**2. Data sources and methods**

#### **2.1 Field measurements of aboveground and foliage biomass**

Field measurements were made in Yukon and North Western Territories (Fig. 1). Aboveground biomass was measured at 43 sites in the summer of 2004 along the Dempster Highway, which goes through the winter and summer ranges of the Porcupine caribou habitat (Fig. 1). Foliage biomass was measured at 10 non-treed sites along the Dempster Highway in the summer of 2006, and again in the summer of 2008 at 11 non-treed sites in the Ivvavik National Park. The Ivvavik National Park is located at northern tip of the Yukon and overlaps with the calving ground and summer range of the Porcupine caribou herd. Details of measurement procedure, calculation method, and results are described as follows.

Fig. 1. Locations of the Porcupine caribou calving ground, summer range, and winter range, as well as that of the aboveground and foliage biomass measurement sites along the Dempster Highway transect during the summers of 2004 and 2006, and in the Ivvavik National Park during the summer of 2008.

We selected sites that were representative of local vegetation conditions, relatively homogenous, and of size at least 3 × 3 Landsat pixels (i.e., > 90 m × 90 m), to ensure that the field measurements can be reliably correlated with Landsat-scale remote sensing indices. In the north where the growing season is very short, biophysical parameters (e.g., foliage biomass) vary significantly both during the beginning and end of the growing season, but are relatively stable and achieve their maximums during the middle of the growing season. Therefore, we measured aboveground biomass and foliage biomass during middle of growing season (e.g., July 18-27, 2004, July 19-27, 2006, and July 15-26, 2008).

We used a systematic approach to sample and measure the aboveground biomass: layer by layer from top to the ground. If a site was sparsely treed woodland, we first measured tree biomass with a variable sampling plot scheme using a prism (Halliwell & Apps, 1997). The basic principle of the variable sampling plot scheme is that each tree is selected using a circular plot with a radius *r* that is proportional to its diameter at the breast height (*DBH*). The constant of proportionality is called the plot radius factor (*PRF*), and thus for tree *i* we can write:

Field measurements were made in Yukon and North Western Territories (Fig. 1). Aboveground biomass was measured at 43 sites in the summer of 2004 along the Dempster Highway, which goes through the winter and summer ranges of the Porcupine caribou habitat (Fig. 1). Foliage biomass was measured at 10 non-treed sites along the Dempster Highway in the summer of 2006, and again in the summer of 2008 at 11 non-treed sites in the Ivvavik National Park. The Ivvavik National Park is located at northern tip of the Yukon and overlaps with the calving ground and summer range of the Porcupine caribou herd. Details of measurement procedure, calculation method, and results are described as follows.

Fig. 1. Locations of the Porcupine caribou calving ground, summer range, and winter range,

We selected sites that were representative of local vegetation conditions, relatively homogenous, and of size at least 3 × 3 Landsat pixels (i.e., > 90 m × 90 m), to ensure that the field measurements can be reliably correlated with Landsat-scale remote sensing indices. In the north where the growing season is very short, biophysical parameters (e.g., foliage biomass) vary significantly both during the beginning and end of the growing season, but are relatively stable and achieve their maximums during the middle of the growing season. Therefore, we measured aboveground biomass and foliage biomass during middle of

We used a systematic approach to sample and measure the aboveground biomass: layer by layer from top to the ground. If a site was sparsely treed woodland, we first measured tree biomass with a variable sampling plot scheme using a prism (Halliwell & Apps, 1997). The basic principle of the variable sampling plot scheme is that each tree is selected using a circular plot with a radius *r* that is proportional to its diameter at the breast height (*DBH*). The constant of proportionality is called the plot radius factor (*PRF*), and thus for tree *i* we

as well as that of the aboveground and foliage biomass measurement sites along the Dempster Highway transect during the summers of 2004 and 2006, and in the Ivvavik

growing season (e.g., July 18-27, 2004, July 19-27, 2006, and July 15-26, 2008).

**2.1 Field measurements of aboveground and foliage biomass** 

Winter range

National Park during the summer of 2008.

can write:

Summer range

Calving ground

**2. Data sources and methods** 

$$PRF = \text{r}\_i \text{ / DBH}\_i \quad \text{or} \quad \text{r}\_i = PRF \times DBH\_i \tag{1}$$

with *DBH* is in centimeters and plot radius is in meters, *PRF* is in m cm-1. A tree is included or "in" if *DBHi* > *ri/PRF*, and excluded or "out" otherwise. A difficulty may arise as whether to include or exclude a tree if it's at the edge, namely, *DBHi* ≈ *ri/PRF*. If this is the case, the distance from the plot centre to the tree is measured. Using the tree's *DBH* and the appropriate PRF, this distance can then be compared to the radius of the plot as calculated by equation 1, and the tree place "in" or "out" of the plot after-the-fact. The measurements included tree height, diameter at breast height (*DBH*), and stand density. The heights of trees are recorded using a laser height measurement instrument (Impulse Forest Pro, Lasertech, Clarkston, MI, USA). Trees for height measurement are selected on basis of the point plot scheme. The value of *DBH* was measured using a tape. The stocking and biomass of the trees were then calculated on basis of allometric equations.

If tree regeneration was present, we measured the biomass of the seedlings using a fixed circular plot of radius of 3.99 m. The measurements included counting the number of stems per tree species, selecting an average-sized seedling to estimate its ground stem diameter, height, and sampling one or two average-sized seedling to measure its fresh and oven-dry weight.

For the understory layers, namely, high shrub, low shrub, herbs/graminoids, moss, and lichen, we harvested samples at five 1 m × 1 m plots to measure the total aboveground biomass: four at four directions and one random (Fig. 2). All the aboveground biomass within the plots were cut and collected into plastic bags and their green biomass weighed. Out of the five plots, we selected one representative plot to conduct an intensive sampling. For this plot, we measured the average height, visually estimate the ground cover percentage, and then cut and weighed the fresh biomass, sequentially, for tall shrub, low shrubs, herbs/graminoids, moss, and lichen. The ratio of green to oven-dry biomass was measured by bringing a sample of the aboveground biomass from the plot back to laboratory.

If a site had no trees, then we applied the aforementioned understory procedure. Note that the plot number and design of a specific site were determined on basis of site conditions: the more heterogeneous and sparse the vegetation is at a site, the more plots are needed (Chen et al., 2009b; Chen et al., 2010b). The procedure was modified for tall shrubs if the tall shrubs (higher than 50 cm) were clustered instead of homogeneous distribution. Our field experience indicated that it was often the case for tall shrubs. In this case, we measured the biomass of tall shrubs using a fixed circular plot of radius = 3.99 m. The measurements included identifying shrub species, counting number of clusters of shrub in the plot, and selecting two average-sized clusters of shrub for more extensive sampling. All aboveground biomass of the two clusters of shrub were cut and weighed for total green biomass. A fraction of the biomass was brought back to measure the ratio of oven-dry to fresh weight. If there are more than one tall shrub species, we repeated the measurements for each of them. The procedure for low shrub, herbs/graminoids, moss, and lichen remain the same.

For foliage biomass measurement during the summers of 2006 and 2008, we selected only non-treed sites. At each sites, five 1 m × 1 m plots were sampled (Fig. 3). Fig. 3 shows a photograph of the field sampling at a coastal plain site in the Ivvavik National Park during the summer of 2008. At each plot, all plants were harvested, sorted into dead and live, different species, and leaves and stem, and recorded for their fresh weights. A fraction of the harvested biomass for all components were brought back to the laboratory, oven-dried, and

Mapping Aboveground and Foliage Biomass Over the Porcupine Caribou

Habitat in Northern Yukon and Alaska Using Landsat and JERS-1/SAR Data 235

Fig. 3. Photograph showing biomass sampling at a coastal plain site in the Ivvavik National

Alexander Gordon, Jayneta Pascal & Kayla Arey), and at the background Weiroing Chen of

The aboveground oven-dry biomass of graminoids, herbs and shrubs in the lower layers, *Bg*

<sup>12345</sup> ( ) <sup>10</sup> 5 *ggggg g g bbbbb B R*

where *bg1*, *bg2, bg3*, *bg4*, and *bg5* are, respectively, the aboveground green biomass of the graminoids/herbs and shrubs mixture for each of the five plots in a site (kg m-2), and *Rg* is the ratio of oven-dry to fresh biomass for the mixture of graminoids/herbs and shrubs. The

> <sup>12345</sup> 1 2 ( ) ( ) <sup>1000</sup> 5 2 *lllll l l l l*

where *Pl*1, *Pl*2, *Pl*3, *Pl*4, and *Pl*5 are, respectively, the percentage of lich ground cover at the 5 plots, *bl*1 and *bl*<sup>2</sup> are, respectively, the fresh lichen biomass of each of the two 10-cm by 10-cm samples collected from one of the plots (kg m-2), and *Rl* is the ratio of oven-dry to fresh

> <sup>12345</sup> 1 2 ( ) ( ) <sup>1000</sup> 5 2 *mmmmm m m <sup>m</sup> m m*

where *Pm*1, *Pm*2, *Pm*3, *Pm*4, and *Pm*5 are, respectively, the percentage of moss ground cover at the 5 plots, *bm*1 and *bm*<sup>2</sup> are, respectively, the aboveground green biomass of each of the two 1-cm depth, 10 cm by 10 cm square moss samples collected from one of the plots (kg m-2), *Dm* is the depth of alive moss in cm, and *Rm* is the ratio of oven-dry to green biomass for moss. The aboveground biomass for a site, *Ba*, was thus the summation of all these

biomass for lichen. The oven-dry biomass of live moss, *Bm* in t ha-1, was calculated by

(3)

*ppppp b b B R* (4)

*ppppp b b <sup>B</sup> R D* (5)

Park, July 25, 2008. Front are 3 northern students from Aklavik, NWT (left to right:

CCRS was sampling root biomass (photo by Wenjun Chen).

oven-dry biomass of lichen, *Bl* in t ha-1, was calculated by

in t ha-1, was calculated by

components where proper.

weighed. Oven-dry foliage biomass of vascular plants were than calculated using the ratio of oven-dry to fresh foliage biomass and the corresponding fresh weight records.

Fig. 2. Plot design for measuring aboveground and foliage biomass at non-forested sites.

The tree oven-dry aboveground biomass at a woodland site was calculated using Canadawide tree biomass equations by Evert (1985) and the Weibull tree-size distribution function within a stand by Chen (2004).

In order to determine the tree-size distribution for a woodland site, the Parameter Prediction Method (PPM) based on basal area was implemented (Chen, 2004). With measured average *DBH*, height, and stand density (trees ha-1) as inputs, the tree-size distribution function gives 15 tree-size classes for each site, and output mean DBH, height, and number of trees for each class. Here, we used trembling aspen functions for hardwood species, jack pine functions for all pine species and pine-mixture, black spruce functions for all spruce and fir species, and mixed-stand function for mixed softwoods/hardwoods.

The Canada-wide biomass equations calculate the oven-dry biomass of stem, bark, and crown of a single tree for 18 Canadian tree species (Evert, 1985), with a given DBH and height. Using the information of tree-size distribution and the biomass equations, we first calculated each biomass component (stem, bark, and crown), added these components to get total aboveground biomass, and then multiplied with the number of trees in each tree-size distribution class to get the total tree biomass per hectare, *Bt*, in t ha-1.

The aboveground oven-dry biomass of clustered high shrub and regenerating trees at a site, *Bs* in t ha-1, was calculated by

$$B\_s = \frac{(b\_{s1} + b\_{s2})}{2} \frac{N\_c}{5} R\_s \tag{2}$$

where (*bs1* + *bs2*)/2 is the mean green biomass of the two clusters of shrub sampled or that of regenerating trees (kg per cluster or regenerating tree), *Nc* is the number of clusters of shrub or regenerating trees in the 3.99 m-radius plot, and *Rs* is the ratio of oven-dry to fresh biomass for shrub or the regenerating trees.

weighed. Oven-dry foliage biomass of vascular plants were than calculated using the ratio

GPS West East

**1m**

**1m**

**1m**

**1m** Random

*b bN B R* (2)

North

**1m**

30m

**1m**

South

**1m**

**1m**

Fig. 2. Plot design for measuring aboveground and foliage biomass at non-forested sites.

The tree oven-dry aboveground biomass at a woodland site was calculated using Canadawide tree biomass equations by Evert (1985) and the Weibull tree-size distribution function

In order to determine the tree-size distribution for a woodland site, the Parameter Prediction Method (PPM) based on basal area was implemented (Chen, 2004). With measured average *DBH*, height, and stand density (trees ha-1) as inputs, the tree-size distribution function gives 15 tree-size classes for each site, and output mean DBH, height, and number of trees for each class. Here, we used trembling aspen functions for hardwood species, jack pine functions for all pine species and pine-mixture, black spruce functions for all spruce and fir species, and

The Canada-wide biomass equations calculate the oven-dry biomass of stem, bark, and crown of a single tree for 18 Canadian tree species (Evert, 1985), with a given DBH and height. Using the information of tree-size distribution and the biomass equations, we first calculated each biomass component (stem, bark, and crown), added these components to get total aboveground biomass, and then multiplied with the number of trees in each tree-size

The aboveground oven-dry biomass of clustered high shrub and regenerating trees at a site,

1 2 ( ) 2 5 *ssc s s*

where (*bs1* + *bs2*)/2 is the mean green biomass of the two clusters of shrub sampled or that of regenerating trees (kg per cluster or regenerating tree), *Nc* is the number of clusters of shrub or regenerating trees in the 3.99 m-radius plot, and *Rs* is the ratio of oven-dry to fresh

of oven-dry to fresh foliage biomass and the corresponding fresh weight records.

**1m**

mixed-stand function for mixed softwoods/hardwoods.

distribution class to get the total tree biomass per hectare, *Bt*, in t ha-1.

30m

within a stand by Chen (2004).

*Bs* in t ha-1, was calculated by

biomass for shrub or the regenerating trees.

**1m**

Fig. 3. Photograph showing biomass sampling at a coastal plain site in the Ivvavik National Park, July 25, 2008. Front are 3 northern students from Aklavik, NWT (left to right: Alexander Gordon, Jayneta Pascal & Kayla Arey), and at the background Weiroing Chen of CCRS was sampling root biomass (photo by Wenjun Chen).

The aboveground oven-dry biomass of graminoids, herbs and shrubs in the lower layers, *Bg* in t ha-1, was calculated by

$$B\_{\mathcal{g}} = \frac{(b\_{\mathcal{g}1} + b\_{\mathcal{g}2} + b\_{\mathcal{g}3} + b\_{\mathcal{g}4} + b\_{\mathcal{g}5})}{5} 10R\_{\mathcal{g}} \tag{3}$$

where *bg1*, *bg2, bg3*, *bg4*, and *bg5* are, respectively, the aboveground green biomass of the graminoids/herbs and shrubs mixture for each of the five plots in a site (kg m-2), and *Rg* is the ratio of oven-dry to fresh biomass for the mixture of graminoids/herbs and shrubs. The oven-dry biomass of lichen, *Bl* in t ha-1, was calculated by

$$B\_{l} = \frac{(p\_{l1} + p\_{l2} + p\_{l3} + p\_{l4} + p\_{l5})}{5} \frac{(b\_{l1} + b\_{l2})}{2} 1000 R\_{l} \tag{4}$$

where *Pl*1, *Pl*2, *Pl*3, *Pl*4, and *Pl*5 are, respectively, the percentage of lich ground cover at the 5 plots, *bl*1 and *bl*<sup>2</sup> are, respectively, the fresh lichen biomass of each of the two 10-cm by 10-cm samples collected from one of the plots (kg m-2), and *Rl* is the ratio of oven-dry to fresh biomass for lichen. The oven-dry biomass of live moss, *Bm* in t ha-1, was calculated by

$$B\_m = \frac{\left(p\_{m1} + p\_{m2} + p\_{m3} + p\_{m4} + p\_{m5}\right)\left(b\_{m1} + b\_{m2}\right)}{5} 1000 R\_m D\_m \tag{5}$$

where *Pm*1, *Pm*2, *Pm*3, *Pm*4, and *Pm*5 are, respectively, the percentage of moss ground cover at the 5 plots, *bm*1 and *bm*<sup>2</sup> are, respectively, the aboveground green biomass of each of the two 1-cm depth, 10 cm by 10 cm square moss samples collected from one of the plots (kg m-2), *Dm* is the depth of alive moss in cm, and *Rm* is the ratio of oven-dry to green biomass for moss. The aboveground biomass for a site, *Ba*, was thus the summation of all these components where proper.

Mapping Aboveground and Foliage Biomass Over the Porcupine Caribou

foliage biomass over the Porcupine caribou habitat, as outlined in Fig. 4.

**Baseline aboveground biomass map**

sensing data to produce aboveground and foliage biomass baseline maps.

**Relationship between aboveground biomass –Landsat & JERS-1 indices**

**JERS-1 SAR summer mosaic**

**3. Results and discussions** 

**Aboveground biomass field measurements** 

Habitat in Northern Yukon and Alaska Using Landsat and JERS-1/SAR Data 237

Linder and Meuser; 1993). In this study, we used *H* = 568000 meter, *θ* =35 degree, and *β* =190 degree in SAR image simulation. Finally, the Digital Number (*DN*) values of JERS mosaic

10

The co-registered and geo-referenced Landsat TM/ETM+ and JERS-1 mosaics were then compared with measurements of aboveground and foliage biomass, with their best-fit relationships being applied back to the mosaics to produce maps of aboveground and

> **Mid-growing season Landsat mosaic**

Fig. 4. Flow chart describing the procedures of using field measurements and remote

**3.1 Measured aboveground and foliage biomass over the Porcupine caribou habitat**  Table 1 summarizes the mean value, standard deviation, and range of aboveground biomass measured at sites within and around the Porcupine caribou winter and summer range along the Dempster Highway, Yukon, during the summer of 2004. Average values of measured aboveground biomass of sparsely treed woodland, low-high shrub lands, and mixed graminoids-dwarf shrub-herb lands were, respectively, 57.3, 11.1, and 2.3 t ha-1. Within each vegetation type, the ranges of measured aboveground biomass were very large. The standard deviations of measured aboveground biomass among sites were often larger than their corresponding mean values, especially for low-high shrubs and mixed graminoidsdwarf shrub-herb, and when all types of vegetation were considered. The measured aboveground biomass ranged from 10 to 100 t ha-1 for sparsely treed woodlands, from 1 to 100 t ha-1 for the low-high shrub sites, from 0.5 to 10 t ha-1 for mixed graminoids-dwarf shrub-herb sites. These measurements indicate that there are significant overlaps in the ranges of aboveground biomass between sparsely treed woodlands and low-high shrub sites, and between low-high shrub sites and mixed graminoids-dwarf shrub-herb sites.

**Foliage biomass field measurements** 

20 lo g *DN* 48.54 (6)

**Mid-growing season Landsat images** 

**Foliage biomass – Landsat SR relationship**

**Baseline foliage biomass map**

**MODIS images** 

could be converted to backscatter coefficients σ (in db format) by the equation:

The calculation of foliage biomass, *Bf* in unit g m-2, was similar to that of aboveground biomass, except that only the foliage component of vascular plants was included.

#### **2.2 Remote sensing data sources and processing**

Nearly clear-sky Landsat TM/ETM+ level 1G orthoimagery was downloaded from the United Stated Geological Survey (USGS) website (http://earthexplorer.usgs.gov) and from the Centre for Topographic Information (CTI) of Natural Resources Canada, available through Geogratis (http://geogratis.cgdi.gc.ca/). 23 scenes are needed to cover the entire Porcupine caribou habitat. Most of them were acquired within the middle growing season (July 10 to August 15) during 1999 – 2003. Only Bands 3 (0.63–0.69 μm), 4 (0.75–0.90 μm), and 5 (1.55–1.75 μm) were used in the study, because Bands 1-3 are highly correlated, as are Bands 5 and 7. Prior to further analysis, all scenes, if necessary, were re-projected to Lambert Conformal Conic (LCC) projection with 95o W, 49o N as the true origin and 49o N and 77o N as standard parallels. Surface reflectance was derived by radiometrically normalizing each scene to 250 m resolution clear sky MODIS imagery, which was a 10-day composite acquired during July 21-31, 2001 with matching bands calibrated to a strip of atmospherically corrected Landsat TM/ETM+ images. A Landsat mosaic of surface reflectance was then generated with further normalizations for individual scenes if substantial discrepancy exists. All radiometric normalization and calibration equations were developed using a Scattergram Controlled Regression method (Elvidge et al., 1995; Yuan & Elvidge, 1996; Chen et al., 2010a). Furthermore, clouds and forested areas were removed and masked, and the mosaic was clipped based on the Porcupine caribou habitat boundary.

The JERS-1/SAR datasets were extracted from the North America JERS mosaics (acquired in the summer of 1998) to cover the Porcupine caribou habitat (Kyle McDonald, JPL, personal communication). In addition, data of 1:50000 DEM covering the habitat were also obtained for orthorectifying the JERS mosaic. The North America JERS summer mosaics were not precisely geo-referenced, because topographic distortions were not removed during the mosaic process due to the lack of adequate DEM (Sheng & Alsdorf, 2005). Visual inspection showed that the offset between the JERS mosaic and the Landsat images may be 300 m ~ 5000 m. The positioning error was expected to be much larger in mountainous regions.

In this study, we employed a SAR image simulation method from DEM data to correct topographic distortions (Sheng & Alsdof, 2005). Briefly speaking, this method includes four steps: (1) simulating a SAR image in an azimuth-range projection from the DEM according to imaging geometry of real SAR image; (2) collecting ground control points that tie the uncorrected SAR image to the simulated SAR image; (3) warping the real SAR image to the simulated SAR image using a polynomial function fitted from the ground control points; and (4) projecting the warped real SAR image back to the DEM map coordinate system. The method requires three types of inputs: individual scene of SAR imagery, DEM data, and SAR imaging geometry parameters (i.e. sensor altitude *H*, minimum look angle *θ*, the orbital azimuth angle *β*). Since the year-day file, which contains the date of specific JERS image's acquisition, was delivered with the JERS mosaics data, we could extract individual path image in the JERS mosaic. The path image was used as an input in place of individual scene of JERS. In addition, since the imaging geometry information of the individual path in the JERS mosaic was unavailable, general JERS imaging geometry parameters were used in the SAR simulation method. For the purpose of image matching for ground control point selection, a correct geometry was preferred but is not necessary (Sheng and Alsdof, 2005;

The calculation of foliage biomass, *Bf* in unit g m-2, was similar to that of aboveground

Nearly clear-sky Landsat TM/ETM+ level 1G orthoimagery was downloaded from the United Stated Geological Survey (USGS) website (http://earthexplorer.usgs.gov) and from the Centre for Topographic Information (CTI) of Natural Resources Canada, available through Geogratis (http://geogratis.cgdi.gc.ca/). 23 scenes are needed to cover the entire Porcupine caribou habitat. Most of them were acquired within the middle growing season (July 10 to August 15) during 1999 – 2003. Only Bands 3 (0.63–0.69 μm), 4 (0.75–0.90 μm), and 5 (1.55–1.75 μm) were used in the study, because Bands 1-3 are highly correlated, as are Bands 5 and 7. Prior to further analysis, all scenes, if necessary, were re-projected to Lambert Conformal Conic (LCC) projection with 95o W, 49o N as the true origin and 49o N and 77o N as standard parallels. Surface reflectance was derived by radiometrically normalizing each scene to 250 m resolution clear sky MODIS imagery, which was a 10-day composite acquired during July 21-31, 2001 with matching bands calibrated to a strip of atmospherically corrected Landsat TM/ETM+ images. A Landsat mosaic of surface reflectance was then generated with further normalizations for individual scenes if substantial discrepancy exists. All radiometric normalization and calibration equations were developed using a Scattergram Controlled Regression method (Elvidge et al., 1995; Yuan & Elvidge, 1996; Chen et al., 2010a). Furthermore, clouds and forested areas were removed and masked, and the mosaic was clipped based on the Porcupine caribou habitat boundary. The JERS-1/SAR datasets were extracted from the North America JERS mosaics (acquired in the summer of 1998) to cover the Porcupine caribou habitat (Kyle McDonald, JPL, personal communication). In addition, data of 1:50000 DEM covering the habitat were also obtained for orthorectifying the JERS mosaic. The North America JERS summer mosaics were not precisely geo-referenced, because topographic distortions were not removed during the mosaic process due to the lack of adequate DEM (Sheng & Alsdorf, 2005). Visual inspection showed that the offset between the JERS mosaic and the Landsat images may be 300 m ~ 5000 m. The positioning error was expected to be much larger in mountainous regions. In this study, we employed a SAR image simulation method from DEM data to correct topographic distortions (Sheng & Alsdof, 2005). Briefly speaking, this method includes four steps: (1) simulating a SAR image in an azimuth-range projection from the DEM according to imaging geometry of real SAR image; (2) collecting ground control points that tie the uncorrected SAR image to the simulated SAR image; (3) warping the real SAR image to the simulated SAR image using a polynomial function fitted from the ground control points; and (4) projecting the warped real SAR image back to the DEM map coordinate system. The method requires three types of inputs: individual scene of SAR imagery, DEM data, and SAR imaging geometry parameters (i.e. sensor altitude *H*, minimum look angle *θ*, the orbital azimuth angle *β*). Since the year-day file, which contains the date of specific JERS image's acquisition, was delivered with the JERS mosaics data, we could extract individual path image in the JERS mosaic. The path image was used as an input in place of individual scene of JERS. In addition, since the imaging geometry information of the individual path in the JERS mosaic was unavailable, general JERS imaging geometry parameters were used in the SAR simulation method. For the purpose of image matching for ground control point selection, a correct geometry was preferred but is not necessary (Sheng and Alsdof, 2005;

biomass, except that only the foliage component of vascular plants was included.

**2.2 Remote sensing data sources and processing** 

Linder and Meuser; 1993). In this study, we used *H* = 568000 meter, *θ* =35 degree, and *β* =190 degree in SAR image simulation. Finally, the Digital Number (*DN*) values of JERS mosaic could be converted to backscatter coefficients σ (in db format) by the equation:

$$
\sigma = 20 \times \log\_{10}{DN} - 48.54 \tag{6}
$$

The co-registered and geo-referenced Landsat TM/ETM+ and JERS-1 mosaics were then compared with measurements of aboveground and foliage biomass, with their best-fit relationships being applied back to the mosaics to produce maps of aboveground and foliage biomass over the Porcupine caribou habitat, as outlined in Fig. 4.

Fig. 4. Flow chart describing the procedures of using field measurements and remote sensing data to produce aboveground and foliage biomass baseline maps.
