Forest woody biomass classification with satellite-based radar coherence over 900 000 km2 in Central Siberia

Forest woody biomass classification with satellite-based

radar coherence over 900 000 km2 in Central Siberia

David L.A. Gaveaua,*, Heiko Balztera, Stephen Plummerb

aCentre for Ecology and Hydrology, Monks Wood, Huntingdon, Cambridgeshire PE28 2LS, UKbESA/ESRIN, Via Galileo Galilei, Casella Postale 64, 00044 Frascati, Italy

Received 23 March 2001; received in revised form 20 September 2001; accepted 18 December 2001

Abstract

In the current context of global deforestation and global warming, a wide range of organisations, with local to international

remits, need estimates of forest biomass to assess the state of the World’s forests and their rate of change. The task would be

impossible without space-based Earth observation, which allows the rapid generation of extensive data sets describing land surface

properties. It is the task of remote sensing scientists to interpret these data into a meaningful source of forest information. Here, a fast

and easily automated method for classifying boreal forests in terms of growing stock volume is presented. The work was conducted

as part of the SIBERIA project, which has resulted in the recent publication of a map of forest growing stock volume covering

900 000 km2 in Central Siberia. The paper describes the use of satellite-based radar coherence to differentiate categories of forest

growing stock volume, the application of this method to classify and map Central Siberian forests, and the characterisation of the

forest classes to help in the interpretation. A list of acronyms and abbreviations used in the text is provided in Appendix A.

# 2002 Elsevier Science B.V. All rights reserved.

Keywords: Coherence; Forest growing stock volume; SAR interferometry; Forest classification

1. Introduction

The forests of Russia cover 7.5–7.7 million km2

and represent roughly 20% of the World’s forested

areas (Krankina and Dixon, 1992; FAO, 1999). Eco-

logically, Siberian forests are regarded as a critical

stabiliser of the planetary climate in maintaining

atmospheric gas balance (Bonan et al., 1992) and

are the habitat to a wide variety of plants and animals,

many of which are rare and/or endemic (WWF, 1994).

At the same time, Russia is going through major

political, social and economic transformations and

these forest resources could be used to help revitalise

the Russian economy. Nilsson and Shvidenko (1997)

comment that the world has seen too many forests used

as a short-term cash crop, without regard for the long-

term economic, environmental and social conse-

quences. The challenge is to avoid the same in Russia.

The SAR Imaging for Boreal Ecology and Radar

Interferometry Applications (SIBERIA) project was

initiated in this context (Schmullius and Rosenqvist,

1997).

Up-to-date information on forest status in the Cen-

tral Siberian region (Fig. 1) was not generally avail-

able or comprehensive until the SIBERIA project

published the first large-scale (900 000 km2) ‘forest

growing stock volume’ map of Central Siberia (Fig. 2).

The map was completed in October 2000 both as a

Forest Ecology and Management 174 (2003) 65–75

* Corresponding author. Tel.: þ44-1487-772-483;

fax: þ44-1487-773-467.

E-mail address: [email protected] (D.L.A. Gaveau).

0378-1127/02/$ – see front matter # 2002 Elsevier Science B.V. All rights reserved.

PII: S 0 3 7 8 - 1 1 2 7 ( 0 2 ) 0 0 0 2 8 - 2

digital data product, and also printed as 123 map

sheets on a scale of 1:200 000. These printed map

sheets will serve those Russian Forest Enterprises

without GIS capabilities, as background information

for sustainable forest management. The map also

provides a baseline for an accurate assessment of

future rates of change of growing stock volume (a

proxy to woody biomass) required to quantify the

amount of carbon either released into or absorbed

from the atmosphere (Nilsson and Shvidenko, 1997).

In SIBERIA, JERS-1 and ERS-1/2 SAR images

were used because microwaves offer the possibility to

‘see’ through clouds (Gerstl, 1990) and are more

directly responsive to differences in woody biomass

than from the optical spectrum (i.e. visible and near-

infrared). The satellites provided three types of ima-

gery: two ‘backscatter’ image types at two different

electromagnetic wavelengths, C-band (ERS-1/2) and

L-band (JERS-1), and a ‘coherence’ image, which is

derived by SAR interferometry (Wegmuller and Wer-

ner, 1995). The backscattering variables characterise

the dielectric properties of the imaged land surface.

Coherence measures the degree of correlation between

two radar images of the land surface. These images

were acquired during the ESA Tandem mission by

ERS-1 and ERS-2 from slightly different viewing

geometries and at a 1-day time lag. By comparing

the state of development in space and time of the

periodic electromagnetic fields captured by both

images through an interferometric average, the degree

of correlation (coherence) could be computed. The

apparent change in the separation between two objects

(e.g. two branches) when viewed from different posi-

tions and at different times introduces a random

parameter in the averaging process, which affects

the degree of correlation. As the random fluctuations

Fig. 1. Central Siberia region classified under the SIBERIA project. The grey areas represent 13 territories (comprising 50 test areas), for

which field-based forest inventory data were available.

66 D.L.A. Gaveau et al. / Forest Ecology and Management 174 (2003) 65–75

increase, which correspond to an increasing number

and height of vegetation elements (leaves, branches,

etc.) mainly because of wind-induced movements in

the canopy, the coherence decreases within a range

from 1 to 0. Occasionally, an unusual event (e.g.

landslide, flood, ploughing activities) happening

between the two ERS-1/2 acquisition dates will make

both electromagnetic signals decorrelate strongly over

any land surface so that coherence loses the ability to

differentiate between forest and non-forest. These

sources of error are ignored in this study because they

are unlikely to occur within such a short time lag

(24 h).

Detailed field information on forest status from 50

test sites dispersed across Central Siberia (Fig. 1)

revealed that coherence images were far more sensi-

tive to changes in forest woody biomass (expressed

in growing stock volume) than the C- and L-band

Fig. 2. Overview of the published map of forest growing stock volume covering 900 000 km2 in Central Siberia. Copyright: EC ENV4-CT97-

0743-SIBERIA; A# ESA 97/98, NASDA GBFM, DLR.

D.L.A. Gaveau et al. / Forest Ecology and Management 174 (2003) 65–75 67

backscatter images. Similar conclusions were reached

by Hyyppa et al. (2000) in a study comparing the

accuracies of various remote sensing techniques to

retrieve forest stand attributes. Therefore the pub-

lished map of forest growing stock volume presented

in Fig. 2 was successfully generated because of the

availability of coherence images over Central Siberia.

In contrast, L-band backscatter images were used

chiefly to map water bodies (lakes and rivers) although

they too contributed to a lesser extent to forest growing

stock volume mapping. An illustration of the coher-

ence image responsiveness to changes in woody bio-

mass is given in Fig. 3.

Here, a novel classification method based on coher-

ence images only, which maps Siberian forests into

classes of growing stock volume is presented. The

method is fast and easily automated because it avoids

co-registration issues between ERS-1/2 and JERS-1

images and reduces the data volume. It is applicable in

principle to the entire ESA ERS-1/2 Tandem data

archive. It only works in areas of low topography.

2. Classification of woody biomass in Siberia

2.1. Study area

The central-east Siberian economic region covers

an area of 4.1 million km2. It has a population of

10 million inhabitants of which 7 million are urban

and 3 million are rural citizens. Most of the land is

therefore sparsely populated (0.02 inhabitant ha�1).

The 900 000 km2 area classified under the SIBERIA

project is a mosaic of taiga forest (pine, spruce, fir,

Fig. 3. A coherence image example for a small area (33 � 34 km2) in the Bolshyamurta territory (578N, 928E). The white patches reveal the

presence of clear-cuts having rectangular shapes. The black patches indicate the presence of dense forest. The grey patches represent forested

areas of intermediate density.


larch, cedar, birch and aspen), wetland, open areas and

rivers. Agricultural land is predominantly seen around

the cities of Krasnoyarsk, Irkutsk and Bratsk. The

overall topography is relatively low except in the

southern part approaching the Mongolian border.

2.2. SAR interferometry data

The Tandem mission during which ERS-1 and ERS-

2 imaged the same area in a 1-day repeat mode

was operated over Central Siberia in autumn 1997.

The data were acquired with a mobile receiving

station of DLR in Ulaanbaatar, Mongolia. A total of

366 ERS 1-day coherence images were processed by

DLR covering an area of 90 426 512 ha. Image co-

registration was achieved within 0.05–0.3 pixel accu-

racy depending on topography. The common band

filtering algorithm (Gatelli et al., 1994) was applied

and an interferometric window size of 20 � 4 pixels

was used to maximise the visual contrast between

forest and non-forest. The final coherence images

were geo-corrected and re-sampled to a 50 � 50 m2

pixel size.

2.3. Ground reference data

Field information on forest status in Central Siberia

was provided by IIASA. The forest inventory data set

comprised 50 test areas (each covering between

20 000 and 100 000 ha) grouped under 13 territories

scattered across the study area (Fig. 1). These GIS data

were polygon-based. Each polygon had a set of attri-

butes: land category, relative stocking, growing stock

volume, age, species composition, average height of

stand and average trunk diameter at breast height

(DBH) of stand. The attribute data were derived from

in situ surveys extrapolated to larger areas, using

1:10 000 or 1:20 000 aerial photography. The average

DBH of stand was calculated as the quadratic mean of

DBH of all living trees in a stand. The average height

of stand, being the average height of all trees of the

dominant species was calculated by a regression

curve, which is a function of DBH (Husch et al.,

1993). In the GIS, growing stock volume was recorded

as rounded integer values in steps of 5–20 m3 ha�1,

and in steps of 10 m3 ha�1 for greater values. The

data rarely exceeded 400 m3 ha�1. The forest cate-

gories (forest classes) required by the Russian Forest

Enterprises and by the scientific community were

expressed in growing stock volume and are given in

Table 1.

2.4. Classification strategy

To convert the coherence data into categories of

growing stock volume, the following strategy was

adopted:

1. Extraction of coherence statistics over areas where

ground reference measurements provided infor-

mation on growing stock volume.

2. Extrapolation of the coherence statistics extracted

in (1) providing a classification of the study area

into categories of growing stock volume (i.e. into

classes of growing stock volume).

3. Assessment of the quality of the classes derived in

(2) by comparing them with ground-based esti-

mates of growing stock volume measured over

different areas from those used in (1) to ensure the

independence of the assessment.

2.4.1. GIS pre-processing and image co-registration

To enable the extraction of the coherence statistics

over areas where ground-based estimates of growing

stock volume were available, the forest inventory data

were incorporated in the coherence images. In total 16

test areas of known forest status grouped under five

territories were registered to their respective coher-

ence image by using ground control points. The co-

registration accuracy was better than one pixel and the

boundary area of every polygon was eroded by one

pixel to further minimise misregistration errors.

The territories were selected for their low topogra-

phy to remove the dependence of coherence on slope

Table 1

Desired forest classes expressed in growing stock volume

Forest classes (m3 ha�1)

0 (bare soil)

0 (sparse shrub)

1–20

21–50

51–80

81–130

131–200

>200


(Lee and Liu, 2001). They were dispersed across the

study area in order to quantify the spatial variability of

the coherence statistics for a given class of growing

stock volume.

Since recent clear-cuts were easily identified in a

coherence image, all the polygons of ground reference

were removed through visual inspection where log-

ging had occurred between the inventory update and

the radar acquisition dates (Fig. 4).

To meet the requirements of the Russian Forest

Enterprises and the scientific community (Table 1), the

polygons in the GIS were aggregated into eight groups

of growing stock volume values (m3 ha�1): bare soil,

sparse shrub, 1–20, 21–50, 51–80, 81–130, 131–200

and >200 m3 ha�1.

2.4.2. Coherence signatures

The extracted coherence statistic results are pre-

sented in Table 2. Table 2 shows a decrease in the

coherence parameter (in a range from 1 to 0) with

increasing growing stock volume at the five territories

of known field-based forest status. The mean of the

Gaussian distributed coherence histogram, jgj ranges

from 0.83 to 0.86 over bare soil to 0.26 to 0.40 over the

highest growing stock volume class (>200 m3 ha�1). It

reaches a low of 0.15 over lakes and rivers because the

water surface is constantly moving. The standard

deviation, stdðjgjÞ is smaller over bare soil and rivers

(0.03–0.06) than over forests (0.11–0.18). This is

because forest stands are more heterogeneous (e.g.

greater spatial variation in height) than the bare soil

and river surfaces. The same explains the higher

variability in the mean coherence, jgj of forest stands

across the study area (e.g. 0.35–0.61 for 81–

130 m3 ha�1).

2.4.3. Generalised coherence signatures and

classification

The variability of the coherence statistics across the

study area was removed by weighted averaging the

coherence signatures of Table 2, thus generating a

generalised set of coherence signatures (Table 3). This

generalised set was used in a Gaussian maximum

likelihood classification (MLC) algorithm (Schowen-

gerdt, 1997), thus providing a classification of the

study area into categories of growing stock volume

(i.e. into classes of growing stock volume). Plots

comparing the GIS growing stock volume polygons

versus the identified eight forest classes were obtained

at three independent territories (eight new test areas)

dispersed across the region (Fig. 5). The clusters

derived from the classification correspond to forest

growing stock volume (Fig. 5) but the separation

between the classes 21–50 and 51–80 m3 ha�1 and

between those above 80 m3 ha�1 is poor, which means

that in practice only four classes of growing stock

volume can be identified: bare soil/sparse shrub, 1–20,

21–80 and >80 m3 ha�1. A classification example for

Fig. 4. A coherence image example for a small area in the Ust-Ilimsk territory (588N, 1038E) and corresponding field-based growing stock

volume information. Note the presence of new clear-cuts in the coherence image (bright rectangular patches) not yet identified in the field-

based inventory data (black rectangular patches) showing evidence of logging activities between 1991 and 1997.


a small area in the Chunsky territory based on the

generalised coherence signatures (Table 3) is given in

Fig. 6.

2.4.4. Quality assessment

A commonly used tool for assessing the quality of a

map produced from remotely sensed images is the

confusion matrix (Aronoff, 1982; Foody, 1992). This

matrix is obtained by calculating the area of correspon-

dence between the growing stock volume polygons

of forest inventory data and the classified categories

of growing stock volume. The most intuitive statistic

from the confusion matrix is the overall accuracy, p0,

which is the total area (expressed in percentage pixels)

for which the growing stock volume polygons of forest

inventory data match the classified categories of grow-

ing stock volume. However, p0 depends on the number

of classes and the expected chance agreement, which

makes it impossible to compare p0 values from dif-

ferent classifications. The kappa coefficient, k was

developed by Cohen (1960) to correct for the expected

chance agreement. A k ¼ 0 indicates a pure chance

agreement and a k ¼ 1 indicates a perfect agreement.

For ranked classes like the growing stock volume

classes, a modified k coefficient, k0 was used. k0 is

weighted by the seriousness of the classification error

so that classifying a pixel of class bare soil/sparse

shrub in the forest inventory data as 1–20 m3 ha�1 is

less serious than classifying it as 21–50 m3 ha�1. This

choice was justified by the important overlap between

two adjacent classes (Fig. 5).

Three new territories of known field-based forest

inventory data (Ust-Ilimsk, Ulkansky and Hrebtovsky)

with low topography and dispersed across the study

area (Fig. 1) were selected for an independent assess-

ment of the coherence-based classification method.

Table 2

Coherence signatures (mean and standard deviation) extracted at five territories of known field-based forest status

Territories

Bolshyamurta Primorsky Chunsky A Chunsky B Nishne

gj j � stdðjgjÞForest classes (m3 ha�1)

0 (bare soil) 0.83 � 0.06 0.86 � 0.03 No data 0.85 � 0.05 0.84 � 0.04

0 (sparse shrub) 0.80 � 0.05 0.76 � 0.05 0.80 � 0.04 0.82 � 0.04 No data

1–20 0.54 � 0.14 0.68 � 0.11 0.61 � 0.15 No data 0.80 � 0.09

21–50 0.49 � 0.14 0.61 � 0.11 0.58 � 0.15 0.60 � 0.11 0.63 � 0.14

51–80 0.44 � 0.14 0.54 � 0.12 0.45 � 0.18 0.54 � 0.11 0.56 � 0.12

81–130 0.35 � 0.14 0.50 � 0.13 0.41 � 0.18 0.49 � 0.11 0.61 � 0.14

131–200 0.31 � 0.12 0.39 � 0.13 0.29 � 0.14 0.43 � 0.13 0.40 � 0.14

> 200 0.30 � 0.13 0.35 � 0.12 0.26 � 0.12 0.40 � 0.13 0.32 � 0.11

Lake/river 0.15 � 0.06 0.15 � 0.06 No data 0.15 � 0.06 No data

Acquisition dates (1997) 25 September 9 October 5 October 8 October 26 September

26 September 10 October 6 October 9 October 27 September

Baseline, B? (m) 219.9 180.3 230 187.4 227.2

Tmin � Tmax (8C) 4–15 5–10 0–8 0–8 3–16

Rainfall (mm) up to 4 days before 6 0 No data No data 6

Forest inventory update 1998 1996 1998 1998 1997

Ground data location 578N, 928E 568N, 1028E 588N, 958E 588N, 968E 558N, 1008E

Table 3

Generalised coherence signatures

Forest classes (m3 ha�1) jgj � stdðjgjÞ

0 (bare soil) 0.85 � 0.04

0 (sparse shrub) 0.79 � 0.05

1–20 0.68 � 0.13

21–50 0.53 � 0.13

51–80 0.45 � 0.13

81–130 0.40 � 0.13

131–200 0.33 � 0.13

>200 0.29 � 0.12


Fig. 5. Plots comparing the GIS growing stock volume polygons (at three independent territories) versus eight classes of growing stock

volume derived from the generalised coherence statistics for (a) Ust-Ilimsk, (b) Ulkansky and (c) Hrebtovsky. The box boundaries are the 25-

and 75-percentiles, the line in the box is the median and the whiskers are the extreme values or 1.5 times the interquartile range whichever is

the lowest. The lines outside the whiskers are outliers.

Fig. 6. A classification example based on the generalised coherence statistics for a small area (33 � 33 km2) in the Chunsky territory (588N,

958E). The presence of clear-cuts having rectangular shapes and road access reveal a characteristic logging pattern.


The overall accuracy, p0 and the weighted k coeffi-

cient, k0 were calculated for four classes of growing

stock volume (bare soil/sparse shrub, 1–20, 21–80 and

>80 m3 ha�1). Results are given in Table 4.

2.5. Forest class characterisation

In the literature on forest mapping by remote sen-

sing, there is usually confusion regarding what con-

stitutes ‘forest’ (Singh, 1988). Lack of an accurate

definition of the forest classes of a given map can

result in selective interpretation of the map and can

therefore lead to erroneous conclusions. To ensure the

quality and usefulness of the forest growing stock

volume map published within the context of the

SIBERIA project (Fig. 2), the complementary forest

attributes such as average height of stand, average

DBH of stand and species composition were added to

the eight categories of forest growing stock volume

initially desired by the Russian Forest Enterprises and

the scientific community. These boreal forest statistics

were extracted from the field-based forest inventory

data set at the five territories of Table 2. The results are

shown in Table 5.

As expected, the results in Table 5 show an increase

in average height of stand and average DBH of stand

with increasing growing stock volume. The results

also reveal that the deciduous birch and aspen species

predominate in the low growing stock volume classes,

to gradually give way to the four main conifer species

(pine, spruce, larch, cedar) in the high volume classes.

This trend is due to the fact that birch and aspen are

pioneer species. The rarity of the cedar specie is

noticed.

3. Discussion

The coherence data were classified into eight

classes of growing stock volume over 16 test areas

scattered across 900 000 km2 in Central Siberia

(Table 2). In all cases, the coherence responsiveness

was to decrease within a range from 1 to 0 with

increasing growing stock volume. This demonstrates

the boreal forest woody biomass mapping capabilities

of coherence images over large areas. A generalised

Table 4

Quality assessment of the coherence-based classification method at

three independent territories dispersed across the study area (Ust-

Ilimsk, Ulkansky and Hrebtovsky)

GIS classes

(m3 ha�1)

Derived classes (m3 ha�1)

Bare soil/

sparse shrub

1–20 21–80 >80

Bare soil/

sparse shrub

9318 22716 251 1046

1–20 451 61988 23922 23738

21–80 4 25721 29702 78529

>80 1 4201 17869 248989

Class accuracy (%) 95 54 41 71

Overall accuracy,

p0 (%)

64

Weighted kappa, k0 0.69

Table 5

Complementary forest attributes of eight growing stock volume classes extracted from the field-based forest inventory database at the five

territories of Table 2

Mensurations Species composition

Growing stock

volume (m3 ha�1)

Average height

of stand (m)

Average DBH

of stand (cm)

Pine (%) Spruce (%) Larch (%) Cedar (%) Birch and

aspen (%)

0 (bare soil) 0 0 No data No data No data No data No data

0 (sparse shrub) <2.21 <1.24 No data No data No data No data No data

1–20 2.21 � 1.29 1.24 � 1.48 20 10 2 2 66

21–50 6.00 � 3.11 5.70 � 3.34 14 14 6 4 62

51–80 9.69 � 4.14 9.64 � 4.96 14 16 8 4 58

81–130 14.45 � 5.50 15.84 � 7.82 14 22 12 4 48

131–200 18.72 � 4.47 21.78 � 7.22 18 24 10 6 42

>200 21.18 � 3.72 25.84 � 6.26 28 24 12 10 26


set of coherence signatures (Table 3) was obtained

from those in Table 2 and was tested in an MLC

algorithm at three independent territories (eight test

areas) scattered across the region. Plots comparing the

GIS forest growing stock volume polygons versus

eight forest classes confirmed the validity of using

the generalised set of coherence statistics (Table 3) to

estimate woody biomass (Fig. 5). Nonetheless, the

poor separation between the classes 21–50 and 51–

80 m3 ha�1 and between those above 80 m3 ha�1

reduced the number of extracted classes to four: bare

soil/sparse shrub, 1–20, 21–80 and >80 m3 ha�1. The

important overlap between two adjacent forest classes

was caused by the high spatial heterogeneity of forest

stands giving them large standard deviation values

(0.11–0.18) and by the high variability in the mean

coherence of forest stands across the study area. This

partly explains why the published map of forest

growing stock volume (Fig. 2) failed to extract classes

beyond 80 m3 ha�1 (see legend in Fig. 2). Still, the

classes 21–50 and 51–80 m3 ha�1 were not merged in

the published map and their validity is questioned

here. The overall accuracy (i.e. the percentage of

correctly classified pixels) of the coherence-based

forest map reached a 64% agreement and the weighted

k0 ranked 0.69. Accuracy results per class gave the

best correspondence values for bare soil/sparse shrub

and >80 m3 ha�1 (95 and 71%, respectively).

Classification results obtained here only apply to

‘ERS repeat-pass 1-day’ coherence images and over

low topography areas. For the published ‘forest grow-

ing stock volume’ map, for which JERS-1 backscatter

images were also used and a topographic mask was

applied to mask out high relief areas, the overall

accuracy reached 80% and the weighted k0 ranked

0.72.

By assigning additional forest attributes to eight

growing stock volume classes initially desired

(Table 5), we have attempted to provide a more

intelligent explanation of what the growing stock

volume classes mean so that anyone using the pub-

lished map (Fig. 2) can relate those classes to other

forest map definitions. However, it is clear that this

needs a more comprehensive examination of inter-

comparability to reconcile classifications, e.g. that of

FAO (1996) that are a function of tree height and

fractional cover. This will be the focus of a further

study.

4. Conclusion

A simple and quick forest growing stock volume

classification method applicable across the whole

Central Siberian region (Fig. 1) based on 1-day coher-

ence images derived from the ESA Tandem mission is

presented. The purpose was to show the potential of

coherence with regard to forest woody biomass clas-

sification and hence explain how growing stock

volume was successfully estimated in the SIBERIA

project. The proposed method is quicker and easier to

use than the classification method used to publish the

forest growing stock volume map shown in Fig. 2 but

still produces very similar results. It is applicable in

principle to the entire ESA ERS Tandem data archive.

The only drawback is that it does not map water bodies

and, like the classification method which generated

Fig. 2, it only works in areas of low topography.

To increase the value of the published map, the

forest classes were further characterised by addition of

other forest attributes. About 110 000 ERS-1/2 pair

images covering nearly the total global land surface

were acquired during the ESA Tandem mission.

Whilst over South America and parts of Southeast

Asia, just one data pair was acquired: as many as five

or six interferometric pairs are available for Europe

and North America. Therefore, there is strong poten-

tial to develop this technique further for forest woody

biomass estimation in other parts of the world.

Acknowledgements

SIBERIA was partly funded by the Framework 4

programme of the European Commission (Contract

No. ENV4-CT97-0743-SIBERIA). ESA and NASDA

provided the ERS and JERS SAR data through data

initiatives. We wish to thank the other members of the

SIBERIA team for their contribution to the project,

namely Kevin Tansey, Adrian Luckman (University of

Wales), Christiane Schmullius, Andrea Holz, Ursula

Marschalk, Wolfgang Wagner, Jan Vietmeier (DLR),

Malcolm Davidson, Thuy Le Toan (CESBIO), Yrjo

Rauste (VTT), Jiong Jiong Yu, Shaun Quegan (Shef-

field University), Michael Gluck, Anatoly Shvidenko

(IIASA), Tazio Strozzi, Urs Wegmuller, Andreas

Wiesmann (Gamma Remote Sensing), Hans Jonsson,

Marianne Orrmalm, Roland Utsi and Torbjørn Westin


(Satellus). In addition, we wish to thank Barry Wyatt

and Robin Fuller for their useful comments and sug-

gestions while drafting this paper.

Appendix A. Glossary of acronyms and

abbreviations

backscatter electromagnetic radiation reflected

back the SAR sensor

C-band a band of microwave frequency

around 5.3 GHz (5.7 cm wavelength)

DLR German Space Agency

ERS-1 European Research Satellite 1

ERS-2 European Research Satellite 2

ESA European Space Agency

GHz gigahertz (measure of the frequency

of electromagnetic radiation)

GIS geographical information system

IIASA International Institute for Applied

Systems Analysis

JERS-1 Japanese Earth Resources Satellite 1

L-band a band of microwave frequency

around 1.25 GHz (24 cm wavelength)

MLC maximum likelihood classification

SAR synthetic aperture radar

SIBERIA SAR imaging for boreal ecology and

radar interferometry applications

References

Aronoff, S., 1982. The map accuracy report: a user’s view.

Photogrammetric Eng. Remote Sens. 48, 1309–1312.

Bonan, G.B., Pollard, D., Thompson, S.L., 1992. Effects of boreal

forest vegetation on global climate. Nature 359, 716–718.

Cohen, J., 1960. A coefficient of agreement for nominal scales. Ed.

Psychol. Measure. 20, 37–46.

FAO, 1996. Forest resources assessment 1990. FAO Forestry Paper

No. 130.

FAO, 1999. State of the world’s forests 1999. FAO Report.

Foody, G.M., 1992. On the compensation for chance agreement in

image classification accuracy assessment. Photogrammetric

Eng. Remote Sens. 58, 1459–1460.

Gatelli, F., Guarnieri, A.M., Parizzi, F., Pasquali, P., Prati, C.,

Rocca, F., 1994. The wavenumber shift in SAR interferometry.

IEEE Trans. Geosci. Remote Sens. 32, 855–865.

Gerstl, S., 1990. Physics concepts of optical and radar reflectance

signatures: a summary review. Int. J. Remote Sens. 11, 1109–

1117.

Husch, B., Miller, Ch.I., Beers, T.W., 1993. Forest Mensuration,

3rd Edition. Krieger Publishing Company, Malabar, FL, 402 pp.

Hyyppa, J., Hyyppa, H., Inkinen, M., Engdahl, M., Linko, S., Zhu,

Y., 2000. Accuracy comparison of various remote sensing data

sources in the retrieval of forest stand attributes. For. Ecol.

Mgmt. 128, 109–120.

Krankina, O.N., Dixon, R.K., 1992. Forest management in

Russia—challenges and opportunities in the era of Perestroika.

J. For. 90, 29–34.

Lee, H., Liu, J.G., 2001. Analysis of topographic decorrelation in

SAR interferometry using ratio coherence imagery. IEEE

Trans. Geosci. Remote Sens. 39, 223–232.

Nilsson, S., Shvidenko, A., 1997. The Russian forest sector. A

Position Paper for the World Commission on Forests and

Sustainable Development. WCFSD, St Petersburg, Russia, p. 66.

Schmullius, C.C., Rosenqvist, A., 1997. Closing the gap—a

Siberian boreal forest map with ERS-1/2 and JERS-1. In:

Proceedings of the Third ERS Symposium on Space at the

Service of our Environment, Florence, Italy, ESA, pp. 1885–

1890.

Schowengerdt, R.A., 1997. Models and Methods for Image

Processing. Academic Press, London.

Singh, K.D., 1988. Comments on: tropical deforestation and remote

sensing, by Norman Myers (1988). For. Ecol. Mgmt. 24, 312–

313.

Wegmuller, U., Werner, C.L., 1995. SAR interferometric signatures

of forests. IEEE Trans. Geosci. Remote Sens. 33, 1153–1161.

WWF, 1994. Conserving Russia’s biological diversity. World

Wildlife Fund Report.


Documents

Forest woody biomass classification with satellite-based radar coherence over 900 000 km2 in Central Siberia