Forest cover change patterns in myanmar

Environmental Conservation 32 (4): 356•364 © 2005 Foundation for Environmental Conservation doi:10.1017/S0376892905002493
Forest cover change patterns in Myanmar (Burma) 1990•2000
PETER LEIMGRUBERi*, DANIEL S. KELLY', MARC K. STEININGER^ JAKE BRUNNER^
THOMAS MÜLLER' AND MELISSA SONGER'
Smithsonian Institution, National Zoological Park, Conservation and Research Center, Front Royal, VA 22630, USA, and Conservation
International, 1919 M Street, NWSuite 600, Washington, DC 20036, USA
Date submitted: 22 April 2005 Date accepted: 26 September 2005
SUMMARY
Myanmar is one of the most forested countries in
mainland South-east Asia. These forests support a
large number of important species and endemics and
have great value for global efforts in biodiversity
conservation. Landsat satellite imagery from the
1990s and 2000s was used to develop a countrywide
forest map and estimate deforestation. The country
has retained much of its forest cover, but forests
have declined by 0.3% annually. Deforestation varied
considerably among administrative units, with central
and more populated states and divisions showing
the highest losses. Ten deforestation hotspots had
annual deforestation rates well above the countrywide
average. Major reasons for forest losses in these
hotspots stemmed from increased agricultural con-
version, fuelwood consumption, charcoal production,
commercial logging and plantation development.
While Myanmar continues to be a stronghold for
closed canopy forests, several areas have been
experiencing serious deforestation. Most notable are
the mangrove forests in the Ayeyarwady delta region
and the remaining dry forests at the northern edge of
the central dry zone.
Keywords: biodiversity, change detection, deforestation rates,
forest, forest dynamics, Landsat
INTRODUCTION
Myanmar's forests represent a globally unique biodiversity
resource (Wikramanayake et al. 2001; Lynam 2003). For
centuries, the country has been known for its teak reserves
and its expansive forests (Bryant 1997), extending from the
lowlands of the Ayeyarwady delta to the hill regions and
the alpine forests of the Himalayas. This Indo-Burma region
harbours a tremendous number of rare and endemic species
and has been recognized for its high value for biodiversity
conservation (Myers et al. 2000). The vast and relatively
intact forests of the region are also reputed to be among the
last strongholds for large mammals species such as tigers and
elephants (Leimgruber et al. 2003; Lynam 2003).
* Correspondence: Dr Peter Leimgruber Tel: +1 540 635 6559 Fax:
+ 1 540 635 6506 e-mail: leimgruberp@crc.si.edu
Myanmar may have retained one of the highest levels
of species richness and most extensive forest cover (UNEP
[United Nations Environment Programme] 1995) of any
country in mainland South-east Asia due in part to its political
and geographic isolation. These conditions sometimes also
make conservation work on the ground difficult. While several
new protected areas have been declared, many lack the
resources and infrastructure necessary to prevent biodiversity
loss from poaching and habitat degradation (Rao et al. 2002;
Myint Aung 2006).
No systematic assessment has been conducted to determine
Myanmar's remaining forest cover, its fragmentation patterns,
or the rate at which forest cover is changing. Recent region-
wide and coarse-scale forest assessments for Asia have
produced alarming estimates of current rates of forest loss
(for example FAO [Food and Agriculture Organization of the
United Nations] 2001a, 2003). Once famed for its extensive
closed canopy forests, Myanmar has been cited in some of
these reports as one of 10 tropical countries worldwide with
the highest annual deforestation rate (FAO 2001«). This pic-
ture is further emphasized by frequent reports about extensive
logging in Myanmar's border regions, particularly since the
Chinese logging ban in 1998 (Global Witness 2003). However,
most of these reports have been limited to data samples
derived from small geographic areas. Even the regional forest
assessments conducted by the FAO were based on a small
sample ofsatellite images and extrapolations (Matthews 2001).
Statistical evaluation of these techniques using countrywide
mapping indicates that the FAO estimates have been very
poor in predicting countrywide and region-wide deforestation
estimates (Tucker & Townshend 2000).
Considering Myanmar's importance for the conservation
of the wider region's unique biodiversity, a countrywide
assessment of forest cover and forest cover change is needed.
This assessment can only be produced via analysis of satellite
imagery and ancillary information, because on-the-ground
records of impacts from agricultural conversion and logging
are either inaccessible or non-existent. Field assessments,
though extremely important for qualitative evaluations, are
not currently feasible because of the remoteness of the hill
forest regions and difficulties in negotiating access to these
areas.
Satellite remote sensing provides objective and consistent
observations suitable for mapping tropical forest cover dy-
namics at a fine scale (Tucker & Townshend 2000). Detection
of land cover changes from mid-resolution, multi-temporal

Forest change in Myanmar 357
satellite images such as Landsat is one of the most valuable
contributions of satellite remote sensing to natural resource
management and biodiversity assessments (Turner et al. 2003;
Leimgruber et al. 2005). Landsat imagery has been the most
heavily used source of satellite data for monitoring forest
change. These images provide encoded radiance data in the
visible near- and middle-infrared spectra, in which most
mature tropical forest can be spectrally distinguished from
farm, fallow land and other non-forest vegetation (for example
Sader et al. 1991; Moran et al. 1994; Steininger 1996, 2000).
The 30-m spatial resolution provided by Landsat images
enables detection of forest clearings as small as one hectare.
Analysis of multi-temporal satellite images has been used to
accurately estimate forest cover and deforestation rates (for
example Tucker & Townshend 2000; Steininger et al. 2001).
We analysed two wall-to-wall Landsat data sets for
Myanmar acquired in the early 1990s and the early 2000s
to address four questions: (1) How much forest is remaining?
(2) How much forest was lost during the last decade? (3)
Where are major deforestation hotspots? (4) What are the
most important patterns of deforestation?
METHODS
Study area
Myanmar has a total area of 678 500 km and shares borders
with Bangladesh, India, China, Laos and Thailand. The
country is rich in natural resources, including petroleum,
natural gas, hydropower and precious stones. Myanmar has an
estimated total population of42.5 million people, the majority
residing within the country's central dry zone. This central
dry zone is surrounded by steep and rugged hill terrain that
extends along the foothills into the higher regions of the
Himalayas. Our study included all of these areas.
Data sources
We acquired complete coverage of Landsat-5 Thematic
Mapper (TM) for 1989-1992, and Landsat-7 Enhanced
Thematic Mapper (ETM-h) for 2000-2001 (43 images for
each date collected for Path: 129-135, Row: 40-53; and for
dates: 2 January 1989-30 January 1993 and 27 October 1999-
19 December 2001). Landsat TM and ETM-h data are
distributed in discrete image tiles designated by a unique row
and path number defined by the World Reference System II.
Images collected in ~ 1990 and ~2000 can be overlaid for
change-detection analysis based on path and row numbers of
the tiles for which they were collected. We also registered all
images to NASA's Geocover, a set of ortho-rectified images
from the 1990s (Tucker et al. 2004). The Geocover ortho-
rectification process uses Global Positioning System (GPS)
data and accounts for elevation to produce an image set
with a root mean square error (RMSE) of < 50 m. Almost
all images were acquired at the end of the monsoon season
and the beginning of the dry season, a time period when
forest vegetation tends to be lush and cloud cover is low. As a
consequence, cloud cover among images used in our analysis
was < 2% and restricted to the north-east ofthe study region.
Selection of images during a period of lush foliage but little
cloud cover and near anniversary date timing for acquiring the
second image was important to reduce confounding effects
of seasonal changes in leaf cover in the country's mixed-
deciduous and dry forests.
Estimation of forest cover and change
For the estimation of forest cover and changes in forest cover
we used an iterative supervised classification technique that
integrates multi-temporal images and classifies forest cover
and forest cover changes in one step. Our analysts were trained
in this technique for assessing deforestation by researchers at
Conservation International's Center for Applied Biodiversity
Science (M. Steininger, personal communication 2003). In
this, satellite images acquired during the same seasons at
different years are combined into one dataset and used in
supervised classification. During classification, the analyst
identifies homogenous areas of forest cover and forest cover
change and derives spectral response statistics for these areas.
Based on the spectral responses, the images are then classified
into maps depicting forest cover and deforestation.
Following this approach we created the multi-temporal
images and refined spectral signatures derived from training
sites in an iterative process: (i) identify training sites for forest
cover and forest cover change, (ii) define spectral signatures,
(iii) run a supervised classification based on these signatures
using maximum likelihood classifiers, and (iv) check for errors
and create additional signatures to refine the classification.
Classification categories were defined as:
(1) Non-change classes: {a) Forest. All closed canopy tall
forest (canopy cover > 50%, tree height > 5 m) observed
in both image dates, including most mature forest, also
savannah-like dry dipterocarp forests (Koy et al. 2005)
and sometimes forests partially degraded by selective
logging or thinning; {h) Water. All water bodies such as
oceans, lakes, reservoirs, rivers, wetlands observed in the
~ 2000 imagery; {c) Non-forest. All areas that were neither
classified as water nor as forest in the ~ 1990 imagery; and
((/) No data. All areas obscured by clouds, cloud shadow
and other shadow in one of the satellite images.
(2) Change classes: {a) Deforestation. All areas observed
as forest in ~ 1990 and non-forest in ~ 2000; {h)
Reforestation. All areas observed as non-forest in ~ 1990
and secondary forest in ~ 2000; and {c) Water change. All
areas changing from water to non-water or vice versa.
Training sites for our supervised classification were
identified based on our detailed knowledge of a wide range of
environments in Myanmar. Collectively, four of the authors
have spent more than 35 months conducting ecological
fieldwork in different parts of that country (Supplementary

358 P. Leimgruher eta.
material ofURL http://www.ncl.ac.uk/ref/journal.htin). To
reduce noise in the result, we smoothed the final classified
maps using a 3 x 3 majority filter.
To determine the forest cover dynamics for all ofMyanmar,
we mosaicked all classified image tiles into a single forest
cover/deforestation map. We used this wall-to-wall satellite
map to calculate countrywide and divisional forest cover
and deforestation rates. For local analysis of forest loss and
identification of deforestation hotspots, we also calculated
forest cover and annual deforestation rates for each of the
image tiles separately. Comparison of the results among the
image tiles allowed us better to analyse spatial variation in
forest cover and deforestation throughout the country.
Accuracy assessment and cross-validation
Determining the accuracy of a broad-scale remote-sensing
product such as a countrywide forest cover change map for
Myanmar is a major methodological challenge. Traditional
'on-the-ground-methods' were not feasible because of the
map's extent and the number of control points necessary
to achieve an acceptable level of accuracy. Overflights,
though ideal for validation across large areas, were currently
impossible. Lack of historical data on the condition of forest
and non-forest land further constrained our ability to evaluate
the accuracy of the maps.
For this study, we evaluated mapping accuracy by
comparing our map with raw images from the Advanced
Spaceborne Thermal Emission and Reflection Radiometer
(ASTER) flying on the Terra satellite (Abrams 2000).
Compared to Landsat TM and ETM-h data, ASTER
imagery has improved spatial resolution (IS m), providing
greater accuracy in delineation of forest and non-forest cover
(for ASTER instrument specifications see Abrams 2000).
Using ASTER imagery it was also more feasible to separate
degraded forests with low canopy cover from dry dipterocarp
forests that frequently also had low canopy closure.
For each of the 43 Landsat image pairs used in the
forest cover-change analysis, we acquired one ASTER image
from the Land Processes Distributed Active Archive Center
(LPDAAC) at the United States Geological Survey (USGS).
We only included ASTER images collected between 2000 and
2002. We generated 40 random points inside the segment of
the Landsat image area that was also covered by the acquired
ASTER images. These points were distributed so as to have
10 random points for each of the mapping classes, namely
forest, non-forest, reforestation and deforestation. This was
not always possible because some classified Landsat tiles
portrayed very little deforestation or reforestation.
In a blind study, image analysts visually categorized the
ground cover in the fine-resolution ASTER images into forest
and non-forest categories. By cross-tabulating these ASTER
categories with the forest cover and change categories in
our deforestation map, we directly estimated the accuracy
of our map in predicting forest cover for the year 2000. For
example, forest and non-forest mapping classes in our map
were considered accurate in 2000 ifthe corresponding ASTER
areas were also forest or non-forest. Similarly, deforestation
and reforestation were considered accurate in 2000 if they
corresponded with ASTER control points that were non-
forest or forest. This assumption was easily justified since
non-forest areas in the ASTER image clearly had not been
regenerating since 1990; and, correspondingly, ASTER areas
in 2000 that were forested were most likely not to have been
logged or deforested previously.
Accuracies from the assessment based on ASTER
represented only partial accuracies for our deforestation map.
We also needed to account for misclassification of forest cover
changes that might have occurred since the 1990s. Since
no ASTER imagery was available for 1990 we could not
determine these errors or accuracies directly but needed to
estimate them based on the available 2000 data. We used
a simple method to approximate total accuracy for change
classes, based on the information derived from the ASTER
2000 data. Our calculations were based on three assumptions:
( 1 ) accuracy in classifying forest or non-forest is approximately
equal for the 1990s and 2000s classifications; (2) accuracies of
change classes can be estimated by summing accuracies for
classifying a 1990 and a 2000 image; and (3) using these sums
we can make a worst-case estimate for accuracy of classifying
deforestation and reforestation using a multi-date analysis.
RESULTS
Forest area and rates of change
In the early 1990s, Myanmar had a total forest cover of about
442 000 km^ over 67% of the nation's land area (Table 1). In
the early 2000s, the forest area had declined to 430 000 km^
(65%).
Over the approximately 10-year period between these forest
measurements, Myanmar lost 12000 km due to human
activities, with an annual rateofforestlossofO.3%. Duringthe
same interval, about 3000 km of forest regenerated, reducing
the annual net deforestation rate to 0.2% (Table 1).
Forest cover changes varied considerably across the country
(Table 1). Clearing rates were highest in Ayeyarwady,
Mandalay and Sagaing Divisions, ranging from 0.4% to 1.2%
a year. All three Divisions encompass significant areas in the
central dry zone, where the majority ofMyanmar's people live.
The Ayeyarwady delta region in particular had experienced
unprecedented levels offorest cover change, losing about 12%
of its remaining forest cover in only 10 years.
The Ayeyarwady and Mandalay Divisions were also among
only four of the country's Divisions and States that had less
than 35% forest cover (Table 1). Half of the Divisions and
States had over 70% forest cover and annual losses in these
areas were frequently well below the annual rates estimated
for the whole country. These included Rakhine State, Shan
State, Kachin State, Tanintharyi Division, Chin State, Karen
State and Kaya State.

Table 1 Remaining forest area
and per cent coverage in ~ 2000,
and average annual deforestation
rate between ~ 1990 and ~ 2000.
Per cent forest cover was
calculated leaving out all areas that
could not be mapped because of
cloud cover. Most divisions had
>2% cloud cover; the maximum
was 4% in Kachin State.
Division/State Total area Forest cover Forest cover Average annual
(1000 km^) (1000 km^) (%) deforestation
rate (%)
Ayeyarwady Division 34 9 26 1.2
Mandalay Division 37 11 31 0.5
Sagaing Division 96 62 66 0.4
Yangon Division 10 1 13 0.2
Rakhine State 35 25 74 0.2
Shan State 157 116 76 0.2
Magway Division 44 14 31 0.2
Kachin State 89 76 89 0.2
Tanintharyi Division 42 31 75 0.1
Bago Division 38 18 47 0.1
Chin State 37 31 87 0.1
Mon State 11 5 45 0.1
Karen State 30 24 78 0.0
Kayah State 12 8 74 0.0
Total 671 430 65 0.2
Table 2 Classification accuracy and error for forest cover map in
2000. Calculations are based on comparing the forest cover map with
ASTER satellite images for ~ 2000.
Forest cover class ASTER class Accuracy (%)
Non-forest
Controlpoints
Forest
Controlpoints
Non-forest
Forest
Reforestation
Deforestation
356
28
46
296
S3
401
282
96
87%
94%
86%
76%
Local patterns of forest cover and losses
To better describe local patterns, we quantified changes in
forest cover based on image tiles, using the 43 pairs ofLandsat
images available for the study. In this analysis oflocal patterns
we found that forest losses were clearly concentrated into 10
deforestation hotspots (Fig. a) which, in decreasing order
of severity, are: (1) the Ayeyarwady delta region (2.2•3.3%),
(2) the northern edge of central dry zone and Ayeyarwady
valley (0.7%), (3) the northern Bago Yoma and Sittoung
valley (0.5%), (4) the Shan plateau (0.5%), (5) northern
Chin State and Myitha River (0.5%), (6) eastern Sagaing
and east bank of Ayeyarwady River (0.4%), (7) Nagaland,
northern Sagaing Division and Uyu River (0.4%), (8) north-
western Rakhine State (0.4%), (9) the border region between
Mon State and Tanintharyi Division (0.4%), and (10) the
southern tip of Tanintharyi Division (0.4%).
The distribution of remaining forest cover also varied
widely across the country, with 2% forest cover in the
Yangon Division and a maximum forest cover of over 90% in
Northern Kachin State (Fig. h). Large forested areas were
found in Kachin State and northern Sagaing Division, in the
international border regions in Chin and Shan State, and along
the Thailand-Myanmar Border.
Accuracy assessments
Based on 1558 control points acquired with ASTER imagery,
our forest cover map in 2000 had an overall accuracy of
86% (Table 2). Forest and non-forest classes in our map
had a high correspondence with forest and non-forest areas
in the ASTER imagery for 2000. Similarly, reforestation
and deforestation corresponded well to ASTER-defined non-
forest or forest areas. All class accuracy levels were well
above 70% and were similar to those reported in previous
satellite-based countrywide studies of tropical deforestation
(for example Steininger et al. 2001).
We used these accuracies from the ASTER comparison
to develop a worst-case estimate of total accuracies for each
change class (Table 3). This was achieved by assuming that
errors in classifying forest and non-forest cover for 1990 and
2000 were roughly equal and that combinations ofthese errors
could provide a worst case estimate of the true error. All of
the estimates were equal or better than 70% and reached as
high as 88% for the areas that remained under forest cover
from 1990 to 2000.
Table 3 Estimated total accuracies and error for forest cover change classes based on ASTER imagery collected in ~ 2000.
determined from Table 2; error for ~ 1990 based on estimates for either classifying forest or non-forest incorrectly.
Error for ~ 2000
Forest change class Direction ofchange Errot^ Combined
error
Cotnbined
1990 2000 1990 2000
accuracy (%)
Non-forest Non-forest Non-forest 0.13 0.13 0.26 74%
Forest Forest Forest 0.06 0.06 0.12 88%
Reforestation Non-forest Forest 0.13 0.14 0.27 73%
Deforestation Forest Non-forest 0.06 0.24 0.30 70%

360 P. Leimgruher et al.
I I Non Forest
Kilometres
Figure 1 («) Per cent annual forest loss from 1990-2000, thick
black lines indicate deforestation hotpots, numbers inside tiles give
annual per cent deforestation and numbers in parentheses
correspond to numbering of deforestation hotspots in the text.
(¿) Per cent remaining forest cover in 2000 indicated by the
numbers inside tiles.
Table 4 Comparison of forest cover estimates from this study
with estimates derived from existing global land cover maps.
'Forest categories from global land cover maps included in this
calculation are: evergreen needleleaf forest, evergreen broadleaf
forest, deciduous needleleaf forest, deciduous broadleaf forest,
mixed forest and woody savannahs. ^GLCC map was derived using
advanced very high resolution radiometer imagery acquired in 1992
(Loveland et al. 2000). 'MGLC map was derived using moderate
resolution imaging spectroradiometer (MODIS) imagery from 2000
(Friedl et al. 2002).
Sources Land area Forest area'
(1000 km^)
1000 km^ %
This study 657 430 65
Global land cover 671 502 75
characterization (GLCC)
MODIS global land 671 509 76
cover (MGLC)^
DISCUSSION
The status of the forests
Myanmar is still among the countries possessing the largest
remaining forest cover in South-east Asia. The countrywide
annual net deforestation rate of0.2% corresponds to the global
average and we found no evidence to support listing Myanmar
among the 10 countries with the highest tropical deforestation,
despite the FAO (2001a) report. However, we did find 10
deforestation hotspots within Myanmar with annual clearing
rates well above the global average. If these trends continue,
the country will face serious and rapid forest loss in the very
near future. Our estimates of remaining forest cover are in
accordance with measurements derived from previous global
land cover mapping (Table 4).
Deforestation rates varied widely among the country's
administrative units (Table 1). Densely populated and
centrally located administrative units showed the greatest
losses, while remote regions, such as Kachin State,
Tanintharyi Division and Chin State had losses below the
global average. This dichotomy can be partly explained by the
increasing resource demands of large populations in central
areas and decreasing state control over forest resources in
remote and sometimes contested regions.
Spatial patterns of forest dynamics suggest three main
processes of forest clearing, including broad-scale conversion
and degradation of forests (Fig. 2), broad-scale shifting
cultivation (Fig. 3a), and conversion to commercial oil palm
plantations (Fig. ?>h). Broad-scale conversion and degradation
of forests is the most common and includes (1) degradation
from fuelwood consumption (FAO 2001^), (2) unplanned and
unrestricted rural agricultural expansion that is encouraged
by local and divisional governments (Myint Aung 2006),
(3) conversion into aquaculture (i.e. shrimp farming in the
Delta region), and (4) commercial clearcutting (Brunner
et al. 1998; Global Witness 2003). Shifting cultivation is
often expanded beyond sustainability (Eberhardt 2003) and

0 10 20
I I I
Kilometres
Deforestation
' Forest
, Non Forest
r
(a) ,
4>'~
<
1
' •'^^
>^ -.>
'.•,. t'
K /» .- %
•- • • (rf^
^
^^1 Deforestation
^ v* Forest
.1 :
w» 1 1 Reforestation
•••V ' '^ 1 j Non Forest /
^ ?> 1 ' No Data
« Ä r » r TT »
!'.?'••
.(b)..
n<b. V
I*- •>•
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k' -if
Sagainq Divison i'Ä^- I ¿.-.^Mandalay
Figure 2 Deforestation hotspots in (a) Ayeyarwady delta region
and (h) at the northern edge of the central dry zone along the
Ayeyarwady valley. Both are examples of broad-scale conversion of
forests into agricultural use.
Figure 3 Examples of spatial patterns in deforestation: (a) patchy
distribution of deforestation and regeneration typical of widespread
shifting cultivation in the Chin Hills; (h) conversion of low-land
rainforest to oil palm plantations in southern Tanintharyi Division.

may result in significant losses of natural resources and
biodiversity. Conversion to commercial oil palm plantations
is occurring rapidly in lowland forests at the southern tip of
Tanintharyi Division (Aung Than, personal communication
2004) threatening a major biodiversity hotspot (Eames et al.
2005).
Overall, Myanmar's forests are in much better shape than
the forests ofalmost any other country in mainland South-east
Asia, partly due to the country's long political and economic
isolation. As global trade reaches remote areas of Asia and
Myanmar, this could change dramatically.
Deforestation hotspots
The 10 deforestation hotspots identified had annual clearing
rates ranging from 0.4% to 2.2%. Most were not tropical
rainforest but more threatened forest types such as mangroves
and tropical dry forests. Two of the deforestation hotspots
merit special mention.
altered immediately, most or all of their natural forest cover
will be degraded, lost, or converted into small, ecologically
denuded forest fragments in the near future. Targeted land
use planning and clearly formulated land use policies would
provide an essential framework to stem the country's rapid loss
ofnatural resources and biodiversity, reduce deforestation and
preserve many of these areas.
Most of the other deforestation hotspots also have unique
fauna and flora and are of special conservation importance
(Tordoff ÍÍ al. 2006). For example, the Bago Yoma is known
for its plant diversity (Davis et al. 1995) and its importance
for the country's teak production (Bryant 1997). Together
with the Chin Hills, another deforestation hotspot, more than
half of the country's recognized centres of plant diversity are
threatened with destruction. The southern tip of Tanintharyi
has some of the last remnants of Sundaic rainforest in South-
east Asia (Eames et al. 2005). These remnants provide habitat
for the largest known population of the endangered Gurney's
pitta, only recently rediscovered in Myanmar.
Ayeyarwady delta region
Deforestation in the Ayeyarwady delta region (Fig. 2a) is
catastrophic, with more than 20% of the mangrove forests
having been lost in only 10 years, the major cause being
fuelwood collection to satisfy the demands of the Yangon
metropolitan area (FAO 2001/»). Originally, delta forests
stretched across the Ayeyarwady and Yangon Divisions. By
the late 19th century most of the dense lowland evergreen
forests, swamp and mangrove forests were cleared following
human settlement (Bryant 1997). Remaining forests were
concentrated in the less accessible estuaries of the southern
Ayeyarwady Division. They represent a recognized priority
area for conservation because of their unique fauna and flora
(TordoffÍÍ al. 2006). The globally threatened species this area
supports include the mangrove terrapin {Batagur haska) and
the saurus crane {Grus antigone) (Tordoff e? al. 2006).
Northern edge ofcentral dry zone and Ayeyarwady valley
In the northern edge of central dry zone and Ayeyarwady
valley (Fig. 2h) at least 7% of the land has been degraded
or converted to other uses in the last decade. Continuous
human encroachments, including widespread and unplanned
agricultural expansion, have caused these major forest losses.
Frequently these expansions have led to people-wildlife
conflicts. For example, Kanbalu township has seen intense
people-elephant conflict in the early 2000s, resulting in the
death ofseveral people (Myint Aung, personal communication
2004). Dipterocarp and dry forests ofthis area represent some
of the best-preserved remnants of this forest type in Asia and
support the largest extant wild population of the vulnerable
Eld's deer {Cervus eldi) (McShea et al. 1999, 2001; Koy et al.
2005). Because of its conservation importance, large fractions
of this area were identified as a conservation priority by a
stakeholder workshop in 2004 (Tordoff e? al. 2006).
Both areas are of special importance to biodiversity
conservation. If land use practices in these two regions are not
Why are deforestation rates lower than previously
estimated?
Our research demonstrates how satellite imagery can be
employed to delineate remaining forest cover and assess forest
cover dynamics in a remote and inaccessible region. The data
our analysis has produced reveal both that Myanmar has more
remaining forest than claimed elsewhere and that its rate of
forest loss is much lower than previously reported. There are
three possible explanations.
( 1 ) Previous estimates were based on small samples ofsatellite
imagery or on expert estimates (FAO 2001«). Tucker and
Townshend (2000) found that randomly selected subsets
of satellite images tend to produce erroneous results in
change calculations unless covering a very large area. We
believe this is the major reason for the extremely high
deforestation rates previously reported by FAO (2001«,
2003). We used complete satellite coverage for Myanmar,
eliminating errors that arise from random selection of a
few images and extrapolation of deforestation rates to the
country scale.
(2) Unlike some previous studies, our assessments included
open-canopy tropical dry forests. These are among the
most threatened and least protected forest ecosystems
in the region (Koy et al. 2005). However, these forest
ecosystems may be classified as non-forest by government.
A more restricted forest cover baseline will increase
estimates of annual forest loss.
(3) Confusion of seasonal changes in canopy cover with
anthropogenic forest degradation is a serious hindrance
to accurate analysis. This is a problem especially in
mixed deciduous, dry dipterocarp and other open-canopy
forests. Errors result when seasonal changes in deciduous
forest trees are interpreted as deforestation or the
converse. Classification accuracies in these forest types

may be lower than for evergreen or mangrove forests.
We attempted to reduce this type oferror by using images
acquired as close to the end ofthe rainy season as possible,
thus minimizing no-data problems from excessive cloud
cover and problems of misclassification from seasonal
change.
Our accuracy assessment demonstrates that our classification
of forest cover and forest cover change dynamics performed
reasonablv well.
ACKNOWLEDGEMENTS
We thank Jenny Gagnon, Kevin Koy, Thiha, Philip DeWitt,
Jamie Robertson and many other interns who assisted in the
image analysis. We owe special gratitude to Melissa Rodden,
who maintained our computing facilities and networks. Kate
Christen, Liz Selig and Chris Wemmer provided thoughtful
comments and ideas throughout the study. This project was
funded primarily by the Conservation and Research Center
Foundation, with additional support from Conservation
International and from the United States Fish and Wildlife
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