Areas of rapid forest-cover change in boreal Eurasia

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Forest Ecology and Management 237 (2006) 322–334

Areas of rapid forest-cover change in boreal Eurasia

Frederic Achard a,*, Danilo Mollicone a,f, Hans-Jurgen Stibig a, Dmitry Aksenov b,Lars Laestadius c, Zengyuan Li d, Peter Popatov e, Alexey Yaroshenko e

a Joint Research Centre of the European Commission, 21020 Ispra, VA, Italyb Socio Ecological Union, 41 Vavilova St., Moscow 117312, Russia

c World Resources Institute, 10 G. St. N.E., Washington, DC 20002, USAd Chinese Academy of Forestry, Wan Shou Shan Hou, Beijing 100091, China

e Greenpeace Russia, 6 Novaya Bashilovka St., Moscow 101428, Russiaf Max Planck Institute for Biogeochemistry, Jena 07745, Germany

Received 13 April 2006; received in revised form 28 July 2006; accepted 28 September 2006

Abstract

Changes in forest cover have become recognized as an important global environmental issue. This article presents a synthesis of what is known

about areas of rapid forest-cover change in boreal Eurasia from the end of the 1990s, based on data compiled from expert opinion and remote

sensing data.

The broad geographic patterns of rapid forest-cover change have been mapped at regional scale with the characterization of the main causes and

drivers of these changes. Around 40 million ha of rapid change areas with clear-cutting activities and 70 million ha with increased fire frequency

are depicted.

The main processes of rapid forest-cover changes in boreal Eurasia are logging and increase of fire frequency. Ancillary processes are forest

conversion for built over area or dam construction, forest regrowths and conversion of bogs. This study revealed that human influence on the

Russian forest landscape has been growing over recent decades, mainly as a consequence of logging activities and human-induced fires, with in

particular: (i) clear cuts; (ii) high intensity selective logging; (iii) increased fire frequency. Rapid land-cover change is not randomly or uniformly

distributed but is clustered in some locations, e.g., high intensity logging mostly takes place in the European part of Russia (e.g., in the Karelian

Isthmus) and along the Southern border of the Taiga. Except for forest regrowths on abandoned agricultural land in the southern Taiga, all other

processes lead to the decrease of forest cover or to its degradation. Logging activities are driven by regular timber harvesting and irregular cutting

for public revenue or individual profit in response to growing demand in national and international markets, particularly in China and Japan. Forest

degradation in Siberia, mostly related to increase of fire frequency and development of logging activities, is extending rapidly.

Annual forest-cover change rates in areas identified as rapid change areas may range from 0.26% for diffuse logging activities to around 0.65%

for areas affected by intense clear-cutting activities, up to 2.3% for areas affected by fires or a combination of fires and logging.

While this approach does not directly lead to quantified estimates of forest-cover changes, it highlights those areas where intensive monitoring

would be devised for an improved estimation of the changes at continental scale.

# 2006 Elsevier B.V. All rights reserved.

Keywords: Forest-cover change; Eurasia; Boreal forest; Remote sensing; Change processes

1. Introduction

Changes in forest cover and in the way people use forests have

become recognized over the last years as an important global

environmental issue in their own right (Rudel et al., 2005). They

are also connected with other global environmental issues, such

* Corresponding author at: CCR/TP 440, I-21020 Ispra, VA, Italy.

Tel.: +39 0332 785545; fax: +39 0332 789073.

E-mail address: [email protected] (F. Achard).

0378-1127/$ – see front matter # 2006 Elsevier B.V. All rights reserved.

doi:10.1016/j.foreco.2006.09.080

as climate change and carbon cycle (Feddema et al., 2005), loss

of biodiversity and sustainability of agriculture (and forestry).

The international scientific community has created new

interdisciplinary research programs to understand the multiple

causes and consequences of land-cover and land-use change

(Lambin et al., 2003) when there has been a concomitant rapid

expansion in the availability of data and information at global

level (Ramankutty et al., 2006).

Covering 32% of all forest area, boreal forests are one of the

last relatively intact terrestrial biomes and are a critical carbon

sink in global climate dynamics. Boreal Eurasia and in

mailto:[email protected]

http://dx.doi.org/10.1016/j.foreco.2006.09.080

F. Achard et al. / Forest Ecology and Management 237 (2006) 322–334 323

particular Russia are endowed with abundant forest resources

(Hytteborn et al., 2005). With 808.8 million ha of ‘forest’ area

and 74.2 of ‘other wooded land’ area in 2005 (FAO, 2006)

Russia alone accounts for over 22% of the world’s forested area

and for 21% of the world’s estimated standing timber volume.

Of the total forested area in Russia, 78% is in Siberia and the

Far East and 22% is in European Russia. From these

809 million ha ‘forest’ area, 77% are designed primarily for

a production function and 11% for a multi-purpose function

(FAO, 2006). However, there has not yet been a systematic

examination using regional observations of the status and trends

in forest-cover change in boreal Eurasia.

There is also an obvious need for good inventory data and

statistics about forest-cover change at sub-national, national,

and international scales. Remote sensing has an important

contribution to make in documenting the actual change in forest

cover on regional and global spatial scales with examples over

the Tropics from the mid 1970s (DeFries et al., 2002; FAO,

2001) and for the humid Tropics from the early 1990s (Achard

et al., 2002). Deforestation ‘‘hot spots’’ in the humid Tropics

were identified with the involvement of around 30 experts

(Achard et al., 1998), this exercise being the first step for the

determination of deforestation rates convening a network of

partners in the tropical countries. There is a need to develop a

similar monitoring system for the boreal ecosystems of the

Eurasian continent. In the long term, we hope to measure

change in forest cover in Eurasia and to understand how this

change impacts on forest sustainability and on climate change

through facilitating carbon emissions. This new monitoring

system should benefit from the latest technological advances in

earth observation. While developing a monitoring method for

boreal forests we followed two steps.

Although information is not complete, several products are

now available that depict the land or forest cover in the last few

years, in particular the forest map of USSR (Isaev, 1990), the

land-cover map for the year of 2000 at a spatial resolution of 1 km

(Bartalev et al., 2003) and the ‘‘Atlas of Russia’s intact forest

landscapes’’ which was generated at 1:500,000 scale (Yar-

oshenko et al., 2001; Aksenov et al., 2002; Greenpeace, 2006).

There are also a number of studies and resultant databases of

ecosystem disturbances in particular in Russia: the Atlas of intact

forest landscapes excludes areas with evidence of significant

human influence (Aksenov et al., 2002) or databases of burned

areas (Sukhinin et al., 2004; Mouillot and Field, 2005).

In addition to the scientific needs for a systematic

documentation of changes in land-cover, there is a need to

integrate such heterogeneous data sources (Lepers et al., 2005).

2. Objectives

The primary objective of this study is to identify areas of

rapid forest-cover change in the boreal forests of Eurasia, i.e.,

areas of significant forest-cover change caused by logging,

fires, mining or other activities, and to get a first indication of

the intensity of these changes.

To reach this objective an expert consultation was held to

collectively identify areas of forest-cover change in the whole

boreal zone of the Eurasian continent. The group of

researchers agreed to produce the best possible current

synthesis of documented change over the last few years (late

1990s to early 2000s). The basic task of the group was to

locate (on a continental map) areas of current or impending

rapid forest-cover change in boreal Eurasia and to character-

ize the main drivers. This consultation was an important step

to produce a reference data set of identified areas of recent and

significant forest-cover change. Keeping in mind the

continental scale of the approach, the expert consultation

was also expected: (i) to agree on characteristics for those

changes that should be measured and (ii) to state whether this

would be possible with present satellite Earth observation

techniques.

Subsequent to the expert consultation, there has been an

extensive review and consultation process throughout the

scientific community, to review the judgments of the

participants. This consultation led to the modification of a

few areas of rapid change, and refinement of the methodology

and terminology. Most importantly, it helped to corroborate and

document reported areas of rapid change. In addition we tried to

validate the results through the interpretation of high-resolution

satellite imagery over six sites and to give an first indication of

the observed rates of changes.

Finally, we have attempted to use this process to bring forth

priorities for future observations and research.

3. Method, concepts and data

3.1. Method

A few main tasks were carried out: identification and

delineation of areas of rapid forest-cover change; description of

change processes; validation of the map through quantification

of the changes in a few validation sites. The first objective was

to locate and delineate areas of recent forest-cover changes on a

map at 1:4 million scale over the whole Eurasian boreal

domain. The second objective was to describe the forest change

processes for each rapid change area, providing the following

information: (i) the main type of change or disturbance (direct

cause), e.g., logging, fire, construction of infrastructure,

mining, clearing for agriculture; (ii) a qualitative estimate of

the rate of change; (iii) the underlying drivers and disturbance

factors (indirect causes) (Geist et al., 2006). A few main

categories of rapid change areas were tentatively identified at

the start of the study:

� T
ype 1. Clear-cutting forest exploitation, followed by natural
regeneration or replanting;

� T
ype 2. Forest exploitation through intense selective logging;
� T
ype 3. Forest degradation due to an increase in fire
frequency; the fire activity is strongly influenced by four

factors: weather/climate, fuels, ignition agents and humans

(Flannigan et al., 2005);

� T
ype 4. Other processes of conversion to non-forest areas
(natural resources exploitation, agriculture or urbanization)

or forest degradation (grazing);

F. Achard et al. / Forest Ecology and Management 237 (2006) 322–334324

� T
ype 5. Conversion of non-forest land to forest land, e.g.,
spontaneous forest regeneration after abandonment of

agriculture land.

Information on potential change indicators was also

compiled during the consultation to evaluate if such indicators

would be retrievable from satellite imagery in a future phase.

We did not attempt to address other questions for which data

sources are more limited, including where which locations are

experiencing a severe impact on ecosystem services even

though the extent of land-cover change might be small, or

where ecosystem services are particularly vulnerable to future

change.

3.2. Definitions

For the purposes of this study, areas of rapid forest-cover

change are intended to represent areas where significant forest

change occurs, leading to a process of degradation, i.e., a

temporary or permanent deterioration in the density or structure

of vegetation cover or its species composition, and thus to a

lower productive capacity of the forest. We consider areas of

significant forest change as areas undergoing change in the

forest cover at rates of 1% or more per year for fires and 0.5% or

more per year for logging or other activities. The change rate

thresholds were set by considering the average annual rate of

fires (i.e., burned forest areas) at 0.5% during the period 1996–

2002 in Russia (Sukhinin et al., 2004) and the average annual

rates of removal of growing stock at 0.2% for the Russian

Federation (FAO, 2006). In our study, the actual rates are

considered in relation to total forest area without taking into

account ‘‘productive’’ forests (i.e., excluding protected and non

productive forests). If rates would be estimated in reference to

productive forests, figures will be much higher.

The study focused on ongoing changes within the last 5

years (i.e., between 2000 and 2005). Changes, which are

expected to occur in the coming 3–5 years were also considered

when possible. Areas with diffused forest-cover change (over

the whole territory), such as in Sweden, Finland and parts of

Russia, were usually not considered as areas of rapid forest-

cover change. Deforestation occurs generally on very limited

area extent when forest is converted to another land-cover, e.g.,

converted for mining or urban expansion.

3.3. Data used

The map of the areas of rapid forest-cover change is mainly

based on expert opinion gathered through formal procedures

(Achard et al., 2005). This method does not allow measuring

directly forest-cover changes.

A continental mosaic of satellite imagery was used as

background layer for supporting the delineation of areas of

current or impending rapid forest-cover change. The mosaic

was based on satellite data at 1 km resolution from the

VEGETATION sensor onboard SPOT-4 (Bartalev et al., 2001)

acquired in 1999 and was printed at 1:4 million scale. Omission

errors may occur because some areas that experienced actual

forest-cover change may be too small to be delineated at this

coarse scale (i.e., <1000 km2). Other types of coarse scale data

sources such as remote sensing-based products (Isaev, 1990;

Aksenov et al., 2002; Bartalev et al., 2003) and sub-national

statistics (Zamolodchikov, 2005) were used as complementary

information. The information based on sub-national statistics

provides average annual rates of logging but was only

considered as secondary to the other sources because of their

coarse regional scale, including very large regions (e.g.,

Yakutia Republic or Krasnoyarskiy krai), which are spatially

heterogeneous for forest-cover change patterns (Zamolodchi-

kov, 2005).

For the validation of our results, we use independent change

assessments for a few sites, over a period covering the last few

years (from mid or late 1990s up to early 2000s). Forest-cover

changes were estimated in these sites using fine spatial

resolution satellite imagery at two dates: Landsat TM or ETM+

sensors at 30 m resolution or ASTER sensor onboard the

TERRA platform at 15 m resolution. The number and location

of the sites were selected empirically to represent a range of

logging activities and fire processes within or along rapid

change areas across Russia. Technical issues such as

availability of recent high-resolution data and suitability of

new remote sensing products for forest-cover change detection

had to be tackled in the selection process. Within each site the

total forest area and the forest area change during the period

covered by the satellite imagery were assessed through digital

classification of the images and visual interpretation (labeling)

of the resulting pixel clusters (Bartalev and Kuryatnikova,

2005; Hlasny and Rasi, 2005; Sambale, 2005).

The change measurements made on these six local sites

allow obtaining an indicative appraisal of the consistency of the

map of rapid change areas, although not statistically valid

(Stibig et al., 2004).

4. Results

4.1. Initial automatic map of recent forest-cover changes

We first produced a map based on a comparison between an

pre-1990 forest map of USSR (Isaev, 1990) and the vegetation

map of Eurasia for the year 2000 (Bartalev et al., 2003). The

Isaev map has been compiled from local forestry maps and

information from around the 1980s. Areas of needleleaf forest

that transformed to broadleaf forests were identified. Forest

type changes due to fires were excluded based on the visual

assessment of Landsat TM imagery of the 1990s. The resulting

map (Fig. 1) shows locations where non-natural forest type

changes did probably occurred during the last 10–20 years,

mainly replacing old-stage coniferous forests (green) by

deciduous forests regrowth (red).

4.2. Overview and location of rapid change areas

The results of the delineation of areas of rapid forest-cover

change were stored as a unique digital spatial database (Achard

et al., 2005). The broad geographic patterns of forest-cover

Fig. 1. Map of forest-type change during last 20 years in Russia. This indicative map shows large-scale non-natural forest type changes (in red), which occurred

during the last 10–20 years, mainly replacing coniferous forests by regrowths of deciduous forests. Forests, which did not change type (mainly coniferous) are

displayed in green (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of the article.).


change can be inferred from the resulting map (Fig. 2). The

change processes dominating in each rapid change area are

described with their main descriptors: location name, direct

causes, characteristics, drivers and factors (Annex Table 1).

Forest degradation in Eurasia, mostly related to unsustainable

logging activities or increase in fire frequency, has been

growing over the recent years. In total we depicted around

40 million ha of rapid change areas with clear-cutting

activities and 70 million ha with increased frequency of fires

(Table 1).

A general stratification of the rapid change areas into eight

main groups or strata was agreed by the experts and described

through the geographical zoning, the type and the cause of the

changes:

1. lo
gging in the Taiga;
2. in
tensive logging on the Karelian Isthmus;
3. lo
gging along the Southern border of the Taiga and in the
‘Birch belt’;

4. f
orest conversion for built over area;
5. r
egrowth of forests in the southern Taiga zone;
6. d
am construction sites;
7. c
onversion of bogs by removing peat and by fires;
8. in
crease of fire frequency.
4.3. Validation

In total for this validation six sites were considered: two in

Western Russia (Northern Karelia and Komi regions), two in

Central Siberia (Southern taiga of the Western Siberian

Lowlands in the Ob river basin and Northern taiga of the

Central Siberian Plateau in the Lena basin) and two in Far East

Siberia (Russian/Chinese border: Heilongjiang region/Pri-

morsky kray; Khabarovsky kray).

The annual change rates in the two sites in Western Russia,

which are covering rapid change areas affected by intense clear-

cutting activities are around 0.65% (Table 2). Two Siberian

sites (sites 3 and 6) are located at the border of rapid change

areas affected by more diffuse logging activities and their

annual change rates are corresponding to lower activity

intensities (around 0.25%). The last two Siberian sites (sites

4 and 6) are affected by fires or a combination of fires and

logging with a much higher annual change rate (at 2.3% for site

5 and 6.1% for site 4). These rates can be compared to the

average annual rates of growing stock removal at 0.2% (FAO,

2006) and to the average annual rate of fires at 0.5% (Sukhinin

et al., 2004) for the Russian Federation during the early 2000s.

These findings confirm that there are different rates of change

for logging activities in different geographical areas (0.65% in

western Russia versus 0.25% in Siberia) and that fires will

result in much higher change rates (>2%).

Considering the spatial resolution of our map of rapid

change areas and the region’s complex forest-cover change

patterns, the change rates obtained in the sites provide an

indicative appraisal of the consistency of the map of rapid

change areas. However, we did not further derive correction

factors for calibrating forest area change estimates from our

map because our validation sites do not statistically represent

the region’s forest-cover change rates as they were selected

empirically, their number is not sufficient and they are

concentrated in the rapid change areas.

5. Discussion

5.1. Processes of change

In the European parts of Russia, logging in the boreal

coniferous forest (Hytteborn et al., 2005) affects mainly

Fig. 2. Location of areas of rapid forest-cover change in Eurasia with location of validation sites. The background image is a mosaic of satellite imagery at 1 km

resolution from the VEGETATION sensor onboard SPOT-4 of the year 1999 (Bartalev et al., 2001). Figure is displayed in Albers conical equal area projection using

the red, near-infrared and short-wave infrared channels of the image mosaic. Magenta squares are displaying the six validation sites. The area numbers are referring to

annex Table 1 (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of the article.).


primary forests with clear cuts of high intensity leading to

some loss of forest cover. The Southern border of the Taiga

and the Birch belt are affected by small-scale logging over

large areas with loss of biomass as a cumulative process. The

intensive logging in the Karelian Isthmus (clear cut and

selective logging of northern European type) raises the

questions with regard to forest degradation and sustainability.

Conversion in conjunction with urban sprawl affects mainly

the Moscow and St. Petersburg regions. The conversion of

bogs, mainly located in the southwest, is usually occurring on

Table 1

Area extent per change process type (in 103 ha)

Change process Area extent

Clear cuts 39,300

High fires frequency 67,200

Intensive logging 7,800

Other processes 5,000

dried peat land and with occasional fires. There are a number

of areas where the fire frequency has increased and usually in

the context of mining. Natural forest regrowth is generally

taking place when land remains forest land in particular after

logging or fires, but there is also a phenomena of spontaneous

‘afforestation’ in the southern Taiga zone as a result of

abandoned agriculture. This afforestation is corresponding to

the overall slight increase of forest land in the Russian

federation as reported by FAO (2001) for the 1990s at

135,000 ha per year. However, the young re-growing forests

are mainly deciduous and very different from the old forests

(coniferous) that are harvested further north.

For Siberia three major groups of rapid change areas have

been identified. The first major group mainly includes logging

activities (clear cuts or high intensity selective logging). The

main factors are regular timber harvesting and irregular cutting

for public revenue or individual profit in response to growing

demand in national and international markets, particularly in

China and Japan (Mayer et al., 2005). Degradation is usually a

Table 2

Rates of forest-cover change in test sites

Test site number 1 2 3 4 5 6

Test site region Northern Kareliaa Komia Ob river basinb Lena riverb Khabarovsky krayc Heilongjiang/

Primorskyc

Dominant tree species Scots pine 70% spruce 20%

pine

Fir and spruce Larch Larch (and spruce) Mixed

coniferous

Change process Clear-cut logging Clear-cut logging

and fires

Clear-cut logging Large scale fires Fires and clear-cut

logging

Small-scale

logging

Date of first image 23 June 1990 1 June 1995 9 July 1999 10 July 2000 7 September 2002 13 July 2000

Satellite sensor path/row Landsat TM 174/16 Landsat TM 186/15 Landsat ETM+

147/20

Landsat ETM+

124/16

Landsat ETM+ 111/23 Landsat ETM+

113/28

Date of second image 16 June 2002 6 June 2002 10 March 2005 3 June 2004 5 September 2004 13 June 2004

Satellite sensor Landsat ETM+ Landsat ETM+ ASTER ASTER ASTER ASTER

Forest area (ha) 546,000 703,800 245,000 280,000 131,800 271,000

Annual change rated (%) 0.73 0.61 0.20 6.1 2.34 0.31

a Derived from Bartalev and Kuryatnikova (2005).b From Sambale (2005).c From Hlasny and Rasi (2005).d Only ‘negative’ forest-cover changes are considered here, i.e., forest cover loss due to logging activities, fires or other processes. Forest regrowth are not included.

Change rates are relative to the total forest area in historical image.


slow process over a number of years. The second group includes

large areas affected by fires when their frequency is considered to

increase in time. Another process of degradation is pest

outbreaks, which are the second factor of forest degradation

after fire. The increase of forest cover is resulting from spon-

taneous forest regrowth on abandoned agricultural land. Such

afforestation is usually happening in the southern Taiga zone.

For Sweden and Finland, it is considered that their specific

forestry management practices do not lead to any particular

area of rapid forest-cover change at the country scale, although

their average annual rates of removal of growing stock are quite

high compared to Russia, at 2.4 and 3.0%, respectively (FAO,

2006). Indeed Swedish and Finnish national statistical Forestry

information shows a distributed spreading of forestry activities

over the territories of the two countries (Stahl et al., 2003;

Peltola, 2003). The forest areas have shown little change during

the last 10 years.

Forests of northeast China are considered in this study

although these forests are cool temperate forests (Qian et al.,

2003). Two main areas of rapid forest-cover change have been

delineated although they are characterized by selective logging

of moderate intensity. The total forest cover of this region

shows an overall trend of slight increase through reforestation

in specific zones.

Table 3

Burned area estimates in Russia

Year

1997 1998 1999

Total burned areas (�103 ha) 1540a 11,500a 5400a

Burned forest areas (�103 ha) 800a 6,900a 2500a

a From Sukhinin et al. (2004) for east of the Urals.b From Schiermeier (2005).c From Goldammer et al. (2003).d Extrapolated from Sukhinin et al. (2004) and Karpachevskiy (2004).

5.2. Increase of fire frequency

Since the last Ice Age fire has been the primary disturbance

process, which organizes the physical and biological

attributes of the boreal biome, with particular impact on

the global carbon cycle (Bergeron et al., 2001; Wirth et al.,

2002; Hytteborn et al., 2005). The frequency of fires has

increased in recent years particularly in Siberia. Mouillot and

Field (2005) show that the average level of burned forests

since the 1950s in the boreal part of Russia is less than

4 million ha/year to be compared to 6.9 million ha in 1998,

7.5 million ha in 2002 and 14.5 million ha in 2003 (Table 3).

The sheer amount of forests burnt in these last years cannot be

explained by the natural dynamic of the forest ecosystem, i.e.,

the natural ecosystem fire cycle which can be as high as 520

years in the case of the central Siberian ‘‘dark taiga’’

(Mollicone et al., 2002). The increase in fire frequency is a

combination of climate anomalies and human impacts

(Mollicone et al., 2006), in particular human disturbances

caused by logging or mining (Dienes, 2004) which have a

multiplicative effect on the consequences of climate

anomalies (Achard et al., 2006). The regions most affected

by fires are located in Siberia. In 2002 and 2003, the forests

around the Lena River in the central part of the Saha Republic

2000 2001 2002 2003 2004

9700a 7600a 12,100a 22,000b n.a.

5100a 3500a 7,500a 14,500c 2900d

Fig. 3. Fire activity from years 2002 to 2005 in the forest domain of Boreal Eurasia compared to areas of rapid forest-cover change due to increase in fire frequency.

Active fires detected by the Terra satellite during the period 2002–2005 are depicted as red dots; this product is a 1 km composite of potential active fires detected by

the MODIS sensor onboard the Terra platform and synthesized over 8-days periods (Justice et al., 2002). Rapid change areas due to fire frequency increase are

displayed as yellow polygons; they correspond to fire frequency increase as considered from the late 1990s (For interpretation of the references to colour in this figure

legend, the reader is referred to the web version of the article.).


and in the river basins of Kolima and Indigirka, respectively,

were most heavily affected. In 2004 and 2005, in coincidence

with oil expansion activities (Dienes, 2004) large forest fires

has been detected in the lower part of the Taz river basin

(Fig. 3).

5.3. Potential change indicators

Fire-location information, which can be derived from

satellite Earth observation data (Sukhinin et al., 2004) is

considered as a potential change indicator. Detection of clear

cut patterns in the boreal coniferous forests of north western

Eurasia is feasible from satellite imagery of medium spatial

resolution (250 m), given that the size of the clear cut is

larger than about 15 ha (Stibig and Bucha, 2005). The use of

such spatial indicators could allow a more automated

identification of rapid change areas in future spatial modeling

exercises.

6. Conclusion

This study produced a synthesis of the best available

information on forest-cover changes at the regional scale for the

early 2000s. Areas of current or impending rapid forest-cover

change in Boreal Eurasia were located on a map and the main

drivers of change were characterized. This result is considered

as an important step to produce an improved reference data set

of identified areas of recent and significant forest-cover change

for the international scientific community (Achard et al., 2005;

Lepers et al., 2005). The map could serve as a baseline for

spatially documenting recent large-scale forest-cover change.

The patterns of rapid forest-cover change observed in this study

have been confirmed with more detailed change assessments

over a few sites. The coarse spatial resolution, however,

remains a limiting factor for local applications and for

monitoring of annual or small-scale change processes, and

consequently, as for any regional map, one should look at the

broad scale patterns. Despite limitations in the data, the

synthesis map produced should help focus attention on the areas

experiencing the most rapid forest-cover changes. Most

notably, this study revealed that:

� H
uman influence on the Russian forest landscape has been
growing over recent decades, mainly as a consequence of

logging activities and human-induced fires, with in particular:

(i) clear cuts; (ii) high intensity selective logging; (iii)

increased fire frequency.

� R
apid land-cover change is not randomly or uniformly
distributed but is clustered in some locations. For example,

high intensity logging mostly takes place in the European part

of Russia.

� A
positive trend is observed in the southern Taiga zone where
deciduous forests naturally re-grow on abandoned agricul-

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T Impact: strong

or (moderate)

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institutional factors

emen

t2

37

able A.1

Description of the change processes for each rapid change area

ID Region/location Main forest type(s) Change process

(20

Pa

h

ctive

ty)

Controlled process affecting

pristine forests

Small FCC;

small patch size

P; O; E Sustainability,

(ecosystem)

Private enterprise Regular harvesting,

sanitary felling,

road construction

h Controlled process affecting

pristine and managed forests

Small FCC;

small patch size


(ecosystem)

Private enterprise Regular harvesting

h natural

eplanting


pristine forests

Medium FCC;

small patch size


ecosystem


h natural Controlled process affecting

pristine forests

Medium FCC;

small patch size


ecosystem


h natural

eplanting



Medium FCC;

small patch size


(ecosystem)


some sanitary

felling

h natural Controlled and uncontrolled

processes affecting pristine

and managed forests

Medium FCC;

small patch size


ecosystem

Public and private

enterprises, local

population (?)

Regular harvesting,

sanitary felling,

irregular cutting


pristine forests

Large FCC;

small patch size


ecosystem



pristine forests

Medium FCC;

small patch size


ecosystem




Medium FCC;

small PS


ecosystem


06

)3

22

–3

34

32

9

rt 1: western Russia

1-1 Murmansk region: Lotta and

Yauriyoki valleys

Evergreen

coniferous forests

Clear-cut logging wit

natural regrowth; sele

logging (high intensi

1-2 Murmansk region: planned

national park, Alakurtti

Evergreen

coniferous forests


natural regrowth

1-3 Karelia republic: Pyaozero area Evergreen

coniferous forests


regrowth and some r

1-4 Karelia republic: Kestenga area Evergreen

coniferous forests


regrowth

1-5 Karelia republic: North Kalevala

planned national park

Evergreen

coniferous forests


regrowth and some r

1-6 Karelia republic: South Kalevala

planned national park

Evergreen

coniferous forests


regrowth

1-7 Karelia republic: Muesersky

district

Evergreen

coniferous forests


regrowth

1-8 Arkhangelsk region: Onega

peninsular

Evergreen

coniferous forests


regrowth

1-9 Arkhangelsk region: north

Vodlozero NP

Mixed coniferous–

broadleaf forests


regrowth

Table A.1 (Continued )

ID Region/location Main forest type(s) Change process Process characteristics F. Cover change;

patch S

T Impact: strong

or (moderate)

Drivers Economic/


1-10 Arkhangelsk region: south-east

Vodlozero

Evergreen

coniferous forests

Clear-cut logging with natural

regrowth



Medium FCC;

small patch size


ecosystem


1-11 Karelia republic: south of

Vodlozero NP

Evergreen

coniferous forests


regrowth

Controlled processes in pristine

and managed forests

Medium FC change

small patch size


ecosystem

Public and private

enterprises

Regular harvesting,

some sanitary felling

1-12 Karelia/Vologda: Andoma area Evergreen

coniferous forests


regrowth

Controlled process in pristine

forests

Medium FCC

small PS

P; E Sustainability,

ecosystem


sanitary felling

(expected)

1-13 St Petersburg region/Karelia

republic: Karelian Isthmus

Evergreen

coniferous forests

Clear-cut logging or selective

logging (high intensity)

Controlled and uncontrolled

processes in managed forests.

Small FCC;

small patch size

P; O Sustainability,

(species, soils,

ecosystem)

Public (FFS) and

private enterprise

Regular harvesting,

sanitary felling,

urban and road

construction

1-14 Arkhangelsk region: White

Sea-Kulay Upland

Evergreen

coniferous forests

Increased fire frequency,

clear-cut logging diamond

mining


(fire) processes in pristine forests

Medium FCC

large (fire) and

small PS


ecosystem

Private enterprise Industrial construction

natural resource

exploitation road

construction

1-15 Arkhangelsk reg/Komi republic:

N-Dvina and Pinega basins

Evergreen

coniferous forests


regrowth, overlogging

Controlled process in pristine

forests

Large FCC;

small patch size


ecosystem


1-16 Komi republic: Timan Range Evergreen

coniferous forests

Increased fire frequency bauxite

mining


processes (fire) in pristine forests

Small FCC;

small patch size

O; E Ecosystem,

(sustainability)

Public and private

enterprises

Industrial

construction natural

resource exploitation

road construction

1-17 Komi republic: west Yugyd-

Va national Park

Evergreen

coniferous forests


regrowth


pristine forests

Medium FCC;

small patch size

P; O Sustainability,

ecosystem


1-18 Komi republic: east Pechoro-

Ilychsky Nature Reserve

Evergreen

coniferous forests


regrowth


pristine forests

Large FCC;

small patch size


ecosystem


1-19 Perm region: western slopes of

Ural Mountains

Evergreen

coniferous forests


regrowth


pristine forests

Medium FCC;

small PS


ecosystem


1-20 Vologda region: Velikiy Ustug

district

Mixed coniferous–

broadleaf forests


regrowth

Controlled processes affecting

managed forests

Medium FCC

small patch size


ecosystem


1-21 Kirov region Mixed coniferous–

broadleaf forests


regrowth

Controlled processes in managed

forests

Medium FCC

small patch size


ecosystem


1-22 Komi Republic and

Kirov region:

upper Letka river

Evergreen

coniferous forests


regrowth

Controlled process affecting pristine

and managed forests

Small FCC;

small PS


ecosystem



broadleaf forests


regrowth


managed forests

Medium FCC;

small patch size


ecosystem



broadleaf forests


regrowth


managed forests

Medium FCC;

small patch size


ecosystem



broadleaf forests


regrowth


managed forests

Medium FCC;

small patch size


ecosystem


1-26 Komi republic:

Ust-Tsilma area

Evergreen

coniferous forests

Increased fire frequency Uncontrolled process affecting

pristine forests

Large FCC;

large patch size

P Species composition,

soils, ecosystem

Occasional fire,

mining companies

Mining (oil, gas)

1-27 Komi republic:

Usinsk area

Evergreen

coniferous forests


pristine forests

Large FCC;

large patch size

P Species composition,

soils, ecosystem

Occasional fire,

mining companies,

Mining (oil, gas)

1-28 Bashkortostan republic Evergreen

coniferous forests


regrowth, selective logging

(high intensity)


pristine forests

Large FCC;

small patch size

P; O; E Species, ecosystem,

(soils)

Public and private

enterprises, local

population

Regular harvesting,

sanitary felling,

irregular cutting

1-29 Manty-Mansiysky

autonomous district:

eastern slopes of Urals

Evergreen

coniferous forests


pristine forests

Medium FC change

large patch size

P; O Species composition,

soils, ecosystem

Public enterprise Gas pipeline

construction

1-30 Sverdlovsk region: eastern

slopes of Urals

Evergreen

coniferous forests


regrowth, increased fire frequency


processes in pristine and

managed forests

Medium FCC;

medium-small PS

P; O; E Species composition,

soils, ecosystem,

(sustainability)

Public and private

enterprises, local

population

Regular harvesting,

irregular cutting,

road and pipeline

construction

1-31 Bashkortostan republic Deciduous

broadleaf forests


regrowth and replanting, selective

logging (high intensity)



Small FCC;

small patch size

P; O; E Ecosystem, (species

composition, soils)

Public and private

enterprises, local

population

Regular harvesting,

sanitary felling,

irregular cutting

F.

Ach

ard

eta

l./Fo

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colo

gy

an

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an

ag

emen

t2

37

(20

06

)3

22

–3

34

33

0

1-32 Moscow city and suburbs Mixed coniferous–

broadleaf forests

Private construction logging

(high intensity)


processes in managed forests

Medium FC change P; O; E Species, soils,

sustainability,

ecosystem

Public and private

enterprises,

individual groups

Urban development

1-33 St. Petersburg city and

suburbs

Evergreen

coniferous forests

Private construction logging

(high intensity)



Medium FC change P; O; E Species, soils,

sustainability,

ecosystem

Public and private

enterprises,

individual groups

Urban development

1-34 Nizhniy Novgorod region:

Volga and Oka valleys

Peatland Increased fire frequency (peat fires) Uncontrolled processes affecting

managed forests and dried peatland

Medium-small

FCC; medium PS

P; E Soils, sustainability,

ecosystem,

Natural factors

(climatic), local

population

Peat mining,

agriculture

1-35 St. Petersburg region:

Tikhvinsky district



Medium-small

FCC; medium PS


ecosystem,

Natural factors

(climatic), local

population

Peat mining,

agriculture

1-36 Novgorod Region: Novgorod

and Malaya Vishera district



Medium-small

FCC; medium PS

P; E soils, sustainability,

ecosystem,

natural factors

(climatic), local

population

peat mining,

agriculture

1-37 Vologda region Peatland Increased fire frequency (peat fires) Uncontrolled processes affecting


Medium-small

FCC; medium PS


ecosystem,

Natural factors

(climatic), local

population

Peat mining,

agriculture

1-38 Tver region: Volga and

Medveditsa rivers

Peatland, mixed

coniferous–

broadleaf forests

Increased fire frequency (peat fires) Uncontrolled processes affecting


Medium-small

FCC; medium PS


ecosystem,

Natural factors

(climatic), local

population

Peat mining,

agriculture

1-39 Moskow, Ryazan and

Vladimir regions: MesFhera

area

Peatland, mixed

coniferous–

broadleaf forests

Increased fire frequency (peat fires) Uncontrolled processes affecting


Medium-small

FCC; medium PS


ecosystem,

Natural factors

(climatic), local

population

Peat mining,

agriculture

1-40 Nizhniy Novgorod region

and Chuvashia republic:

Cheboksarskoye Reservoir

Evergreen

coniferous forests

Conversion (dam construction) Controlled and uncontrolled


Small FC change O; E Public Dam construction

1-41 Perm region: Upper Kama

Reservoir

Evergreen

coniferous forests

Conversion (dam construction) Controlled processes affecting

managed forests

Medium FC change O Species composition,

soils, sustainability,

ecosystem

Public Dam construction

1-42 Bashkortostan republic: Y

umagusino Reservoir on

Belaya River

Evergreen

coniferous forests

Conversion (dam construction) Controlled processes affecting

managed forests

Medium FC change O Species composition,

soils, sustainability,

ecosystem

Public Dam construction

1-43 Yekaterinburg city and

suburbs

Evergreen

coniferous forests

Conversion (mining) Controlled processes affecting


P; O; E Soils, ecosystem Private enterprise Natural resource

exploitation

Part 2: Siberia and the far East

2-1 Yamalo-Nenetskiy

autonomous District:

North of Surgut

Spruce and Scots

Pine sparse forests


regrowth; increased fire frequency;

oil extraction


intact/pristine forests

Large FCC;

medium PS

P; O; E Soils; sustainability Public or private

enterprises large-

small FFS

Regular timber

harvesting, natural

resource exploitation,

road/pipeline construct.

2-2 Altai Kray Scots Pine forests Selective logging (moderate

intensity); increased fire

frequency

Large FCC;

medium PS

P; O; E Species composition;

soils; ecosystem

Individual groups

local population

Irregular cutting for

profit conversion

to agricultural

2-3 Altai Kray:

northern Altai

Broadleaf forests Selective logging (high intensity);

increased fire frequency; over

grazing (degradation)


processes affecting pristine and

managed forests

Large FCC;

medium PS


soils; (ecosystem)

Public or private

enterprises local

population

Irregular cutting for

profit, industrial

construction, natural


2-4 Altai Kray: near

Barnaul city

Broadleaf forests;

Scots Pine forests


regrowth, selective logging

(high intensity), increased

fire frequency

Uncontrolled process affecting

managed forests

Large FCC;

medium/small PS


(sustainability)

Large-small FFS

local populations

Sanitary felling,

irregular cutting

for profit

2-5 Novosibirsk Oblast: near

Novosibirsk city

Birch forests Selective logging (moderate

intensity), over grazing (degradation),

reforestation



Medium FCC P; O; E Species composition;

(soils); ecosystem

Public or private

enterprises local

populations

Conversion from

agriculture (collective

farm collapse)

2-6 Altai Kray/Kemerovo

Oblast: Salair ridge

Broadleaf forests;

evergreen

coniferous forests

Selective logging (high intensity),

road construction


processes

Medium FCC P; O; E (Species); soils;

sustainability

Public or private

enterprises;

large-small FFS

Regular timber

harvesting, natural


(gold mining)

F.

Ach

ard

eta

l./Fo

restE

colo

gy

an

dM

an

ag

emen

t2

37

(20

06

)3

22

–3

34

33

1

Table A.1 (Continued )

ID Region/location Main forest type(s) Change process Process characteristics F. Cover change;

patch S

T Impact: strong

or (moderate)

Drivers Economic/


2-7 Kemerovo Oblast: western

slope of Kuznetsky Alatan

Broadleaf forests Selective logging (high intensity),

industrial pollution

Uncontrolled process P; O; E (Species); soils Public or private

enterprises

(prov. development

scheme)

Regular timber

harvesting, industrial



2-8 Krasnoyarsky Kray:

Jarcevo district

Scots Pine forests Clear-cut logging with natural

regrowth



Large FCC;

large PS

P; O; E Soils; sustainability;

ecosystem

Local enterprises Regular timber

harvesting

2-9 Krasnoyarsk Kray: south of

Jenisey Range

Evergreen

coniferous forests


regrowth, road or pipeline

construction, expected fires



Small FCC;

small PS

P; O Species composition;

sustainability;

ecosystem

Private enterprise Regular timber

harvesting, industrial



2-10 Krasnoyarsk Kray: near

Angara river

Scots Pine forests;

evergreen

coniferous forests

Degradation due to pest outbreak

(Siberian moss)



Large FCC P; O; E Species composition;

soils; ecosystem

Natural factors:

climate, windfalls,

clear-cuts, burnt

2-11 Krasnoyarsk Kray/Irkutsk

Oblast: Angara

River/Ilimsky district

Scots Pine forests;

evergreen

coniferous forests


regrowth or replanting


managed forests

Large PS Sustainability Public or private

enterprises

Regular timber

harvesting

2-12 Irkutsk region:

Zhitolovskiy district

Evergreen

coniferous forests

Gas-extraction field Controlled process E (Ecosystem) Private enterprise Natural resource

exploitation,

pipeline construction

2-13 Irkutsk Oblast:

near Baykal lake

Scots Pine forests Selective logging (moderate

intensity); increased fire

frequency

Affecting managed forests Small FCC;

medium PS

(Species composition;

ecosystem)

Small FFC local

population

Survival of local

people

2-14 Buryat Republic: near

Baykal lake

Larch forests Clear-cut logging with natural

regrowth, increased fire

frequency


(fire) processes

Medium FCC;

medium PS

P; O Species composition;

(ecosystem)

Small FFS Regular timber

harvesting

2-15 Buryat Republic/Chita

Oblast

Larch forests Increased fire frequency Uncontrolled process affecting

managed forests

Medium PS O (Ecosystem) Natural factors

(climatic), local

population

Lack of control

2-16 Chita Oblast:

Chikoy Upland

Evergreen

coniferous forests;

Scots Pine forests

Clear-cut logging with

natural regrowth



Medium/small

FCC; small PS


sustainability;

ecosystem

Private enterprise Regular timber

harvesting

2-17 Amur Oblast: border

with China

Larch forests Increased fire frequency Uncontrolled process Natural factors

(climatic), local

population

Lack of control

2-18 Khabarosvk Kray/

Yevrey Oblast

Mixed coniferous–

broadleaf forests;

evergreen

coniferous forests

Clear-cut logging Uncontrolled process affecting


Large FCC;

large PS


sustainability;

ecosystem

Public or private

enterprises,

individual groups,

local population

Regular timber

harvesting irregular

cutting for profit

2-19 Khabarosvk Kray:

southern Sikhote

Alin mountains

Larch forests mixed

coniferous–broadleaf

forests



Large FCC;

large PS

Ecosystem Natural factors

(climatic), local

population

Lack of control

2-20 Khabarosvk Kray:

central Sikhote Alin

mountains; Khorsky

leskhoz

Mixed coniferous–

broadleaf forests

Selective logging (high/moderate

intensity)


processes affecting managed forests

Large FCC;

small PS

P; O; E Species composition Public or private

enterprises;

illegal logging

brigades

Regular timber


cutting for profit

2-21 Primorski Kray/

Khabarosvk Kray:

central Sikhote Alin;

Samarga river

Evergreen

coniferous forests

(Spruce-fir); larch

forests

Conversion to non-forest Clear-cut

logging Selective logging (high/

moderate intensity)



Large FCC;

small PS

P; O; E (Sustainability);

ecosystem

Large private

timber companies

Regular timber


cutting for profit

2-22 Primorski Kray: central

Sikhote Alin mountains;

Svelinsky Leskhoz

Evergreen

coniferous forests

Clear-cut logging Permanent

conversion to non-forest



Large FCC;

small PS

P; O; E (Species composition);

sustainability;

ecosystem

Large timber

companies

Regular timber

harvesting

2-23 Primorski Kray: central

Sikhote Alin mountains;

Ussuri river

Mixed coniferous–

broadleaf forests

(Korean Pine)

Selective logging (high/

moderate intensity)


processes affecting managed

forests

Large FCC;

small PS

P; O; E Species composition Public or private

enterprises;

individual groups

Regular timber


cutting for profit

F.

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ard

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ag

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37

(20

06

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22

–3

34

33

2

2-24 Primorski Kray: border

with China

Dry oak forests; mixed

coniferous–broadleaf

forests

Increased fire frequency

Degradation caused by

frequent ground fires


managed forests

Small FCC;

medium PS

P; O; E Ecosystem;

biodiversity;

(sustainability

Local population Lack of control,

poverty

2-25 Sakhalin Oblast Evergreen

coniferous forests

Clear-cut logging Uncontrolled process affecting


Large FCC;

large PS

Ecosystem Public or private

enterprises,

individual groups,

local population

Regular timber

harvesting

2-26 Yakutia Republic Larch forests Increased fire frequency Uncontrolled process affecting

managed forests

Large FCC;

large PS

P; O Sustainability;

ecosystem

Natural factors

(climatic), local

population

Lack of control

2-27 Yakutia

Republic/Magadan

Oblast: Cherskyi

mountains

Larch forests Increased fire frequency Uncontrolled process affecting


Medium FCC;

large PS

O Ecosystem Natural factors

(climatic)

2-28 Khabarovsky kray Larch and Spruce

forests

Clear-cut logging Increased

fire frequency



Large FCC;

small PS

O; E (Species composition);

sustainability;

ecosystem

Large timber

companies

Regular timber

harvesting

2-29 Taz river basin Larch forests Increased fire frequency Uncontrolled process affecting


Medium FCC;

large PS

O; E Ecosystem Private enterprise Natural resource

exploitation (oil)

Part 3: China

3-1 Heilongjiang province:

Da Hinggan Ling prefec

Larch forests;

pine forests

Selective logging (moderate

intensity)


managed forests

Small FCC;

small PS

P; O; E (Species

composition;

sustainability)

Public enterprises

(national

development plan)

Regular timber

harvesting

3-2 Heilongjiang province:

Xiao Hinggan

Ling prefec

Mixed coniferous–

broadleaf forests

Selective logging (moderate

intensity)


managed forests

Small FCC;

small PS

P; O; E (Species

composition;

sustainability)

Public enterprises

(national

development plan)

Regular timber

harvesting

ID: identification number. Region/location: name of administrative region and local place. Main forest type: main forest cover type inside the rapid change area. Change process: this parameter describes the direct cause

and includes the following possibilities: clear-cut logging (with natural regeneration or replanting); selective logging (high or moderate intensity); increased fire frequency; conversion to non-forest (mining, dam

building. . .); degradation (overgrazing, pest outbreak. . .); afforestation (on abandoned agricultural land). Process characteristics: control or uncontrolled process affecting intact/pristine or managed forests. F Cover

change: forest cover change percentage over last 5 years: large (>10%); medium (5–10%); small (<5%). Patch S: Average patch size (in case of clear-cut or fire): large (>100 ha); medium (50–100 ha); small (<50 ha).

T: time: past (1998–2003); ongoing (2003–2005); expected (next 3 years). Impact: Negative impact [strong or (moderate)] on species composition, soils, sustainability or ecosystem. Drivers: Drivers of the change

process, e.g., public or private enterprises; large-small FFS; individual groups or local population; natural factors (climatic). Economic/institutional factors: Economic/institutional factors of change process including,

regular timber harvesting; irregular cutting for profit; natural resource exploitation; road or pipeline construction; sanitary felling; conversion from agriculture.

F.

Ach

ard

eta

l./Fo

restE

colo

gy

an

dM

an

ag

emen

t2

37

(20

06

)3

22

–3

34

33

3


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Areas of rapid forest-cover change in boreal Eurasia

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