developed and applied to estimate woody structures and aboveground biomass in tropical seasonal forests in northern Thailand. The tree height and stand density were estimated by model inversion with an optimization technique that had an RMSE of 4 m and 161 trees/ha at a relative error around 30%. The aboveground woody biomass was calculated with a general allometric equation. The major conclusions in this study are the following: (1) Leaf volume scattering, branch volume scattering and trunk–soil double bounce are the three major scattering components in tropical forests. With the LAI data from the JERS-1 VNIR image, leaf contribution can be quantified and subtracted from total backscatter in the modified canopy scattering model. (2) The optical/SAR synergy improves the sensitivity of SAR signals to woody structures and therefore improves the woody biomass estimation in tropical forests. The modelled woody backscatter was sensitive to aboveground biomass at .100 tonnes/ha, in agreement with past studies in boreal and temperate forests. (3) In areas with steep slopes or dense mature forests, the approach is less reliable because of severe topographic effects with regard to SAR images and strong leaf attenuation to the total backscatter.

Acknowledgements This research was funded by a NASA GOFC grant (NAG 5-9286) and NASA Land Use Land Cover Program grant (NNG05GD49G) at the Center for Global Change and Earth Observations, Michigan State University. We thank Dr C. Navanujraha and S. Lawavirojwong at Mahidol University, Thailand, and Dr N. Wiangwang and P. Varnakovida at Michigan State University and local foresters for help with field data collection and processing. References BROWN, S. and LUGO, A.E., 1992, Aboveground biomass estimates for tropical moist forests of the Brazilian Amazon. Interciencia, 17, pp. 8–18. CHEN, J.M. and CIHLAR, J., 1996, Retrieving leaf area index of boreal conifer forests using Landsat TM images. Remote Sensing of Environment, 55, pp. 153–162. DOBSON, M.C., ULABY, F.T., LE TOAN, T., BEAUDOIN, A. and KASISCHKE, E.S., 1992, Dependence of radar backscatter on coniferous forest biomass. IEEE Transactions on Geoscience and Remote Sensing, 30, pp. 412–415. DOBSON, M.C., ULABY, F.T., PIERCE, L.E., SHARICK, T.L., BERGEN, K.M., KELLNDORFER, J., KENDRA, J.R., LI, E., LIN, Y.C., NASHASHBI, A., SARABANDI, K. and SIQUEIRA, P.,


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1995, Estimation of forest biophysical characteristics in northern Michigan with SIRC/X-SAR. IEEE Transactions on Geoscience and Remote Sensing, 33, pp. 877–895. FUNG, A.K., LEE, Z. and CHEN, K.S., 1992, Backscatter from a randomly rough dielectric surface. IEEE Transactions on Geoscience and Remote Sensing, 30, pp. 356–369. GOEL, N.S. and STREBEL, D.E., 1984, Simple beta distribution representation of leaf orientation in vegetation canopies. Agronomy Journal, 76, pp. 800–802. HARRELL, P.A., KASISCHKE, E.S., BOURGEAU-CHAVEZ, L.L., HANEY, E.M. and CHRISTENSEN, N.L., Jr., 1997, Evaluation of approaches to estimating aboveground biomass in southern pine forests using SIR-C data. Remote Sensing of Environment, 59, pp. 223–233. KARAM, M.A., AMAR, F., FUNG, A.K., MOUGIN, E., LOPES, A., LE VINE, D.M. and BEAUDOIN, A., 1995, A microwave polarimetric scattering model for forest canopies based on vector radiative transfer theory. Remote Sensing of Environment, 53, pp. 16–30. KELLNDORFER, J.M., PIERCE, L.E., DOBSON, M.C. and ULABY, F.T., 1998, Toward consistent regional-to-global-scale vegetation characterization using orbital SAR systems. IEEE Transactions on Geoscience and Remote Sensing, 36, pp. 1395–1411. LE TOAN, T., BEAUDOIN, A. and GUYON, D., 1992, Relating forest biomass to SAR data. IEEE Transactions on Geoscience and Remote Sensing, 30, pp. 403–411. LUCAS, R.M., HELD, A.A., PHINN, S.T. and SAATCHI, S., 2004, Tropical forests. In Manual of Remote Sensing, vol. 4, Remote Sensing for Natural Resources Management and Environmental Monitoring, S. Ustin (Ed.), pp. 237–315 (New Jersey: John Wiley & Sons). LUCKMAN, A., BAKER, J., HONZAK, M. and LUCAS, R., 1998, Tropical forest biomass density estimation using JERS-1 SAR: seasonal variation, confidence limits, and application to image mosaics. Remote Sensing of Environment, 63, pp. 126–139. LUCKMAN, A., BAKER, J., KUPLICH, T.M., YANASSE, C.C.F. and FRERY, A.C., 1997, A study of the relationship between radar backscatter and regenerating tropical forest biomass for spaceborne SAR instruments. Remote Sensing of Environment, 60, pp. 1–13. MOGHADDAM, M. and DUNGAN, J., 2000, Fusion of SAR and TM data for quantitative estimation of forest variables over an extended range of validity. In Proceedings of IEEE International Geoscience and Remote Sensing Symposium, 24–28 July, Honolulu, Hawaii, Vol. 3, pp. 954–956 (New Jersey: IEEE). MONTEITH, D.B., 1979, Whole Tree Weight Tables for NY, AFRI Research Report 40 (New York: University of Syracuse). PAIRMAN, D., BELLISS, S.E. and MCNEILL, S.J., 1997, Terrain influences on SAR backscatter around Mt. Taranaki, New Zealand. IEEE Transactions on Geoscience and Remote Sensing, 35, pp. 924–932. PASTOR, J., ABER, J.D., MCCLAUGHERTY, C.A. and MELILLO, J.M., 1984, Biomass production using generalized allometric regressions for northeast tree species. Forest Ecology Management, 7, pp. 265–274. RANSON, K.J. and SUN, G., 1997, Mapping of boreal forest biomass from spaceborne synthetic aperture radar. Journal of Geophysical Research, 102, pp. 29599–29610. RIGNOT, E., SALAS, W.A. and SKOLE, D.L., 1996, Mapping deforestation and secondary growth in Rondonia, Brazil using imaging radar and Thematic Mapper data. Remote Sensing of Environment, 59, pp. 167–179. SAATCHI, S.S. and MOGHADDAM, M., 2000, Estimation of crown and stem water content and biomass of boreal forest using polarimetric SAR imagery. IEEE Transactions on Geoscience and Remote Sensing, 38, pp. 697–709. SAATCHI, S.S. and MCDONALD, K.C., 1997, Coherent effects on microwave backscattering models for forest canopies. IEEE Transactions on Geoscience and Remote Sensing, 35, pp. 1032–1044. SALAS, W.A., DUCEY, M.J., EIGNOT, E. and SKOLE, D., 2002, Assessment of JERS-1 SAR for monitoring secondary vegetation in Amazonia: I. Spatial and temporal variability in



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backscatter across a chrono-sequence of secondary vegetation stands in Rondonia. International Journal of Remote Sensing, 23, pp. 1357–1379. SHABANOV, N.V., YANG, W., KNYAZIKHIN, Y., MYNENI, R.B. and AHL, D.E., 2005, Analysis and optimization of the MODIS leaf area index algorithm retrievals over broadleaf forests. IEEE Transactions on Geoscience and Remote Sensing, 43, pp. 1855–1865. SKOLE, D.L. and TUCKER, C.J., 1993, Tropical deforestation and habitat fragmentation in the Amazon: satellite data from 1978 to 1988. Science, 260, pp. 1905–1910. SUN, G. and RANSON, K.J., 1995, A three-dimensional radar backscatter model of forest canopies. IEEE Transactions on Geoscience and Remote Sensing, 33, pp. 372–382. SUN, G., SIMONETT, D. and STRAHLER, A., 1991, A radar backscatter model for discontinuous coniferous forests. IEEE Transactions on Geoscience and Remote Sensing, 29, pp. 639–650. TUCKER, C.J. and TOWNSHEND, J.R.G., 2000, Strategies for monitoring tropical deforestation using satellite data. International Journal of Remote Sensing, 21, pp. 1461–1471. TURNER, D.P., COHEN, W.B., KENNEDY, R.E., FASSNACHT, K.S. and BRIGGS, J.M., 1999, Relationships between leaf area index and Landsat TM spectral vegetation indices across three temperate zone sites. Remote Sensing of Environment, 70, pp. 52–68. TURNER, D.P., KOEPPER, G.J., HARMON, M.E. and LEE, J.J., 1995, A carbon budget for forests of the conterminous United States. Ecological Applications, 5, pp. 421–436. UHL, C., BUSCHBACHER, R. and SERRAO, A.S., 1988, Abandoned pastures in eastern Amazonia. I. Patterns of plant succession. Journal of Ecology, 76, pp. 663–681. ULABY, F.T., SARABANDI, K., MCDONALD, K., WHITT, M. and DOBSON, M.C., 1990, Michigan microwave canopy scattering model. International Journal of Remote Sensing, 11, pp. 1223–1253. VAN ZYL, J.J., CHAPMAN, B.D., DUBOIS, P. and SHI, J., 1993, The effect of topography on SAR calibration. IEEE Transactions on Geoscience and Remote Sensing, 31, pp. 1036–1043. WAGNER, W., LUCKMAN, A., VIETMEIER, J., TANSEY, K., BALZTER, H., SCHMULLIUS, C., DAVIDSON, M., GAVEAU, D., GLUCK, M., LE TOAN, T., QUEGAN, S., SHVIDENKO, A., WIESMANN, A. and YU, J.J., 2003, Large-scale mapping of boreal forest in SIBERIA using ERS tandem coherence and JERS backscatter data. Remote Sensing of Environment, 85, pp. 125–144. WANG, C. and QI, J., 2008, Biophysical estimation in tropical forests using JERS-1 VNIR imagery, I: Leaf area index. International Journal of Remote Sensing, 29, pp. 6811–6826 (this issue). WANG, C., QI, J. and COCHRANE, M., 2005, Assessment of tropical forest degradation with canopy fractional cover from Landsat ETM + and IKONOS imagery. Earth Interactions, 9, pp. 1–18. WANG, C., QI, J., MORAN, S. and MARSETT, R., 2004, Soil moisture estimation in a semiarid rangeland using ERS-2 and TM imagery. Remote Sensing of Environment, 90, pp. 178–189. WHITTAKER, R.H., BORMANN, F.H., LIKENS, G.E. and SICCAMA, T.G., 1974, The Hubbard Brook ecosystem study: forest biomass and production. Ecological Monographs, 44, pp. 233–254.